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Article

Prediction of Structural Type for City-Scale Seismic Damage Simulation Based on Machine Learning

1
Beijing Key Laboratory of Urban Underground Space Engineering, School of Civil and Resource Engineering, University of Science and Technology Beijing, Beijing 100083, China
2
Guangdong Provincial Key Laboratory of Durability for Marine Civil Engineering, Shenzhen University, Shenzhen 518060, China
3
Key Laboratory of Civil Engineering Safety and Durability of China Education Ministry, Department of Civil Engineering, Tsinghua University, Beijing 100084, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(5), 1795; https://doi.org/10.3390/app10051795
Received: 27 January 2020 / Revised: 23 February 2020 / Accepted: 2 March 2020 / Published: 5 March 2020
Being the necessary data of the city-scale seismic damage simulations, structural types of buildings of a city need to be collected. To this end, a prediction method of structural types of buildings based on machine learning (ML) is proposed herein. Specifically, using the training data of 230,683 buildings in Tangshan city, China, a supervised ML solution based on a decision forest model was designed for the prediction. The scale sensitivity and regional applicability of the designed solution are discussed, respectively, and the results show that the supervised ML solution can maintain high accuracy for different scales; however, it is only suitable for cities similar to the sample city. For wide applicability for various cities, a semi-supervised ML solution was designed based on sampling investigation and self-training procedures. The downtowns of Daxing and Tongzhou districts in Beijing were selected as a case study for the designed semi-supervised ML solution. The overall prediction accuracies of structural types for Daxing and Tongzhou downtowns can reach 94.8% and 99.5%, respectively, which are acceptable for seismic damage simulations. Based on the predicted results, the distributions of seismic damage in Daxing and Tongzhou downtown were output. This study provides a smart and efficient method for obtaining structural types for a city-scale seismic damage simulation. View Full-Text
Keywords: machine learning; structural types; decision forest; self-training procedures; city-scale seismic damage simulation machine learning; structural types; decision forest; self-training procedures; city-scale seismic damage simulation
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MDPI and ACS Style

Xu, Z.; Wu, Y.; Qi, M.-z.; Zheng, M.; Xiong, C.; Lu, X. Prediction of Structural Type for City-Scale Seismic Damage Simulation Based on Machine Learning. Appl. Sci. 2020, 10, 1795. https://doi.org/10.3390/app10051795

AMA Style

Xu Z, Wu Y, Qi M-z, Zheng M, Xiong C, Lu X. Prediction of Structural Type for City-Scale Seismic Damage Simulation Based on Machine Learning. Applied Sciences. 2020; 10(5):1795. https://doi.org/10.3390/app10051795

Chicago/Turabian Style

Xu, Zhen; Wu, Yuan; Qi, Ming-zhu; Zheng, Ming; Xiong, Chen; Lu, Xinzheng. 2020. "Prediction of Structural Type for City-Scale Seismic Damage Simulation Based on Machine Learning" Appl. Sci. 10, no. 5: 1795. https://doi.org/10.3390/app10051795

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