Regular inspections on track components such as the clip, spike, and rail are essential to maintain track quality and ensure railroad operating safety. Unfortunately, traditional image processing (IP)-based systems have limited accuracy. Existing convolutional neural network (CNN)-based approaches are designed for either detection or saliency segmentation of a specific track component (e.g. fastener or rail only). The overall track condition could not be evaluated because not all the track components are inspected simultaneously. This paper presents an all-in-one YOLO (AOYOLO) framework for multi-task track component inspection. First, a newly developed ConvNeXt-based backbone is constructed in AOYOLO to produce suitable hyperfeatures for both detection and segmentation tasks. Second, a novel U-shaped salient object segmentation branch is incorporated into AOYOLO to supplement the object detection branch, improving both the rail surface defect segmentation and the detection of other components. Advanced data augmentations are integrated to further enhance the accuracy and scalability of the network. Extensive experiments conducted on a track dataset established with images taken by drone indicate that the proposed system is able to: (1) achieve 95.6% mAP for track components inspection at a real-time speed of 147 fps, and (2) reach 93.6% accuracy on RSDs detection, which surpass the current state-of-the-art models. The exceptional inference speed and superior detection accuracy have great potential for field applications.

Accurately measuring visible cracks in bridges is crucial for their structural health diagnosis, damage detection, performance evaluation, and maintenance planning. The primary means of visual crack detection still relies heavily on manual visual inspection, an inefficient process that can pose significant safety risks. This article develops a unmanned aerial vehicle (UAV) vision-based surface crack measurement methodology and visualization scheme for the bridges that can detect and measure cracks automatically with improved efficiency. The surface crack measurement methodology is achieved by designing a three-stage crack sensing system including the You Only Look Once-based crack recognition, U-shaped network-based crack segmentation, and deep-vision-based crack width calculation. This workflow is integrated into a comprehensive UAV inspection system, which is intended for operation at the field. The surface crack visualization scheme is accomplished by taking advantage of time-series image fusion, GPS information migration, and three-dimensional (3D) point cloud technique to reconstruct the 3D geometrical model of the tested bridge, which is convenient for unveiling the crack information in the bridge. The proposed methodology was successfully validated by a case study on an arch bridge. The achievement of this article promotes the UAV vision-based bridge’s surface crack inspection technology to a new status that no preparation for pasting calibration marker is needed, and crack identification, segmentation, and width calculation are realized promptly during the UAV flying on-site, as well as damage evaluation for bridges is visually fulfilled based on the reconstructed digital-graphical 3D model. The working environments and influencing factors to the developed system are sufficiently discussed. Certain limitations in the current application are pointed out for future improvements.

Regular crack detection is essential for extending the service life of bridges. However, the image data collected during bridge crack inspections are complex to convert into physical information and construct intuitive and comprehensive Three-Dimensional (3D) models incorporating crack information. An intelligent crack detection method for bridge surface damage based on Unmanned Aerial Vehicles (UAVs) is proposed for these challenges, incorporating a three-stage detection, quantification, and visualization process. This method enables automatic crack detection, quantification, and localization in a 3D model, generating a bridge model that includes crack details and distribution. The key contributions of this method are as follows: (1) The DCN-BiFPN-EMA-YOLO (DBE-YOLO) crack detection network is introduced, which improves the model’s ability to extract crack features from complex backgrounds and enhances its multi-scale detection capability for accurate detection; (2) a more comprehensive crack quantification method is proposed, integrating the crack automation detection system for accurate crack quantification and efficient processing; (3) crack information is mapped onto the 3D model by computing the camera pose for each image in the 3D model for intuitive crack visualization. Experimental results from tests on a concrete beam and an urban bridge demonstrate that the proposed method accurately identifies and quantifies crack images captured by UAVs. The DBE-YOLO network achieves an accuracy of 96.79% and an F1 score of 88.51%, improving accuracy by 3.19% and the F1 score by 3.8% compared to the original model. The quantification accuracy is within 10% of the error margin of traditional manual inspection. A 3D bridge model was also constructed and integrated with crack information.

This study focuses on the active earth pressure distribution of finite soil masses in urban proximity engineering, with a specific emphasis on nonlimit conditions. Through laboratory model tests, the relationship between the nonlimit earth pressure and displacement of the finite soil mass is investigated. Furthermore, the distribution law of the nonlimit earth pressure is explored, considering the influence of the existing structure, proximity distance, and soil cover thickness of the existing structure on the nonlimit earth pressure distribution within the finite soil mass. The findings indicate the following: (1) the distribution of the nonlimit active earth pressure along the depth of the retaining walls is nonlinear, exhibiting an initial increase, followed by a decrease near the bottom of the existing structure and then another decrease after an initial increase; (2) the nonlimit active earth pressure and ultimate active earth pressure of semi-infinite soil are greater than those of finite soil; (3) as the proximity distance increases, the nonlimit active earth pressure of finite soil gradually increases, and the rate of decrease in earth pressure at the existing structure floor decreases; (4) with increasing soil cover thickness of the existing structure, the nonlimit active earth pressure and ultimate active earth pressure of finite soil gradually increase, and the position of change along the depth of the retaining walls initially increases, then decreases, and finally continues to increase with increasing depth. The aforementioned research offers a foundation for determining the appropriate finite soil pressure in proximity engineering.

The calculation of the frost-heaving pressure is one of the key issues in the frost-resistant design of tunnels in cold regions. To reveal the frost heave mechanism of partial ponding behind the tunnel linings, a three-dimensional geo-mechanical model test was developed that only considered the frost heave load. A self-designed water bladder equipment was set up to simulate the partial ponding. The frost heave response characteristics of the surrounding rock with partial ponding were simulated to analyze the change law of temperature field and frost-heaving pressure under freezing by using air-conditioning to reduce the ambient temperature of the model test. The changes in the internal temperature of the surrounding rock and the evolution of the frost-heaving pressure over time under different thicknesses of partial ponding and different levels of surrounding rock were compared. The theoretical calculation value of frost-heaving pressure and the test values of the model test were compared and analyzed using the numerical simulation method. The results showed that the temperature of the surrounding rock presented three-stage changes, which showed a lagging characteristic relative to the environmental temperature. The empirical equation for the relationship between the frost-heaving pressure and time was obtained by nonlinear fitting of the experimental results. Taking advantage of the regular tetrahedron calculation model, the paper established a semiempirical frost-heaving pressure model considering time and space effects, which was identical with the frost-heaving phenomenon from the experiments. Theoretical analysis, experiments, and numerical simulation show that the frost-heaving pressure increased along with the depth of partial ponding and the elastic resistance coefficient of surrounding rock, which could provide references for revealing the frost-heaving pressure of partial ponding behind tunnels of cold regions and has a significant meaning in the engineering application.