Influence of Material Toughness on Fracture Reliability in Steel Bridges

The fracture limit state of the AASHTO LRFD Bridge Design Specifications is addressed by requiring minimum impact toughness values for base material and mitigating potential fracture initiators through proper structural detailing. This fracture control approach has been successful in minimizing the number of fractures in steel bridges designed since its inception; however, it is not a calibrated limit state and the structural reliability against sudden brittle fracture has not been previously established. Accordingly, the objective of this study was to quantify the relationship between material toughness and fracture reliability in steel bridge members, considering the probabilistic distribution of fracture toughness and applied stress for a variety of structural steels and assumed crack sizes. The master curve approach is used to account for the probabilistic distribution of fracture toughness, and reliabilities are determined using Monte Carlo simulation and the Hasofer-Lind approach. The results indicate that the fracture reliability for modern bridge steels is consistent with the reliability of AASHTO strength limit states, and that certain steels currently available on the market can provide enough reliability against fracture to essentially eliminate brittle fracture as a limit state of concern. This finding holds the potential for a new way of approaching the design of fracture-critical members.

Quantitative Deterioration Evaluation of Heavy-Duty Anticorrosion Coating by Near-Infrared Spectral Characteristics

Heavy-duty anticorrosion coatings are applied on the surface of steel bridges for protecting against corrosion. By aging deterioration, the coating is worn from the surface year by year. Appropriate re-painting construction programs should be adopted for the maintenance of the bridges according to the evaluation of wear extent. Experimental studies were conducted with the aim of quantitative estimation of the degree of abrasion of the top coat thickness. It was found that there was a correlation between the top coat thickness and the observed infrared intensity and that this calibration relationship could be used to estimate the top coat thickness.

Computer Vision-Based Detection for Delayed Fracture of Bolts in Steel Bridges

The delayed fracture of high-strength bolts occurs frequently in the bolt connections of long-span steel bridges. This phenomenon can threaten the safety of structures and even lead to serious accidents in certain cases. However, the manual inspection commonly used in engineering to detect the fractured bolts is time-consuming and inconvenient. Therefore, a computer vision-based inspection approach is proposed in this paper to rapidly and automatically detect the fractured bolts. The proposed approach is realized by a convolutional neural network- (CNN-) based deep learning algorithm, the third version of You Only Look Once (YOLOv3). A challenge for the detector training using YOLOv3 is that only limited amounts of images of the fractured bolts are available in practice. To address this challenge, five data augmentation methods are introduced to produce more labeled images, including brightness transformation, Gaussian blur, flipping, perspective transformation, and scaling. Six YOLOv3 neural networks are trained using six different augmented training sets, and then, the performance of each detector is tested on the same testing set to compare the effectiveness of different augmentation methods. The highest average precision (AP) of the trained detectors is 89.14% when the intersection over union (IOU) threshold is set to 0.5. The practicality and robustness of the proposed method are further demonstrated on images that were never used in the training and testing of the detector. The results demonstrate that the proposed method can quickly and automatically detect the delayed fracture of high-strength bolts.