Autopoietic Architecture: Can Buildings Think? by Dennis Dollens

Autopoietic Architecture: Can Buildings Dennis Dollens Think?

DBA3: Digital-Botanic Architecture 3 Autopoiesis, eTrees, & Digital Nature 1 Dennis Dollens

Autopoietic Architecture : Can Buildings Think?

Cover & Above: PagodaTower. 2010-ongoing. Dennis Dollens. Digitally grown eTrees, branches, leaves, and flowers programmed for environmentally active, metabolic architectural functions (See: Fig 1). • Right: e-Learning Tower for Edinburgh. 2010-2014. Small leaf-like panels for bioactive shading with stems and branches intertwinning to host metabolic panels. • Back Cover: Los Angeles Tower. Parametrically generated leaf formation used to fold interlocking facade panels of differing-sized modules. (See: Figs 12-14). •

Dennis Dollens

Autopoietic Architecture: Can Buildings Think? 5

Autopoietic Digital-Botanic Specimens 25

eTees, Digital Nature & BioArchitecture 78

Autopoietic Architecture : Can Buildings Think?

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he points that I would emphasize are: First,

that this sharp division between mentality and Nature

has no ground in our fundamental observation. We find ourselves living within Nature. Second, I conclude that we should conceive mental operations as among the factors which make up the constitution of Nature. Third, that we should reject the notion of idle wheels in the process of Nature. Every factor which emerges makes a difference, and that difference can only be expressed in terms of the individual character of that factor. Fourth, that we have now the task of defining natural facts, so as to understand how mental occurrences are operative in conditioning the subsequent course of Nature.

rough division can be made of six

types of occurrences in Nature. The first type is human existence, body and mind. The second type includes all sorts of animal life, insects, the vertebrates, and other genera. In fact all the various types of animal life other than human. The third type includes all vegetable life. The fourth type consists of the single living cells. The fifth type consists of all large-scale inorganic aggregates, on a scale comparable to the size of animal bodies or larger. The sixth type is composed of the happenings on an infinitesimal scale, disclosed by the minute analysis of modern physics.â&#x20AC;? â&#x20AC;˘

Alfred North Whitehead Nature and Life, II. 1933

Dennis Dollens

Autopoietic Architecture: Can Buildings Think? I What follows is a theory-based proposition for research methods directed to support design in the context of biogenerative, metabolic architecture. The text is formulated to underpin architectural experiments and prototyping both theoretically and in practice. It’s a technologically enabled set of options for considering breakthroughs in science, informatics, robotics, mathematics and their implications for metabolic architecture. I argue that hybridized theories of autopoiesis and extended cognition — what I call, autopoietic-extended design — accommodate methods of research and practice to reconceptualize technology, biology, intelligence, and architecture as bonded within nature (Jonas 1966. Odling-Smee et al. 2013). In this context, two theoretical systems: 1), biological selforganization via autopoiesis (Dollens 2015a. Maturana & Varela 1980); and 2), cognition-to-environment communication and perception via extended cognition (Clark 2008) are hybridized to support design research methods. Autopoiesis, with its organizational breakdown of components, unities, and domains expressed in theory — and extendable to practice — is metaphorically speaking, a blueprint for gauging minimally proscribed rules and structural coupling in organisms, living technologies, and environments. To outline autopoiesis I look first to Maturana and Varela’s mechanistic approach — “no forces or principals will be adduced which are not found in the physical universe” (1980 75), from which they maintain: If living systems are machines, that they are physical autopoietic machines is trivially obvious: they transform matter into themselves in a manner such that the product of their operation is their own organization [self-monitoring and self-maintenance]. However we deem the converse is also true: a physical system if autopoietic is living (1980 82).

Autopoietic Architecture : Can Buildings Think?

And: Our aim was to propose the characterization of living systems that explains the generation of all the phenomena proper to them. We have done this by pointing at autopoiesis in the physical space as a necessary and sufficient condition for a system to be a living one (1980 83-84). In a further defining aspect of autopoiesis Weber and Varela view selforganizing systems as reaching into the realm of thinking and cognition where: . . . autopoiesis is a singularity among self-organizing concepts . . . provid[ing] the decisive entry point into the origin of individuality and identity . . . connecting . . . into the [living] phenomenological realm (Weber & Varela 2002 116). Since I am looking to translate matter, phenomena, and intelligence from nature to metabolic architecture, I deploy autopoiesis as a scaffold for recognizing and transporting typologies of life and intelligence to design practice and pedagogy. To enact theory in practice and learning, I adapt Maturana and Varelaâ&#x20AC;&#x2122;s text to help designers distinguish living properties in nature while investigating and conceptualizing co-intelligences in objects, spaces, events, technology, and buildings. Consequently, in a procedural partnership between autopoiesis and extended cognition, autopoietic-extended design may be activated. Therein, autopoiesis prescribes organization to characterize and identify living systems, while Andy Clarkâ&#x20AC;&#x2122;s (2008) theory of extended cognition defines ways cognition, objects, environments, phenomena, and nature interact in cognitive/sensory interchanges: It is possible that sometimes at least, some of the activity that enables us to be the thinking, knowing, agents that we are occurs

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outside the brain . . . . Minds like ours are the products not of neural processing alone but of the complex and iterated interplay between brains, bodies, and the many designer environments in which we increasingly live and work (Clark 2010). Challenging notions of cognition as purely brain/body phenomena, Clark and David Chalmers advocated distributed, collaborative environmental intelligence and sensory-data sourcing as extended mind or extended cognition. Their 1998 paper, “The Extended Mind,” still reverberates in philosophy and cognitive science, stressing: “Cognitive processes ain’t (all) in the head!” (Clark & Chalmers 1998 29). In parallel, they offered a sound-bite brief — but nevertheless critical question: “Where does the mind stop and the rest of the world begin?” (Clark & Chalmers 1998 27). Toward an answer, they offered: [T]he biological brain has in fact evolved and matured in ways which factor in the reliable presence of a manipulable external environment (Clark & Chalmers 1998 31). • In the wake of the above four quotations, I hybridize autopoiesis and extended cognition for a design methodology and operating system (OS). Thereafter, autopoietic-extended design, as a protocol/ scaffold supports students investigating biomimetic data and biological phenomenology. Students’ and designers’ cognitive input/output shapes data to help generate processes necessary for metabolic/intelligent architecture and theory. And, I contend, that process may be considered biologically akin to human extended phenotypes (Dawkins 1982). As organizational for research and pedagogy, autopoieticextended design further aids theorizing an architectural machinic composed of molecular (cognitive) fabricating systems, matter, and buildings biologically responsive to environmental problems while operating as living technology (Bedau et al. 2010. Rieffel et al. 2013. Spiller 2009). Henceforth, living technology is defined as:

Autopoietic Architecture : Can Buildings Think?

. . . based on the powerful core features of life . . . [and] explained and illustrated with examples from artificial life software, reconfigurable and evolvable hardware, autonomously self-reproducing robots, chemical protocells, and hybrid electronic-chemical systems (Bedau et al. 2010). In this frame, Bedau’s “hybrid electronic-chemical systems” suggests pathways between metabolic life and allopoietic machinics we could learn from. That pathway includes setting raw data from nature — observed, programmed, or streamed — as steps for designers to interpret and program. Biological input from nature is essential for morphological, phenomenological, and metabolic functions necessary for generative architecture to explore the domains of autopoietic intelligence (Clark 2008. Di Paolo 2005. Maturana & Varela 1980. Weber & Varela 2002). Input of this sort is fundamental to decode life functions required by bioremedial formulations to support the encoding of algorithmic models and simulations. Researchers and students herewith observationally and technologically source design-relevant data to develop systems supporting their ideas for metabolically intelligent buildings. To construct an autopoietic-extended design OS supporting such an endeavor, I track biocomputation and algorithmic simulation back to Allan Turing’s AI and morphogenetic research (Dollens 2014. 2015. Turing 1952). Thereafter, I date generative architecture to Turing’s research in order to provide a trailhead through which we approach biosimulation and AI as they emerge in living technology (Langton 1988. Markoff 2013. Modha 2013. Turing 1936). Deploying biological simulation and computation based in nature then foregrounds, Can buildings think? as parallel to Turing’s (1950) question: “Can machines think?” By emphasizing, Can buildings think? as an investigatory quest, I situate autopoietic-extended design as a pedagogical OS and scaffold directed to biologically performative architectural research. •

Dennis Dollens

eTrees & Autopoietic-Extended Design Practice At the heart of my own practice, figures 2-4, 16, 20, 24-25 illustrate experiments for investigating adaptive botanic intelligence and morphology in order to yield ideas for metabolic architectures. I developed procedures for biodigitally simulating plant growth in order to model armatures for intelligent, bioresponsive building structures. Within this frame, I consider these models as physical manifestations of the autopoietic-extended design program that generated them and that, thereafter, they helped generate — causal and recursive design components evolving toward theoretical articulation and materialized prototypes. These simulations carry embedded and extended data as cognitive-state objects explained via autopoiesis and Clark’s (2008. 2010) hypothesis of extended cognition. As presented, the Xfrog/L-system simulations (Figs 2, 5, 7, 13, 16, 20) and 3D stereolithographic — STL — models (Figs 3-4, 10-11, 23-25) demonstrate digitally hybridized plant growth, balance, distribution, and scale. The plant attributes are enacted by numerically manipulating botanic characteristics first observed, visualized, and prototyped in order to biomimetically harness properties of phyllotaxy (Fig 6), parastichy, gravitropism, and phototropism. Moreover, the models and their digital files embed genetic algorithms (dormant or active) that influence branch section, length, spiraling, tapering, budding, and leafing in subsequent phases of component growth. The models’ branching, whirling, and exaggerated overgrowth enables alternative anatomical performance delivered in self-bracing (or triangulation-like) reinforcement controlled by the intersecting, perforated, or bonded assembly of components. Contrast, for example, figure 11 to figure 23 — the generative differences are coded in thickbranch (Fig 23) vs. thin branche (Fig 11) crossections. Metabolic or ALife intelligence is thereby framed to hypothesize and guide shapeshifting performance via structural morphology. While choice of a model and/or STL file from the serial Xfrog images (Figs 3, 7, Bottom 11, 16, Top 23) may appear aesthetically

Autopoietic Architecture : Can Buildings Think?

arbitrary, no single preference determines their selection. Satisfying requirements (e.g., branch intersections, dimensions, and joinery) for overall eTree movement leaves few generative configurations capable of flex in single or combined x-, y-, or z-orientation(s). So, only in the initial programming, or far into the generative process after an eTree demonstrates twist, folding, or collapsibility can aesthetic decisions come into play. The eTrees’ evolvable configurations (all relating differential, proportional growth) populate a new typology comprised of simulated trusslike structures, matrices, substrates, or armatures. At present, the eTrees exhibit the limited range of the proto-animate abilities of flexing, stretching, and twisting already mentioned. Yet these movements demonstrate necessary morphological and anatomical attributes as modes of expression (extrapolations) from which properties of living systems (e.g., bone shear, blood circulation, muscle pathways) may be contemplated. Consequently, the structural systems of eTrees are critical for metabolic buildings in the category of digitalbotanic architecture that I investigate (Baluška & Mancuso 2009. Brenner et al. 2006. Mancuso 2010). At the next stage (Figs 1, 16-18), the skeletal trusses are intended to test aerodynamic, seismic, and tropic input/output responses assessable in simulations or in prototyped skin and/or musclelike responsive models. The input/output properties just listed are not foreign to plants or animals — but, animate/metabolic skills are new to buildings and are only now emerging in theoretical architecture (Armstrong 2012. Cronin 2011). As the physical expressions of design ideas, the STL eTrees drive autopoietic and biomimetic form-finding to cultivate architectural performance intended to meet environmental challenges. Pedagogically parallel, eTrees are examples of design-by-research processes from which students may develop their own ideas/visualizations by transferring morphological, metabolic, and sense-making attributes from plants and bacteria to architecture (Baluška & Mancuso 2009. Brenner et al. 2006. Dollens 2015b. Mancuso 2010. Pollan 2013).

Dennis Dollens

Moving design research toward living, self-maintaining, sense-making organisms is thus one goal of autopoietic-extended design. To continue that move vis-à-vis eTrees, I contemplate refabrication of the current models with a living, 3D printable bioresin (Atala 2011. Fountain 2013). After that, I imagine hybridizing secondary cellular-intelligent (leaf/skin/membrane) systems with — or sprouting from — eTree matrices. The second option, I visualize (Figs 1, 16-18) as hypothetical membrane-clad intelligent structures appropriate to urban-scale building panels. Such rendered models, and their 3D files, embody working concepts at preliminary stages of process/structure integration, where eTrees generate building anatomy and frameworks for algorithmically growing leaflike and podlike facade components (Fig 26 & pp83, 90-91). Enlisted for architectural research, the models prompt discussions that impact evolving design and learning ideas predicated on metabolic intelligence or on plant/ bacteria sense-making (Brenner et al. 2006. Pollan 2013). • II To start with, it is useful to recognize landmark biological and technological breakthroughs because they set markers impacting architecture from fields such as plant biology, biomaterials, and biofabrication. For example, contemporary with this text, Stanford University and the J. Craig Venter Institute (Markoff 2012) announced a fully computational bacterium, and IBM (Markoff 2013) announced SyNapse chips (Systems of Neuromorphic Adaptive Plastic Scalable Electronic chips). Based on biological cognition, SyNapse are the first “cognitive chips” not programmed, but capable of learning (IBM 2011). The digital bacterium (Covert 2012) is a “simulation, which runs on a cluster of 128 computers, [and] models the complete life of the cell at the molecular level.” The Stanford/Venter model then provides scientists with “computerized laboratories,” from which they may experiment “without the need for traditional instruments” (Markoff 2012). In a landscape where biology and technology are evolving

Autopoietic Architecture : Can Buildings Think?

to reinterpret biological performance, AI and robotic design are far in advance of architecture for recognizing sensory intelligence, networked communication, and synthetic life. To catch up, architects need experience with laboratory equipment supporting design initiatives and experimentation consistent with the observation that “traditional architectural and engineering ways of thinking about materials as something independent of form and structure are obsolete” (Weinstock 2006). Architecture further needs scientific imaging and advanced scanning equipment of its own (pp84-85) — new types of design labs, tools, and research facilities that are collaborative with existing fablabs to enable bio-experimentation linking visualization, simulation and theory. Synthetic biology, learning chips, and computational intelligence, or their equivalents, are then of theoretical and practical autopoietic and extended cognition significance for ways of thinking about, and making, buildings. Theories of autopoiesis and extended cognition are capable of helping to focus and influence researcher/ student’s conceptualizations and resulting metabolic design ideas. A multidisciplinary example supporting this view is Openworm. org. The research website functioning through crowd-sourced expertise, illustrates for generative architecture, the potential of virtual biological structure, metabolism, and organization digitally group sourced. For Openworm, CAD designed structure (body), and biosimulation (organs), come into being through a social media-based, crossdisciplinary, research model appropriate to theories of autopoiesis, biomimetics, extended cognition, and metabolic architecture. If the synthetic bacterium and the SyNapse chips foretell lines of experimentation, they nevertheless exist as mainline research. I think we should also seek examples of emergent, yet equally rigorous radical research. Living technology and biorobotics (Bedau et al. 2010. Webb & Consi 2001) exemplify potential non-conscious intelligence (AI, ALife, simulated bacteria) that even the most basic single-cell life possesses — but that no machine or building yet possesses. More

Dennis Dollens

controversial, but conceivably critical to metabolic architectural experimentation, entails finding alternative models to animal cognition and intelligence (and subsequent problems of consciousness.) We may focus, for one case, on plants to define alternative multi-cellular intelligence, perception, and memory currently under investigation in the new discipline of “plant neurobiology” (Brenner et al. 2006. Dollens 2015b. Gagliano et al. 2014. Pollan 2013). Visualization of cellular intelligence in materials extrapolated from biology could, for example, influence the design of pliable building skins — able to fold like a leaf — as shape-shifting morphological facades demonstrating aerodynamic efficiency (Rieffel et al. 2013). In this respect, Rachel Armstrong has written about the role of synthetic biology where: “The building envelope could be constructed not with . . . traditional inert surfaces but with a ‘living’ cladding, which could house a range of synthetic-biology-based technologies” (Armstrong 2012 loc 483). With leaf performance as an example of nature-to-architecture interfacing, buildings themselves may become biologically relevant — endowed with intelligence driving bioremedial functions. Changing a building’s surface impacts architectural performance through functions such as light control, passive ventilation, air filtration, aerodynamic stress (wind-load and sheer), and communication networking. Thereafter, changing the ontological state of a building from inert to intelligent (via bio- and syntheticlife materialization) changes its status in nature to favor autopoietic performance (Dollens 2015a). Performative facades are then indicative of current-generation architectural bioremediation and biorobotics — fully metabolic systems are a next step (Spiller 2009. Venter 2013). In this hypothesis, one living example is Physarum polycephalum, a single-cell amoeba that, under stress, self-assembles into a multicellular organism popularly known as slime mold. As an organism, it demonstrates emergent intelligence currently under investigation for living computational uses (Adamatzky 2013). For architecture, such

Autopoietic Architecture : Can Buildings Think?

living technologies (Bedau et al. 2010. Rieffel et al. 2013) potentially align metabolic building functionality with both animate and machine intelligence for a degree of environmental autonomy. As designer-environmentalists, I think we need to conceptualize how to work with living organisms, biochips, simulated bacteria, and learning algorithms as near-future architectural components. To this end, I read Humberto Maturana and Francesco Varela’s (1980) autopoiesis (Dollens 2015a) as a framework for living “machines” to theorize metabolic architectures. Early on, Maturana and Varela’s thinking helped situate biology as systemic for AI and cybernetics. Likewise Openworm, robotics, SyNapse chips, and bacterial life are current signposts that inorganic or synthetic-biological intelligence may sooner-than-later push us toward living technology and computation once acknowledged only in a limited range of organisms (Bedau et al. 2010. Modha 2013. Covert 2013). As examples of pervasive AI and then digital sensing, we read first from The Atlantic and then from The New York Times: AI pervades heavy industry, transportation, and finance. It powers many of Google’s core functions, Netflix’s movie recommendations, Watson, Siri, autonomous drones, the selfdriving car (Somers 2013). And of neuromorphic processors — chips that learn — we hear: “Instead of bringing data to computation as we do today, we can now bring computation to data,” said Dharmendra Modha, an I.B.M. computer scientist who leads the company’s cognitive computing research effort. “Sensors become the computer, and it opens up a new way to use computer chips that can be everywhere” (Markoff 2013). •

Dennis Dollens

III Lifelike computing systems and reformulated AI are now emerging after decades of experiments theoretically supported, though not conceptualized, in Alan Turing’s papers: “Intelligent Machinery” (1948), “Computing Machinery and Intelligence” (1950), and “The Chemical Basis of Morphogenesis” (1952). Turing’s papers were hypothetical projections of future-possible computation and algorithmic simulation with far reaching implications for today’s technologies, scripted architecture, and fabrication-by-code. From them I see the origins of generative architecture in the biological simulations Turing first programmed after joining the University of Manchester in September 1948 (Copeland 2013. Dollens 2014. Swinton 2004. 2011). Ideas from his research still grow, prompted by new scholarship, algorithms, and computational power branching through iterations of his imitation-game (Turing Test) and reaction-diffusion simulations (Reinitz 2012. Turing 1952). In aggregate, Turing’s ideas still support reexamining and expanding his question: “Can machines think?” (Dennett 1998. Turing 1950). Asked in “Computing Machinery and Intelligence,” Turing’s (1950) query was substantiated by later computational programming and computer simulations. John Reinitz wrote in Nature that algorithmic models from this period were: “the first computer simulations of [biological] pattern formation.” He continued: “What Turing should receive credit for is opening the door to a new view of developmental biology” (Reinitz 2012 464). “Computing Machinery and Intelligence” (Turing 1950) and his morphogenetic research (Turing 1952) quietly impacted 1960s and 1970s botanic coding (L-systems) and development of computational biology as instrumental for generative architecture (Dollens 2015. Floreano & Mattiussi 2008. Hayles 1999. Lindenmayer 1968. Prusinkiewicz & Lindenmayer 1990). Today, deep-rooted complexity and recursion (Luhmann 1990) mix generative data feeding design, synthetic biology, algorithmic botany, and cognition in the environment; sometimes instantiating

Autopoietic Architecture : Can Buildings Think?

autopoiesis and living technologies in nature (Bedau et al. 2010. Clark 2008. Maturana & Varela 1980). Without direct reference, Maturana and Varela (1980) carried onward Turing’s search for machine intelligence and morphogenesis. Their search included programming cellular automata to study properties of autopoiesis (Langton 1988. McMulling & Varela 1997. Ray 1994. Turing 1952. Uribe 1981. Varela et al. 1974. Zeleny 1981). Turing’s plant observations and simulations may thus be retrospectively seen as origin points where translated data from nature to hand drawings, algorithms, computer simulations, and theory (Dollens 2014. Swinton 2004) informed later attempts at computational autopoiesis — that is, machinic life different from machine intelligence. I discuss those computational, morphological, and embryological translations in an issue of Leonardo (Dollens 2014), and note them here as experiments framing data from nature as biologically relevant to metabolic architecture. Seen as foundational, Turing brings to biogenerative design a computational heritage that includes machines, code, AI, plant observations, and biological simulation as they have been referenced and evolved into generative design and programming (Dollens 2014. 2015). Now, as much as anything, the acceptance of metabolic, intelligent architecture is a matter of understanding that architecture could (wholly or partially) function biologically in nature, taking on biochemical roles in ecological remediation — buildings alive, but not conscious. Against the backdrop of Turing’s algorithmic morphology and pioneering digital simulations, Maturana and Varela’s autopoiesis (1980) should be factored in when biological properties and attributes are contemplated for generative and bioremedial design. Their text charts a theory of life and cognition continually amended by later cognitive scientists, biologists, engineers, and philosophers (Di Paolo 2005. Thompson 2007. Weber & Varela 2002). Autopoiesis theorizes necessary minimal components to define and distinguish life, sense-making, and cognition in nature (e.g.

Dennis Dollens

bacteria) and in autonomous social systems (Maturana & Varela 1980). If we deploy the amended theory we open new pathways for AI, living technology (Bedau et al. 2010), and computational botany as well as systems theory, cognitive science, and cybernetics to evolve design (Clarke & Hansen 2009. Luhmann 1990. Hayles 1999). As I develop elsewhere (Dollens 2015. 2015a), Maturana and Varelaâ&#x20AC;&#x2122;s hybridized thesis, paired with the environmentally oriented hypothesis of extended cognition underpins autopoietic-extended design (Clark 2008. Di Paolo 2005. Maturana & Varela 1980. Weber & Varela 2002. Zeleny 1981). From that foundation, I posit results to support a mobile OS methodology and practice fusing observational, computational, and biological experimentation for architecture. The two systems, autopoietic biological self-organization and extended cognition actively theorize and propel research-by-design where: . . . autopoiesis is a singularity among self-organizing concepts . . . close to strictly empirical grounds, yet provid[ing] the decisive entry point into the origin of individuality and identity, . . . connecting . . . into the phenomenological realm (Weber & Varela 2002). And where extended cognition stipulates that: It matters that we recognize the very large extent to which individual human thought and reason are not activities that occur solely in the brain . . . it drives home the degree to which environmental engineering is also self-engineering [autopoietic self-maintenance]. In building our physical and social worlds, we build (or rather, we massively reconfigure) our minds and our capacities of thought and reason (Clark 2008 loc 382). In the wake of these quotations, I hybridize autopoiesis with extended cognition. Thereafter, autopoietic-extended design, as a protocol (OS),

Autopoietic Architecture : Can Buildings Think?

aids researchers and students investigating biological input/output for the development of metabolic/intelligent generative architecture, materials, systems, and theory. Maintaining “origin of individuality and identity” (Weber & Varela 2002) is then important in order to recognize the thinking of the architect as an autonomous architectural attribute: creative and inseparable from matter and other organisms — nature. Coded simulations and scripted living technology need to be subsequently coordinated and phased with architecture so that design processes and pressures play constructive, intelligent roles driven by living functions. In this way, new buildings will range as cognitive extensions (Clark 2008), tuned (Coyne 2010) with their designer’s thinking, as well as with autonomous living technology adapted to specific environments. Related to those processes, I listen to Katherine Hayles strategizing the transfer of theory into practice. We should, according to Hayles, be “using for different ends the very technologies applying this [generative] pressure” (Hayles 2012). In that statement, she supports subverting technology to experimental research goals. Subversion for generative architecture is ontologically feasible since machines and buildings exist in the same phylum (category) of human construction and may be conceptualized in related existential and “phenomenological realms.” In this view, evolutionary and phenomenological input gets acknowledged in physical buildings as specialized extended phenotypes (Dawkins 1982. Hansell 2005. Langton 1988. Turner 2000) cognitively and physiologically generated in conjunction with an autopoietic scaffold partnered by extended cognition (Clark 2008. 2010. Maturana & Varela 1980). I express the process in a three-part iterative equation: nature = machine: machine = architecture: architecture = nature.

Dennis Dollens

Consider: we build machines and we build architecture — architecture is a big machine anchored in the ground. Then, if architecture is a big machine and the problems of technology, embryology, and code-asintelligence foregrounded in Turing’s search for types of intelligence and pattern formation — exist for machines — they simultaneously exist for architecture and cities. Thereafter, Turing’s (1950): “Can machines think?” = Can buildings think? The Clark (2008), Turing (1952), Dawkins (1982), Di Paolo (2005), and Maturana and Varela (1980) hybrid now enacts new OSlike network links to support learning underwritten with autopoieticextended design. And that OS, again in Hayles’s words: “intervene[s] in the cycles of continuous reciprocal causality [feedback]” (Hayles 2012). We may then physically, technologically, and phenomenologically nurture network intervention — feedback/feedforth — because it is theoretically embodied in autopoietic-extended design methods generating perceptions, ideas, and design continuous in, and with, nature. Furthermore, the deployment of theory supports observing and prototyping environmental operations as they impact analogue and digital evolutionary processes posited as akin to human extended phenotypes (Dawkins 1982. Hansell 2005. Hayles 2012. Jonas 1966. Odling-Smee et al. 2013). The autopoietic-extended design program then intervenes in Maturana and Varela’s, Turing-like formulation, guiding our theorizing of machinic intelligence, smart materials, and biological systems as contributing to intelligent buildings (Clark 2008. Maturana & Varela 1980. Turing 1950). To recap that scenario, cultivated design ideas feedback from objects/nature to cognition via technology/observation as biodata and metabolic research information (Luhmann 1990. Turing 1952). Therein, we may channel information’s flow and affordances as sources of insight from nature and biology, or as components of living technology, toward architectural functions (Clark 2008. Gibson

Autopoietic Architecture : Can Buildings Think?

1986). The cultivation of data-from-nature for buildings then becomes a fundamental task for design research and architectural pedagogy. This lineage supports design learning and teaching hybridized from autopoiesis (Maturana & Varela 1980), extended cognition (Clark 2008. 2010), and computational simulation (Turing 1952. 1953). Activated as an OS for studio, e-, or m-learning, the hybrid enables design and research conjectures consistent with AI and biological intelligence as observed in animal/plant/machine domains (Brenner et al. 2006. Dawkins 1982. Hansell 2005. Turing 1936. 1952. Turner 2000). Those research conjectures then enrich dialectical potential to impact cognitive, metabolic, and synthetic phenomena intended to prompt theorizing for how buildings can be evolved for bioremedial performance and, thereafter, as ecologically proactive. Fast-forwarded as tool-, machine-, knowledge-, and building-making — autopoietic-extended design participates in phenomenological, material, and intelligent nature (Clark 2008. Dollens 2015a. Maturana & Varela 1980). It sets up theory for design application in pedagogical practice through which research and observation methods mutually aid debate, testing, extending, amending, fabricating, and distributing sense-making intelligences in buildings. The autopoietic-extended design OS is now ready for beta release (Dollens 2015). It cognitively, phenomenologically, and materially extends the provenance of buildings, biology, simulation, technology, programming, and theory as viably interactive through autopoieticextended design, to recursively ask: Can Buildings Think? • References Adamatzky, Andrew. (2013) “Slimeware: Engineering Devices with Slime Mold.” Artificial Life. 19. 317-330.

Armstrong, Rachel. (2012) Living Architecture: How Synthetic Biology Can Remake Our Cities and Reshape Our Lives. New York. TED Conferences. Kindle Edition. Atala, Anthony. (2011) “Printing a Human Kidney.” TED. Accessed: 23 June 2014. www.ted.com/talks/anthony_atala_printing_a_human_kidney Baluška, František. & Mancuso, Stefano. (2009) “Deep evolutionary origins of neurobiology.” Communicative & Integrative Biology. 2:1, 60-65; January/February 2009.

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Bedau, Mark A. McCaskill, John S. Packard, Norman H. & Rasmussen, Steen. (2010) “Living Technology: Exploiting Life’s Principles in Technology.” Artificial Life. Cambridge, MA. The MIT Press. 16: #1. 89-97. Accessed online May 2012. http:// www.mitpressjournals.org/toc/artl/16/1 Brenner, Eric D. Stahlberg, Rainer. Mancuso, Stefano. Vivanco, Jorge. Baluška, František. & Van Volkenburgh, Elizabeth. (2006) “Plant Neurobiology: An Integrated View of Plant Signaling.” Trends in Plant Science. 11:8. 413-419. Clark, Andy. & Chalmers, David J. (1998) “The Extended Mind.” Analysis 58. 1023. _______________. (2008) Supersizing the Mind: Embodiment, Action, and Cognitive Extension. New York. Oxford University Press. _______________. (2010) “Out of Our Brains.” The New York Times. New York. 12 December 2010. http://opinionator.blogs.nytimes.com/2010/12/12/out-of-our-brains/ Clarke, Bruce & Hansen, Mark B.N. Eds. (2009) Emergence and Embodiment: New Essays on Second-Order Systems Theory. Durham, NC. Duke University Press. Copeland, B. Jack, (2013) Turing: Pioneer of the Information Age. Oxford. Oxford University Press. Covert, Markus W. Karr, Jonathan R. Sanghvi, Jayodita C. Macklin, Derek N. Gutschow, Miriam V. Jacobs, Jared M. Bolival Jr., Benjamin. Assad-Garcia, Nacyra. & Glass, John I. (2012) “A Whole-Cell Computational Model Predicts Phenotype from Genotype.” Cell. 150:2. 389-401. 20 July 2012. Coyne, Richard. (2010) The Tuning of Place: Sociable Spaces and Pervasive Digital Media. Cambridge, MA. The MIT Press. Cronin, Leroy. (2011) “Defining New Architectural Design Principles With ‘Living’ Inorganic Materials.” In: Spiller, Neil. & Armstrong, Rachel. Eds. (2011) “Protocell Architecture.” AD: Architectural Design. Wiley. 81:2. 34-43. March/April 2011. Dawkins, Richard. (1982) The Extended Phenotype: The Long Reach of the Gene. New York. Oxford University Press. Dennett, Daniel C. (1998) Brainchildren: Essays on Designing Minds. London. Penguin Books. Di Paolo, Ezequiel. (2005) “Autopoiesis, Adaptivity, Teleology, Agency.” Phenomenology and the Cognitive Sciences. 4: 429–452. DOI: 10.1007/s11097-0059002-y C _ Springer 2005 Dollens, Dennis. (2014) “Alan Turing’s Drawings, Autopoiesis and Can Buildings Think?.” Leonardo: The International Society for the Arts, Sciences and Technology. Cambridge, MA. The MIT Press. May/June 2014. 47:3. _______________. (2015) “Autopoietic-Extended Architecture: Can Buildings Think?” PhD Thesis. University of Edinburgh. School of Architecture ESALA. _______________. (2015a) Autopoiesis for Metabolic Architecture: A Reading of, & Guide to: “Autopoiesis: The Organization of the Living.” Kindle (Amazon) e-book. _______________. (2015b) “Autopoiesis + Extended Cognition + Nature = Can

Autopoietic Architecture : Can Buildings Think?

Buildings Think?” Communicative & Integrative Biology. Accepted: 13 November 2014. In press for 2015. Floreano, Dario & Mattiussi, Claudio. (2008) Bio-Inspired Artificial Intelligence: Theories, Methods, and Technologies. Cambridge, MA. The MIT Press. Fountain, Henry (2013) “Printing Out a Biological Machine.” The New York Times. 18 August 2013. http://www.nytimes.com/2013/08/20/science/printing-out-abiological-machine.html?ref=science Gagliano, Monica. Renton, Michael. Depczynski, Martial. & Mancuso, Stefano. (2014) “Experience Teaches Plants to Learn Faster and Learn Slower in Environments Where it Matters.” Oecologia. 5 January 2014. Gibson, James J. (1986/1979) The Ecological Approach to Visual Perception. Hillsdale, New Jersey. Lawrence Erlbaum Associates. Publishers. Hansell, Mike. (2005) Animal Architecture. Oxford. Oxford University Press. Hayles, N. Katherine. (1999) How We Became Posthuman: Virtual Bodies in Cyberspace, Literature, and Informatics. Chicago. University of Chicago Press. _______________. (2012) How We Think: Digital Media and Contemporary Technogenesis. Chicago. The University of Chicago Press. IBM. (2011) IBM Unveils Cognitive Computing Chips. NY. Armonk. 18 Aug 2011. Press release. Consulted: 29 May 2012. http://www-03.ibm.com/press/us/en/ pressrelease/3521.wss Jonas, Hans. (1966) The Phenomenon of Life: Toward a Philosophical Biology. Chicago. The University of Chicago Press. Langton, Christopher G. (1988) “Artificial Life.” In: Langton, Christopher G. (1988) Artificial Life. Santa Fe, NM. Addison Wesley & The Santa Fe Institute. 6:1-47. Lindenmayer, Aristid. (1968) “Mathematical Models for Cellular Interaction in Development.” Journal of Theoretical Biology. 18. 1968. Luhmann, Niklas. (1990) “The Cognitive Program of Constructivism and a Reality that Remains Unknown.” 64-85. In: Krohn, Wolfgang. Kuppers, Gunter. Nowotny, Helga. (1990) Selforganization: Portrait of a Scientific Revolution. Dordrecht. Kluwer Academic Publishers. Mancuso, Stefano. (2010) “The Roots of Plant Intelligence.” TED Global. http://www.ted.com/talks/stefano_mancuso_the_roots_of_plant_intelligence.html Markoff, John. (2012) “In First, Software Emulates Lifespan of Entire Organism.” The New York Times. 20 July 2012. Accessed: 20 July 2012. http://www.nytimes. com/2012/07/21/science/in-a-first-an-entire-organism-is-simulated-by-software.html _______________. (2013) “Brainlike Computers, Learning from Experience.” The New York Times. 29 December 2013. Accessed 29 December 2013. http://www. nytimes.com/2013/12/29/science/brainlike-computers-learning-from-experience. html?ref=technology

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Maturana, Humberto & Varela, Francisco. (1980) Autopoiesis and Cognition: The Realization of the Living. Dordrecht, Holland. D. Reidel Publishing Company. McMulling, Barry & Varela, Francisco J. (1997) “Rediscovering Computational Autopoiesis.” SFI (Santa Fe Institute) Working Paper 97-02-012. Accessed 26 June 2013. www.santafe.edu/media/workingpapers/97-02-012.pdf Modha, Dharmendra S. (2013) “IBM Research: Our People, Dharmendra Modha.” Accessed: 29 December 2013. http://researcher.watson.ibm.com/researcher/view. php?person=us-dmodha Odling-Smee, John; Erwin, Douglas H; Palkovacs, Eric P; Feldman, Marcus W; & Laland, Kevin N. (2013) “Niche Construction Theory: A Practical Guide for Ecologists.” The Quarterly Review of Biology. 88:1. Pollan, Michael. (2013) “The Intelligent Plant: Scientists Debate a New Way of Understanding Flora.” The New Yorker. 23 & 30 December 2013. 92-105. Prusinkiewicz, Przemyslaw & Lindenmayer, Aristid. (1990) The Algorithmic Beauty of Plants. New York. Springer-Verlag. Ray, Thomas S. (1994) “An Evolutionary Approach to Synthetic Biology: Zen and the Art of Creating Life.” Artificial Life. 1. 179-209. Reinitz, John. (2012) “Pattern Formation.” Nature. 482. 464. 23 February 2012. Rieffel, John. Knox, Davis. Smith, Schuyler. & Trimmer, Barry. (2013) “Growing and Evolving Soft Robots.” Artificial Life. 20. 143-162. Accessed: 4 December 2013. http://www.mitpressjournals.org/doi/abs/10.1162/ARTL_a_00101 Somers, James. (2013) “The Man Who Would Teach Machines to Think.” The Atlantic. November 2013. Accessed: 29 October 2013. http://www.theatlantic.com/ magazine/archive/2013/11/the-man-who-would-teach-machines-to-think/309529/ Spiller, Neil. (2009) “Plectic Architecture: Toward a Theory of the Post-Digital in Architecture.” Technoetic Arts: A Journal of Speculative Research. Bristol, UK. 7: 2. 95-104. Swinton, Jonathan. (2004) “Watching the Daisies Grow: Turing and Fibonacci Phyllotaxis.” 477-498. In: Teuscher, Christof. Ed. (2004) Alan Turing: Life and Legacy of a Great Thinker. Berlin. Springer-Verlag. _______________. (2011) “Turing, Morphogenesis, and Fibonacci Phyllotaxis: Life in Pictures.” Preprint provided by author. Submitted to Elsevier. 2011. Thompson, Evan. (2007) Mind in Life: Biology, Phenomenology, and the Science of Mind. Cambridge, MA. Harvard University Press. Turing, Alan M. (1936) “On Computable Numbers, With an Application to Entscheidungsproblem.” London. Proc. London Maths. Soc., ser.2 42. 230-265. http://plms.oxfordjournals.org/content/s2-42/1/230.full. pdf+html?ijkey=bvNIrAXLJ7n4ODP&keytype=ref

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_______________. (1948) “Intelligent Machinery National Physics Laboratory Report.” In: Metzer, B. and Michie, David. Eds. (1969) Machine Intelligence v5. Edinburgh. Edinburgh University Press. Originally: Turing, Alan M. (1948) “Intelligent Machinery.” National Physics Laboratory Report. 1-20. In: Evans, C.R. and Robertson, A.D.J. Key Papers: Cybernetics. London. Butterworths. 1968. _______________. (1950) “Computing Machinery and Intelligence.” Mind, New Series, Vol. 59, No. 236 (Oct., 1950), pp. 433-460. See also: Turing, Alan M. (1950) “Computing Machinery and Intelligence.” In: Evans, C.R., Robertson, A.D.J. (1966) Key Papers: Brain Physiology and Psychology. London. Butterworths. _______________. (1952) “The Chemical Basis of Morphogenesis.” Philosophical Transactions of the Royal Society B, 237, 37-72. Reprinted in: Saunders, P.T. (1992) Collected Works of A.M. Turing: Morphogenesis. London. North-Holland. Turner, Scott J. (2000) The Extended Organism: The Physiology of Animal-Build Structures. Cambridge, MA. Harvard University Press. Uribe, Ricardo B. (1981) “Modeling Autopoiesis.” In: Zeleny, Milan. Ed. (1981) Autopoiesis: A Theory of Living Organization. New York. North Holland. 51-62. Varela, Francisco J; Maturana, Humberto R; & Uribe, R. (1974) “Autopoiesis: The Organization of Living Systems, its Characterization and a Model.” BioSystems. 5: 187-196.

Venter, Craig. (2012) “What is Life? A 21st Century Perspective.” Lecture. Dublin. Trinity College Dublin. http://www.tcd.ie/Communications/news/news. php?headerID=2606&vs_date=2012-07-24 _______________. (2013) Life at the Speed of Light: From the Double Helix to the Dawn of Digital Life. [Kindle Edition.] New York. Little, Brown Book Group. Webb, Barbara & Consi, Thomas R. Eds. (2001) BioRobotics: Methods & Applications. Cambridge, MA. The MIT Press. Weber, Andreas & Varela, Francisco. (2002) “Life after Kant: Natural Purposes and the Autopoietic Foundations of Biological Individuality.” Phenomenology and the Cognitive Sciences. 1. 97–125. Weinstock, Michael. (2006) “Self-Organization and Material Constructions.” 34-41. In: Hensel, Michael, Menges, Achim, & Weinstock, Michael. Eds. (2006) AD: Techniques and Technologies in Morphogenetic Design. West Sussex, UK. Wiley-Academic. 76.2. Zeleny, Milan. Ed. (1981) Autopoiesis: A Theory of Living Organization. New York. North Holland.

Dennis Dollens

Pagoda Tower

Fig 1. Pagoda Tower. Experimental growth from Xfrog (L-systems) for digital-frame supporting forms for biorobotic controllers moving and activating leaflike heat/light/sensor screens. Leaf-forms hold materialization potential for metabolic functions for bioremedial, building participation. Right insert: four Xfrog screenshots illustrating schematic leaf growths and distribution (phyllotaxis and parastichy) along a tubular, controlling stem. Xfrog/Rhino/3DS Max. 25

Autopoietic Architecture : Can Buildings Think?

eTree Anatomy & Morphology

Capsicum annuum Chile pepper leaves & fruit: model for pods & platforms

eTree & branches

Branch support to cradle pods & balconies

Balconyâ&#x20AC;&#x2122;s double curvature modeled from leaves

Xfrog plant generation

Tree & branches in an algorithmic, Fibonacci spiral

DIY â&#x20AC;˘ Trees as Architecture

Habitation Pods based on seedpods

Dennis Dollens

Fig 2. Autopoiesis & Digital Botanic Architecture. This series of experiments with simulated digital trees, hybridized into architectural elements, illustrates botanic forms and their morphological and mathematical attributes applied to design systems and structures. Using generative processes demonstrates how the transference of some biological properties, held in algorithmic notation, such as phyllotaxy (Fig 6), allometry, and phototropism, may be inherited by architectural and design elements derived from plant simulations and their corresponding biological maths.

Helianthus annuus Sunflower: model for solar tracking

Triticum aestivum Wheat, model for natural stacking & clustering

Prismatic geometries as circulation core

Tracking Solar Panels: modeled from the example of multidirectionalfacing flowers

Morphological Hybrid Digitally generated tree, branches, leaves, pods, & flowers as a schematic building

Autopoietic Architecture : Can Buildings Think?

eTree Branch & Tendril Morphology STL Truss #10. 2009. Generative sequence from Xfrog animation.

STL Two Branch Column. 1999. The projectâ&#x20AC;&#x2122;s starting-point based on a tree & then machine fabricated

STL Truss #1. 2000-2003. Tendrils & structurally intersecting branches. Above: STL with adobe-pulp skin. Below. STL eTrees with membrane surfaces

STL Truss #2. Intersecting and self-reinforcing branches grown to reinforce the columnâ&#x20AC;&#x2122;s center

STL Truss #4. Central trunk model with spiraling, interlinking branches for structural reinforcement

STL Truss #3. Gravitropic, intersecting branches grown to link and structurally pierce and graft with/ into lower branches

Dennis Dollens

Fig 3. STL & SLS eTree models. 1999-Ongoing. Branch and tendril development evolving as multi-directional, flexing structural trusses that phase out the tree trunks. Simultaneously, the branches sprout secondary growths based on flowers, leaves, tendrils, and pods that are eventually reprogrammed as living or mechanical spaces for prototype buildings.

STL Truss #7. This model is of the ArizonaTower (Figs 7-8). It is a collection of eTrees linked by branches—each of which sprouts both pods programmed for circulation stairways, and pods reprogrammed as elongated cubes for habitation

STL Truss #10. Sharing many of the attributes of #9, this structure departs, having greater branch asymmetry and flex (Figs 9-10) while also acquiring greater strength

STL Truss 11. No central trunk supports this structure (Figs 20-23). Its strength comes from asymmetrically reprogramming one of the branches’ 3D coordinates in order to extend it, elongated and flattened in one direction

STL Truss #9. The first design to eliminate the eTree’s central trunk (Figs 11. 24-25) and therefore become a flexible structure of interlinking, spiraling branches with nodes for connecting joints, stems, and tendrils DIY Looking • Recursive Branching

http://algorithmicbotany.org/

Autopoietic Architecture : Can Buildings Think?

DIY • Botany • Tendrils • Knotting • Connectors

www.xfrog.com

Dennis Dollens

Four eTrees / One Frame

Fig 4. STL eTree models. 2000-2003. These four eTrees with equally proportioned trunks and branches were digitally simulated. Half of the branches were programmed to loop and intersect, thus reinforcing each of the four central trunks (detail, left), while the other branches were grown straight, intersecting at the corners of the building cage.

Autopoietic Architecture : Can Buildings Think?

Fig 5. Predatory Frame. 2005. Above and right: Predatory Structureâ&#x20AC;&#x201D;four eTrees with vine and tendril branches grown as framing structures with tendrils ready to reach out and anchor the building. Below: pod clusters stacked and held within the vine and tendril frame. Bottom: Earlier, related growth strategy for prototype canopies, Paris metro, 2001-2002.

Dennis Dollens

Predatory eTree Vines

Autopoietic Architecture : Can Buildings Think?

Fig 6. Phyllotaxy & Algorithmic Growth from Digital Software. Plant leaves and flowers (and shells and bones and horns) follow geometric spiraling patterns that can be captured in algorithmic formulas and thus digitally simulated. Above left and right, are19th-century scientific diagrams of botanic, spiraling progression. Right top: illustrates phyllotaxic branch spiraling overlaying an Xfrog drawing whose branches have been programmed into regular polygons (a basic eTree); the branch tips sprout overscaled leaves (modeled here as panels) that illustrate the embedded Fibonacci directional flow. Photo inserts, right: spider web with concentric web construction; and, far right spiraling succulent leaves of Euphorbia myrsinites (Myrtle Spurge).

Dennis Dollens

Photo: Barbara Riley

eTrees, Nature’s Numbers, & Spiral Growth

DIY Botanic Spiraling • Phyllotaxy http://en.wikipedia.org/wiki/Fibonacci_number

Autopoietic Architecture : Can Buildings Think?

eTree Simulation: ArizonaTower

Fig 7. ArizonaTower Xfrog/L-system Growth. Generative sequence illustrating the digital growth of multiple branches and pods. 36

Dennis Dollens

Fig 8. ArizonaTower. Rendering of the ArizonaTowerâ&#x20AC;&#x2122;s pods and branches with solar panels and rooted biodigesters developed from digital leaves. Bottom: ArizonaTower STL models. 37

Autopoietic Architecture : Can Buildings Think?

Dennis Dollens

SnapPods, Seedpods, Barbs, & Tendrils Fig 9. SnapPod Connectors. 2008-Ongoing. eTrees whose branches link with tendril-like snapping pods. Xfrog screen (below) shows the generation of the structure and 3DS Max renderings (left) show the SnapPod connectors and eTrees. Below middle: Squash tendrils growth spiral reaching and attaching to tree stump.

DIY Microscopy Robert Hooke Micrographia 1666 http://www.gutenberg.org/etext/15491

Autopoietic Architecture : Can Buildings Think?

SnapPods, Seedpods, Barbs, & Tendrils

Fig 10. STL SnapPods. 2008-Ongoing. Below and right: the first generation of connector/snaps for linking structural eTrees. Below bottom: Rhino screen captures of the snaps derived from flower seedpods, tendrils, barbs, and thorns.

DIY Citizen Science: Toy Digital Microscope http://www.microscopy-uk.org.uk/primer/index.htm

Dennis Dollens

Autopoietic Architecture : Can Buildings Think?

eTree Branches & Tendrils Fig 11. TreeTruss. 2007-Ongoing. Developed first as a horizontal, interior ceiling structure for a club, this eTree supported projectors, lights, sensors, and acoustic baffles. Since 2007 the ceiling structure has been revised with additional branching for several projectsâ&#x20AC;&#x201D;most prominently, the cylinderlike body for the Los Angeles Tower (Figs 12-14). Below: renderings of the early versions of the eTree with sound baffles (originally generated as leaves). Middle: eTree with tendrils; STL model seen in horizontal and vertical positions. Bottom: Xfrog stills from the generative sequence of eTree growth. This multidirectional eTree, whose central trunk has been repressed in the software code, suggests a structural form and system for environmentally flexing column and beam typologies and is a subject of ongoing autopoietic research.

Dennis Dollens

Autopoietic Architecture : Can Buildings Think?

Dennis Dollens

Self-Shading Tower for Los Angeles Fig 12. 2007-2013. As seen (p46 Fig 13 next page), the eTree generating this tower’s cylinder is also a component of other projects—a kind of spine whose generative code lends itself to multiple design paths resulting in different kinds of structural leafing and branching forms (Figs 24-25). Prominent in the developmental stages of the tower’s panels, the eTree is eventually repressed in favor of an open interior while the 2000+ piece monocoque facade sheathes the building’s fifteen floors.

DIY • Scales • Membranes • Skins

Autopoietic Architecture : Can Buildings Think?

DIY • Botany • Clusters • Orientation • Shape http://en.wikipedia.org/wiki/Monocoque

Dennis Dollens

Self-Shading Tower for Los Angeles

Fig 13. Skin / Monocoque Panels. 2007-2013. Left: The first parametric expression of leaves populating the cylinderlike volume created from a point cloud determined by the eTree’s tendril tips. Above: Further parametric development of a leaf form, interlocked as a continuous surface, creates a monocoque facade generated in ParaCloud. The linking, chainmail-like components are part of an ongoing search for panel systems that can take on environmental performative duties—such as filtering and ventilation—as well as, in other design formulations, the housing of sensor-embedded monitors. Additionally, the panel designs adjust to produce pockets where plant, algae, or other biological agents may be housed.

Autopoietic Architecture : Can Buildings Think?

Dennis Dollens

Tower for Los Angeles: Almond Skin Fig 14. Skin / Monocoque Panels. 2007-2013. Left: Populated 3D components generated in ParaCloud with individual panels intended to function as monocoques inspired by almond shells (bottom left) and mechanically related to the structures of airplanes. Bottom: Screenshot of ParaCloud running a solar calculation for dispersing three different components around the towerâ&#x20AC;&#x2122;s perimeter, each with different environmental sensitivity and controls.

DIY Design Research â&#x20AC;˘ Leaf Folds

Autopoietic Architecture : Can Buildings Think?

Dennis Dollens

Branch Truss & Yucca Skins

Fig 15. BioTower. 2009-Ongoing. Top & Left: Canopies installed at the Santa Fe Art Institute, 2001. Series of branch structuresâ&#x20AC;&#x201D;asymmetrical trussesâ&#x20AC;&#x201D;supporting paper membranes hand-made from yucca blades (leaves), demonstrating the idea of clustered panels stabilizing and strengthening branching struts; the Canopy project became the physical prototype for the monocoques and then the hovering leaf clusters later developed for the Los Angles Tower (Figs 12-14) and BioTower (above and Figs 16-18).

Autopoietic Architecture : Can Buildings Think?

Fig 16. BioTower. 2009-Ongoing. Digital growth sequence. Left to right top: 1. eTree branches. 2. Sensor nodes (pods). 3. Branches & nodes. 4. Leaf clusters. 5. Leaf clusters, branches, & sensor nodes. Bottom left & right: Xfrog screen shots for the BioTowerâ&#x20AC;&#x2122;s exterior systems. 52

Dennis Dollens

BioTower: Generative Leaf Sequence

Autopoietic Architecture : Can Buildings Think?

Fig 17. BioTower. 2009-Ongoing. Above: BioTower with branch matrix, sensor nodes. Right: 54 BioTower with leaf-cluster systems for air filtration, sound baffling, & heat / light control.

Dennis Dollens

BioTower

Autopoietic Architecture : Can Buildings Think?

BioTower Facade & BioScreen

Fig 18. 2009-Ongoing. Top: Series of branch panels with an origamilike folded paper skin modeled from the observation of leaves, as an early study for a hovering screen facade with a faceted surface. Above: Schematic for outer biomechanical sensor-node pods, biological filters, and passive cooling system embodied in digital leaf panels. Right: Inner structural panel and glass study. Below, righthand page: sketch for branches, nodes, and flower petals or leaves.

Dennis Dollens

DIY Electron Microscope Stomata http://www.jstor.org/stable/3066303

Autopoietic Architecture : Can Buildings Think?

e(palm)Tree Column & Skins Fig 19. Right: Photo collage. Los Angeles. Washingtonia robusta (Mexican fan palm) and five Xfrog bark simulations for imbricated, interlinked tiles as prototypes for architectural scales, panels, and interlocking structures. Below: Xfrog digital growths as a stylized palm columns.

DIY Biological Skins

Dennis Dollens

Autopoietic Architecture : Can Buildings Think?

eTree Branches: Braided, Interlaced, & Imbricated Pod Nests (Cradles)

DIY • Bud/Pod • Volume • Cultivation

Dennis Dollens

Fig 20. Untitled. 2009-11. Each structural cluster is comprised of three eTrees with asymmetrically grown branches programmed as imbricated armatures nesting and stacking spherical pods. While weaving and knotting in nature may most obviously come from bird nests and spider webs, allied procedures, such as the interlacing of the cane cholla (background) illustrate one of nature’s wide ranging structural growths to borrow and extrapolate from.

Right and bottom left: Project for an apartment building along Glasgow’s Strathcylde River, Scotland. Stacked and spiraling pods grown on eTree frames as an experiment for pod clustering and orientation. (See pp72-73. 90-91).

DIY • Form & Movement Research

Autopoietic Architecture : Can Buildings Think?

Fig 21. Above & Right: Untitled. 2009-2011. Digital sketches using an eTree encased in a clear membrane for visualizations based on cellular forms, diatoms, and protozoa. 62

Dennis Dollens

eTree Branch & Membranes

DIY Spiraling Tendril Connections

Autopoietic Architecture : Can Buildings Think?

eTree Branches, Stacking Pods, & STL

Fig 22. Untitled. 2009-2011. Below: Study for spiraling and stacking pod clusters. Right: eTree STL model as branch armature. Bottom: Membrane test model (1996-97) for surfacing pods. Handmade yucca paper tested for fiber alignment, strength, and translucency.

DIY Spatial Volumes from Seed Pods

Dennis Dollens

Autopoietic Architecture : Can Buildings Think?

Fig 23. Top: eTree branch-to-root Xfrog generation sequence. Above: Double eTree branch armatures, STL models. 66

Dennis Dollens

eTree Spine-Branch STL

DIY • Botany Transforming • Pods • Leaves • Branches

Autopoietic Architecture : Can Buildings Think?

DIY Citizen Science: Robert Hooke Micrographia 1666

Fig 24. Left & Right. e-Tree with Glass Leaves. 2008-Ongoing. Leaves sprouted from the eTree (Fig 11) in a study for populating and surfacing branching structures with scale-like panels.

Dennis Dollens

eTree & Glass Leaves

Autopoietic Architecture : Can Buildings Think?

Dennis Dollens

eTree & Glass Leaves Fig 25. e-Trees with Glass Leaves. 2008-Ongoing. Leaves sprouted from an eTree (Fig 11) in a study for populating and surfacing branching structures with scalelike panels.

Digitalis: Foxglove

Autopoietic Architecture : Can Buildings Think?

Yucca glauca

Penstemon palmari

DIY Digital-Botanic Software Grown • Pods • Leaves • Branches

Dennis Dollens

Flower Stalks, Stacks, & Clusters

ever take the “I shan’t see it” attitude. By exercising a little vision you will come to realize that the tree, which has a possible future, perhaps a great one, may be more important than yourself . . . • Christopher Lloyd The Adventurous Gardener. 1983

Fig 26. PodTowers. 2004/2005-Ongoing. Above: eTree stacked and clustered. An early model from tall flower stalks with overscaled seedpods schematically defining habitation units (See: pp 90-91). Left: Penstemon palmari and Yucca glauca. Tall flowering stalks studied for clustering, asymmetry, and light orientation. The stalk curvatures later influenced the self-shading tower for Los Angeles (Figs 12-14).

Autopoietic Architecture : Can Buildings Think?

Fig 27. Leaf Models. 2004-Ongoing. Foreground: Six living Penstemon palmari leaves configured into a study model. Background: The digital model follows the form of one of the penstemon leaves for a studioâ&#x20AC;&#x2122;s roof. It was developed from the idea that surface facets create self-shading topographies, thus reducing heat gain, while potentially increasing surface area for emerging technologies such as sprayed-on photovoltaics.

Dennis Dollens

Flower Stalks & Leaves

Left: Penstemon palmari Below left: Plaza Canopy based on Penstemon leaf.

Autopoietic Architecture : Can Buildings Think?

Leaf Whirling & Twirling

Fig 28. Procedures such as folding and twirling have been used throughout these pages. These movement- and growth-distribution formulations are sometimes seen in nature. For example, in the opposite upper-right photo of a datura flower unfolding while exhibiting directional spiraling at the same time (Fig 6). With other examples, directional patterns are difficult to detect, as in the spiraling spines of the datura seedpod, opposite. Near right: a single Xfrog generated leaf, visually modeled from a tobacco leaf, given a twirl with 8 leaf iterations.

DIY â&#x20AC;˘ Future Digital Seeds

Dennis Dollens

Flowers & Pods / Folding & Twirling

DIY Digital-Botanic • Spirals • Twirls • Folds

Autopoietic Architecture : Can Buildings Think?

eTrees, Digital Nature, & BioArchitecture Introduction The idea is not to make buildings look like botanic organisms. The idea is to interlace nature and architecture to enable the design of hybridized, biological structures. For this process, investigating nature is design research mapped in accordance with the theory of autopoiesis (pp5-24). The overall aim is to create new architectural species incorporating natural attributes ordered in metabolic performative materials, mechanics, communications, and form. Designing prototype structures to autonomously sense and execute tasks such as passive air filtration, heat transfer, and water reclamation justifies the expectation that experimental bioarchitecture will necessarily collaborate with science and technology to initiate metabolic architecture. Buildings derived from growth algorithms, biofabrication, and living technology—nurturing biointelligent, bioremedial systems—are inevitable. New architectural skins, panels, floors, and skeletal systems, shouldering biological responsibilities, will evolve new bioaesthetics. My perspective, filtered through today’s generative and computational software and plant science, (Baluška et al. 2009) is also historically influenced. I appropriate DIY method from 18th- and 19th-century science (right) while also looking to, the origins of modern buildings—specifically, to Louis Sullivan’s (1967/1924) morphological design in relation to Alan Turing’s 1950s botanic computer simulations (Dollens 2014. 2015). To demonstrate, I have digitally simulated experimental structures, grown from software, and projected them as bioclimatically operative. Toward this objective of responsive biological architecture, I use

Dennis Dollens

Stephen Hales. Vegetable Staticks. 1723. Learning how roots function and branch underground. (See digital generative sequence, top pp 66-67; and Fig 8.).

Xfrog, ParaCloud, Generative Components, and Rhino to develop branching tree structures (Figs 1-28 pp25-77). The software also comes into subsequent use for surfaces, panels, and pods (pp86-89) with attributes appropriated from individual and/or massed leaves, roots, flowers, barbs, and tendrils. (Or sometimes from shells, skeletons, scales, and minerals.) Digitally generated architecture, hybridized from computational plant simulation, is part of a process of visual and technological observation for infusing design forms with botanical properties. This search, linking design and nature, involves finding ways to conceptualize and algorithmically model plants and trees as bioresponsive architectural components. Doing so addresses generative programming, biological structure, and ecology in the context of bioremediation and biofabrication. Biology and botany are, of course, not new sources for architectural development. Design inspired by nature, articulated by idea-eye-hand production has been used for tens of thousands of years. Architectureâ&#x20AC;&#x2122;s ancient craft origins viewed through ur-building technologies, such as weaving, knotting, and pottery may be understood as appropriations from nature (Herrmann 1984). But contemporary design looks less toward nature for inspiration than it does toward industry. Accordingly, design could learn from, and collaborate with, ecology, biotechnology, biochemistry, genetics, and theory. Designers might tap and collaborate with emergent science taking inspiration from research that is second nature to biologists and ecologists. Consider the design implications of ideas and information generated by scientists constructing synthetic bacteria that off-gas methane as an alternative to oil-based fuels (Ball 1999. Benyus 1997. Mattheck 1998. Vincent 1990. Wade 2007).

Autopoietic Architecture : Can Buildings Think?

Instead of burning fossil-fuel buildings might eventually have tanks of bacteria-farmed methane. Standard architecture may have vats of bacteria, processing sewage and gray water. Both of these bacterial scenarios bring life forms into mechanical devices and those systems may eventually be controlled by bacterial intelligences hybridized in metabolic architecture (Dawkins 1982. Estévez 2003. Wilson 1999).

Above: Flexible STL eTree, digitally grown in Xfrog, whose trunk has been repressed in favor of piercing, interlocking, looped branches and tendrils. (Figs 11, 24-25). • Right: STL eTree illustrating branches looped and fused into the trunk, creating a 3D truss, column, or beam. (Fig 3).

eTree Generation One program for simulating plant morphology is Xfrog. The software is generally employed to computationally “grow” lifelike digital trees, shrubs, and flowers for special effects in film. Xfrog has the ability to produce forms based on botanic attributes, imparting to its 3D files selected attributes of living organisms—for example, branching, leafing, and spiraling. But its design-growth parameters can also be tasked to generate original structures based on the organic-derived algorithms it uses to mimic, say, an oak or an elm. Metaphorically, such manipulation may result in species of digitally grown design. For example, branching in trees may be transformed—computationally hybridized—to produce experimental structures embedding botanic performance and heritage. In Figures 4 and 5 (pp 30-34) you see models of building frames originated as simulated eTrees. For these frames, selected tree branches were programmed to loop and reinforce the central trunks (eliminating the collar beams, straight braces, tie beams, and queen posts from a traditional truss). Alternating with the looped branches, other branches were programmed out-stretched as beams/struts and configured into geometric frames. The eTree trusses employ simulated tree trunks and branches following natural geometries formulated by

Dennis Dollens

both the software’s modified L-systems and Xfrog’s proprietary growth and environmental rules (Prusinkiewicz & Lindenmayer 1990. Lintermann 1998). The tree-totruss design process relies on natural proportions and processes, such as phyllotaxy, phototropism, and/or gravitropism. While this process does not copy nature, it numerically models facets of nature’s growth patterns, calculated from the biological analysis of plants and trees (Fig 28. pp28-29) (Jean. 1995. Niklas 1994). The digitally grown, STL- trusses have implications for machine fabrication. Their curved, looping, tubular forms (left) have springlike qualities causing them to continually curl and fuse back into their trunks, or to each other in later versions, (opposite). This spiraling, looping operation braces the structure in X and Y directions. The overall unit is then a self-reinforcing, interlocking, three-dimensional sprung brace—effectively a flexible, asymmetrical truss. Stabilization of seismic movement is one obvious requirement that the eTree trusses look to fill. Equally valuable, if further away, are shape-shifting facades or components reconfiguring themselves as weather, data, network, or social conditions change. These types of environmental responses are directly inspired from observing plants—bringing to mind Claus Mattheck’s (1998) idea for “trees as instructors for designers.” The idea behind such design research is to fuse botanical attributes, biological intelligence, digital programming, and structural performance—looking first to natural forms and organisms, then finding useful properties, and finally integrating that information and visualization into a project’s design.

Autopoietic Architecture : Can Buildings Think?

Digital-Botanic Heritage Initially I attempt to identify design principles, generative strategies, or aesthetic logic embedded in plants; second, I apply that information in digital simulations; and third, develop the simulations as bioresponsive projects with physical models. In a recombinatory sense, I hybridize ideas from biology into architectural forms—evolve new systems from them—and then articulate the new design into parts and pieces capable of supporting and sheathing experimental buildings. For example, developing projects to clad the eTree structures with leaflike skins, biomembranes, or monocoques (pp86-87). Design experiments of this kind lead toward botanically informed architectures carrying the generative heritage of digital files originally modeled as simulated plants. The projects do not exactly mimic a plant’s aesthetic, morphology, or anatomy but are, nevertheless, algorithmic cousins infused with plantlike proportions and morphological mathematics. Mobilizing environmental conditions asks a building’s structure and surface to sense changes and address them. Integrated components such as remote sensors, robotic actuators, and digital intelligence are currently options—and good ones-—but ultimately, biological living materials, organs, and hybrid, semi-living/semi-mechanical systems will be necessary. Then botany, technology, experimental gardening, chance-viasoftware, aesthetic decisions, citizen science, and DIY ingenuity can result in visual hypotheses aiding emerging architectural species.

Yucca glauca. Narrow leaf yucca.

Dennis Dollens

Hybridizing Architecture Beyond trusses and structural design, digitally-grown component façades, panels, surfaces, pods, and modular units are subjects of this research. By morphologically transforming algorithmically derived leaves, flowers, stems, roots, and seedpods, the resulting design components retain simulated plant attributes for clustering, massing, fusing, and connecting (Figs 27-28 pp74-77). I think of these transformations as leaders for procedural sets of digital operations encoding biological-like properties in projects and models. Additionally, the procedures help reveal how spaces and forms, digitally generated from plants, can environmentally enhance aesthetic ends while assuming environmental performance. For the software-grown, parametric PodHotel (left & pp90-91) and BioTower (Figs 16-18 pp52-57), I concentrated on interlocking branch armatures — resembling a woven cylindrical basket in the case of the BioTower (Fig 16. 1 & 3 left to right pp52-53). The BioTower’s branches sprout a series of spiraling and clustered digital leaves and biomechanical systems acting as filtration membranes for the building’s interior. The metabolically activated façades were inspired by, and modeled on, the stalks of blooming flowers from narrow leaf yuccas (Yucca glauca). The yucca’s floral spikes express a growth pattern following Fibonacci (Fig 6 pp34-35) spiraling up the stalk (opposite). They illustrate sequential and punctuated placement of forms (flowers/seedpods) responding to environmental orientation factoring in heat/shade/light/air distribution around a cylindrical stalk. This pattern information, genetically determined in the yucca and numerically translated through Xfrog, anchors both the PodHotel’s and the BioTower’s botanical — Turing — heritage (Dollens Detail: PodHotel. Barcelona. Pod clusters influenced by the Yucca glauca (Left. See also: pp90-91).

Autopoietic Architecture : Can Buildings Think?

Scanning Electron Microscope (SEM) view of leaf stoma (top) and stomata (bottom). • Right: five SEM views of Opuntia phaeacantha for the digital-botanic project to reformulate traditional adobe products as thin, lightweight, and strong hybrid materials. • Microscopy photographs: Alberto T. Estévez. Genetic Architectures Research. ESARQ. Universitat Internacional de Catalunya, Barcelona.

2014). The plant geometries and hierarchies inherited through L-systems and Xfrog help realize shapes for enhancing or avoiding heat and light while maximizing photovoltaic and passive wind control. And, experientially looking out from the inside of the BioTower, the view is like that filtered through a tree’s canopy. From a design perspective, the thrust of the ArizonaTower (Figs 7-8 pp36-37) represents an attempt to hypothetically root a building—to bring into an architectural dialogue, not only the aesthetics of what is seen, but also the potential of what is hidden. Yet, to be clear, I am not, at least at this point, suggesting that there are, or should be, architectural roots. My intention is to think of underground anchoring, low-pressure pumping, and water circulation. Furthermore, reasons for investigating root networks are multiple: they anchor and foot, and they are biological ecologies counterbalancing above-ground components. And, since Darwin and Darwin’s (1881) The Power of Movement of Plants—root apicals—what they called radicles, have been investigated as the location of plant intelligence (Baluška et al. 2009). Equally important, underground biological systems bring to mind mechanisms for water storage and distribution, bacterial sensors, and networked information. New underground forms and configurations may be inspired not only by roots, but also by rhizomes, tubers, and bulbs (as well as their bacterial symbionts), culled for ideas to deploy biointelligent and sense-making controllers and actuators. An overview of the prototype STL models made since 1999 (Fig 3 pp28-29) illustrates a lineage of eTree development and structural performance. From the first simple tree with two gnarly branches, the eTrees’complex branching increases until models illustrate design programmed

Dennis Dollens

for growth scaled to habitable, if hypothetical, spaces. For example, in the final image of the sequence, the ArizonaTower (Figs 7-8 pp36-37) sprouts forms at forking botanical nodes where pods and polygons, programmed enormously out of scale, become roomlike architectural capsules. Leaves and Monocoques Leaves as shifting, aggregate clusters, responding to directional winds, or as profile- and surface-reducing organisms (e.g., wilting in extreme heat), have implications for architecture and industrial design. I have been examining the physiological pores (stomata) that leaves use to breatheâ&#x20AC;&#x201D;millions on the underside of a single leaf (opposite page). Using images from scanning electron microscopy, you see individual, biomechanically organized cells tasked with opening and closing (as in a cameraâ&#x20AC;&#x2122;s aperture) that serve low-pressure hydraulic (turgor) plant systems. Scientists use information from microscopes in specifically professional waysâ&#x20AC;&#x201D;designers could respond to it in equally legitimate, if differently implemented, ways (but do not usually have channels to such information). With information from microscopy, designers might grapple with visualized translations of biomechanics for architectural structures, materials, and fabrication to re-direct molecular and cellular nature thus hybridizing buildings with embodied biological functions. It is to the cellular level I sometimes look for ideas to translate leaf (and other plant) functions into design potential. Ideas for developing architectural skins, not as lungs but as filtration membranes, has occupied a place in my thinking since 1996 and continues today on two fronts. One: phase-change materials, though not a

Autopoietic Architecture : Can Buildings Think?

Above: 3D test components populated over a warped surface by ParaCloud for testing the idea of individual monocoques as part of an aggregate curtain wall. • Right page 87: Seed to Panels—Almond shell interior, exterior, and structural in between = nature’s monocoque. • Below the almond: three panel designs for interchanging interior and exterior air, developed between 1999 and 2006 and related to the self-shading Tower for Los Angeles (Figs 12-14).

focus of this text, involves envisioning, formulating, and making prototype organic adobe products that are thin, lightweight, and strong; specific electron microscope research for this project focuses on the prickly pear cactus, Opuntia (p85). A parallel line of research charts the observations that almond shells have different inner and outer surfaces (polished inside, rough out) connected by a filamentous, porous structure. After grasping how almond shells breathe and ventilate through their shell pores, I fit the data into seedpod simulations influencing monocoque and panel design (right). I came to think of them as my test specimens for design research, akin to botany’s Arabidopsis or biology’s genetic test fly, Drosophila. I now consider the aesthetic and technical performance of porous surfaces across a range of shapes and folds found in the forms and curvatures of leaves and seed pods—leading to monocoque prototypes whose perforations are intended to inhale, filter, and exhale. Conclusion One vision for integrating buildings and biological design includes inventing new architectural systems—thinking of them first as natural and then as metabolic and intelligent; thinking that architecture is part of nature. A parallel strategy fosters collaborations between design, biology, and industry/technology thereby encouraging designers to enter industrial and manufacturing production in order to create new biomaterials and intelligent structures. Biology and technology will define our buildings’ increasing ability to interact with nature. Such buildings are likely to be nurtured, and their functions guided, from software, computation, environmental sensors and actuators, and later from living systems. In this scenario, software and

Dennis Dollens

scripting become interpretive tools for generating, analyzing, and integrating design into nature. Presently, branches, leaves, and flowers are pushing me in new directions. In 2007 I began using ParaCloud, not only to populate components onto irregular surfaces, but also to understand parametrics as a way of generating iterative, individually-scaled panels, hybridized with natural properties. Figures 12-14 (pp4449) illustrate tests for parametrically linked components of façade panels as possible elements for deformable metabolic skins. These units, based on the shape-shifting and biointelligence of leaves and seeds, require parametric generative abilities to biomimetically activate resulting morphologies. For example, the LA Tower exterior (Figs 12-14 pp44-49) was generated from an Xfrog grown tree-truss whose branch tips defined a point cloud that in turn, articulated a glass surface. That surface defined the underlying matrix hosting the façade’s 2,000+ ParaCloud generated panels — each folded from a 2D leaf template. Buildings, cities, and their infrastructures are going to be environmentally beneficial, contributing to cleaner air, their skins functioning like sense-making leaves, alerting us to pollution and allergens. Architectures will be adjusting, folding, accommodating, and reorienting themselves to reduce solar gain in hot periods and heat loss in cold, they will be aerodynamically reconfiguring in response to shifting wind loads. Such bioremedial functions may also assist interior air exchange, noise abatement, and toxic filtration. And, I see no reason why buildings should not metabolically and biochemically address carbon sequestration, photosynthesis, and watershed reclamation while, at the same time, providing new habitats for urban bird and native plant life.

Autopoietic Architecture : Can Buildings Think?

Prototype panel. 2004 Adobe, hemp, and Opuntia formulated as part of a thin-wall panel system or monocoque.

If we consider architecture as bioremedial, we need to begin reconceptualizing nature without exclusionary categoriesâ&#x20AC;&#x201D;consequently realigning design/nature in education and design practice. Using the tools of technology, science, and cognition to give buildings and cities biological properties, architecture may be reanimated as an environmental asset, rather than a liability. We may look to biodigital generation and fabrication as one pathway from toxic, formulaic architecture, seeing it instead as a driver of architectural speciation. Viewing design in an evolutionary frame holds promise for creative, technical advancement as todayâ&#x20AC;&#x2122;s highly lethal species of buildings, products, and urbanisms die out, replaced with fitter, performative species. Still, before bioarchitecture or cities can be tested or publicly and professionally considered, before residents and viewers can react to biologically living structures, there have to be alternative examples or prototypes to consider, debate, and refine. What has preceded then is a set of ideas realized as drawings and models for contemplating nature, architecture, digital nature, and the integration of metabolic intelligence into cities and buildings. In an elemental way, the work samples an ongoing experiment in generative simulation from plants-to-software. Such works also illustrates potential directions for environmentally related design linked to botany, biology, and computation that encourages design-by-research for living and hybrid architectures.

Dennis Dollens

Bubble Panel & Block. Related to, and derived from, investigations of almond shells, panels, monocoques, and skins. This permeable block was designed for circulation of air and light as an interior partition.

Ball, Philip. (1999) Made to Measure: New Materials for the 21st Century. Princeton University Press. Princeton. Baluška, František. Mancuso, Stefano. Volkmann, Dieter. & Barlow, Peter W. (2009) “The ‘Root-Brain’ Hypothesis of Charles and Francis Darwin.” Plant Signaling & Behavior. 4:12. 1121-1127. Dec. 2009. Benyus, Janine, M. (1997) Biomimicry: Innovation Inspired by Nature. Quill. New York. Darwin, Charles. & Darwin, Francis. (1881) The Power of Move ment in Plants. New York. D. Appleton and Company. (Available at guttenberg.org) Dawkins, Richard. (1982) The Extended Phenotype. Oxford University Press. New York. Dollens, Dennis. (2014) “Alan Turing’s Drawings, Autopoiesis and Can Buildings Think.” Leonardo: The International Soci ety for the Arts, Sciences and Technology. Cambridge, MA. The MIT Press. 47:3. 249-253. Dollens, Dennis. (2015) “Autopoietic-Extended Architecture: Can Buildings Think?” PhD Diss. Edinburgh School ofArchi tecture and LandscapeArchitecture. University of Edinburgh. Estévez, Alberto, T., Dollens, Dennis, L. Pérez Arnal, Ignasi., Pérez- Méndez, Alfonso., Planella, Ana., Puigarnau, Alfons., and Ruiz Milllet, Joaquim. (2003) Genetic Architectures / Arquitecturas genéticas. UIC. Barcelona. Herrmann, Wolfgang. (1984) Gottfried Semper: In Search of Architec ture. The MIT Press. Cambridge. Jean, Roger V. (1995) Phyllotaxis: A Systemic Study in Plant Morpho genesis. Cambridge University Press. Cambridge. Lintermann, Bernd and Deussen, Oliver. (1998) “A Modelling Method and User Interface for Creating Plants.” Computer Graph ics Forum, Vol 17, No 1, March 1998. PDF, http://www. xfrogdownloads.com/greenwebNew/company/compa nyStart.htm Mattheck, Claus. (1998) Design in Nature: Learning from Trees. Springer. Berlin. Niklas, Karl, J. (1994) Plant Allometry: The Scaling of Form and Process. The University of Chicago Press, Chicago. Prusinkiewicz, Przemyslaw and Lindenmayer, Aristid. (1990) The Algorithmic Beauty of Plants. Springer-Verlag. New York. And: Center for Algorithmic Botany. http://www.algorith micbotany.org/ Sullivan, Louis H. A System of Architectural Ornament: According with a Philosophy of Man’s Powers. The Eakins Press, New York. 1967. Vincent, Julian. (1990) Structural Biomaterials. Princeton University Press. Princeton. Wade, Nicholas. (2007) “Genetic Engineers Who Don’t Just Tinker.” New York. The New York Times. Ideas and Trends. 8 July 2007. Wilson, Edward O. (1999) Consilience: The Unity of Knowledge. Knopf. New York.

Autopoietic Architecture : Can Buildings Think?

Pod Towers

Glasgow Tower. Foreground. Penstemon palmeri seedpod clusters along a vertical stalk studied as a model for morphological pattern, airflow, and light distribution using Xfrog/Rhino/3DS Max. Background, the 3DS Max rendering of the residential tower collaged onto an image of the Glasgow site (Fig 26. pp72-73). 90

Dennis Dollens

PodHotel. Barcelona. Project for grouped eTrees sprouting habitation pods around three tubular trunks (See also pp90-91).

Autopoietic Architecture : Can Buildings Think?

Dennis Dollens

Autopoietic Architecture : Can Buildings Think?

Dennis Dollens

Architecture • Autopoiesis • Theory • Digital & Generative Design • Environment ISBN: 978-0930829735 • $5 Autopoietic Architecture : Can Buildings Think?

Looking through a filter of autopoietic theory as it defines living

intelligent systems, DBA3 Autopoietic Architecture: Can Building

Think? asks questions of how AI, synthetic biology, and living technology can merge with aesthetics, geometry, and plant research in order to visually and systemically aid extrapolation of generative procedural rules, geometries, and metabolic forms to consider

intelligent architectures. Autopoietic Architecture proposes build-

ings hybridized through algorithmic plant simulation, living bacteria, plant metabolism, computational simulation, and living technology. The text discusses and illustrates an induced evolution in one

emerging method — autopoietic-extended design — for metabolic

architecture realized through software-simulated, plant-to-architecture morphology and biological intelligence. The resulting auto-

poietic-generative architecture is manifested in prototype ideas,

theory, structures, surfaces, materials, and systems documented with drawings, renderings, and STL models.

Dennis Dollens, PhD in architecture from the University of Edinburgh, teaches in the Biodigital Master Program at the school of architecture (ESARQ), 96 Universitat Internacional de Catalunya. His companion title to this book is, Autopoiesis for Metabolic Architecture: A Reading of & Guide to, Autopoiesis: The Organization of the Living is available as a Kindle ebook.