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.

Rapid and accurate detection of internal defects in bridges has always been a major concern of the management and maintenance departments. In the present study, an intelligent method for the detection of structural internal defects is proposed based on infrared thermography and deep learning. Through theoretical analysis, numerical simulations and laboratory experiments, the classification, localization and quantification of internal defects of concrete structures were achieved with the infrared thermography and deep learning method. The mean average precision for classification and localization of internal defects is 96.59%, the mIoU for pixel-level segmentation is 95.19%, and the average relative error for damage quantification is 0.70%. The feasibility of the trained model is verified with new images, and the results show that the trained model can capture infrared thermal features of internal defects with different sizes and depths. This method has the advantages of low cost, high accuracy, easy operation, and large area scanning of concrete structures, which can provide a good reference for the detection of internal defects of concrete structures.

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.

Fatigue of steel bridges is a major concern for bridge engineers. Previous fatigue-based weight-limiting method of steel bridges is founded on the Palmgren–Miner’s rule and S-N curves, which overlook the effect of existing cracks on the fatigue life of in-service steel bridges. In the present study, based on the theory of linear elastic fracture mechanics, a framework combining the artificial neural networks and Monte Carlo simulations is proposed to analyze the fatigue reliability of steel bridges with the effects of cracks and truck weight limits considered. Using the framework, a new method of setting the gross vehicle weight limit for in-service steel bridges with cracks is proposed. The influences of four key parameters, including the average daily truck traffic, the gross vehicle weight limit, the violation rate, and the detected crack size, on the fatigue reliability of a steel bridge are analyzed quantitatively with the new framework. Results show that the suggested framework can enhance the fatigue reliability assessment process in terms of accuracy and efficiency. The method of setting gross vehicle weight limits can effectively control the fatigue failure probability to be within 2.3% according to the desired remaining service time and the detected crack size.

The objective of this study was to present a new approach for determining the truck weight limit of simply supported steel girder bridges. This approach is based on the fatigue reliability of bridge girders under the action of random traffic flows considering the presence of multiple trucks. A simply supported steel girder bridge designed according to the current codes was used as the bridge model to illustrate the presented approach. Based on the collected traffic data from Wisconsin, random traffic flows were generated using the Monte Carlo method and were used as the traffic loading. Numerical simulations were carried out to investigate the effects of three important parameters, including the fraction of traffic in the fast lane ($FTFL$ ), violation rate ($VR$ ), and truck weight limit ($TWL$ ), on the average fatigue damage accumulation ($AFDA$ ) induced by each truck in the random traffic flow. Based on the Miner’s cumulative damage model and the S-N curve, the effects of these parameters on the fatigue reliability index of bridge girders were also analyzed. The truck weight limit that can ensure the target fatigue reliability of the bridge girders after 75 years of service was determined based on the fatigue reliability analysis of the bridge girder.