Road infrastructure suffers irreversible damage during service due to periodic or seasonal freeze-thaw (F-T) cycles, resulting in significant economic losses to the nation. However, systematic evaluation parameters for the entire road F-T environment are lacking, and there is insufficient understanding and short-term prediction of the underlying F-T environment changes and distribution. In this study, 13 F-T environmental parameters in three categories were summarized by combining meteorological parameters and multi-field evaluation parameters. Taking Shiquan River Town as an example, the statistical probability of road F-T environmental parameters was clarified by using the generalized extreme value (GEV) distribution. The study also clarified the spatiotemporal characteristics of road F-T environment across regions based on key F-T environmental parameters. The frequency of F-T events in China increases from south to north and from east to west, and F-T processes are influenced by regional altitude and latitude. Meanwhile, the temporal variation of the F-T environment in China shows significant seasonality. The F-T environment transitions from short-duration, high-frequency F-T cycles to longer-duration, low-frequency F-T cycles. The initial moisture condition of the road F-T environment in the northeastern regions increases, but the additional moisture during the F-T process decreases. This study provides critical insights into understanding the underlying mechanisms and short-term prediction of the road F-T environment changes, which can be used as a basis for the planning, construction, and maintenance of road infrastructure in China.

The minimum miscibility pressure (MMP) is a crucial parameter in assessing the miscibility of CO₂ displacement and evaluating the effectiveness of oil displacement. Traditional methods for calculating MMP are intricate and time-consuming, involving numerous related parameters. Therefore, precise and efficient determination of MMP is highly significant in the development of CO₂-driven reservoirs. This study first utilized the Pearson correlation coefficient to analyse the correlation factor mechanism of 36 sets of fine-tube experimental data. Subsequently, the physical information neural network prediction model was employed with reservoir temperature, crude oil composition, and injected gas type as input parameters. The PRI state equation and Glaso correlation equation drove the model, with parameter optimization and training conducted under both physical and data driving. The model demonstrates high prediction accuracy and strong generalization ability. Finally, Validation of the model was performed using fine-tube experimental data from 5 other wells, revealing a relatively small relative deviation between calculated and experimental values, with an average coefficient of determination of 0.95 and an average relative error of 4.42%. The prediction accuracy was improved by about 75% compared to other machine learning algorithms. This model holds potential for application in on-site reservoir development, enhancing the measurement accuracy of the minimum miscible pressure of pure CO₂ flooding, greatly shortening the design cycle of reservoir development, expediting the process of reservoir development, and providing technical guidance for improving oil and gas recovery and pure CO₂ flooding exploration and development.

To enhance the application of alkali-activated materials in mine filling, cemented tailings backfill was prepared using slag, fly ash, sodium silicate, and NaOH as primary constituents. The effects of the raw material type and dosage on the backfill were examined through a single-factor experiment. Additionally, response surface methodology (RSM) was utilized to optimize the mixing ratios of the backfill, with a focus on fluidity and compressive strength as key objectives. The evolution of backfill quality and compressive strength under the combined effects of dry–wet and freeze–thaw (DW-FT) cycles was analyzed. The hydration products, microstructure, and pore characteristics of the specimens were analyzed using X-ray diffraction (XRD), scanning electron microscopy with energy dispersive spectroscopy (SEM-EDS), and nitrogen adsorption tests (NATs) across varying cycles. The results demonstrate that the optimal backfill composition includes 47.8% fly ash, 6.10% alkali equivalent, and a 1.44 sodium silicate modulus. The macroscopic behavior of the backfill under DW-FT coupling followed this progression: pore initiation → pore expansion → crack formation → crack propagation → structural damage. After a minor initial increase, the backfill strength steadily decreased. Microscopic analysis revealed that the decline in internal cementation products and the deterioration of pore structure were the primary causes of this strength reduction. Thus, the DW-FT coupling can cause significant erosion of the backfill. The technical solutions presented in this paper offer a reference for solid waste utilization and provide valuable insights into the durability of backfill under DW-FT coupling.

To scientifically enhance user perception in decision-making for designing interactive spaces in schools for children with intellectual disabilities, we propose an innovative design model that integrates the Kano model, Analytic Hierarchy Process (AHP), and Axiomatic Design (AD) theories based on user needs. Initially, multi-method research was used to gather the original user requirements which were then refined through data cleaning to establish the initial user needs. The Kano model was then employed to categorize these initial user needs. AHP was then used to construct a hierarchical analysis model for the interactive spaces in schools for children with intellectual disabilities, creating a judgment matrix to accurately calculate demand weight values at each level. Subsequently, AHP was used to select the most important demand items. The independence axiom of AD theory was used to achieve a “Z”-shaped mapping between the functional requirements (FRs) and design parameters (DPs) for the interactive spaces in schools for children with intellectual disabilities. This mapping was analyzed using a matrix approach to assess the design rationality and optimize solutions, thereby transforming user needs into design parameters. Finally, the design parameters were used to create interactive spaces through computer-aided design, and the resulting design plans were evaluated. Experimental results indicate that this design scheme effectively translates subjective concepts into specific design parameters through a qualitative and quantitative approach. This significantly enhances the user needs of interactive spaces in schools for children with intellectual disabilities and provides a scientific basis for the architectural design of these schools.