Aiming at the problems of low efficiency， strong reliance on manual labor， high risk of high-altitude work， and secondary damage that is easily caused by contact detection in traditional methods for building exterior wall disease detection， this paper proposes an intelligent non-destructive detection method based on machine vision and deep learning. This method enables rapid identification of three types of apparent diseases： spalling， hollowing， and cracking. Using UAV high-precision collection equipment， disease images were collected from typical exterior wall types such as tiles， paint， and cement mortar. A building exterior wall disease image database containing 1018 images of three types of diseases was constructed. Through LabelMe software， disease annotation was performed， forming 1168 spalling labels， 1619 hollowing labels， and 1515 cracking labels. Based on the deep learning YOLO11n model， multiple training schemes were implemented on the training set. This study found that， with 300 training epochs， an image size of 1280 pixels， and data augmentation enabled， a detection performance of mAP50 = 0.753 was achieved. This model relatively accurately identified the three types of apparent diseases： spalling， hollowing， and cracking. Finally， engineering instance applications were carried out in multiple residential communities in the Chengdu area， further proving that the model has good generalization ability and can provide a new technology for non-destructive rapid detection of building exterior wall diseases.

Dovetail profiled steel sheets are characterized by a unique rib configuration that ensures a flat plate surface. Compared with conventional flat steel plates， they offer higher buckling resistance， greater out-of-plane stiffness， and more effective interaction with concrete. These superior properties render them well-suited for enhancing the mechanical properties of wall claddings， lateral force-resisting components， and steel-concrete composite structures. To clarify their in-plane shear mechanism， pure shear diagonal loading tests were conducted on two dovetail profiled steel sheet specimens： DPS-V with vertically oriented ribs and DPS-D with ribs inclined at 45°. Experimental observations were focused on buckling modes， deformation evolution， and failure modes， while finite element analysis （FEA） was employed to further elucidate the underlying working mechanism. The results indicated that the profiled ribs provided effective boundary restraint to the plate strips， thereby inhibiting global penetrating buckling. Both specimens exhibited localized buckling within the plate strips， with DPS-V undergoing shear buckling and DPS-D experiencing compressive buckling. Owing to the boundary restraint provided by the ribs， the plate strips were capable of developing post-buckling strength； however， the tensile effect induced by the formation of local tension fields ultimately led to flexural-torsional instability of the ribs， resulting in overall failure. The shear resistance of DPS-V was primarily provided by the plate strips， whereas that of DPS-D was derived from the combined action of the plate strips and ribs， exhibiting significant anisotropic behavior—its bearing capacity under diagonal tension was 38% higher than that under diagonal compression. Although the initial stiffness and ultimate bearing capacity of DPS-V were slightly lower than those of DPS-D， DPS-V demonstrated superior ductility and deformability beyond the peak load. Based on the superposition principle， design formulas for predicting the shear capacity of the two types of steel sheets were proposed. The relative error between the calculated and experimental values was within 4%， providing a reliable reference for the engineering design of such components.

Many existing rural houses face structural safety hazards due to material performance degradation. This study investigated the residual bearing capacity and the strengthening effect of carbon fiber reinforced polymer （CFRP） sheets on old precast prestressed concrete （PC） hollow-core slabs， using specimens obtained from a demolished 45-year-old rural house in Shanghai. The test involved two PC slabs： one served as the control specimen， and the other was strengthened with CFRP sheets. Static loading tests were conducted to compare and analyze their failure modes， deformation capacity， and energy dissipation capacity. Based on the measured data， the strain development pattern of the CFRP sheets was analyzed. The results showed that the 45-year-old PC hollow-core slabs still had a certain bearing capacity but with a low safety margin， exhibiting a typical brittle flexural failure due to under-reinforcement. After CFRP strengthening， the failure mode changed to shear failure with obvious ductile characteristics， because the CFRP sheets bore the main tensile stress in the later loading stage.

The stop-hole method is a commonly used technique for repairing fatigue cracks of steel structures in engineering practice； however， it suffers from issues such as unreliable repair effectiveness and damage to the cross-section. By combining externally bonded CFRP plates with the stop-hole to form a combined repair method， the limitations of a single method can be compensated for， achieving efficient repair of fatigue cracks. To investigate the enhancement effect of CFRP plates on stop-hole repair， a numerical analysis of the fatigue performance was conducted on single-edged cracked steel plates repaired with combined stop-hole and CFRP plate， based on the local stress-strain approach and fracture mechanics theory. A two-stage fatigue life assessment method for combined repaired steel plates was established and validated through comparisons with existing experimental studies. The results showed that， compared with the stop-hole repair， the combined repair method significantly reduced the stress around the hole edge and the stress intensity factor after crack re-initiation， thereby delaying crack propagation and significantly reducing the crack growth rate. Compared with CFRP plate repair， the combined repair method provided additional crack initiation life， demonstrating its high repair efficiency.

The concrete-filled steel tubular （CFST） column-double laminated slab composite shear wall is a novel composite structural form for prefabricated buildings. A refined three-dimensional finite element model was established using ABAQUS and validated against quasi-static test data. Combined with test and numerical results， the mechanical mechanism under quasi-static loading was revealed， and the shear slip at the precast-cast-in-situ concrete interface was identified as the intrinsic cause of composite action degradation and performance deterioration. On this basis， a trilinear backbone curve model applicable to composite shear walls with a shear span ratio greater than 1.5 was proposed， along with formulas for equivalent stiffness and cross-sectional bearing capacity. Hysteretic rules were established based on a modified Clough model to develop a complete restoring force model. Validation results showed that the theoretical predictions agreed well with the test （numerical） results. Furthermore， an interface strengthening scheme using angle steel shear keys was proposed， which increased the ultimate drift ratio by 34% and the ductility coefficient by 26%， significantly improving the plastic deformation capacity.

Efficient and reliable structural health monitoring is essential for ensuring the safety and extending the service life of steel structures. Owing to the advantages of non-contact nature， high efficiency， and a high degree of automation， computer vision （CV） has gradually become an important technology for the inspection and maintenance of steel structures. Focusing on surface cracks and corrosion damage of steel structures， this review systematically summarizes the recent research progress in CV-based damage detection and outlines the major approaches， including image classification， object detection， and image segmentation. Particular attention is paid to key optimization strategies for small object detection， robustness under complex backgrounds， few-shot learning， and on-site deployment. Existing studies indicate that CV has significantly improved the automation， intelligence， and precision of damage detection for steel structures. However， further advances are still required in dataset standardization， model robustness to interference， generalization capability across scenarios， and lightweight real-time inference.

To address the safety disturbance caused by urban rail transit construction to adjacent tall structures， this study conducted a systematic structural safety assessment on a TV tower in the context of construction adjacent to a metro transfer station and a shield tunnel. Field structural inspection， long-term deformation monitoring， and three-dimensional numerical simulation were integrated to assess the current condition of the TV tower structure， the evolution patterns of horizontal and vertical foundation displacements， and the inclination characteristics of the tower body. These methods also accurately quantified the impacts of metro foundation pit excavation and shield tunnel construction on the tower's foundation and superstructure. The results showed that the current concrete strength of the TV tower meets the design requirements， with a historically accumulated foundation inclination ratio of 1.3‰. Numerical simulation predicted that subsequent construction would increase the inclination ratio by 0.31‰， resulting in a total inclination ratio of 1.61‰， which still meets safety requirements. Based on these findings， targeted deformation control standards and engineering recommendations were proposed.

In order to accurately separate the contributions of foundation settlement and structural damage to the deformation of port approach bridges and realize the physical attribution of structural damage， an integrated framework of "multi-source perception–physical modeling–deviation diagnosis" was adopted to develop a synergistic method combining time-series PS-InSAR-based foundation settlement monitoring， high-resolution optical image shadow analysis， and BIM-based parametric mechanical modeling. First， under a unified spatiotemporal datum， time-series PS-InSAR technology was applied to extract the foundation settlement field， while an improved Normalized Shadow Index （NSI） was used to invert the relative deformation at the tops of bridge piers. Second， an LOD350-level BIM model was converted into a parametric beam-grid mechanical model， and foundation settlement as well as thermal loads were taken as inputs to calculate the theoretical deformation response. Finally， a Damage Risk Index （DRI） was constructed to quantify the deviation between monitored and theoretical deformations， enabling damage early warning and localization. Closed-loop verification was further performed using an actual engineering case. The results showed that the proposed method achieved a mean absolute error （MAE） of approximately 1.14 mm and a root mean square error （RMSE） of approximately 1.46 mm in deformation monitoring， with 88% of data points having an error no greater than 2 mm and a damage identification accuracy of 93.7%. This method also supports the full-chain diagnostic process of "large-scale early warning – localized positioning – on-site verification – repair validation". It is concluded that this method effectively overcomes the limitations of single remote sensing techniques in interpreting deformation causes， achieves the transition from "phenomenon perception" to "mechanism interpretation"， and thus provides a reliable technical paradigm for the intelligent operation and maintenance of long linear steel-structure infrastructures such as port approach bridges.

To meet the demand for optimizing long-term maintenance decisions in intelligent bridge operation and maintenance， considering the characteristics of time-varying bridge deterioration under a finite horizon， financial discounting of maintenance costs， and difficulty in long-term reward propagation， a life-cycle maintenance decision model incorporating non-homogeneous deterioration and discounting effects was developed. The bridge deterioration process was characterized by non-homogeneous Markov state transitions. Based on discrete health states and maintenance actions， maintenance costs and risk costs were integrated into a unified cost function， while cash-flow discounting was introduced into the decision-making process. To address the limitations of conventional reinforcement learning methods in handling finite-horizon stage-wise decision tasks， non-homogeneous state transitions， and unstable training， a reinforcement learning framework combining state augmentation and backward curriculum learning was proposed. Expanded state representation enhanced the policy’s capability to capture finite-horizon characteristics， while backward curriculum learning gradually extended the training interval to improve learning stability and convergence efficiency. Numerical results demonstrated that the proposed method effectively adapted to finite-horizon maintenance decision problems under non-homogeneous bridge deterioration and achieved favorable performance in both policy quality and training stability， thereby providing methodological support for maintenance planning in life-cycle bridge operation and maintenance management.

This study investigated the structural response of a 24-year-old， 160 m span concrete-filled steel tubular （CFST） arch bridge after the fracture of a single tie rod. By comparing monitoring data before and after the tie rod fracture， this study systematically analyzed the variation characteristics of key mechanical indicators， including arch foot displacement， suspender cable force， and the alignment of the arch ribs and the bridge deck. Furthermore， it evaluated the influence pattern of the tie rod fracture on the bridge structure. The results showed that after the fracture of a single tie rod， the arch feet underwent outward displacement， and both the arch ribs and the bridge deck experienced downward deflection， with the deformation values on the fractured side being significantly larger than those on the non-fractured side. The suspender cable force showed no obvious change. The deformations of the arch ribs and the deck exhibited an asymmetric distribution： the vertical deflection of the free-end half-span on the fractured side was larger than that of the fixed-end half-span， while the opposite was true for the non-fractured side. Furthermore， a full-bridge finite element model was established to simulate the structural response of the bridge induced by the tie rod fracture， and the calculated results reproduced the monitoring data of the actual bridge well. Based on this model， a dynamic amplification factor was introduced to evaluate the stress state of the remaining tie rods and the arch ribs upon the sudden fracture of multiple tie rods.