In recent years, the rapid development and proliferation of highways in China have made asphalt pavement maintenance increasingly complex, requiring maintenance management departments to make practical choices of preventive maintenance measures within limited budgets. To improve comprehensiveness, scientific rigor, and the economy of decision-making, the Analytic Hierarchy Process (AHP) was employed to conduct a decision-optimization study of preventive maintenance measures for asphalt pavements. Taking the preventive maintenance project of the Liuzhou North Ring Expressway in Guangxi as a case study, maintenance measures were initially selected through road condition assessment and investigation. A multi-level, multi-objective decision-making AHP model was constructed, including an objective layer, a criterion layer, an indicator layer, and a scheme layer. By comprehensively considering maintenance needs and assigning values to multi-level factors, the weights and priorities of each maintenance measure were determined. The results show that the ranking and weight calculation of measures such as ultra-thin cover, composite seal coat, micro-surfacing, thin layer cover, and seal coat are relatively rational, and the theoretical analysis results are in good agreement with actual needs.

This study presents a statistical evaluation of cementitious composites incorporating rice husk ash (RHA) and recycled steel fibers using a structured experimental design. A Central Composite Rotatable Design (CCRD) within the framework of Response Surface Methodology (RSM) was employed to examine the combined influence of RHA content, recycled steel fiber aspect ratio, and water–cement ratio on selected properties of concrete. A total of twenty experimental mixes were prepared according to the design matrix, and compressive strength, flexural strength, and water absorption were measured as response variables. Material characterization was limited to X-ray fluorescence–based oxide composition for cement and RHA and scanning electron microscopy–energy dispersive spectroscopy (SEM–EDS) based morphological documentation for RHA. The experimental results were analyzed using analysis of variance to identify statistically supported trends and interaction effects within the investigated parameter ranges. The findings indicate that strength-related responses and water absorption are governed primarily by interaction effects among mixture parameters rather than by individual variables acting independently. The results are interpreted within the investigated design space (10%–20% RHA replacement), and no comparison with control mixtures (0% RHA) is implied. This study provides statistically supported, trend-level insights into the behavior of RHA- and recycled steel fiber–modified cementitious composites under the defined experimental conditions. The results contribute experimental and statistical evidence relevant to structural engineering applications where controlled modification of concrete mixtures is of interest.

Water leakage in metro systems poses a persistent threat to structural durability and operational safety, particularly in water-bearing environments where leakage-induced deterioration may propagate through interconnected stations. While previous studies have extensively investigated leakage mechanisms and local structural responses, limited attention has been paid to the system-level vulnerability of metro networks under leakage disturbances, particularly the lack of an integrated framework linking leakage susceptibility and network characteristics. To address this gap, this study aims to develop an integrated framework for assessing station-level vulnerability in metro networks by incorporating both network characteristics and leakage susceptibility factors. Methodologically, network properties are first quantified by integrating topological structure and passenger flow characteristics. Leakage susceptibility is then evaluated using a fuzzy comprehensive evaluation method based on field investigation data. A combined weighting approach is further employed to integrate network and leakage indicators into a unified vulnerability assessment framework. Finally, a Monte Carlo probabilistic failure model is introduced to evaluate system robustness, and station vulnerability is ranked using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. The results indicate that leakage-related factors contribute dominantly to station vulnerability, accounting for approximately 62.4% of the overall weight, highlighting their critical role in metro system performance degradation. Several stations are identified as high-risk nodes due to the combined effects of unfavorable hydrogeological conditions and topological importance. The proposed framework can support infrastructure managers in prioritizing inspection scheduling, preventive maintenance, and targeted reinforcement, thereby enhancing the resilience of metro systems against leakage-induced disruptions. Future work should incorporate multi-temporal operational data and real-time monitoring information, and further validate the model using long-term maintenance records to improve its practical applicability.