Performance-Driven Generative Design in Buildings: A Systematic Review

In multi-objective Bayesian optimization, an infill criterion is an important part, as it is the indicator to evaluate how much good a new set of solutions is, compared to a Pareto-front approximation set. This paper presents a deterministic algorithm for computing the Expected R2 Indicator for bi-objective problems and studies its use as an infill criterion in Bayesian Global Optimization. The R2-Indicator was introduced in 1998 by M. Hansen and A. Jaszkiewicz for performance assessment in multi-objective optimization and is more recently also used in indicator-based multi-criterion evolutionary algorithms (IBEAs). In Bayesian Global Optimization, we propose the Expected R2-indicator Improvement (ER2I) as an infill criterion. It is defined as the expected decrease of the R2 indicator by a point that is sampled from a predictive Gaussian distribution. The ER2I can also be used as a pre-selection criterion in surrogate-assisted IBEAs. It provides an alternative to the Expected Hypervolume-Indicator Improvement (EHVI) that requires a reference point, bounding the Pareto front from above. In contrast, the ER2I works with a utopian reference point that bounds the Pareto front from below. In addition, the ER2I supports preference modelling with utility functions and its computation time grows only linearly with the number of considered weight combinations. It is straightforward to approximate the ER2I by Monte Carlo Integration, but so far a deterministic algorithm to solve the non-linear integral remained unknown. We outline a deterministic algorithm for the computation of the bi-objective ER2I with Chebychev utility functions. Moreover, we study monotonicity properties of the ER2I w.r.t. parameters of the predictive distribution and numerical simulations demonstrate fast convergence to Pareto fronts of different shapes and the ability of the ER2I Bayesian optimization to fill gaps in the Pareto front approximation.

In the last decade, many studies focused on finding optimal design solutions considering trade-offs between different aspects of building design. Accordingly, multi-objective optimization (MOO) approaches have been increasingly applied in the building industry. However, certain aspects must be deepened to ensure a more effective decision-making process in the early planning phase. On the one hand, uncertainties should be considered before making decisions to ensure the robustness of the optimal solutions; on the other hand, decisions are made at different times in building planning, and the sequential order of making decisions should be modeled. This paper presents a holistic two-stage multi-objective stochastic optimization (MOSO)-II framework to minimize the environmental impact of global warming potential (GWP) and cost throughout the entire life cycle of a building (phases of A1-A3, B4, B6 and C3-C4), considering passive and active design strategies in two consecutive stages, under uncertainty. Herein, individual/use and political/market uncertainties are considered. As a proof of concept, the proposed framework is applied in a case study for a generic zone in a multi-family terraced residential building type with solid brick construction. The advantages of the proposed framework are validated by comparing it with alternative single-stage MOSO frameworks. Results show that the proposed two-stage MOSO-II framework can deliver a smaller range of solutions with a better performance in terms of lower GWP and cost. It indicates that the proposed framework can effectively assist planners in decision-making by reducing the effort in choosing the proper solution. Secondly, the results also emphasize the importance of passive design strategies in sustainable building planning. In addition, the energy mix structure and cost of energy sources should be carefully adjusted in the future to promote a more ecologically sustainable building design.

Due to the adversarial limitations between the output solutions in the multi-objective optimization stage, the energy efficiency optimization of large pumping stations has the disadvantage of difficult coordination between power consumption and efficiency. Therefore, a multi-objective Bayesian hyperparameter optimization algorithm design for energy efficiency optimization of large pumping stations has been carried out. A multi-objective optimization model for energy efficiency of large pumping stations was constructed by combining the input power of the main motor, transmission energy loss, transformer loss, and energy consumption of auxiliary equipment, with efficiency as the constraint. We extract sequence features that affect pump water demand, and use feature labels as hyperparameters for the multi-objective optimization model of energy efficiency in large pumping stations. We transform the multi-objective optimization problem into a hyperparameter optimization problem, avoiding adversarial output solutions. We iterate the query points collected by EI, so the output model reaches a convergent optimal solution. The test results show that the designed algorithm can improve the pumping efficiency of the pump station while controlling the power consumption of the pump station, achieving collaborative optimization of efficiency.

Parametric multi-objective optimization tools bear the potential to integrate, optimize, and explore design spaces to support interdisciplinary decision-making. A parametric optimization and decision support tool was developed (POD tool), and an evolutionary multi-objective optimization algorithm implemented (POD MOO tool) to automate design search for flexible integrated industrial building design. Both tools were tested and compared within a user study, simulating an interdisciplinary industrial building design process to evaluate if the MOO creates advanced building options in design search. Evaluations of questionnaires show the preference to search for a design by manipulating parameters instead of automatically generated designs from the algorithm.

Computational fluid dynamics (CFD), as the most sophisticated airflow modelling method, can simultaneously predict airflow, heat transfer and contaminant transportation in and around buildings. This paper introduces the roles of CFD in building design, demonstrating its typical application in designing a thermally conformable, healthy and energy-efficient building. The paper discusses the primary challenges of using CFD in the building modelling and design practice. Furthermore, it analyses the developing trends in applying CFD to building design, by thoroughly reviewing the literatures in all the proceedings of the International Conference on Building Simulation, one of the most influential symposiums in the building simulation field.

A performance comparison of multi-objective optimization algorithms for solving nearly-zero-energy-building design problems

Pressure swing adsorption (PSA) is an energy-efficient technology for gas separation, while the multi-objective optimization of PSA is a challenging task. To tackle this, we propose a hybrid optimization framework, which integrates three steps. In the first step, we establish surrogate models for the constraints using Gaussian processes (GPs) and employ multi-objective Bayesian optimization to search for feasible points that satisfy the constraints. In the second step, we establish surrogate models for the objective function and constraints using GPs and utilize constrained multi-objective Bayesian optimization to search for an approximate Pareto front. In the third step, we perform a local search based on the approximate Pareto front. By employing the trust region filter method, we construct quadratic models for each constraint and objective function and refine the Pareto front to achieve local optimality. This framework demonstrates the efficiency of Bayesian optimization and the local optimality of the trust region method. A comparison with the popular evolutionary algorithm, Nondominated Sorting Genetic Algorithm II (NSGA-II), showed that this framework had a higher hypervolume of the Pareto front while halving the runtime and reducing the number of simulations by a factor of 20.

Digital technologies enable realistic and highly refined representations early in the design process, yet designers frequently use rudimentary objects in their design practice. This research proposes a framework to explain characteristics of objects that make them appropriate for different roles in complex design projects. Objects created by designers in three research settings are explored through interviews. Through the lens of sociomateriality, objects are seen in roles of joining conversation across knowledge boundaries or encapsulating conversation. Four characteristics of fidelity, investment, ambiguity, and history are proposed to determine which role an object is suited to. The framework helps explain and guide effective use of technology and appropriate use of objects in design.

Conventional electromagnetic linear actuators(ELA) have difficulty in balancing efficiency, response and accuracy, so a novel compound ELA is researched, featuring a coaxial integrated design of moving coil and moving iron actuators that switch between co-drive mode and separate drive mode based on service environment. In order to further enhance the comprehensive performance of the actuator and improve its adaptability to extreme working conditions, a multi-objective hierarchical optimization strategy was proposed based on NSGA-II algorithm by analyzing its structural principle, magnetic circuit characteristics and electromagnetic force characteristics. The moving coil actuator, as the main driving component, and the moving iron actuator, as the auxiliary driving component, are selected as the first and second layer optimization objects for multi-objective optimization design. Results indicate that the actuator achieves starting forces of 587.6N and 191.4N under cooperative drive mode and separate drive mode, respectively. Following optimization, these forces increased by approximately 2.2% and 22.4%, while the mover mass was reduced by about 7.2%. The actuator's thrust density, response speed, and structural compactness were effectively enhanced. The proposed approach can be used to solve the complex multi-objective optimization tasks in ELA designing.

International audience

:A robust multi-objective wing design optimization procedure using CFD is presented in this work. Instead of directly applying CFD to wing design optimization, a Kriging aerodynamic model that approximates CFD result is proposed to save computational cost. By introducing 6σ robust approach as a sub-objective function, multi-objective wing design problems can be solved with aerodynamic robustness. Non-Uniform Rational B-Spline curves (NURBS) are introduced to depict the wing geometry in design optimization process. The most important parameters of the wing geometry can then be screened by design of experiment and treated as design variables due to the local variation property of NURBS. Under this formulation, a multi-objective genetic algorithm based on non-dominated sorting is employed to handle multiple flight conditions in wing design. The optimum wing geometry is selected according to trade-off among design objects on the non-dominated front. To validate the proposed approach, two robust design optimization cases are studied for ONERA-M6-Wing. It turns out that the drag coefficient is insensitive to Mach number between Ma0.8–Ma0.9 after robust optimization. The result also verifies the effectiveness of our method in boosting performance and robustness of multiple flight conditions: the lift curve slope (Ma0.3) of optimum wing and the 6σ objective function of transonic drag (Ma0.8–Ma0.9) increase by 11.9% and 25.4%, respectively. Observations in the optimization cases are concluded as follows: 1) For wing aerodynamic design problems, the multi-objective robust optimization can both improve the performance at different flight conditions and provide robustness. 2) The Kriging aerodynamic model derived from CFD result can satisfy the precision requirement of wing design. 3) Even though the optimization result is subject to the weights of the 6σ function in sub-object, the influence is not comparable to the trade-off among design objects. 4) The optimum solution obtained by proposed approach is superior to gradient based optimization method, while the computational cost is acceptable.

Consideration of indoor air quality is a known practice in building and home design in developed Countries, and appropriate measures are taken during design for residential health. This is not so in developing economies. There is a dearth of research work on indoor air quality. Yet there is high level of exposure of the population to indoor air pollutants in residential buildings in these regions of the world. Building designers are yet to consider this problem in their designs. A major cause of indoor air pollution is the traditional method of cooking using dirty fuels. At present, no designs in these regions take cognizance of the use of these dirty fuels. This study addresses the problem of indoor air pollution of residential buildings in these areas. The study adopts ‘International Environmental Protection Agencies’ Evaluation Methodology, involving the use of Building Physics Equipment; Light House Laser Particle Counters; Carbon Monoxide and Extech DCO 1001, that measures particulates, carbon monoxide, indoor relative humidity, indoor temperature, and carbon dioxide quality respectively. Subjective assessment using questionnaire is also adopted. The data were analyzed with Statistical Package for the Social Sciences (SPSS). The results reveal that indoor air quality problem is common in the study area. The major pollutants identified in the study included: gases, steam, particles of dust and fibers, most of which are from internal indoor combustions. The result of the analysis also reveals that the factor loadings yielded a high cumulative percentage of the measured variables, in other words, a high concentration of the pollutants in the indoor air mass. The study recommends a new approach in residential buildings design that ensures immediate removal of indoor air pollutants from their source of generation.

Aiming at the problems such as low computational efficiency and long process in dynamic topology optimization, combined with bi-directional evolutional structure optimization, the dynamic optimization problem is transformed into the static optimization problem in multiple load cases. Newmark method is used to analyze the structure dynamics load. The topology optimization model based on equivalent static load is established. On the basis of the equivalent static load, multi-objective dynamics evolutionary structure optimization is studied. And the mathematical model of the dynamic stiffness and natural frequency about the multi-objective optimization is established. The mathematical formula of the sensitivity is deduced. Then a complete optimization iterative process is given. The numerical example illustrates that it is feasible to solve the multi-objective dynamic topology optimization with equivalent static loads. Modal tracking technique is introduced to the multi-objective dynamic topology optimization. The numerical example shows that modal tracking technology can track the required modal in the optimization process.

A new approach in multi-objective evolutionary optimization is decomposition. Decomposition is a basic method in old multi-objective optimization which in evolutionary multiobjective optimization, has not been widely used. Using this method, a multi-objective optimization problem is converted into a number of scalar subproblems, and all the subproblems are simultaneously optimized. In this paper, the performance and efficiency of the algorithm MOEA/D (multi-objective evolutionary algorithm based on decomposition) with the performance of two algorithms NSGA-II and MOPSO (evolution optimization methods based on dominance) for solving constrained portfolio optimization in Tehran Stock Exchange, has been compared. Portfolio return and its risk, has been considered as the optimization objectives and CvaR has been considered as a risk measure. The results indicate the high potential of these algorithms for constrained portfolio optimization. Also, the results indicate that the optimization algorithm based on decomposition has lower computational complexity, and Pareto front is more extensive than the other two methods. (Abstract)

Urban Nature-Based Solutions have emerged as sound strategies for urban climate change adaptation, but they lack effective decision-support tools. This paper proposes a decision-support framework that relies on Multi-Objective Optimization (MOO), the consideration of actual space availability, the consideration of the simultaneous objectives of climate change adaptation and water management, the integration of demand for co-benefits, and the participatory exploration of trade-offs for decision-makers. The MOO is applied to the case study of the French city of Bordeaux, to identify the optimal location of Groundwater Recharge Infrastructures (GRI) that include Nature-Based Solutions. It provides decision-makers with spatially explicit solutions for developing GRI, with the objectives of maximizing groundwater recharge, maximizing urban cooling, while minimizing opportunity costs, in the context of urban climate change adaptation. Results indicate an array of solutions between two polar strategies: large scale specialized grey solutions in most effective recharge areas, or diffused Nature-Based Solutions to satisfy citizen demand for multiple benefits. The participatory trade-off analysis proposed in this paper is a novel way to co-design spatial climate change adaptation strategies in urban contexts.