Probabilistic cracking prediction via deep learned electrical tomography
Probabilistic cracking prediction via deep learned electrical tomography
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DOI：
10.1177/14759217211037236
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发表时间：
2021-08-10
影响因子：
6.6
通讯作者：
Smyl, Danny
中科院分区：
文献类型：
--
作者：
Chen, Liang;Gallet, Adrien;Smyl, Danny
近年来，电阻层析成像（ERT）已成为一种可行的方法来检测，定位和重建混凝土结构的结构裂缝模式。然而，高保真ERT重建通常需要计算上昂贵的优化机制和复杂的约束和正则化方案，这阻碍了结构健康监测框架中的实用实施。为了应对这一挑战，本文提出使用预测性深度神经网络来直接快速地解决类似的ERT逆问题。具体而言，交叉熵损失的使用用于优化网络，形成从ERT电压测量到二进制概率空间裂纹分布（裂纹/未裂纹）的非线性映射。在这项工作中，人工神经网络和卷积神经网络首先使用模拟的电气数据进行训练。接下来，预测网络的可行性进行了测试和肯定，考虑从钢筋混凝土元件观察到的弯曲和剪切开裂模式的实验和模拟数据。
In recent years, electrical tomography, namely, electrical resistance tomography (ERT), has emerged as a viable approach to detecting, localizing and reconstructing structural cracking patterns in concrete structures. High-fidelity ERT reconstructions, however, often require computationally expensive optimization regimes and complex constraining and regularization schemes, which impedes pragmatic implementation in Structural Health Monitoring frameworks. To address this challenge, this article proposes the use of predictive deep neural networks to directly and rapidly solve an analogous ERT inverse problem. Specifically, the use of cross-entropy loss is used in optimizing networks forming a nonlinear mapping from ERT voltage measurements to binary probabilistic spatial crack distributions (cracked/not cracked). In this effort, artificial neural networks and convolutional neural networks are first trained using simulated electrical data. Following, the feasibility of the predictive networks is tested and affirmed using experimental and simulated data considering flexural and shear cracking patterns observed from reinforced concrete elements.