Irregular-shaped bridge is an important component of urban overpass and is prone to damage due to severe overloading and material deterioration. Structural damage detection is necessary to prevent bridge failure and guarantee the safe operation of urban traffic. For vibration-based damage detection methods, mode shape of full-scale structure is difficult to be measured with the limited number of sensors, while modal frequency can be obtained accurately and conveniently. This paper aims to propose a two-stage scheme for damage identification using the ratios of modal frequency changes and uniform load surface curvature difference (ULSCD) in damage region. FCM algorithm improved by PSO algorithm (FCM-PSO) is employed to locate damage and predict the damage extent. Firstly, the ratios of modal frequency changes from training cases are classified into several clusters based on FCM-PSO analysis. And the cluster centers for damage locations are constructed. Damage location can be identified by calculating the fuzzy memberships between identification indicator vector and cluster centers of damage locations. After obtaining the damage location, ULSCD values in damage region are established to assess damage severity based on the memberships in damage grades. Damage identification results for typical irregular-shaped bridge demonstrate that the two-stage damage identification method is efficient and accurate to identify the occurrence, location and extent of structural damage

The durability and safety of modern concrete architecture and infrastructure are critically impacted by early-stage surface cracks. Timely and appropriate identification and management of these cracks are therefore essential to enhance structural longevity and stability. This study utilizes computer vision technology to construct a large-scale database, comprising 106,998 concrete surface crack images from various research sources. Through data augmentation, the database is extended to 140,000 images to fully leverage the advantages of deep learning models. For concrete surface crack detection, this study proposed a lightweight convolutional neural network (CNN) model, achieving 92.27% accuracy, 94.98% recall, and a 92.39% F1 score. Notably, the model runs smoothly on lightweight office notebooks without GPUs. Additionally, an image stitching algorithm that seamlessly stitches multiple images was proposed to generate high-quality panoramic views of bridges. The image stitching algorithm demonstrates robustness when applied to multiple images, successfully achieving stitching without visible seams or errors, providing efficient and reliable technical support for bridge panorama generation. The research outcomes demonstrate significant practical value in bridge inspection, providing robust technical support for safe and efficient bridge inspection. Moreover, our findings offer valuable references for future research and applications in related fields.

This study investigated the effects of varying water–binder (w/b) ratios and internal curing materials—superabsorbent polymer (SAP) and ceramsite—on the shrinkage behavior and crack resistance of ultra-high-performance concrete (UHPC). Although internal curing has been extensively studied, the comparative effectiveness of different internal curing materials on early-age shrinkage and restrained cracking behavior of UHPC under consistent mixture proportions remains unclear. To address this gap, a systematic experimental comparison of SAP and ceramsite was conducted. The influences of w/b ratio and different amounts and addition methods (dry and pre-absorbed addition) of SAP and ceramsite on the flowability, mechanical properties, early autogenous shrinkage, drying shrinkage, and early crack resistance of UHPC were discussed. Findings indicate that increasing the w/b ratio reduces autogenous shrinkage but compromises mechanical properties, altering the cracking mode from primary microcracks to a few wider cracks. Pre-saturated ceramsite (less than 10% volume) and SAP effectively mitigate autogenous and drying shrinkage, enhancing crack resistance without significantly reducing mechanical properties. However, exceeding a ceramsite volume dosage of 10% or using the dry addition method increased the flowability of UHPC, while decreasing crack resistance. Microstructural analysis reveals that internal curing materials facilitate hydration and enhance structural density through the formation of ettringite and calcium silicate hydrate. To optimize shrinkage reduction while maintaining mechanical properties, SAP should be incorporated in a dry state, with a dosage limited to 0.4% of the mass of the cementitious material; ceramsite needs to be pre-saturated and limited to 5% of the total volume.

The basic object of damage diagnosis for bridges is to ascertain the damage occurrence, location, and severity based on extracted modal parameters from the monitored vibration response. In practice, modal parameters are extensively affected by ambient environment, especially temperature. In this paper, a novel damage diagnosis approach was proposed to identify the structural damage under the influence of temperature. Firstly, temperature field in the concrete slab was numerically simulated through thermodynamic analysis. Secondly, mechanisms of temperature affecting modal frequencies were analyzed, and the reference finite element model was constructed considering stress stiffening effect and temperature-dependent characteristics of concrete. Finally, damage was simulated by adding concrete specimens onto the surface of concrete slab. And, damage of concrete slab was identified using probabilistic finite element model updating technique. The feasibility of the proposed approach was demonstrated using experimental study of concrete slab monitored outside the laboratory.

To investigate the evolution of acoustic emission (AE) parameters during the flexural failure of reinforced concrete (RC) beams subjected to freeze–thaw and corrosion, four RC beams were fabricated and assigned to four conditioning regimes: no deterioration, freeze–thaw only (75 cycles), corrosion only (4.8% mass loss), and combined. Three-point bending tests were conducted, combining AE and digital image correlation (DIC) techniques. The damage process was divided into four stages: micro-crack initiation, stable crack propagation, unstable crack propagation, and failure. The evolution of AE parameters including ring count, energy, amplitude, peak count, and duration was analyzed. Each parameter is positively correlated with load level and rises as the damage stage advances. The slope of cumulative parameters reflects crack development more reliably than instantaneous values. The effect of corrosion on these parameters is significantly greater than that of freeze–thaw. For corroded beams, AE parameter levels are higher during the micro-crack initiation stage but lower during the stable crack propagation stage. The overall AE activity decreases with increasing deterioration degree. High-amplitude events increase with damage progression, but fewer high-amplitude events are observed at the failure stage of severely deteriorated beams. This study reveals the correspondence between AE parameters and damage stages, providing an experimental basis for damage assessment using AE techniques.