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.

近年来，电断层扫描，即电阻断层扫描 (ERT)，已成为检测、定位和重建混凝土结构中结构开裂模式的可行方法。然而，高保真 ERT 重建通常需要计算成本高昂的优化机制以及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。在这一努力中，人工神经网络和卷积神经网络首先使用模拟电数据进行训练。接下来，考虑从钢筋混凝土元件观察到的弯曲和剪切开裂模式，使用实验和模拟数据测试和确认预测网络的可行性。