Pattern recognition of wood structure design parameters under external interference based on artificial neural network with BIM environment

Author:

Qiuling Zheng1,Ke Yang1,Qiang Xu1,Chenglong Zhang1,Liguang Wang1

Affiliation:

1. Jilin Jianzhu University, Changchun, Jilin, China

Abstract

Under the influence of novel corona virus pneumonia, the staff are controlled. Therefore, it is a difficult problem to measure the parameters of wood structure building on site. The measurement error of traditional wood structure parameters in complex environment is large, so an efficient and accurate measurement and recognition method is needed. In this paper, a method combining random decrement method and ITD method is proposed to measure the frequency, damping ratio and other structural dynamic parameters of ancient building timber structure under crowd random load excitation. In this paper, the frequency and damping ratio of the typical ancient building timber structure are predicted by using the artificial neural network model trained by the known data. The experimental results show that the population density has a great influence on the measurement of the dynamic parameters of the wooden structure of ancient buildings. Using this method, combined with the long-term monitoring data of temperature and humidity, the influence of various environmental factors on the dynamic characteristics of the structure can be analyzed. This provides data support for structural damage identification and health monitoring.

Publisher

IOS Press

Subject

Artificial Intelligence,General Engineering,Statistics and Probability

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Design Study of 3D Cable Line BIM Parameter Modeling System based on Ground-Penetrating Radar Identification Results;2023 Asia-Europe Conference on Electronics, Data Processing and Informatics (ACEDPI);2023-04

2. BIM and ANN-based rapid prediction approach for natural daylighting inside library spaces;Journal of Intelligent & Fuzzy Systems;2023-01-30

3. Data mining model for predicting the quality level and classification of construction projects;Journal of Intelligent & Fuzzy Systems;2021-12-31

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