|Table of Contents|

Research on Safety Assessment Model of Building Structure Based on Neural Network(PDF)

《建筑科学与工程学报》[ISSN:1673-2049/CN:61-1442/TU]

Issue:
2022年04期
Page:
174-182
Research Field:
Publishing date:

Info

Title:
Research on Safety Assessment Model of Building Structure Based on Neural Network
Author(s):
WANG Jun-jie;  JIAO Ke;  PENG Zi-xiang
(Guangdong Architectural Design & Research Institute Co., Ltd., Guangzhou 510010, Guangdong, China)
Keywords:
structural safety assessment;  neural network;  small sample problem;  missing value imputation;  precision rate;  recall rate
PACS:
TU312.3
DOI:
10.19815/j.jace.2021.09065
Abstract:
To achieve fast civil structure safety assessment, safety assessment model of building structure based on neural network was proposed. Based on the requirements of inspection and investigation of Standard for Appraisal of Reliability of Civil Buildings, 45 input parameters included bearing capacity, durability, historical condition, and environmental situation were selected as the input parameter. The safety level in the Standard for Appraisal of Reliability of Civil Buildings was selected as the output parameter. Deep belief network was adopted to study the nonlinear map relationship between the input and output parameters. The selection of input parameter, the problem of missing data, small sample problem and the reliability of neural network were discussed and validated. The results show that the evaluation model using all input parameters has higher robustness under the premise that the correlation between input parameters and output parameters cannot be accurately judged. Miss forest algorithm has better interpolation performance than other commonly used missing interpolation algorithms. Using variational autoencoder to expand training dataset can effectively improve the generalization ability and classification accuracy of neural network. Improved deep belief network by introducing weighted cross entropy loss function can increase the sensitivity of training to unsafe categories, and the recall rate can be greatly improved by sacrificing a small amount of precision rate of unsafe categories. The safety assessment model of building structure based on neural network is capable to predict the safety level of the structure. With the fast and mass operation ability, it is an effective mean to realize structural safety monitoring of large-scale buildings.

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Last Update: 2022-07-10