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

Zhen Xu,Ming Zheng,Ming-Zhu Qi,Chen Xiong,Xinzheng Lu,Yuan Wu

doi:10.3390/app10051795

Abstract

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.

Highlights

Cities are densely organized, with many buildings and civil infrastructures
Using the training data of 230,683 buildings in Tangshan city, China, a supervised machine learning (ML) solution based on a decision forest model was designed for the prediction
The results indicate that the designed semi-supervised ML solution can achieve a high prediction accuracy even when using 1% of all the building data

Summary

Introduction

Cities are densely organized, with many buildings and civil infrastructures. Two third of cities beyond one million people in China are located in high risk areas of earthquakes (i.e., the corresponding seismic precautionary intensities of these cities are more than 6 according to the seismic design code of China [3]). Beijing, the capital of China, and Taiyuan, a large city in the north of China, are both located in the area of seismic precautionary intensity 8. The earthquake risk of these two districts are very high (i.e., the precautionary seismic intensity is 8). In this intensity, the peak ground acceleration corresponding level earthquake whose probability of. The ML solution will be applied in the downtowns of Daxing and Tongzhou for obtaining the Introduction of Case Study data of structural types

Objectives

Results

Conclusion