Volume 22, Issue 1 (2022)                   MCEJ 2022, 22(1): 143-159 | Back to browse issues page

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Ghaderi P, abdolmaleki A. A novel unsupervised deep neural network based method for damage detection in civil structures. MCEJ 2022; 22 (1) :143-159
URL: http://mcej.modares.ac.ir/article-16-52478-en.html
1- Iran University of Science and Technology , p_ghaderi@iust.ac.ir
2- Iran University of Science and Technology
Abstract:   (1034 Views)
   Civil structures may experience unexpected loads and consequently damages during their life cycle. Damage identification has been a challenging inverse problem in structural health monitoring. The main difficulty is characterizing the unknown relation between the measurements and damage patterns. Such damage indicators would ideally be able to identify the existence, location, and severity of damages. In order to solve such problems, biologically inspired soft-computing techniques have gained traction. The most widely used soft-computing method, called neural networks is designed such that it can learn from data without a need of feature design process. Damage pattern can be detected using neural network. A deep unsupervised neural network can recognize patterns and extract features from data. In this paper a methodology is described for global and local health condition assessment of structural systems using vibration response of the structure. The model incorporates Fast Fourier Transform and unsupervised deep Boltzmann machine to extract features from the frequency domain of the recorded signals. Restricted boltzmann machine is a shallow neural network with two layer. First layer of restricted boltzmann machine called input layer and second layer of restricted boltzmann machine called hidden layer.Deep Boltzmann machine created by setting some restricted Boltzmann machine sequentional. Hidden layer of each restricted boltzmann machine is input layer of next restricted boltzmann machine. Each layer of restricted Boltzmann machine extract features form input data Recorded data divided to smaller vectors. Fast fourier transformation used to transform divided vectors into frequency domain.  A benefit of the proposed model is that it does not require costly experimental results to be obtained from a scaled version of the structure to simulate different damage states of the structure and only vibration response of the healthy structure is needed to training deep neural network. The input consists of a set of records obtained from the healthy state of the structure and another set of records with unknown health states. The model extracts information from both healthy and unknown sets to determine the health states of the unknown set. The healthy records are low intensity vibrations of the structure at least in one planar direction in the healthy state in the form of time series signals and The unknown records are low intensity vibrations of the structure on unknown state of health. Ambient vibrations can be due to wind, traffic, or human/pedestrian activities. An appropiate health index is defined and calculated for each part of the structure. The value of this index is between 0 and 1. The closer the value is to 1 the healthier the structure. To evaluate the efficiency of the proposed method a building structures with 35 story has been simulated in OPENSEES. Data collection should be selected appropriately to prevent errors. Obtained result demonstrate that proposed method has about 95 percent efficiency to predict damages and their severity. Different damage state put on due to three earthquakes with different severity. Structural health index calculated after each earthquake. Calculated structural health index demonstrate efficieency of proposed method for detecting damages and severity of damages.
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Article Type: Original Research | Subject: Earthquake
Received: 2021/05/12 | Accepted: 2021/10/29 | Published: 2023/01/30

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