The intelligent crack detection method is an important guarantee for the realization of intelligent operation and maintenance, and it is of great significance to traffic safety. In recent years, the recognition of road pavement cracks based on computer vision has attracted increasing attention. With the technological breakthroughs of general deep learning algorithms in recent years, detection algorithms based on deep learning and convolutional neural networks have achieved better results in the field of crack recognition. In this paper, deep learning is investigated to intelligently detect road cracks, and Faster R-CNN and Mask R-CNN are compared and analyzed. The results show that the joint training strategy is very effective, and we are able to ensure that both Faster R-CNN and Mask R-CNN complete the crack detection task when trained with only 130+ images and can outperform YOLOv3. However, the joint training strategy causes a degradation in the effectiveness of the bounding box detected by Mask R-CNN.

https://www.mdpi.com/1424-8220/22/3/1215/pdf?version=1644460512

Crack Detection and Comparison Study Based on Faster R-CNN and Mask R-CNN

Developing a climbing robot for repairing cables of cable-stayed bridges

Intelligent robotic systems for structural health monitoring: Applications and future trends

Review on automated condition assessment of pipelines with machine learning

Evaluation-oriented façade defects detection using rule-based deep learning method

Inspection of surface defects on stay cables using a robot and transfer learning

Stay cables are important structural members of cable-stayed bridges, which play a significant role in the health monitoring and assessment of cable-stayed bridges The in-service cable force, which varies from the effects of vehicle load, wind load and other environmental factors, may cause fatigue damage in stay cables Traditional force identification methods can only calculate the time-average cable force instead of the instantaneous force A novel method has been proposed in this paper for identifying time-varying cable tension based on the variational mode decomposition (VMD) method This recent method decomposes signals and adaptively estimates instantaneous frequency combined with the Hilbert–Huang transform method In the proposed study, the time-varying modal frequencies were identified from stay cable acceleration data, and then the time-varying cable tension was identified by the relationship between cable tension and identified fundamental frequency Scaled and full-scale models of stay cables were implemented successively to illustrate the validity of the proposed method The results showed that the variational mode decomposition (VMD) method has a good effect on identifying the time-varying cable forces, even the sudden changes in cable force According to the cable force identification results, the maximum error was 84%, which meets the actual application of time-varying cable force measurements An on-site test was also implemented to monitor the cable force during a construction period, and the results showed that the proposed method can provide accurate real-time results for evaluation and decision-making

Variational Mode Decomposition Based Time-Varying Force Identification of Stay Cables

Underwater inspection of bridge substructures using sonar and deep convolutional network

Scour is one of the primary reasons for the collapse of bridges, as it severely reduces the stability of their piers. This study developed a model updating method based on an improved genetic algorithm (GA) to identify scour depth, combined with a parallel computing technique to accelerate the model updating procedure. The proposed GA adopted a gradient-like calculation as the mutation operation, while the population in GA was initialized based on the exponential distribution. A finite element model of a continuous girder bridge, whose soil-structure interaction was simulated by winkle beam theory, was established to give a sensitivity analysis of the scour effect. A moving mass-spring model was used to simulate the vehicle-bridge interaction, and dynamic responses under various scour damages were generated to verify the method’s performance. The results of the numerical simulation indicated that the transverse vibration modes of a continuous girder bridge were sensitive to the scour effect, and the proposed method can identify the scour depth with an error of less than 0.31 m.

Haochen Wang

Papers

Inspection of surface defects on stay cables using a robot and transfer learning

Variational Mode Decomposition Based Time-Varying Force Identification of Stay Cables

Underwater inspection of bridge substructures using sonar and deep convolutional network

Identification of the scour depth of continuous girder bridges based on model updating and improved genetic algorithm

Laboratory Investigation of Early Damage Detection for an Old-Aged Reinforced Concrete Beam using Acoustic Emission and Digital Image Correlation