Accurate travel route optimization is essential to promote and grow tourism in modern society. This paper investigates a travel route optimization problem alongside the urban railway line and proposes a hybrid teaching–learning-based optimization (HTLBO) algorithm. First, a mathematical programming model is established to minimize the total traveling time, in which the routes between and in different cities have to be appropriately determined. Then, a hybrid metaheuristic named HTLBO is proposed for solution generation. In HTLBO, depth first search (DFS) is utilized to obtain the optimal routes of any two stations in railway network, and a three-level coding method is designed to accommodate the problem characteristic. Besides, opposition-based learning (OBL) is embedded into teaching-learning-based optimization (TLBO) for enhancing HTLBO’s exploration ability, while variable neighborhood descent (VND) is used to enhance the algorithm’s exploitation ability. Finally, a case study is presented and simulation results verify HTLBO’s feasibility and effectiveness.

Warm-mix recycled asphalt (WMA-R) technology for reclaimed asphalt pavement (RAP) significantly reduces energy consumption and environmental pollution while maintaining the performance of asphalt mixtures. Significant progress has been made at home and abroad in evaluating the impact of regenerated asphalt mixtures on the performance of regenerated asphalt. However, the performance improvement of WMA-R depends on the effective diffusion of regenerated agents and their interaction mechanism with aged asphalt, which has not been fully studied. This paper systematically studies the diffusion characteristics of biomimetic-based warm-mix regenerant in aged asphalt and its impact on the high- and low-temperature performance of asphalt mixtures through MD and experimental verification. The results show that biomimetic-based warm-mix regenerant can significantly improve the diffusion performance of aged asphalt. Through the rutting test and low-temperature bending test, the significant improvement of the biomimetic-based warm-mix regenerant in the rutting resistance and crack resistance of asphalt mixtures was verified.

Historic buildings often exhibit high energy use intensity (EUI), while conservation constraints limit envelope retrofits, making it difficult to identify robust and actionable operational predictors. Using four in-use historic buildings in Shenyang, China, this study presents a pilot methodological demonstration with a controlled-comparability workflow consisting of two linked layers: (i) a Driver layer of intervenable operational variables and (ii) a Pathway layer of calibrated EnergyPlus heat-balance terms for physics-informed interpretation. Three importance approaches (Spearman, wrapper RFE with XGBoost, and Random Forest) are compared; rankings are fused via reciprocal rank fusion, and stability is tested using cross-period rolling validation across Top-K feature sets. After similarity screening, EUI variation is better explained by operational predictors and the corresponding simulated loss channels than by macro-scale structural heterogeneity. Infiltration-related indicators and envelope/infiltration loss components remain consistently prominent, while Spearman importance is less stable in the Pathway layer under seasonal switching and nonlinear coupling. A Top-10 subset provides a favorable accuracy–stability trade-off. The proposed Driver–Pathway mapping supports conservation-compatible prioritization hypotheses within a simulation-consistent interpretive framework; findings are associational and context dependent and should be validated through field measurements and experimental or quasi-experimental studies before prescriptive claims are made.

Under the condition of incomplete information, the research proposed a classification method based on ELECTRE TRI to assess the risk of the deep foundation pit. Firstly, twenty indicators of the deep foundation pit are selected, and indifference threshold, preference threshold, veto threshold are determined. Then the consistency index and the inconsistency index are calculated by ELECTRE TRI on which the composite reliability of risk rating is obtained. Finally, the method is applied to the deep foundation pit of metro station for empirical analysis. The results show that ELECTRI uses thresholds as the nodes of classification, which can effectively solve the problems of excessive difference in subjective evaluation by experts, and heterogeneity and negative value of indicators, and considers the problem of mutual compensation between indicators, providing a new method for grade determination.

Historic buildings account for a significant proportion of the existing building stock, yet their HVAC systems often operate inefficiently and consume substantial energy. To identify typical HVAC energy use patterns and support energy management, this study used a calibrated EnergyPlus v22.2.0 model with outdoor air temperature as the independent variable and HVAC electricity, sensible heating energy, and sensible cooling energy as the dependent variables within a change-point analysis framework. Latin hypercube sampling (LHS) was applied to five variables within predefined ranges, and 2800 parametric variants were generated from four archetype models. Base load, change-point temperature, and heating and cooling slopes were extracted, and three clustering methods—GMM, K-means, and HDBSCAN—were compared. HVAC electricity was primarily characterized by a 5P change-point model, whereas sensible heating energy and sensible cooling energy were stably represented by 3P heating and 3P cooling models, respectively. K-means performed best overall for HVAC electricity pattern recognition, and both sensible heating energy and sensible cooling energy showed advantages for K-means over HDBSCAN. Robustness analysis indicated high stability for all three energy variables, with sensible heating energy showing the best performance. These findings provide a basis for HVAC operation diagnosis and energy-saving optimization in historic buildings.