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.

A vision-based detection method for bolt missing and loosening using the optimal hexagon rule is proposed. High-quality images are obtained through preprocessing operations such as perspective transformation and distortion correction. Based on the optimal hexagon rule， combined with a regional attention mechanism and a residual efficient layer aggregation network， a keypoint detection algorithm based on YOLOv26 is developed to precisely locate the six vertex coordinates of the head of standard hexagonal-head bolts. The Graham scan algorithm is adopted to obtain the convex hull of the circumcircle of the six bolt vertices. Taking the centroid of the convex hull as the center， the sum of deviations between the actual and estimated values of the six vertices is minimized. The Broyden-Fletcher-Goldfarb-Shanno （BFGS） quasi-Newton optimization algorithm is used to adjust the vertex coordinates and generate optimized regular hexagon vertex coordinates. By comparing the coordinate states of bolts， the rotation angle of the bolt can be calculated. Bolt joint rotation tests using 16-hole three-color bolt heads （gray， blue， and red） showed that within the loosening angle range of 0–60°， the absolute error of the identified bolt loosening angle ranged from -2° to 2°， with a maximum relative error of 8%， demonstrating that the proposed optimal hexagon method provided high detection accuracy and stability.

To address the low efficiency， high risk， and limited quantitative capability of manual inspection for fatigue cracks in steel box girders， a study was conducted on the force analysis and motion control of a magnetic wall-climbing robot for crack inspection. According to the crack inspection requirements for deck plates and diaphragms， a wall-climbing robot equipped with an eddy current testing device was designed， and key parameters including overall dimensions， payload capacity， and operating speed were determined. Static and dynamic models of the robot were established to analyze the minimum magnetic adhesion force and driving torque required on steel plate surfaces. Based on the Webots platform， simulations were carried out to investigate the robot’s motion under different payload and weld obstacle conditions. The results showed that the robot could move continuously between the diaphragm and deck plate. As the payload increased， the start-up time on the diaphragm became longer and speed fluctuation became more pronounced， while the motion on the deck plate remained relatively stable. When crossing a weld， short-term speed fluctuations occurred， and the pitch angle increased significantly with the rising payload. Overall， the robot still maintained good obstacle-crossing capability and motion stability.

The Nanjing City Wall represents the pinnacle of ancient Chinese city wall construction and holds significant cultural heritage value. However， due to the deterioration of its structural integrity and external environmental factors， it faces substantial safety concerns requiring urgent restoration and reinforcement. First， this study examined the structural configuration and damage conditions of the section from Jiefang Gate to Xuanwu Gate based on field surveys and literature review. Second， finite element analysis using ANSYS software was conducted on the wall structure. The analysis focused on evaluating the mechanical properties and safety under various combined conditions， including the effects of air-raid shelters and moisture absorption/expansion of internal brick-rubble-soil fill， to identify potential hazards. Finally， adaptive restoration and conservation measures were proposed， balancing both the preservation of historical appearance and the reinforcement of structural safety. This study implemented targeted reinforcement measures for different types and grades of deterioration， including structural strengthening of wall bodies， rampart top surfaces， and arches. Under the premise of preserving historical appearance， the wall structure was reinforced to achieve minimal intervention conservation for cultural heritage buildings， providing valuable insights and references for the preservation and restoration of ancient city walls.

To enhance the intelligent supervision of photovoltaic （PV） project progress and address the issue of low recognition accuracy caused by component occlusion in complex scenarios， this study proposed an automated recognition approach for key PV components that integrates UAV images with an improved object detection algorithm. In response to common challenges such as small-scale targets and occlusion interference in UAV images， this study developed an optimized non-maximum suppression mechanism and a dynamic screening strategy based on target size and category features. Experimental results showed that the improved model achieved stable convergence of the loss function during training. Its key performance indicators， including detection precision， recall， mAP50， and mAP50-95， reached 94.8%， 93.2%， 94.8%， and 96.5%， respectively. In the practical application of the Anduo PV project in Nagqu， Tibet， the average recognition accuracy for core components such as pile foundations， PV supports， and PV modules exceeded 95%， significantly outperforming traditional manual inspection methods. These findings demonstrated that the proposed approach effectively reduces target omission under occlusion through dynamic detection optimization， providing a feasible technical solution for intelligent progress monitoring in complex PV construction environments and possessing considerable practical engineering value.

Taking the renovation project of C10 Laboratory at Tsinghua University’s Nankou Base for State Key Laboratories as the engineering background， this study focused on the core contradiction between industrial heritage conservation and the functional needs of national key laboratories in the adaptive reuse of old industrial buildings， and proposed a full-cycle renovation framework of "Conservation–Reconstruction–Symbiosis". With multi-source heterogeneous data fusion， this study completed a comprehensive diagnosis of the workshop， established a three-level industrial heritage conservation system， and developed integrated renovation technologies for heritage feature preservation and building performance improvement. The project achieved the coordinated goals of industrial context inheritance， scientific research function implementation and low-carbon development， and provided replicable engineering reference for the sci-tech innovation-oriented regeneration of similar old industrial areas.