Cracks are often the most intuitive indicators for assessing the condition of in-service structures. Intelligent detection methods based on regular convolutional neural networks (CNNs) have been widely applied to the field of crack detection in recently years; however, these methods exhibit unsatisfying performance on the detection of out-of-plane cracks. To overcome this drawback, a new type of region-based CNN (R-CNN) crack detector with deformable modules is proposed in the present study. The core idea of the method is to replace the traditional regular convolution and pooling operation with a deformable convolution operation and a deformable pooling operation. The idea is implemented on three different regular detectors, namely the Faster R-CNN, region-based fully convolutional networks (R-FCN), and feature pyramid network (FPN)-based Faster R-CNN. To examine the advantages of the proposed method, the results obtained from the proposed detector and corresponding regular detectors are compared. The results show that the addition of deformable modules improves the mean average precisions (mAPs) achieved by the Faster R-CNN, R-FCN, and FPN-based Faster R-CNN for crack detection. More importantly, adding deformable modules enables these detectors to detect the out-of-plane cracks that are difficult for regular detectors to detect.

Residual strength of corroded textile-reinforced concrete (TRC) is evaluated using the deep learning-based method, whose feasibility is demonstrated by experiment. Compared to the traditional method, the proposed method does not need to know the climatic conditions in which the TRC exists. Firstly, the information about the faster region-based convolutional neural networks (Faster R-CNN) is described briefly, and then procedures to prepare datasets are introduced. Twenty TRC specimens were fabricated and divided into five groups that were treated to five different corrosion degrees corresponding to five different residual strengths. Five groups of images of microstructure features of these TRC specimens with five different residual strengths were obtained with portable digital microscopes in various circumstances. With the obtained images, datasets required to train, validate, and test the Faster R-CNN were prepared. To enhance the precision of residual strength evaluation, parameter analysis was conducted for the adopted model. Under the best combination of considered parameters, the mean average precision for the residual strength evaluation of the five groups of the TRC is 98.98%. The feasibility of the trained model was finally verified with new images and the procedures to apply the presented method were summarized. The paper provides new insight into evaluating the residual strength of structural materials, which would be helpful for safety evaluation of engineering structures.