Hyperbolic cooling tower shells are mostly cast-in-place reinforced concrete thin-walled structures. Their high-altitude construction poses significant challenges， and geometric imperfections often occur due to issues in construction layout accuracy. Focusing on a specific engineering case， this study employed terrestrial laser scanning （TLS） technology to capture precise geometric imperfection data of the tower shell. Based on the scanned data， finite element models of the hyperbolic cooling tower， both with and without geometric imperfections， were developed using ABAQUS. The effects of geometric imperfections on the mechanical properties of the cooling tower， as well as the sensitivity of different load effects to these imperfections， were systematically investigated. The results indicated that when the actual imperfection magnitude was introduced based on the measured distribution pattern， the bearing capacity and crack resistance of the tower shell decreased significantly. Moreover， no deterioration in mechanical properties was observed when the imperfection magnitude remained below 150 mm. The effects of dead load and external wind pressure were highly sensitive to geometric imperfections， whereas temperature effects remained almost unaffected. Furthermore， the locations along the meridian lines of the tower shell where the maximum external wind suction occurs were identified as critical regions for safety assessment.

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.

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.

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.

Asphalt pavement joints are prone to distresses such as cracking， faulting， and water infiltration. Existing studies mainly focus on material modification and construction techniques， while systematic design of joint components in asphalt layers remains limited. To improve the structural integrity and service performance of pavement joints， this paper proposes a novel joint component with a telescopic double-tube structure and establishes the corresponding construction technology system. First， based on the principle of load transfer and deformation coordination， a telescopic structure with square steel tubes was designed， and its load transfer efficiency under different parameter combinations was analyzed using the finite element method. Second， field monitoring was conducted on an in-service expressway to investigate the strain response of the component under coupled traffic loading and temperature effects. Finally， the construction process and key control measures were summarized. The results showed that the proposed component exhibited excellent load transfer capability and deformation adaptability， with a load transfer coefficient exceeding 0.88 and a low degree of dispersion. Field monitoring results verified its stable mechanical properties， and its strain response was highly consistent with temperature variations and traffic load distribution. The proposed construction method ensured installation accuracy and long-term durability.

This study investigated the fatigue life of steel crane beams through fatigue testing and combined finite element analysis to reveal the stress distribution changes at the support of variable-section beams before and after prestress strengthening. In addition， the equivalent structural stress method was employed to predict the fatigue life corresponding to the initiation of 20 mm cracks at the support. The results indicated that prestress strengthening significantly reduced the stress level at critical welds， while the location of maximum stress and the failure mode remained unchanged. With increasing prestress control values， both the overall failure life of the beam and the crack initiation life at the support were significantly extended. Under a prestress control value of 12.5 t， the strengthening effectiveness factors for overall beam failure and visible support cracks reached 4.08 and 4.23， respectively， demonstrating the effectiveness of high-level prestressing.

This study applied machine learning to predict and optimize the hygrothermal performance of bamboo-woven mud walls， highlighting their potential in addressing environmental challenges. Generative adversarial networks （GANs） were first used to augment limited experimental data， addressing small-sample constraints. A back propagation （BP） neural network was employed to analyze and predict the performance of the wall materials. After optimization via a genetic algorithm （GA）， the model’s R² improved to 0.77， indicating significantly enhanced predictive performance. These findings confirm the feasibility of using machine learning in the reuse of traditional building materials and provide a digital theoretical basis and technical support for the preservation and renewal of bamboo-woven mud walls.

To address the problem of missing multi-source monitoring data of offshore wind turbines caused by sensor failures or communication interruptions under harsh operating conditions， this paper proposes a novel imputation model based on a multi-head gated residual network. This method achieves collaborative fusion of supervisory control and data acquisition （SCADA） data and structural vibration monitoring data through feature concatenation， employs a gated residual network to extract deep nonlinear coupling features， and uses a multi-head parallel output architecture for the independent reconstruction of these two heterogeneous data types. During the training stage， a dynamic masking mechanism combined with a hybrid loss function is adopted to enhance the model’s adaptability to complex aerodynamic operating conditions. Validated with field data from a 10 MW offshore wind turbine， the proposed model achieved a high coefficient of determination under training conditions， enabling accurate reconstruction of missing multi-source data. In generalization tests for non-training periods， although the coefficient of determination of the model’s predictions fluctuated slightly， the model still effectively captured the overall trends of monitoring signals. Notably， the degradation in generalization performance for vibration data was less pronounced than that for SCADA data， demonstrating its greater stability. The proposed method can significantly improve the completeness and reliability of multi-source monitoring data for wind turbines and holds considerable potential for engineering applications.

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.

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.