Automated topology design of high‐rise diagrid buildings by genetic algorithm optimization

Orhan, Talha; Taşkin, Kıvanç

doi:10.1002/tal.1853

Cited by 13 publications

(5 citation statements)

References 13 publications

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“…In order to carry out a targeted study on the design of building electromechanical systems, seven representative energy system parameters, including energy consumption of air conditioning equipment, energy consumption of renewable energy equipment, energy consumption of cooling system, energy consumption of heating system, energy consumption of lighting, energy consumption of elevator, and building energy consumption monitoring system, were selected as the main optimization objects in this optimized design, where the target cost was determined by Chinese fixed standards combined with market prices. In order to unify the standards, the energy consumption in the above indicators is calculated by multiplying the electricity consumption during the operation period by the carbon emission factor [36], and the carbon emission factor is 0.5810 according to the latest Notice on Doing a Good Job in the Management of Greenhouse Gas Emission Reports of Enterprises in 2022 issued by the Ministry of Ecology and Environment of China. See Table 2 for specific index requirements of each parameter.…”

Section: Mathematical Model Constructionmentioning

confidence: 99%

Research and application of energy consumption optimization design of electromechanical system based on genetic algorithms

Guo,

Zhang,

Peng

2024

JIMO

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In recent years, most of the new buildings in China are high-energy buildings, and electromechanical system, as a construction project, occupies a large proportion of energy consumption, which is an important way to improve the green design of buildings. The components of electromechanical system are complex, which have dialectical relationship between performance indexes under the multi-objective optimization of low energy consumption and low cost. Aiming at the common problem of high energy consumption in China's new buildings, this paper comprehensively applied NSGA-II method in genetic algorithm and building information technology (BIM), combined with Matlab, Revit, Ecotect and other computing tools. Seven indexes of electromechanical energy consumption have been analyzed for multi-objective optimization design, which namely ACE, REE, CE, HE, LE, BMS and EE The case study of Shandong Province hospital project is selected to reveal the high share of building building electromechanical system in energy consumption through the optimised energy saving effect, and at the same time, there is a huge space for energy saving optimisation. On the basis of BIM technology, genetic algorithm is used to find the optimal solution set of multi-factors of building electromechanical system, so as to provide data support for solving multi-objective and multi-variable problems in green building design. The main conclusions are as follows: (1) The NSGA-II method is suitable for the energy consumption optimization calculation of electromechanical equipment in green building projects, and the efficiency and quality of index optimization analysis can be improved by quickly searching Pareto optimal solution set through multiple iterations, which provides an important reference for scheme optimization in the design stage; (2) Pareto optimal solution sets of seven indexes of electromechanical energy consumption are obtained. The highest variation rates are CE(3.23%) and HE(2.67%), and the lowest variation rate is BMS(0.16%). (3) Through optimization simulation calculation, it is found that ACE, REE, HE and LE have a large contribution to reducing the overall energy consumption, while CE, BMS and EE have certain fluctuations before and after optimization.

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Section: Mathematical Model Constructionmentioning

confidence: 99%

Research and application of energy consumption optimization design of electromechanical system based on genetic algorithms

Guo,

Zhang,

Peng

2024

JIMO

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“…It can be observed that the proposed Graph‐GEN is remarkably advantageous (an increase of 500 times), over YJK, which calculates the CMQs of each structural component after detailed structural analysis and reinforcement design. When using genetic algorithms for structural optimization, hundreds of times of CMQ estimation are typically needed (Orhan & Taşkın, 2021; X. Zhou et al., 2022). For example, a population size of 20 and an iteration number of 50 will result in 1000 possible schemes and 1000 times of CMQ estimation.…”

Section: Case Studymentioning

confidence: 99%

Knowledge‐enhanced graph neural networks for construction material quantity estimation of reinforced concrete buildings

Fei,

Liao,

et al. 2023

Computer aided Civil Eng

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The construction material quantity (CMQ) is widely concerned in the structural design of reinforced concrete buildings and is often included among the objective functions of computer‐aided optimization design techniques. To minimize construction cost and carbon emissions, an accurate and efficient CMQ estimation method is timely required. In this study, a novel graph neural network (GNN) is proposed, whose architecture and loss function are specifically designed for CMQ estimation. With a heterogeneous feature fusion mechanism, the GNN can automatically extract features from all CMQ‐related information, in contrast to the existing data‐driven methods that rely heavily on manually selected features. By further incorporating a prior knowledge inclusion strategy, the GNN can avoid fundamental errors that might be encountered by purely data‐driven methods. To enrich the diversity of the CMQ dataset, a data augmentation method is proposed incorporating generative adversarial networks and parametric modeling. Numerical experiments and case studies show that the proposed CMQ estimation method is superior to the existing data‐driven methods in terms of accuracy and is 500 times faster than typical commercial structural design software. This study is anticipated to benefit the objective evaluation of computer‐aided design, thereby facilitating the promotion of low‐cost and low‐carbon‐emission building designs.

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“…This is primarily because design optimization entails the evaluation of hundreds of potential design schemes. 3,4 If a general-purpose FE software is employed, then seismic-performance evaluation alone will consume dozens of hours, which is unacceptable in practical design scenarios when time is a critical factor.…”

Section: Introductionmentioning

confidence: 99%

“…However, the computational time for a single design scheme typically extends to several minutes or longer, which renders these software packages inadequate for satisfying design optimization demands. This is primarily because design optimization entails the evaluation of hundreds of potential design schemes 3,4 . If a general‐purpose FE software is employed, then seismic‐performance evaluation alone will consume dozens of hours, which is unacceptable in practical design scenarios when time is a critical factor.…”

Section: Introductionmentioning

confidence: 99%

Hybrid surrogate model combining physics and data for seismic drift estimation of shear‐wall structures

Fei,

Liao,

Zhao

et al. 2024

Earthq Engng Struct Dyn

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To address the issue of costly computational expenditure related to high‐fidelity numerical models, surrogate models have been widely used in various engineering tasks, including design optimization. Despite the successful application of the existing surrogate models, physics‐based models depend largely on simplifications and assumptions, which render parameter calibration challenging; whereas data‐driven models require substantial data to reach their full potential, with their performance often being constrained in tasks when obtaining massive data is difficult. In this study, a hybrid surrogate model is proposed combining physics‐based and data‐driven models to rapidly estimate building seismic responses. The application of this model is exemplified through effective estimation of inter‐story drift ratios (IDRs), being a critical factor in shear‐wall structure design. Initially, a data augmentation technique and a parametric modeling procedure are introduced to significantly enhance the dataset diversity. Subsequently, a task decomposition strategy is proposed to effectively integrate a data‐driven graph neural network (GNN) and a physics‐based flexural‐shear model. Additionally, the output layer and the loss function of the GNN are modified to enhance the estimation accuracy by eliminating fundamental errors. Results of numerical experiments indicate that the proposed hybrid model can complete IDR estimations in an average time of 0.56 s, with a mean absolute percentage error of 12.7%. This performance significantly surpasses that of existing purely data‐driven and physics‐based models. A case study shows that the efficiency of the proposed hybrid model is approximately 100 times greater than that of conventional finite element software. This enables an accurate assessment of the design compliance with code requirements. The results of this study can be applied to the design optimization of seismic‐resistant building structures.

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Automated topology design of high‐rise diagrid buildings by genetic algorithm optimization

Cited by 13 publications

References 13 publications

Research and application of energy consumption optimization design of electromechanical system based on genetic algorithms

Research and application of energy consumption optimization design of electromechanical system based on genetic algorithms

Knowledge‐enhanced graph neural networks for construction material quantity estimation of reinforced concrete buildings

Hybrid surrogate model combining physics and data for seismic drift estimation of shear‐wall structures

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