A clustering-based analysis method for simulating seismic damage of buildings in large cities

Earthquakes are major threats to cities. Refined simulations of urban earthquake damage are significant for disaster prevention planning, including post-earthquake emergency rescue operations. However, this approach is time-consuming and computationally demanding. This study presents a novel machine...

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Vydáno v:	Engineering structures Ročník 307; s. 117860
Hlavní autoři:	Chen, Xianan, Zhang, Lingxin, Lin, Xuchuan, Skalomenos, Konstantinos A., Chen, Zifeng
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier Ltd 15.05.2024
Témata:	Batch clustering algorithm Clustering algorithms Machine learning Seismic damage simulation Urban building groups Urban building groups Batch clustering algorithm Clustering algorithms Seismic damage simulation Machine learning
ISSN:	0141-0296, 1873-7323
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Popis
Shrnutí:	Earthquakes are major threats to cities. Refined simulations of urban earthquake damage are significant for disaster prevention planning, including post-earthquake emergency rescue operations. However, this approach is time-consuming and computationally demanding. This study presents a novel machine learning method for simulating seismic damage of large urban building groups, thus minimizing the computational time and resources associated with the simulation process. First, the proposed method clusters building structures based on their seismic damage index (DI) under specific ground motions. Subsequently, one structure is selected from each cluster for seismic analysis. This approach significantly reduces the number of analyzed structures. The efficiency and accuracy of this method are studied based on a parametric study that involved an improved K-means clustering algorithm and a grid-based clustering algorithm. A batch-clustering algorithm is also developed to further improve the speed of clustering and efficiency of regional seismic simulations. A large Chinese city is considered as research application example. The results indicated the following: (1) the method based on the improved K-means clustering algorithm was superior; (2) the batch-clustering algorithm significantly speeded up clustering analysis and enhanced efficiency of the seismic simulation; (3) the clustering-based simulation method demonstrated high efficiency and accuracy, with a significant decrease of 90.5% in the calculation time compared with direct nonlinear time history analysis. In addition, the average relative error of the DI was only 11.0%. Moreover, over 85.0% of the structures were estimated to be in the correct damage state. •A method is established to efficiently simulate seismic damage of urban buildings.•Computation cost is reduced by grouping buildings based on their damage indices.•Two clustering algorithms are compared and algorithm parameters are optimized.•Clustering in batch is developed to shorten clustering time of thousands of buildings.
ISSN:	0141-0296 1873-7323
DOI:	10.1016/j.engstruct.2024.117860