Generative design and performance optimization of residential buildings based on parametric algorithm

Zhang, Jingyu; Liu, Nianxiong; Wang, Shanshan

doi:10.1016/j.enbuild.2021.111033

Cited by 67 publications

(22 citation statements)

References 12 publications

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“…Design processes can be supported by parametric and generative design methods to evaluate large numbers of design solutions (Feng et al 2019, Zhang et al 2021. Furthermore, engineers make use of simulation methods to predict the performance of a building, and optimisation methods to find optimal design solutions within certain criteria.…”

Section: A1 Building Design Process and Support Systemsmentioning

confidence: 99%

Machine learning and genetic algorithms for conformal geometries in design support systems

Hassel¹,

Hofmeyer²,

Ezendam³

et al. 2023

ECPPM 2022 - eWork and eBusiness in Architecture, Engineering and Construction 2022

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Section: A1 Building Design Process and Support Systemsmentioning

confidence: 99%

Machine learning and genetic algorithms for conformal geometries in design support systems

Hassel¹,

Hofmeyer²,

Ezendam³

et al. 2023

ECPPM 2022 - eWork and eBusiness in Architecture, Engineering and Construction 2022

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“…Recent research has tried to adapt MOO for real-world design applications, from improvement in workflow, algorithms, and combination with other techniques. Zhang et al 58 created a generative design process for residential building design with MOO. Numerous design parameters were categorised into qualitative (e.g.…”

Section: Overview Of Previous Studiesmentioning

confidence: 99%

Solving the comfort-retrofit conundrum through post-occupancy evaluation and multi-objective optimisation

Wang

et al. 2023

Building Services Engineering Research and Technology

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Developing appropriate building retrofit strategies is a challenging task. This case study presents a multi-criteria decision-supporting method that suggests optimal solutions and alternative design references with a range of diversity at the early exploration stage in building retrofit. This method employs a practical two-step method to identify critical comfort and energy issues and generate optimised design options with multi-objective optimisation based on a genetic algorithm. The first step is based on a post-occupancy evaluation, which cross-refers benchmarking and correlation and integrates them with non-linear satisfaction theory to extract critical comfort factors. The second step parameterises previous outputs as objectives to conduct building simulation practice. The case study is a typical post-war highly glazed open-plan office in London. The post-occupancy evaluation result identifies direct sunlight glare, indoor temperature, and noise from other occupants as critical comfort factors. The simulation and optimisation extract the optimal retrofit strategies by analysing 480 generated Pareto fronts. The proposed method provides retrofit solutions with a criteria-based filtering method and considers the trade-off between the energy and comfort objectives. The method can be transformed into a design-supporting tool to identify the key comfort factors for built environment optimisation and create sustainability in building retrofit. Practical application: This study suggested that statistical analysis could be integrated with parametric design tools and multi-objective optimisation. It directly links users’ subjective opinions to the final design solutions, suggesting a new method for data-driven generative design. As a quantitative process, the proposed framework could be automated with a program, reducing the human effort in the optimisation process and reducing the reliance on human experience in the design question defining and analysis process. It might also avoid human mistakes, e.g. overlooking some critical factors. During the multi-objective optimisation process, large numbers of design options are generated, and many of them are optimised at the Pareto front. Exploring these options could be a less human effort-intensive process than designing completely new options, especially in the early design exploration phase. Overall, this might be a potential direction for future study in generative design, which greatly reduce the technical obstacle of sustainable design for high building performance.

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“…According to Krish [ 13 ], the concept of generative design was first proposed by Frazer in the 1970s. In 1989, with the advent of parametric CAD tools, generative design was further studied [ 14 ]. In 1997, Bentley and Wakefield [ 15 ] developed and optimized the first generation of generative design systems based on genetic algorithms.…”

Section: Introductionmentioning

confidence: 99%

Generative Design and Integrated 3D Printing Manufacture of Cross Joints

Han

Xia

et al. 2022

Materials

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The integrated process of design and fabrication is invariably of particular interest and important to improve the quality and reduce the production cycle for structural joints, which are key components for connecting members and transferring loads in structural systems. In this work, using the generative design method, a pioneering idea was successfully realized to attain a reasonable configuration of the cross joints, which was then consecutively manufactured using 3D printing technology. Firstly, the initial model and generation conditions of a cross joint were constructed by the machine learning-based generative design algorithm, and hundreds of models were automatically generated. Then, based on the design objective and cost index of the cross joint, three representative joints were selected for further numerical analysis to verify the advantages of generative design. Finally, 3D printing was utilized to produce generative joints; the influences of printing parameters on the quality of 3D printing are further discussed in this paper. The results show that the cross joints from the generative design method have varied and innovative configurations and the best static behaviors. 3D printing technology can enhance the accuracy of cross joint fabrication. It is viable to utilize the integrated process of generative design and 3D printing to design and manufacture cross joints.

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Generative design and performance optimization of residential buildings based on parametric algorithm

Cited by 67 publications

References 12 publications

Machine learning and genetic algorithms for conformal geometries in design support systems

Machine learning and genetic algorithms for conformal geometries in design support systems

Solving the comfort-retrofit conundrum through post-occupancy evaluation and multi-objective optimisation

Generative Design and Integrated 3D Printing Manufacture of Cross Joints

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