Population growth and urbanisation trends bring many consequences related to the increase in global energy consumption, CO2 emissions and a decrease in arable land per person. High‑rises have been one of the inevitable buildings of metropoles to provide extra floor space since the early examples in the 19th century. Therefore, optimisation of high-rise buildings has been the focus of researchers because of significant performance enhancement, mainly in energy consumption and generation. Based on the facts of the 21st century, optimising high-rise buildings for multiple vital resources (such as energy, food, and water) is necessary for a sustainable future. This research suggests “self-sufficient high-rise buildings” that can generate and efficiently consume vital resources in addition to dense habitation for sustainable living in metropoles. The complexity of self-sufficient high-rise building optimisation is more challenging than optimising regular high-rises that have not been addressed in the literature. The main challenge behind the research is the integration of multiple performance aspects of self-sufficiency related to the vital resources of human beings (energy, food, and water) and consideration of large numbers of design parameters related to these multiple performance aspects. Therefore, the dissertation presents a framework for performance optimisation of self-sufficient high-rise buildings using artificial intelligence focusing on the conceptual phase of the design process. The output of this dissertation supports decision-makers to suggest well-performing high-rise buildings involving the aspects of self sufficiency in a reasonable timeframe.@en

The increase in global population, which negatively affects energy consumption, CO2 emissions, and arable land, necessitates designing sustainable habitation alternatives. Self-sufficient high-rise buildings, which integrate (electricity) generation and efficient usage of resources with dense habitation, can be a sustainable solution for future urbanisation. This paper focuses on transforming Europoint Towers in Rotterdam into self-sufficient buildings considering energy consumption and food production (lettuce crops) using artificial intelligence. Design parameters consist of the number of farming floors, shape, and the properties of the proposed façade skin that includes shading devices. Nine thousand samples are collected from various floor levels to predict self-sufficiency criteria using artificial neural networks (ANN). Optimisation problems with 117 decision variables are formulated using 45 ANN models that have very high prediction accuracies. 13 optimisation algorithms are used for an in-detail investigation of self-sufficiency at the building scale, and potential sufficiency at the neighbourhood scale. Results indicate that 100% and 43.7% self-sufficiencies could be reached for lettuce crops and electricity, respectively, for three buildings with 1800 residents. At the neighbourhood scale, lettuce production could be sufficient for 27,000 people with a decrease of self-sufficiency in terms of energy use of up to 11.6%. Consequently, this paper discusses the potentials and the improvements for self-sufficient high-rise buildings. @en

High-rise building optimisation is becoming increasingly relevant owing to global population growth and urbanisation trends. Previous studies have demonstrated the potential of high-rise optimisation but have been focused on the use of the parameters of single floors for the entire design; thus, the differences related to the impact of the dense surroundings are not taken into consideration. Part 1 of this study presents a multi-zone optimisation (MUZO) methodology and surrogate models (SMs), which provide a swift and accurate prediction for the entire building design; hence, the SMs can be used for optimisation processes. Owing to the high number of parameters involved in the design process, the optimisation task remains challenging. This paper presents how MUZO can cope with an enormous number of parameters to optimise the entire design of high-rise buildings using three algorithms with an adaptive penalty function. Two design scenarios are considered for quad-grid and diagrid shading devices, glazing type, and building-shape parameters using the setup, and the SMs developed in part 1. The optimisation part of the MUZO methodology reported satisfactory results for spatial daylight autonomy and annual sunlight exposure by meeting the Leadership in Energy and Environmental Design standards in 19 of 20 optimisation problems. To validate the impact of the methodology, optimised designs were compared with 8748 and 5832 typical quad-grid and diagrid scenarios, respectively, using the same design parameters for all floor levels. The findings indicate that the MUZO methodology provides significant improvements in the optimisation of high-rise buildings in dense urban areas.@en

Designing high-rise buildings is one of the complex tasks of architecture because it involves interdisciplinary performance aspects in the conceptual phase. The necessity for sustainable high-rise buildings has increased owing to the demand for metropolises based on population growth and urbanisation trends. Although artificial intelligence (AI) techniques support swift decision-making when addressing multiple performance aspects related to sustainable buildings, previous studies only examined single floors because modelling and optimising the entire building requires extensive computational time. However, different floor levels require various design decisions because of the performance variances between the ground and sky levels of high-rises in dense urban districts. This paper presents a multi-zone optimisation (MUZO) methodology to support decision-making for an entire high-rise building considering multiple floor levels and performance aspects. The proposed methodology includes parametric modelling and simulations of high-rise buildings, as well as machine learning and optimisation as AI methods. The specific setup focuses on the quad-grid and diagrid shading devices using two daylight metrics of LEED: spatial daylight autonomy and annual sunlight exposure. The parametric model generated samples to develop surrogate models using an artificial neural network. The results of 40 surrogate models indicated that the machine learning part of the MUZO methodology can report very high prediction accuracies for 31 models and high accuracies for six quad-grid and three diagrid models. The findings indicate that the MUZO can be an important part of designing high-rises in metropolises while predicting multiple performance aspects related to sustainable buildings during the conceptual design phase.@en

Urbanization and population growth lead to the construction of higher buildings in the 21st century. This causes an increment on energy consumption as the amount of constructed floor areas is rising steadily. Integrating daylight performance in building design supports reducing the energy consumption and satisfying occupants’ comfort. This study presents a methodology to optimise the daylight performance of a high-rise building located in a dense urban district. The purpose is to deal with optimisation problems by dividing the high-rise building into five zones from the ground level to the sky level, to achieve better daylight performance. Therefore, the study covers five optimization problems. Overhang length and glazing type are considered to optimise spatial Daylight Autonomy (sDA) and Annual Sunlight Exposure (ASE). A total of 500 samples in each zone are collected to develop surrogate models. A self-adaptive differential evolution algorithm is used to obtain near-optimal results for each zone. The developed surrogate models can estimate the metrics with minimum 98.25% R2 which is calculated from neural network prediction and Diva simulations. In the case study, the proposed methodology improves daylight performance of the high-rise building, decreasing ASE by approx. 27.6% and increasing the sDA values by around 88.2% in the dense urban district.@en

In this study, the implementation of evolutionary algorithms to the form-finding problem of masonry shell models is presented using Autoclaved Aerated Concrete material. Regarding the significance of design decisions, the study is focused on the conceptual stage of the design process. In this context, the applied method is addressed as multi-objective real-parameter constrained optimization. For the sake of dealing with the shell design problem, two objective functions are considered: minimization of global displacement and minimization of mass. Two multi-objective evolutionary algorithms, namely, Non-Dominated Sorting Genetic Algorithm II and Real-coded Genetic Algorithm with mutation strategy of Differential Evolution Algorithms are compared in terms of computational and architectural performance. As a result, the solutions generated by these algorithms are found much competitive.@en

As integrated components of the building envelopes, shading devices are the elements designed for stopping excessive amount of direct and indirect sunlight passing through and for avoiding undesirable admission of light into glazed buildings. Shading devices also reduce the operational cost of active systems, mostly heating and cooling, by providing considerable energy saving without completely blocking the daylight. However, the conventional shading device types in real world applications and even the ones presented in the literature stick to non-amorphous shapes providing limited improvement of the energy performance with negligible rates. Considering the lack of amorphous solutions in the literature, we propose novel design alternatives of energy-efficient shading device with panels in amorphous forms generated by parametric modeling and performance evaluation-based optimization in contrast with the conventionally designed structures. Initially, a performance evaluation-based optimization model was developed by employing evolutionary multi-objective optimization algorithms to overcome the complexity of the design process. Moreover, minimization of TEC (Total Energy Consumption) and maximization of the UDI (Useful Daylight Illuminance) are defined as the main objective functions to be optimized by non-dominated sorting genetic algorithm (NSGA II) and self-adaptive continuous genetic algorithm with differential evolution (JcGA-DE) in the shading model. According to the numerical results of the annual energy consumption, we managed to reach considerable energy saving up to 14%, while keeping the daylight availability above 50%.@en

This study presents a systematic review and summary of performative computational architecture using swarm and evolutionary optimisation. The taxonomy for one hundred types of studies is presented herein that includes different sub-categories of performative computational architecture, such as sustainability, cost, functionality, and structure. Specifically, energy, daylight, solar radiation, environmental impact, thermal comfort, life-cycle cost, initial and global costs, energy use cost, space allocation, logistics, structural assessment, and holistic design approaches, are investigated by considering their corresponding performance aspects. The main findings, including optimisation and all the types of parameters, are presented by focussing on different aspects of buildings. In addition, usage of form-finding parameters of all reviewed studies and the distributions for each performance objectives are also presented. Moreover, usage of swarm and evolutionary optimisation algorithms in reviewed studies is summarised. Trends in publications, published years, problem scales, and building functions, are examined. Finally, future prospects are highlighted by focussing on different aspects of performative computational architecture in accordance to the evidence collected based on the review process.

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Architectural design is a process that considers many objectives to satisfy. In general, these objectives are conflicting with each other. On the other hand, many design parameters are associated with these conflicting objectives, too. Therefore, architectural design is described as a complex task. To handle the complexity, computational optimization methods can be employed to investigate architectural design process in detail. This paper focuses on investigating Pareto-front solutions for theatre hall design using multi-objective evolutionary algorithms. To formulate the theatre hall acoustic design problem, we consider three objectives. Two objectives are minimization of both reverberation time, and total initial cost whereas the third objective is the maximization of seating capacity. In addition, several designs and acoustical performance constraints are defined. To tackle this problem, a multi-objective self-adaptive differential evolution algorithm (JDEMO) is proposed and compared with a well-known non-dominated sorting genetic algorithm-II (NSGA-II) from the literature. Computational results show that the proposed JDEMO algorithm achieves competitive results when compared to the NSGA-II.@en

Most of the architectural design problems are basically real-parameter optimization problems. So, any type of evolutionary and swarm algorithms can be used in this field. However, there is a little attention on using optimization methods within the computer aided design (CAD) programs. In this paper, we present Optimus, which is a new optimization tool for grasshopper algorithmic modeling in Rhinoceros CAD software. Optimus implements self-adaptive differential evolution algorithm with ensemble of mutation strategies (jEDE). We made an experiment using standard test problems in the literature and some of the test problems proposed in IEEE CEC 2005. We reported minimum, maximum, average, standard deviations and number of function evaluations of five replications for each function. Experimental results on the benchmark suite showed that Optimus (jEDE) outperforms other optimization tools, namely Galapagos (genetic algorithm), SilverEye (particle swarm optimization), and Opossum (RbfOpt) by finding better results for 19 out of 20 problems. For only one function, Galapagos presented slightly better result than Optimus. Ultimately, we presented an architectural design problem and compared the tools for testing Optimus in the design domain. We reported minimum, maximum, average and number of function evaluations of one replication for each tool. Galapagos and Silvereye presented infeasible results, whereas Optimus and Opossum found feasible solutions. However, Optimus discovered a much better fitness result than Opossum. As a conclusion, we discuss advantages and limitations of Optimus in comparison to other tools. The target audience of this paper is frequent users of parametric design modelling e.g., architects, engineers, designers. The main contribution of this paper is summarized as follows. Optimus showed that near-optimal solutions of architectural design problems can be improved by testing different types of algorithms with respect to no-free lunch theorem. Moreover, Optimus facilitates implementing different type of algorithms due to its modular system.

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Floating neighborhoods are innovative and promising urban areas for challenges in the development of cities and settlements. However, this design task requires a lot of considerations and technical challenges. Computational tools and methods can be beneficial to tackle the complexity of floating neighborhood design. This paper considers the design of a self-sufficient floating neighborhood by using computational intelligence techniques. In this respect, we consider a design problem for locating each neighborhood function in each cluster with a certain density within a floating neighborhood. In order to develop a self-sufficient floating neighborhood, we propose multi-objective evolutionary algorithms, namely, a self-adaptive real-coded genetic algorithm (CGA) as well as a self-adaptive real-coded genetic algorithm (CGA_DE) employing mutation operator of differential evolution algorithm. The only difference between CGA and CGA_DE is the fact that CGA uses random immigration of certain individuals into the population as a mutation operator whereas in the mutation phase of CGA_DE algorithm, the traditional mutation operator DE/rand/1/bin of DE algorithms. The arrangement of individual functions to develop each neighborhood function is further elaborated and formed by using Voronoi diagram algorithm. An application to design a self-sufficient floating neighborhood in Urla district, which is on the west coast of Turkey, İzmir, is presented.@en

This paper discusses a set of façade design alternatives for form-finding problem focusing on conceptual phase. In this respect, the aim of the research is to propose a multi-objective optimization approach for a façade design of public pool building. We present a set of solution belonging to Self-adaptive Multi-objective Ensemble Differential Evolution (JE_DEMO) and Self-adaptive Multi-Objective Differential Evolution (JDEMO) algorithm. We focus on maximization of daylight performance and minimization of structural displacement. Based on results, two algorithms presented competitive results. Contributions are presented based on objectives functions as new trade-offs and proposed JE_DEMO algorithm for design problems.@en

In this paper, the design of a specific case study of a foyer space is concerned in healthcare facility. The design task of a healthcare facility in architectural perspective is one of the most challenging tasks in the architectural design field since it involves different spaces that have unique requirements. Specifically, a foyer space has been considered as a gathering area that answers people’s needs and expectations. The study shows an application of computational intelligence for a skylight design in foyer space. For this reason, objective functions are considered to minimize skylight cost and to maximize the daylight performance of the interior space. Multi-Objective Self-Adaptive Ensemble Differential Evolution Algorithm and Non-Dominated Sorting Genetic Algorithm-II are proposed to tackle this complex problem. According to results, jE_DEMO algorithm presents satisfactory solutions as well as NSGA-II.@en

This paper presents an implementation of multi-objective optimization for a rectangular façade design proposal in a healthcare building’s common space. Objectives are to maximize daylight performance and to minimize façade construction cost. The aim of this study is to enhance indoor comfort of an existing healthcare building by concerning cost-effective façade design alternatives subject to several constraints. To handle the problem, we formulate a multi-objective real-parameter constraint problem. In order to solve this, Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and Multi-objective Self-Adaptive Ensemble Differential Evolution (jE_DEMO) algorithms are used. Finally, both algorithms are capable to discover desirable set of design alternatives.@en

This paper presents the results obtained by NSGA-II and jDEMO on a restaurant design optimization in the conceptual phase. A multi-objective problem is formulated by considering the minimization of investment and the maximization of customer count and maximization of visual perception, subject to several constraints. The main problem requires the configuration of restaurant spaces with different seating groups, decisions regarding the customer capacity, fraction and position of the windows. The contributions of the paper can be summarized as follows. We show that most architectural design problems are basically real-parameter multi-objective constrained optimization problems. So, any type of evolutionary and swarm optimization methods can be used in this field. A multi-objective self-adaptive differential evolution algorithm (jDEMO), inspired from the DEMO algorithm from the literature with some modifications, is developed and compared to the well-known fast and non-dominated sorting genetic algorithm so called NSGA-II in order to solve this complex problem and identify alternative design solutions to decision makers. Through the experimental results, we show that the proposed algorithm is competitive with the NSGA-II algorithm.

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This paper presents a multi-objective self-adaptive differential evolution algorithm to solve the form-finding problem of high-rise building design in the conceptual phase. The aim of the research is to reach suitable high-rise design alternatives for hard and soft objectives, which are construction cost per square meter, structural displacement, and visual perception of the spaces from the inside out subject to several constraints that are related with both high-rise construction regulations, and profitability of the spaces. We formulate the problem as a multi-objective realparameter constrained optimization problem for three objectives that are inherently conflicting. To tackle this problem, we developed two different optimization algorithms, namely, a Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and a Self-Adaptive Differential Evolution Algorithm (jDE) in order to obtain Pareto fronts with diversified non-dominated solutions. The extensive computational results show that the jDE algorithm yields much more desirable Pareto front than the NSGA-II algorithm.

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