Fei Xu

Reliable separation of dynamic and fault-slip-induced components in near-fault ground motions remains a critical challenge for regional seismic analysis because existing approaches have been primarily developed for individual records and lack spatial consistency. This study proposes a unified frequency-based framework in which a single optimal cutoff frequency is determined through a global objective function evaluated over all spatial locations. The results indicate that an optimal cutoff frequency of 0.29 Hz provides the best overall decomposition, corresponding to a mean objective function value of 0.208 and a standard deviation of 0.078, reflecting both good decomposition performance and limited spatial variability. The proposed framework enables consistent separation of acceleration-dominated dynamic motion and displacement-dominated fault-slip-induced deformation across the regional ground-motion field. Based on the decomposed components, spatial distributions of peak ground-motion parameters and response spectra are analyzed to quantify their respective contributions. The results show that dynamic components control acceleration-related responses, whereas fault-slip-induced components dominate permanent displacement and long-period spectral behavior. Directional spectral analysis further reveals a clear difference between the fault-parallel and fault-normal directions. In the fault-parallel direction, the fault-slip-induced component becomes dominant at long periods, leading to a pronounced transition from dynamic to fault-slip-controlled response. In contrast, in the fault-normal direction, the dynamic component remains significant even at long periods, and the contribution of the fault-slip-induced component is comparatively limited. These results indicate that the influence of fault slip on seismic response is strongly direction dependent, with a much greater impact in the fault-parallel direction than in the fault-normal direction. These findings demonstrate that a unified cutoff frequency can effectively ensure consistent decomposition across a regional ground-motion field while revealing the distinct and direction-dependent roles of dynamic motion and fault-slip-induced deformation in near-fault seismic response.

The mechanical performance of concrete structures under corrosive environments largely depends on the bond behavior between rebar and concrete. Existing studies primarily focus on predicting the peak bond stress, paying limited attention to the complete degradation process of bond strength. To address the deterioration of bond strength caused by internal rebar corrosion in concrete structures, a comprehensive bond–slip dataset was constructed based on extensive pull-out test data from existing literature. Nine input features were selected: corrosion rate, bond length, rebar diameter, concrete compressive strength (both cube and cylinder), concrete cover thickness, rebar yield strength, rebar type, and slip. This dataset captures the full evolution of bond strength at the corroded rebar–concrete interface as a function of slip. A bond–slip prediction model for corroded rebar was developed using a stacking GBDT–SVR (Gradient Boosting Decision Tree–Support Vector Regression) machine learning approach. Feature importance analysis was conducted using the SHAP method. The results showed a strong agreement between predicted and actual values. Performance metrics such as R², σ, η, and γ confirmed the high accuracy of the model, with prediction outside 0.8–1.2 confidence band only at low bond stress values. Compared to traditional empirical formulas, the proposed model demonstrates superior precision.

Tunnel lining voids, a common latent defect induced by the coupling effects of complex geological, environmental, and load factors, pose severe threats to operational and personnel safety. Traditional detection methods relying on Ground-Penetrating Radar (GPR) combined with manual interpretation suffer from high subjectivity, low efficiency, frequent missed or false detections, and an inability to achieve real-time monitoring. Thus, this paper proposes an intelligent identification methodology for tunnel lining voids based on an improved version of YOLOv8. Key enhancements include integrating the RepVGGBlock module, dynamic upsampling, and a spatial context-aware module to address challenges from diverse void geometries—resulting from interactions between the environment, geology, and load—and complex GPR signals caused by heterogeneous underground media and the varying electromagnetic properties of materials, which obscure void–background boundaries, as well as interference signals from detection processes. Additionally, the C2f-Faster module reduces the computational complexity (GFLOPs), parameter count, and model size, facilitating edge deployment at detection sites to achieve real-time GPR signal interpretation for tunnel linings. Experimental results on a heavy-haul railway tunnel’s lining defect dataset show 11.57% lower GFLOPs, 14.55% fewer parameters, and 13.85% smaller weight files, with average accuracies of 94.1% and 94.4% in defect recognition and segmentation, respectively, meeting requirements for the real-time online detection of tunnel linings. Notably, the proposed model is specifically tailored for void identification and cannot handle other prevalent tunnel lining defects, which restricts its application in comprehensive tunnel health monitoring scenarios where multiple defects often coexist to threaten structural safety.

Accurately and rapidly estimating bridge flexibility is crucial for structural health diagnosis and performance evaluation. Traditional impact testing methods for flexibility identification use numerous sensors placed on the bridge deck, these methods suffer from low efficiency. To address this issue, a substructure mobile impact testing strategy using noncontact vision-based structural displacement measurements is proposed to estimate bridge flexibility. The advantages of this method include (1) impact testing is performed using only a movable single camera and an impact hammer, improving testing efficiency; (2) the use of multipoint vision-based noncontact measurements of dynamic displacements based on the adaptive kernel correlation filters (AKCF), reduces testing costs; and (3) overcoming field-of-view limitations, the flexibility matrix of the entire bridge is obtained through a reference-free mobile impact testing method by integrating the substructure results. Finally, the effectiveness of this method is validated through laboratory experiments with a simply supported beam model.

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.

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.

An accurate prediction of the future service state of long-span bridges is crucial for the structural reliability evaluation, maintenance planning, and further life-cycle cost analysis. By extending the average conditional exceedance rate (ACER) statistical model and applying input–output data collected through a structural health monitoring (SHM) system, this paper proposes a novel methodology for predicting the future service state of long-span bridges. The advantages lie in the consideration of the main excitation load as the structural input and the strain response of the bridge as the output. Therefore, a mapping relationship between the extreme excitation load and extreme strain could be established, and the future service state of long-span bridges could be predicted. The proposed method comprises three steps: (1) extraction of the ambient temperature-induced strain and vehicle-induced strain from the measured strain series through the SHM system using the baseline estimation and denoising with sparsity (BEADS) method, (2) establishing statistical models of the extreme values of different excitations (input) and structural strains (output) using a cascade of conditioning approximations and the ACER to obtain the tail trend of the data and extrapolating it, and (3) establishing a functional relationship between the input and output extreme values based on the same conditions of the regression period at the target prediction level, after which the future service state of long-span bridges can be predicted. The proposed method is applied to a case study of the Jinchao Bridge in Guangdong Province, China, and the results are expected to provide a scientific reference for the design of new bridges and in the maintenance of existing ones in service.

In order to explore the fracture evolution characteristics of existing defect lining structures under unsymmetrical loads, unsymmetrical failure model tests of three working conditions, namely, intact lining, vault defect, and arch waist defect, were carried out. Acoustic emission (AE) technology was used to detect the lining cracks from microscopic to macroscopic. The relationship between the accumulated energy of AE and the relative load was established, and a fracture evolution model of lining based on the cumulative energy of AE was proposed. The results show that the failure process of the intact lining under unsymmetrical load has three stages: “initial crack cracking at the unsymmetrical position → development and formation of main crack → specimen cracking failure”. The load ratio of initial crack cracking is 16.5%, and the load ratio of main crack development and formation is 52.2%. The initial cracking position of the lining under unsymmetrical loads has nothing to do with the existing defect position, with defects occurring at the unsymmetrical position, and the remaining cracks are mainly distributed in the vault, inverted arch and arch foot position. Compared with the intact lining, the ultimate bearing capacity of the vault defect and arch waist defect decreased by 14.2% and 21.3%, and the maximum deformation decreased by 44.6% and 50.6%, respectively. The radial deformation degree in the unsymmetrical position at each stage is basically the same under unsymmetrical load conditions, and it is not affected by the position of the existing defects. The deformation proportion of each stage is concentrated in 5%, 40%, 53%, respectively. We derived the facture evolution of lining based on the accumulated energy of AE, and the quantitative relationship between the damage variable and deformation of the lining under unsymmetrical load was established. When the fracture degree reaches 0.04 and 0.35, the deformation of vault defect lining and the arch waist defect lining is 58.3%, 75.0%, 40.2% and 50.6% earlier than those of the complete lining, respectively. When the fracture degree exceeds 0.6, the growth rate of damage degree is no longer affected by the location of the existing defect.

Concrete structures are subjected to various forms of damage in cold regions. However, the interfacial bonding properties of traditional concrete (NC) reinforced with textile-reinforced cementitious composite (TRECC) under freeze–thaw cycle damage have not been fully studied. In this paper, different control groups were obtained by adjusting the types and layers of Fiber Reinforced Plastics (FRP) in TRECC and the interfacial roughness level between TRECC and NC. After experiencing 0–300 freeze–thaw cycles, each group underwent the uniaxial tensile test, three-point bending test, and scanning electron microscope observation. The results show that FRP type in TRECC can determine the strength of specimens. After 100 freeze–thaw cycles, the ultimate tensile strength of TRECC with two different FRP types increased by 38.4% and 55.3%, respectively, compared with TRECC. Furthermore, the bond strength and resistance to freeze–thaw damage of TRECC-NC interface increased with the increase of roughness under the action of freeze–thaw cycles. After 100 freeze–thaw cycles, the bonding strength of the repair system reached the highest. Compared with 0 freeze–thaw cycles, the ultimate tensile strength of the TRECC-NC reinforcement system under low roughness and high roughness increased by 50.05% and 61.25%, respectively. Meanwhile, the internal cracks of TRECC gradually developed and penetrated, reducing the cooperative working ability between TRECC-NC.