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Balancing design intent and performance: an algorithmic design approach
Architectural Science Review
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The design of building envelopes requires a negotiation between qualitative and quantitative aspects belonging to different disciplines, such as architecture, structural design, and building physics. In contrast to hierarchical linear approaches in which various design aspects are considered and conceived sequentially, holistic frameworks allow such aspects to be taken into consideration simultaneously. However, these multi-disciplinary approaches often lead to the formulation of complex high-dimensional design spaces of solutions that are generally not easy to handle manually. Computational optimisation techniques may offer a solution to this problem; however, they mainly focus on quantitative aspects, not always guaranteeing the flexibility and interactive responsiveness designers need in the early design stage. The use of intuitive geometry-based generative tools, in combination with machine learning algorithms, is a way to overcome the issues that arise when dealing with multi-dimensional design spaces without necessarily replacing the designer with the machine. The presented research follows a human-centred design framework in which the machine assists the human designer in generating, evaluating, and clustering large sets of design options. Through a case study, this paper suggests ways of making use of interactive tools that do not overlook the performance criteria or personal preferences of the designer while preserving the simplicity and flexibility needed in the early design stage.
This paper posits a design approach that integrates multi-agent generative algorithms and structural topology optimisation to design intricate, structurally efficient forms. The research proposes a connection between two dichotomous principles: architectural complexity and structural efficiency. Both multi-agent algorithms and Bi-directional evolutionary structural optimisation (BESO) (Huang and
Xie 2010), are emerging techniques that have significant potential in the design of form and structure.This research proposes a structural behaviour feedback loop through encoding BESO structural rules within the logic of multi-agent algorithms. This hybridisation of topology optimisation and swarm intelligence, described here as SwarmBESO, is demonstrated through two simple structural models. The paper concludes by speculating on the potential of this approach for the design of intricate, complex structures and their potential realisation through additive manufacturing.
The growing interest in sustainability and environmental design promoted the development of tools that respond to the architects' demand for efficient ways to evaluate building performance and optimize their designs. To optimize a design, a parametric model of that design is iteratively instantiated and evaluated by an optimization algorithm that searches for the solutions that best fulfill a set of performance objectives. However, according to Wolpert´s No-Free-Lunch Theorem, different optimization problems are best served by different optimization algorithms and, thus, architects should test multiple ones. Unfortunately, this is not a straightforward task for the currently available algorithmic design tools because different optimization algorithms have different requirements, forcing architects to spend considerable time and effort to adapt their parametric models and simulation tools, to configure the optimization algorithms, and to visualize the optimization results. This paper addresses the beforementioned problems by presenting the integration of a wide range of optimization within the same algorithmic design tool. We discuss the use of this tool in a case study that demonstrates the usefulness of multiple optimization algorithms to avoid the potential non-optimality that emerges from using just one of them.
Computation-based approaches are increasingly important in architecture, namely Algorithmic Design (AD), which is based on the use of algorithms to generate designs. Besides enhancing design exploration, AD helps architects deal with recurrent design changes and with the pressure to quickly obtain results. Moreover, AD supports the search for better-performing solutions that satisfy environmental demands. Unfortunately, the complexity and specialized knowledge required by AD are still restraining architects due to the amount of effort and time needed to implement the necessary algorithms. To make AD and design optimization techniques more accessible, we propose a theoretical framework to help architects with the algorithmic generation, evaluation, and manufacturing of a large variety of designs, by following a continuous design workflow that merges the typically detached design stages. In order to be useful, the framework needs to focus on a domain of application, and, in this paper, we target the development of buildings' façades due to their aesthetical and environmental relevance. We evaluate the framework in the context of a real case study resulting from a collaboration between a team of architects specialized in AD and a traditional design studio.
Performance-Based Design (PBD) helps architects shift towards a more environmentally-conscious design process. To take advantage of this paradigm, the performance of multiple design variants must be evaluated and compared. However, due to its repetitive and tiresome nature, architects feel discouraged to use such an approach. In this context, to promote optima-oriented strategies, PBD approaches can be complemented with optimization processes that automatically evaluate design variants to find the best-performing ones. Unfortunately, building performance evaluations typically require simulations, which tend to be very time-consuming. Moreover, architectural projects often require the assessment of multiple performance criteria, drastically increasing the complexity of the optimization problem and the number of evaluations required. Additionally, as no optimization algorithm consistently outperforms all the others, each design optimization problem requires testing multiple algorithms to identify the best one(s). This paper proposes a parallelized approach to multi-objective optimization that reduces the time needed to test multiple algorithms by more than one order of magnitude, thus addressing the time-sensitiveness of architectural projects while promoting more efficient PBD approaches. We evaluate the proposed approach in the optimization of the daylight performance and material cost of an exhibition space by comparing different optimization algorithms in a parallelized and non-parallelized scenario.
Virtual prototypes enable performance simulation for building components. The presented research extended the application of generative design using virtual prototypes for interactive optimisation of structural nodes. User-interactivity contributed to the geometric definition of design spaces rather than the final geometric outcome, enabling another stage of generative design for the micro-structure of the structural node. In this stage, the micro-structure inside the design space was generated using fixed topology. In contrast to common optimisation strategies, which converge towards a single optimal outcome, the presented design exploration process allowed the regular review of design solutions.
User-based selection guided the evolutionary process of design space exploration applying Online Classification. Another guidance mechanism called Shape Comparison introduced an intelligent control system using an inital image input as design reference. In this way, aesthetic guidance enabled the combined evaluation of quantitative and qualitative criteria in the custom-optimisation of structural nodes. Interactive node design extended the potential for shape variation of custom-optimized structural nodes by addressing the geometric definition of design spaces for multi-scalar structural optimisation.
This paper analyzes eight assumptions that underlie the general consensus in the computer-aided architectural design community that multi-objective optimization is more appropriate for and more analogous to architectural design processes than single-objective optimization. The paper discusses whether (a) architectural problems are best formulated as multi-objective optimization problems, (b) architectural design optimization is only about negotiating tradeoffs, (c) multiple objectives require multi-objective optimization, (d) Pareto fronts represent design spaces, (e) Pareto fronts require multi-objective optimization, (f) multi-objective algorithms are efficient and robust, (g) evolutionary operators make multi-objective algorithms efficient and robust and whether (h) computational cost is negligible. The paper presents practical examples of combining multiple objectives into one and concludes with recommendations for when to use single-and multi-objective optimization, respectively, and directions for future research.
Algorithmic Design is an approach that uses algorithms to generate designs. These algorithms are built using either a Visual Programming Language (VPL) or a Textual Programming Language (TPL). In architecture, there is a clear propensity to the use of VPLs, e.g., Grasshopper or Dynamo, over the use of TPLs, e.g., Python or AutoLisp. In addition to all the user-friendly and interactive features that make VPLs more appealing to architects, most of them already integrate components for textual programming. In contrast, TPLs have not been as successful in incorporating visual features. Given the user-friendliness of VPLs and the relevance of TPLs for large-scale and complex designs, we discuss Visual Input Mechanisms (VIMs) in the context of TPLs. In this paper, we extend previous research in this area by exploring and implementing the most valuable VIMs in a TPL adapted for architectural design.
This paper presents a performance oriented parametric approach for a façade's skin design to enhance indoor daylight quality. This approach utilizes daylight performance evaluation to search for skin design that improves the distribution of the daylight indoors by achieving the requirements of daylight metrics of both Leadership in Energy and Environmental Design (LEED V4) and Illuminating Engineering Society (IES) in early design stages. Parametric design procedure is proposed and consists of four integrated phases: (1) generate the skin, (2) analyse the skin's daylight performance, (3) evaluate the daylight performance and (4) search for the near optimum solution. "Genetic Algorithms" and "Exhaustive Search" as two different search and optimization techniques where implemented to understand the potentials of each one to be integrated in the proposed procedure in the forth phase. The study outlines the challenges and opportunities opened to architects to apply the proposed procedure to integrate daylight performance evaluation in early design stages where the most influential design decisions are taken.
Digital design paradigms in architecture have been rooted in representational models which are geometry centered and therefore fail to capture building complexity holistically. Due to a lack of computational design methodologies, existing digital design workflows do little in predicting design performance in the early design stage and in most cases analysis and design optimization are done after a design is fixed. This work proposes a new computational design methodology, intended for use in the area of conceptual design of building design. The proposed methodology is implemented into a multi-agent system design toolkit which facilitates the generation of design alternatives using stochastic algorithms and their evaluation using multiple environmental performance metrics. The method allows the user to probabilistically explore the solution space by modeling the design parameters’ architectural design components (i.e. façade panel) into modular programming blocks (agents) which interact in a bottom-up fashion. Different problem requirements (i.e. level of daylight inside a space, openings) described into agents’ behavior allow for the coupling of data from different engineering fields (environmental design, structural design) into the a priori formation of architectural geometry. In the presented design experiment, a façade panel is modeled into an agent-based fashion and the multi-agent system toolkit is used to generate and evolve alternative façade panel configurations based on environmental parameters (daylight, energy consumption). The designer can develop the façade panel geometry, design behaviors, and performance criteria to evaluate the design alternatives. The toolkit relies on modular and functionally specific programming modules (agents), which provide a platform for façade design exploration by combining existing three-dimensional modeling and analysis software.
Stringent requirements of efficiency and sustainability lead to the demand for buildings that have good performance regarding different criteria, such as cost, lighting, thermal, and structural, among others. Optimization can be used to ensure that such requirements are met. In order to optimize a design, it is necessary to generate different variations of the design, and to evaluate each variation regarding the intended criteria. Currently available design and evaluation tools often demand manual and time-consuming interventions, thus limiting design variations, and causing architects to completely avoid optimization or to postpone it to later stages of the design, when its benefits are diminished. To address these limitations, we propose Algorithmic Optimization, an algorithmic-based approach that combines an algorithmic description of building designs with automated simulation processes and with optimization processes. We test our approach on a daylighting optimization case study and we benchmark different optimization methods. Our results show that the proposed workflow allows to exclude manual interventions from the optimization process, thus enabling its automation. Moreover, the proposed workflow is able to support the architect in the choice of the optimization method, as it enables him to easily switch between different optimization methods.
This article presents benchmark results from seven simulation-based problems from structural, building energy, and daylight optimization. Growing applications of parametric design and performance simulations in architecture, engineering, and construction allow the harnessing of simulation-based, or black-box, optimization in the search for less resource- and/or energy consuming designs. In architectural design optimization (ADO) practice and research, the most commonly applied black-box algorithms are genetic algorithms or other metaheuristics, to the neglect of more current, global direct search or model-based, methods. Model-based methods construct a surrogate model (i.e., an approximation of a fitness landscape) that they refine during the optimization process. This benchmark compares metaheuristic, direct search, and model-based methods, and concludes that, for the given evaluation budget and problems, the model-based method (RBFOpt) is the most efficient and robust, while the tested genetic algorithms perform poorly. As such, this article challenges the popularity of genetic algorithms in ADO, as well as the practice of using them for one-to-one comparisons to justify algorithmic innovations.
Highlights Benchmarks optimization algorithms on structural, energy, and daylighting problems. Benchmarks metaheuristic, direct search, and model-based optimization methods. Challenges the popularity of genetic algorithms in architectural design optimization. Presents model-based methods as a more efficient and reliable alternative.
This paper presents the first stage of a study examining the digital design and fabrication of a parametrically defined sculptural concrete façade element employing fibre reinforced concrete. On the background of a literature review of related precedent studies, the paper extends the scope of previous studies by offering a detailed insight into the process of integrating architectural considerations with material properties of fibre reinforced concrete, detailed structural analysis and construction constraints. The paper offers technical details with a focus on material to similar ongoing studies.
To paraphrase Robert le Ricolias, the art of the structure is where to put the folds. Using that inspiration, fundamental concepts from origami, topology optimization and architecture are combined in parametric design studies for structurally and architecturally efficient facades. With the rapid rise in computing power, engineers and architects have been enabled to design buildings that are willfully inefficient for the sake of architectural expression. This research provides a way to couple artistic expression with structural efficiency by examining the potential efficiencies inherent in origami-inspired building envelope. Several architectural strategies from constructed high-rise buildings are examined as examples of how architectural expression can help or hinder facade efficiency. The studies use daylighting or "self-shading" (architectural constraint) and stress ratios (structural constraint) to evaluate the impact of adjusting panelized geometry. A detailed parametric study of origami-inspired structural components and systems are presented as a baseline procedure for designers. Architectural implications of the layouts are evaluated using Grasshopper scripts and post-processing macros. The structural implications of the layouts have been analyzed with 3D FEA software using scripts to automate the analysis and results are interpreted in terms of material efficiency. Preliminary results of the method used on the Beijing Greenland Center case study indicate that origami-inspired topology resulted in a façade system that is 30% more energy efficient with 10% less material than the original geometry.
Computational design tools based on Autodesk’s DesignScript language have been used with geometry and topology modelling techniques in the design of a climatised free-form building envelope. This project involves structural and performance analysis tools applied to structural engineering, façade engineering and fabrication planning. The project has progressed from concept through tender phases. The particular geometry presented unique conditions that required non-standard solutions to be used; to this end
DesignScript was introduced to allow the design and engineering team to build a number of scripted topological façade models that explored alternative façade configurations. This paper combines a discussion about the specific fabrication project with a more generalised discussion of the role of computational tools in design and fabrication. The main interest is to explore the two-way relationship between practice and tool building by considering how computation can contribute to a practical fabrication project and equally important, how computational tools can be tested and refined by being used in practice on demanding projects.
During the last decades, standards on building construction have risen sharply to integrate new, ambitious demands regarding energy efficiency, as well as thermal and optical comfort in the design procedure. Building simulation software assists in the accurate calculation of a hypothetical or existing building's performance on several aspects; but they are, in their vast majority, assessment-oriented, rather than focused on dynamically supporting the decision-making procedure. During the last two decades, a clear shift of design professionals and academia towards addressing performance issues from the conceptual stages of a building's design is observed. In this review, the methodology of performance-driven design optimization using computational/parametric design and optimization is presented, and the core literature available on the topic is reviewed in order to identify the current status, different approaches, obstacles, and areas of future research on the subject. The review findings confirm that there is enormous potential for the design of better-performing buildings using this technique, but there are still many obstacles to overcome and areas for future research.
This paper examines Parametric Design (PD) in contemporary architectural practice. It considers three case studies: The Future of Us pavilion, the Louvre Abu Dhabi and the Morpheus Hotel. The case studies illustrate how, compared to non-parametrically and older parametrically designed projects, PD is employed to generate, document and fabricate designs with a greater level of detail and differentiation, often at the level of individual building components. We argue that such differentiation cannot be achieved with conventional Building Information Modelling and without customizing existing software. We compare the case studies' PD approaches (objected-oriented programming, functional programming, visual programming and distributed visual programming) and decomposition, algorithms and data structures as crucial factors for the practical viability of complex parametric models and as key aspects of PD thinking.
The paper focuses on possibilities of already known engineering procedures such as Finite Element Method or Topology Optimisation for effective implementation in architectural design process. The existing attempts of complex engineering algorithms implementation, as a form finding approach will be discussed. The review of architectural approaches utilising engineering methods will be supplemented by the author's own solution for that particular problem. By intersecting architectural form evaluation with engineering analysis complemented by optimisation algorithms, the new quality of contemporary architecture design process may appears.
Most architectural modelling software provides the user with geometric freedom in absence of performance, while most engineering software mandates pre-determined forms before it can perform any numerical analysis. This trial-and-error process is not only time intensive, but it also hinders free exploration beyond standard designs. This paper proposes a new structural design methodology that integrates the generative (architectural) and the analytical (engineering) procedures into a simultaneous design process, by combining shape grammars and graphic statics. Design tests presented will demonstrate the applicability of this new methodology to various engineering design problems, and demonstrate how the user can explore diverse and unexpected structural alternatives to conventional solutions.
Parametric modelling software often maintains an explicit history of design development in the form of a graph. However, as the graph increases in complexity it quickly becomes inflexible and unsuitable for exploring a wide design space. By contrast, implicit low-level rule systems can offer wide design exploration due to their lack of structure, but often act as black boxes to human observers with only initial conditions and final designs cognisable. In response to these two extremes, the authors propose a new approach called Meta-Parametric Design, combining graph-based parametric modelling with genetic programming. The advantages of this approach are demonstrated using two real case-study projects that widen design exploration whilst maintaining the benefits of a graph representation.
Recent developments in computational design tools have bridged a gap between a well-established parametric building modeling[1] and analysis or simulation software such as EnergyPlus[2], Radiance[3], Daysim[4] and OpenStudio[5], opening up the possibility for architects to use the computational power to model and simulate real environmental behavior of the architectural artefact and its components. Now architects are able to evaluate the behavior of a project, whether it is a building, a city, a landscape or infrastructure and a new road towards an architecture based on performance is opened[6]. Therefore we can put the idea of performance as a precedent to shape development and the architectural form becomes informed by the performative aspects. We can use various computational tools in order to gather qualitative and quantitative aspects of the architectural artefacts performance in the early stages of design, and we can go further from just optimizing a form after it has been defined.
This dissertation focuses on computational strategies for incorporating structural considerations into the earliest stages of the architectural design process. Because structural behavior is most affected by geometric form, the greatest potential for structural efficiency and a harmony of design goals occurs when global formal design decisions are made, in conceptual design. However, most existing computational tools and approaches lack the features necessary to take advantage of this potential: architectural modeling tools address geometry in absence of performance, and structural analysis tools require an already determined geometrical form. There is a need for new computational approaches that allow designers to explore the structural design space, which links geometric variation and performance, in a free and interactive manner. The dissertation addresses this need by proposing three new design space strategies. The first strategy, an interactive evolutionary framework, balances creative navigation of the design space with a focus on performance. The original contributions of this strategy center on enhanced opportunities for designer interaction and control. The second strategy introduces structural grammars, which allow for the formulation of broad and diverse design spaces that span across typologies. This strategy extends existing work in geometry-based shape grammars by incorporating structural behavior in novel ways. Finally, the third strategy is a surrogate modeling approach that approximates the design space to enable fast and responsive design environments. This strategy contributes new ways for non-experts to use this machine-learning-based methodology in conceptual design. These three complementary strategies can be applied independently or in combination, and the dissertation includes a discussion about possibilities and techniques for integrating them. Finally, the dissertation concludes by reflecting on its potential impact on design in practice, and by outlining important areas for future work. Key words: conceptual structural design, design space exploration, structural optimization, interactive evolutionary algorithm, structural grammar, surrogate modeling, structural design tools
The importance of Algorithmic Design (AD) is growing due to its advantages for the design practice: It empowers the creative process, facilitating design changes and the exploration of larger design spaces in viable time, and supports the search for better-performing solutions that satisfy environmental demands. Still, AD is a complex approach and requires specialized knowledge. To promote its use in architecture, we present a mathematics-based framework to support architects with the algorithmic development of designs by following a continuous workflow embracing the three main design stages: exploration, evaluation, and manufacturing. The proposed framework targets the design of buildings’ facades due to their aesthetical and environmental relevance. In this paper, we explain the framework’s structure and its mathematical implementation, and we describe the predefined algorithms, as well as their combination strategies. We focus on the framework’s algorithms that generate different geometric patterns, exploring their potentialities to create and modify different facade designs. In the end, we evaluate the flexibility of the framework for generating, modifying, and optimizing different geometrical patterns in an architectural design context.
In accordance with national and international regulations, the energy usage and emissions of buildings need to be reduced. Both new constructions and retrofit actions should consider the strict requirements of a more sustainable built environment. In many cases, passive and active strategies are only added to the project after an initial conceptual design of the building has already been drawn up, thus limiting their effective integration into the construction as well as their efficacy. Moreover, the complexity linked to different and often contrasting objectives calls for a multi-objective optimization of the process that simultaneously considers energy and emissions in addition to costs. Given the above-mentioned challenges, we propose a novel approach to the multi-objective optimization of new buildings and retrofit actions that considers geometry and envelopes variables, allowing for architectural variability. We demonstrate the efficacy of the proposed method by means of its application to a case study building that is located in the Mediterranean climate and is already compliant with current regulations. The proposed multi-objective optimization approach, based on a genetic algorithm and conducted by means of open access software, made it possible to save 60% of the annual energy demand by means of geometry optimization and, once the geometry was fixed, 23% of the annual energy cost by means of passive and active strategies, while reducing the energy simulations required from 3 × 1012 to less than 8000.
Typogenetic Design is an interactive computational design system combining generative design, evolutionary search and architectural optimisation technology. The active tool for supporting design decisions during architectural shape generation uses an aesthetic system to guide the search process. This aesthetic system directs the search process toward preferences expressed interactively by the designer. An image input as design reference is integrated by means of shape comparison to provide direction to the exploratory search. During the shape generation process, the designer can choose solutions interactively in a graphical user interface. Those choices are then used to support the selection process as part of the fitness function by online classification.
Enhancing human decision making capabilities in human-in-the-loop design systems addresses the complexity of architecture in respect to aesthetic requirements. On the strength of machine learning, the integral performance trade-off during multi-criteria optimisation was extended to address aesthetic preferences. The tacit knowledge and subjective understanding of designers can be used in the shape generation process based on interactive mechanisms. As a result, an integrated support system for performance-based design was developed and tested.
Closing the loop from design to construction using design optimisation of structural nodes in a set of case studies confirmed the need for intuitive design systems, interfaces and mechanisms to make architectural optimisation more accessible and intuitive to handle. This dissertation investigated Typogenetic Design as a tool for initial morphological search. Novel instruments for human interaction with design systems were developed using mixed-method research. The present investigation consists of an in-depth technological enquiry into the use of interactive generative design for exploratory search as an integrated support system for performance-based design. Associated project-based research on the design potential of Typogenetic Design showcases the application of the design system for architecture.
Generative design as an expressive tool to produce architectural geometries was investigated in regard to its ability to drive initial morphological search of complex geometries. The reinterpretation of processes and boosting of productivity by artificial intelligence was instrumental in exploring a holistic approach combining quantitative and qualitative criteria in a human-in-the-loop system. The shift in focus from an objective to a subjective understanding of computational design processes indicates a perspective change from optimisation to learning as a computational paradigm.
Integrating learning capabilities in architectural optimisation enhances the capability of architects to explore large design spaces of emergent representations using evolutionary search. The shift from design automation to interactive generative design introduces the possibility for designers to evaluate shape solutions based on their knowledge and expertise to the computational system. At the same time, the aesthetic system is trained in adaptation to the choices made by the designer. Furthermore, an initial image input allows the designer to add a design reference to the Typogenetic Design process. Shape comparison using a similarity measure provides additional guidance to the architectural shape generation using grammar evolution.
Finally, a software prototype was built and tested by means of user-experience evaluation. These participant experiments led to the specification of custom software requirements for the software implementation of a parametric Typogenetic tool. I explored semi-automated design in application to different design cases using the software prototype of Typogenetic Design. Interactive mass-customisation is a promising application of Typogenetic Design to interactively specify product structure and component composition. The semi-automated design paradigm is one step on the way to moderating the balance between automation and control of computational design systems.
The building sector presents one of the largest economic and environmental footprints. Building performance optimization can minimize this impact by combining (1) algorithmic approaches, to generate multiple building design variants, (2) simulation tools, to evaluate building's performance regarding distinct aspects, and (3) optimization algorithms, to seek more efficient building designs. Unfortunately, despite the existence of several optimization algorithms, their application to architectural optimization problems is not well-studied and this often drives architects towards the application of the simplest
available algorithm. However, this rarely is the most efficient option for addressing a specific problem, in particular, due to the time-intensive simulations required to evaluate building designs. As a result, poor algorithm selection might lead to unacceptable optimization times and less efficient designs.
This dissertation addresses optimization algorithms specially tailored for handling simulation-based optimization problems. In particular, we develop and assess an optimization framework in the context of three architectural case studies involving single- and multi-objective optimization of simulation-based lighting, structural, and cost aspects of buildings. Obtained results reveal that solutions' quality and the time spent in optimization are strongly dependent on the algorithm's choice. Thus, different algorithms
should be tested in order to determine the one that better �ts an optimization problem. Finally, this dissertation shows the benefits of optimization to reduce the impact of the building sector and motivates its introduction as an indispensable phase of the architectural design process.
This article describes how cutting‐edge, parameter‐based computational engineering techniques have been used to undertake the conception, analysis and documentation of the 2500 complex steelwork connections in the exoskeleton of the new Morpheus Hotel in Macau. It discusses the tools, methodologies and strategies devised by the engineering team to automate the time‐consuming model creation and data‐handling operations associated with the finite element analysis, enabling them to complete this challenging part of the project in just 12 months.
In the past, there has been a rapid evolution in computational tools to represent and analyze
architectural designs. Analysis tools can be used in all stages of the design process, but they are
often only used in the final stages, where it might be too late to impact the design. This is due to
the considerable time and effort typically needed to produce the analytical models required by the
analysis tools. A possible solution would be to convert the digital architectural models into analytical
ones, but unfortunately, this often results in errors and frequently the analytical models need to be
built almost from scratch. These issues discourage architects from doing a performance-oriented
exploration of their designs in the early stages of a project.
To overcome these issues, we propose Algorithmic Design and Analysis, a method for analysis that is
based on adapting and extending an algorithmic-based design representation so that the modeling
operations can generate the elements of the analytical model containing solely the information
required by the analysis tool. Using this method, the same algorithm that produces the digital
architectural model can also automatically generate analytical models for different types of analysis.
Using the proposed method, there is no information loss and architects do not need additional work
to perform the analysis. This encourages architects to explore several design alternatives while
taking into account the design’s performance. Moreover, when architects know the set of design
variations they wish to analyze beforehand, they can easily automate the analysis process.
Current green building practice has been largely advanced by an integrated design process. This integrated design process involves multiple disciplines, such as architecture, civil, mechanical, and electrical engineering. The design method heavily relies on utilizing building performance simulation to illustrate how design parameters affect the energy consumption and quality of the indoor environment before actual design decisions are made (Anderson, 2014). The architectural design tools in the integrated design process supersede traditional geometrical exploration instruments, such as Sketchup, Revit, ArchiCad, and Rhino (Negendahl, 2015). More building performance simulating tools, such as Ecotect, Computational Fluid Dynamics (CFD), Radiance, and EnergyPlus, have been developed to help architects measure building performance (e.g., natural ventilation, daylighting, solar radiation, and energy uses) in the design process and attain green building standards such as Leadership in Energy and Environmental Design (LEED). The information presented by these tools guide architects at a certain level in achieving green building goals. However, building simulation is generally beyond the architect's knowledge domain. Many architects have difficulty in understanding these technical terms and models, as well as their design implications. Therefore, specific consultants have emerged to help architects grasp the meanings of these numbers and models, which require architects to implement a high level of design collaboration and coordination (Aksamija, 2015; Gou & Lau, 2014). Simulation consultants can work in parallel with architects at the early design stage to intervene in the conceptual and schematic design; they may also work behind architects to verify the building performance after the design is finished and make their design green through technical alterations.
Most existing literature argues for an early intervention of building performance simulation in the architectural design process and explores different algorithms or models for optimal intervention (Degens, Scholzen, & Odenbreit, 2015; Sick, Schade, Mourtada, Uh, & Grausam, 2014; Svetlana Olbina & Yvan Beliveau, 2007). However, the difference between early intervention and late verification is often not investigated. Few qualitative studies can help understand how the building performance simulation is actually implemented, and how it influences the quality of design solutions in addition to the quantity of performance outcomes. The current research presents two case studies that compare building performance simulation as an early intervention and a late verification tool in the architectural design process, which contextualizes the building simulation research in real building practices.
The aim of this study is to illustrate and compares the use of Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO) for multi-objective optimization of two and three dimensional moment resisting steel structures subjected to earthquake loads. For this purpose, steel buildings with different characteristics are designed under earthquakes using the Non-dominated Sorting Genetic Algorithm (NSGA-II) and PSO as a tool to achieve the best structure in terms of: minimize the total structural weight (which is directly related with the costs), control of the maximum inter-story drift, and to satisfy the strength requirements of the AISC-LRFD specification. It is considered that all the steel structures are constituted by elements with W section (256 in total) taken from the LRFD-AISC Database. Although, the GAs and PSO are applied for moment resisting steel structures, the concepts can be extended for other structural systems. It is concluded that the use of NSGA-II and PSO reduce the structural weight and they are a very useful tools to improve the structural performance of the buildings. Finally, the structural buildings obtained via PSO are in general better solutions in comparison with the NSGA-II approach.
Engineering and architectural problems most of the times are not dealing with only one single objective to be satisfied. To the contrary almost always these problems contain different objectives to be fulfilled: problems such as how to select the optimized option among objectives such as energy consumption, comfort, and beauty of the building, or construction cost, operation and maintenance costs, and expected life-span of the design building.
In the field of Generative Design (GD), Visual Programming Languages (VPLs), such as Grasshopper, are becoming increasingly popular compared to the traditional Textual Programming Languages (TPLs) provided by CAD applications, such as RhinoScript. This reaction is explained by the relative obsolescence of these TPLs and the faster learning curve of VPLs. However, modern TPLs offer a variety of linguistic features designed to overcome the limitations of traditional TPLs, making them hypothetical competitors to VPLs. In this paper, we reconsider the role of TPLs in the design process and we present a comparative study of VPLs and modern TPLs. Our findings show that modern TPLs can be more productive than VPLs, especially, for large-scale and complex design tasks. Finally, we identify some problems of modern TPLs related to portability and sharing of programs and we propose a solution.
This paper presents a comprehensive review of all significant research applying computational optimisation to sustainable building design problems. A summary of common heuristic optimisation algorithms is given, covering direct search, evolutionary methods and other bio-inspired algorithms. The main summary table covers 74 works that focus on the application of these methods to different fields of sustainable building design. Key fields are reviewed in detail: envelope design, including constructions and form; configuration and control of building systems; renewable energy generation; and holistic optimisations of several areas simultaneously, with particular focus on residential and retrofit. Improvements to the way optimisation is applied are also covered, including platforms and frameworks, algorithmic comparisons and developments, use of meta-models and incorporation of uncertainty. Trends, including the rise of multi-objective optimisation, are analysed graphically. Likely future developments are discussed.
Façade design is a cross-disciplinary multi-objective optimization process. The major barrier to devising an optimal façade solution is the evaluation of the true values of alternative façade design options. A simple approach is to focus on a limited number of design criteria that can be evaluated through mono-disciplinary commercial software, while overlooking other cross-disciplinary design criteria. This paper describes a prototype whole-life value optimization tool for façade design, which accounts for the functional, financial, and environmental sustainability of alternative façade options. The tool adopts an integrated approach involving accurate simulation, systematic parametric analysis, and automatic design optimization. The tool is trialled on a real-world façade design project, and it successfully identified optimal façade solutions that outperform the original solutions obtained from the conventional façade design process.
Climate adaptive building shells (CABS) are receiving increasing attention because they can enable high-performance building design that combines low energy consumption with good indoor environmental quality (IEQ). Various studies have acknowledged the potential of CABS with seasonal adaptation, but thus far, there is no method available to quantify their performance potential. This paper presents a framework for design and performance analysis of CABS with optimal seasonal adaptation strategies. The framework is based on a sequence of multi-objective optimization scenarios and uses a genetic algorithm in combination with coupled building energy and daylighting simulations. Findings from a case study with an office building in the Netherlands demonstrate the effectiveness of the framework in quantifying the potential of seasonal CABS. Results of the case study show that monthly adaptation of six facade design parameters can lead to improved IEQ conditions and 15–18% energy savings compared to the best performing non-adaptive building shell. Through post-optimization analysis of monthly and annual solutions, a better understanding of the key elements of seasonal facade adaptation is obtained.
Fernando Romero and Armando Ramos of Fernando Romero EnterprisE (FREE) describe how the firm's design for an iconic museum in Mexico City, which adopted complex computational techniques, required them to develop an integrated and highly collaborative approach to design; with a central digital 3-D model being applied throughout the construction phase.
Building design is quite a complicated task with the design team trying to counterbalance various antagonistic parameters, which in turn are subject to various constraints. Due to this complexity, performance simulation tools are employed and as a consequence, optimization methods have just started being used, mainly as a decision aid. There are examples, amongst the architectural community, where probabilistic evolutionary algorithms or other derivative-free methods have been used with various decision variables and objective goals. This paper is a review of the methods and tools used for the building design optimization in an effort to explore the reasoning behind their selection, to present their abilities and performance issues and to identify the key characteristics of their future versions.
Passive solar design strategies comprise important ways of reducing the heating, cooling and lighting energy consumption of buildings. Although it is relatively simple to reduce the energy use up to some extent by applying individual strategies, very high levels of energy performance require application of the optimal combination of several strategies, verified through building energy simulations. Here we give an exhaustive review of the previous studies of simulation-based optimization of passive solar design strategies, with particular focus on recent research results.
