Black-Box Optimization of Lighting Simulation in Architectural Design

Design as a critical action can be defined as a concrete-utilitarian construction process and a state-related symbolic ritual. The process of design is defined by the abstraction area, which is accompanied by abstract forms of representation for the physical environment in a built environment and the response to problems. Black Box is defined as tool, system, or object that in art and science is evaluated by inputs and outputs and does not include any internal information. The Black Box problem, which is part of the architectural design process, has been reconstructed as a critical stance to the closed and implicit architectural production process, supporting open thought to the end but formally designing houses that remain faithful to this black box. Hence, this article is an example of a physical and semantic representation production with an emphasis on design as a critical action and metaphor. This article, which presents the black box problem through a theoretical perspective and architectural design studio examples, focuses on the semantic and formal representation of all conditions of a cube. This aspect aims to provide a creative cross section from the potential of infinite design.

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Genetic evolution vs. function approximation: Benchmarking algorithms for architectural design optimization

Journal of Computational Design and Engineering ◽

10.1016/j.jcde.2018.09.001 ◽

2018 ◽

Vol 6 (3) ◽

pp. 414-428 ◽

Cited By ~ 3

Author(s):

Thomas Wortmann

Keyword(s):

Genetic Algorithms ◽

Design Optimization ◽

Architectural Design ◽

Parametric Design ◽

Fitness Landscape ◽

Optimization Methods ◽

Black Box ◽

Direct Search ◽

Model Based ◽

Simulation Based

Abstract 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.

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Performing Form: Judgement and Subjectivity in Algorithmic Architectural Design

10.35483/acsa.teach.2019.26 ◽

2019 ◽

Author(s):

Theodora Vardouli ◽

◽

François Sabourin ◽

Keyword(s):

Architectural Design ◽

Computational Thinking ◽

Computational Design ◽

Active Role ◽

Black Box ◽

Computational Techniques ◽

Digital Revolution ◽

Digital Architecture ◽

Software Interfaces ◽

Digital Computers

It is an oft-made claim that digital computers are changing architectural discourse and professional practice. These changes are plural, varied, and often prosaic. They do not fit one definition of “digital architecture”, nor one manifesto of “digital revolution.” While historians, theorists, and ethnographers of architectural practice are beginning to map the disciplinary valencies and professional effects of digital computers, architectural curricula grapple with questions about when, where, how, and why to introduce computers in an architecture student’s education.1 Professionally accredited architecture curricula negotiate a stifling demand for student proficiency in various kinds of commercial software, with the broader pedagogical possibilities that emerge from the many variances of computational design and making.2 In the parts of a curriculum that integrate a “digital” component, this negotiation usually manifests as a dilemma between training students in software skills and teaching computational processes of thinking, designing, and making architecture. In courses that teach software, computational techniques are often hidden, or “black boxed,” behind the screen. Students deploy them indirectly (through software interfaces) to produce drawings, output construction documents, simulate, and analyze a design’s various performances. Meanwhile, in courses that focus on computational thinking and making, rules and algorithms are out in the open and take on an active role in the creation of architectural space and form.These two approaches echo distinct attitudes toward design processes themselves that surrounded early work on design and computing. In a report on the first international conference of the Design Methods Group—a North American “coalition” of researchers working on “rational” theories and methods of environmental design,3 often through the use of digital computers—architect and urban designer Jonathan Barnett called these two attitudes “black box” and “glass box.”4 “Glass box” approaches were concerned with an analyti-co-mathematical rendition of the design process—asking the question of whether architectural design, or rather which parts of it, could be conceived as a kind of computation: a step-wise process amenable to logico-mathematical description and analysis. Examples of “glass box” work included systematic methods for “fitting” geometric form to functional goals and various methods for enumerating possible geo-metric configurations based on certain rules and constraints, broadly falling under the label of “generative design.” “Black box” approaches, on the other hand, aspired to enhance specific tasks that designers faced in a traditional process through the aid of new graphical and interactive technologies. “Black box” examples included computer aids of different kinds, from drafting tools to conversational interfaces that informed the designer about the impacts of their decisions. In other words, “glass box” approaches recast design as a kind of computation (a step-wise, algorithmic process), while “black box” approaches used computation as a tool for various familiar design tasks.

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