卷积神经网络
刚度
结构工程
结构健康监测
计算机科学
悬臂梁
有限元法
振动
弹簧(装置)
还原(数学)
人工智能
工程类
数学
声学
物理
几何学
作者
Mohammad Almutairi,Nikolaos Nikitas,Osama Abdeljaber,Onur Avcı,Mateusz Bocian
出处
期刊:Structures
[Elsevier]
日期:2021-12-01
卷期号:34: 4435-4446
被引量:17
标识
DOI:10.1016/j.istruc.2021.10.029
摘要
Evaluating the severity of structural damage is a critical component of Structural Health Monitoring (SHM). Convolutional Neural Networks (CNNs) have been used before to detect structural damage and evaluate its severity by utilising only raw vibration data. However, these vibration-based CNN applications were limited to discrete user-defined levels of damage. To provide a more accurate representation of structural damage, this paper aims to design and validate a framework for evaluating structural damage severity within a continuous range of damage levels, using 1D CNNs and distributed raw acceleration data. To this purpose, a simple Finite Element (FE) cantilever model with non-rigid rotational spring support was adopted. Damage was simulated at the support as reduction of the rotational spring stiffness. The performance of the proposed framework was assessed under different excitation scenarios and data pre-processing techniques. The results demonstrate the ability of 1D CNNs to evaluate damage severity with high accuracy. By estimating the reduced value of the rotational spring stiffness, the proposed framework can also be used towards FE model updating in parallel with damage severity evaluation.
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