Automatic crack segmentation is crucial for ensuring the safe and stable operation of civil concrete buildings. However, due to the irregularity of cracks, low image quality, and complex background environment, automatic crack segmentation on concrete building surfaces still faces significant challenges. To address these issues, an automatic segmentation network (LKT-Net) based on a large kernel pooling Transformer is proposed, aiming to improve the comprehensiveness and accuracy of crack feature extraction while maintaining a lightweight design. First, the large kernel pooling Transformer (LKT) is proposed as the fundamental building block of LKT-Net, which combines large kernel convolution with pooling layers and attention mechanisms to effectively enhance global perception and capture local details at a lower computational cost. To extract edge information accurately, the Feedforward network is improved by integrating the Laplacian operator with multi-scale convolutions, thereby enhancing multiscale edge detection capabilities. Finally, to mitigate information loss during downsampling, we propose a feature enhancement module (FEM) to replace traditional skip-connections, thereby enhancing cross-level feature interactions. The experimental results showed that on three public datasets (DeepCrack537, CrackLS315, and CrackTree260), compared with eight advanced networks, LKT-Net achieved mean Intersection over Union (mIoU) scores of 86.23%, 70.82%, and 83.67%, respectively, demonstrating excellent segmentation performance. The codes are available at: https://github.com/wjxcsust2024/LKT-Net .

Traffic loads are the main source of live loads on bridge structures, and accurately identifying the dynamic axle load of vehicles is of great importance for Bridge Structural Health Monitoring (BSHM). Most of the past research established the relationship between moving forces and structures through analytical or finite element methods to estimate axle loads, which was proved to be ill-conditioned and inefficient. In this paper, a Moving Force Identification (MFI) method based on U-shaped Network (U-Net) is proposed, which does not require axle position information as a prior knowledge, but is completely based on monitoring data. Firstly, the dynamic response of the finite element model under traffic load is used to establish a data set. In this process, a large number of random traffic flows with different axle loads, axle spacings and vehicle speeds are generated to simulate the operation process of real bridges. Then, the deep learning model is trained with dynamic displacement data as input and dynamic axial load as output. The accuracy of the model is verified by numerical simulation and the robustness of the model is tested. Finally, the practicality of the proposed method is tested by the actual bridge experiment. The results of the actual bridge test show that the mean error of the axle force identification of the proposed method is 6.28 %, indicating that the proposed method has certain application prospects in practical engineering.

Rat-trap bond brick masonry is commonly found in historic and heritage structures. During reinforcement, achieving complete unloading is often impractical, making it necessary to consider the impact of existing load. Reinforcement of brick masonry with steel plates offers advantages such as ease of construction, safety, reliability, and good reversibility. This study presents axial compression tests on six rat-trap bond brick masonry specimens (including one unreinforced specimen, one steel plate-reinforced specimen without preload, and four steel plate-reinforced specimens under preload). The results demonstrate that steel plate reinforcement effectively enhances the load-bearing capacity and ductility of rat-trap bond brick masonry while improving its failure mode. The preload ratio, defined as the ratio of the axial compressive load applied at strengthening point to the peak load of the unreinforced specimen, is found to have a certain impact on the reinforcement effectiveness. Additionally, increasing the steel plate thickness further enhances load-bearing capacity; however, compatibility must be taken into consideration. A finite element model was developed based on the experimental results and extended for further analysis. Furthermore, an axial load-bearing capacity formula for rat-trap bond brick masonry reinforced with steel plates under preload is proposed, incorporating existing calculation theories for steel plates and masonry.

This study investigates the seismic performance of shallow socket bridge piers used in the Zhengzhou-Jinan high-speed railway. In-situ full-scale quasi-static cyclic load tests and finite element simulations using ABAQUS were conducted through quasi-static analyses. After validating the finite element model’s accuracy, a comprehensive parametric study was performed to assess the effectiveness of the shallow socket connection and systematically evaluate the influence of key design parameters, including socket depth, pier diameter, and aspect ratio. The results indicate that reasonably designed shallow socket bridge piers exhibit mechanical performance comparable to cast-in-place integral bridge piers. The mechanical performance of socket bridge piers is governed by the weakest component. Increasing the socket depth markedly improves the post-peak ductility of the pier, resulting in ductile bending failure. While a large pier diameter improves the load-bearing capacity, it also raises the likelihood of brittle failure in the socket connection components. In addition, increasing the height of the pier columns substantially compromises seismic performance, altering the failure mode of the bridge piers.

This paper presents an experimental and numerical investigation into the fire performance of reinforced concrete (RC) beams strengthened with externally bonded carbon fiber-reinforced polymer (CFRP) sheets and protected by various anchorage and insulation systems. Seven full-scale beams were tested, including three specimens under ambient conditions and four exposed to the ISO 834 standard fire curve. The key parameters examined included insulation schemes, such as a 20 mm cement-based fire-resistant plaster (SJ-2) and a hybrid system combining 2 mm intumescent coating with a 20 mm mortar or SJ-2 layer, as well as the anchorage configurations including mechanical anchors and CFRP U-wraps. Experimental results showed that all insulated specimens achieved a fire-resistance rating of at least 2.5 hours without failure. The combined application of insulation and anchorage systems effectively reduced midspan deflections during fire exposure, primarily by preserving the tensile contribution of the CFRP sheets. A coupled thermal-mechanical numerical model was developed by integrating transient heat transfer analysis with a fiber-based sectional approach. This model explicitly accounted for the temperature-dependent degradation of concrete, steel reinforcement and CFRP sheets. Numerical predictions closely aligned with the experimental results, which accurately predicted the temperature distributions across the beam cross-sections and the degradation of flexural capacity. The validated model serves as a reliable tool for simulating the fire response of CFRP-strengthened RC beams with various anchorage and insulation systems, thereby offering a basis for optimizing fire protection designs and providing a reference for engineering practice in enhancing fire resistance using anchorage systems.

This study proposes a novel method for identifying impulsive seismic motions by integrating the Enhanced Adaptive Multi-Resolution Chirplet Method (EAMRCM) with a Support Vector Machine (SVM) classifier. EAMRCM provides refined time-frequency representations that effectively extract intricate pulse characteristics from near-fault seismic motion records, thereby enhancing both the accuracy and robustness of the extraction process. Principal Component Analysis (PCA) is employed to distill key features from the seismic data, which is subsequently coupled with an SVM classifier to automatically distinguish between impulsive and non-impulsive events. Compared to existing approaches, the proposed method effectively adapts to complex seismic motion patterns, significantly reduces manual intervention, and enhances both automation and classification accuracy. Simulation tests performed in MATLAB demonstrate that this approach markedly improves the identification accuracy for impulsive seismic events, offering a novel tool and methodological foundation for seismic analysis and disaster mitigation engineering.

The rapid industrial development has led to a global shortage of fresh water and river sand. A promising solution involves using seawater and sea-sand as alternative raw materials to produce seawater sea-sand concrete (SSC), and using fiber-reinforced polymer (FRP) owing excellent corrosion resistance as confinement material to produce FRP-confined SSC with reasonable performance. This study aims to investigate the axial compressive behavior of FRP-confined SSC experimentally and evaluate the existing predictive models. A total of 48 short columns and 48 standard cubes were tested. The studied parameters in the experiments covered the type of concrete as well as the type and thickness of FRP. The test results indicated that concretes incorporating seawater exhibited higher early-age strength but comparable 28-day strength to normal concrete; under the same FRP confinement condition, different types of concrete showed similar behavior, including dilation performance, axial stress-strain response and ultimate condition. It can be deduced that seawater and sea-sand substitutions have negligible influence on the performance of FRP-confined concrete. Moreover, a database including existing FRP-confined SSC specimens was established and used to evaluate the applicability of existing analysis-oriented models for FRP-confined SSC. The results showed that the model developed by Jiang and Teng demonstrates the highest predictive accuracy.

The application of bolt anchoring method has been found effective for enhancing the flexural performance of steel plate-strengthened reinforced concrete (RC) beams. However, bond defects that may develop at the steel-concrete interface and the loss of bolts due to corrosion can compromise the strengthening effectiveness. Thus, this study conducted four-point bending tests to examine the impact of bond defects and the loss of bolts on the flexural behavior of RC beams strengthened with steel plates. The results exhibited that an 11% bond defect area and the loss of 20% number of bolts led to premature debonding due to stress concentrations around the defects and the loss of mechanical anchorages that can reduce the peeling stress, resulting in a 32.8% reduction in the debonding load. Nevertheless, the remaining bolt anchorages effectively impeded the debonding development, allowing the steel plates to sustain the load and achieve an ultimate load comparable to beams without bond defects. Finite element (FE) simulations were performed and verified against experimental test data, demonstrating good agreement with an R 2 value of 0.98. The verified model was extended for parametric analysis, revealing that the increase in ultimate load of the beams diminished with a larger bond defect area and fewer bolts. Finally, an analytical model was proposed to predict the ultimate load of the strengthened beams, considering the influence of carrying bond defect area and bolt quantity.

As important parameters reflecting the fire resistance of restrained steel columns, buckling and critical temperatures have been extensively studied. However, the existing simplified design methods for buckling or critical temperature are based on column models with either pin-ended or with large rotational restraints, their applications on steel columns with limited rotational restraint stiffness ratios, i.e., rotational restraint stiffness ratios smaller than the critical rotational restraint stiffness ratio, is questionable. In this paper, parametric analyses were performed on a validated numerical column model with rotational restraint stiffness ratio spanning from 0.0001 to 10.0. Additionally, the influences of slenderness ratio, load ratio, and axial restraint stiffness ratio were also considered. Results reveal significant errors when using the existing simplified design methods to predict the buckling and critical temperatures of steel columns with limited rotational restraint stiffness ratios. Based on the parametric analyses results, new prediction formulas for buckling and critical temperatures of box-section steel columns were proposed. Results of the proposed formulas are in close alignment with those of the parametric analyses across the full range of considered parameters. Moreover, the effectiveness of the proposed formulas was further validated by test results in literatures.

The “strong columns-weak beams” principle is widely adopted in seismic design for RC frame structures, yet “column hinge” failures remain prevalent in both experimental tests and earthquake damage investigations. Current design methods often underestimate the influence of cast-in-place slabs, typically assuming a uniform effective flange width without accounting for actual boundary constraints. To better understand the yielding mechanism of RC frames under seismic loading, this study employs a multiscale modeling approach. Sensitivity analyses validate the finite element model, while parametric studies reveal that slabs significantly alter the reinforcement yielding sequence in beams and columns. The effective flange width is found to depend on beam geometry, joint position, and beam end rotation. These findings emphasize the need to revisit the yielding mechanism and further investigate spatial effects and flange width in the collapse mechanism of RC frames.