Search Results (112)

Despite increasing academic interest in return migration, limited understanding remains of how individual resources, workplace experiences, and perceptions of the origin country interact to shape return migration intention among migrant workers in major industrial destinations. This study investigates return migration intention among Myanmar migrant workers in Samut Sakhon Province, Thailand, using a multilevel framework that links micro-level individual and household characteristics, meso-level workplace and social experiences, and macro-level assessments of conditions in Myanmar. A quantitative research design was employed, with data collected from 506 Myanmar migrant workers using proportional stratified random sampling. The data were analyzed using descriptive statistics, chi-square tests, t-tests, and binary logistic regression. The results indicate that the majority of respondents did not intend to return to Myanmar within the next 10–15 years. Workplace discrimination emerged as the strongest positive predictor of return migration intention, while higher income and annual remittance behavior also increased the likelihood of intending to return. Conversely, having family in Thailand, perceived opportunities for job change or promotion, satisfaction with wages and welfare, and perceived safety in Myanmar reduced the likelihood of return migration intention. The findings suggest that future mobility plans cannot be explained solely by economic calculation. They are also shaped by family arrangements, workplace treatment, and migrants’ assessments of the feasibility and desirability of return. The study advances return migration scholarship by demonstrating the pivotal role of workplace discrimination within a multilevel explanation of return migration intention. Full article

Road-railway combined steel truss bridges are increasingly adopted in urban infrastructure due to their structural efficiency and versatility. This study proposes a three-level multi-scale finite element framework to investigate the safety reserve and progressive failure mechanism of a four-span (80 + 120 + 120 + 80 m) continuous steel truss bridge carrying both highway and railway traffic. At the macro level, a beam element model was established in Midas/Civil to determine the most unfavorable loading configurations, yielding a minimum buckling load factor of 31.0 under dead load and a maximum vertical displacement of 175 mm at mid-span under combined traffic loading. At the meso level, a mixed beam–shell element model incorporating geometric and material nonlinearities was developed in ABAQUS, revealing an ultimate load factor of 6.61 with distinct progressive failure characteristics: initial yielding occurs near the intermediate pier supports, where deformation is constrained, while final instability develops at Joint A17 due to its lower relative stiffness. At the micro level, a refined solid-shell submodel of the critical joint, driven by displacement boundary conditions extracted from the global model, was constructed to capture the local failure mechanism. The results demonstrate that the governing failure mode is shear buckling of the gusset plate, induced by a vertical displacement differential of approximately 30 mm between the web members on opposite sides of the joint arising from differential stiffness. The stress analysis further reveals pronounced stress concentrations in the splice plates adjacent to the more flexible web member, confirming the asymmetric load distribution mechanism. Based on these findings, strengthening measures including increased gusset plate thickness at pier-top joints, optimized chord sections, and the use of higher-strength steel in critical regions are recommended. Full article

Metal magnetic memory (MMM) is a promising non-destructive evaluation method for ferromagnetic materials, allowing early detection of stress concentration and micro-defects under weak geomagnetic excitation. However, current magneto-mechanical coupling models are computationally complex and insufficient to characterize the full-cycle evolution of mesoscale physically short cracks. This work proposes a magnetic dipole model and its decomposed formulation for V-shaped cracks. Combined with theoretical derivation, finite element simulation, and in situ three-point bending tests on Q345 steel, the magneto-mechanical coupling mechanism and magnetic signal evolution during crack propagation are investigated. Results show that the MMM normal component exhibits obvious peak-peak features at the crack tip, while the tangential component shows a single-peak characteristic. Two critical signal mutations are observed at crack lengths of about 100 μm and 3000 μm, corresponding to micro-meso and meso-macro crack transitions, respectively. The model is verified with relative errors of 15.2% for H_x and 17.6% for H_y. This study reveals the quantitative correlation between MMM signals and full-lifecycle crack growth, supporting damage assessment and fatigue life prediction for ferromagnetic engineering structures. Full article

Marine anthropogenic particle pollution is a major environmental concern due to its persistence and widespread distribution. Microplastics are widely recognized as a subset of anthropogenic particles originating from synthetic polymers. This study examines the occurrence, characteristics, and biological associations of anthropogenic particles ingested by Atlantic chub mackerel from the Saronikos Gulf. A total of 179 specimens were analyzed for anthropogenic particles in the gastrointestinal tract, while muscle tissue was examined in 51 individuals. Anthropogenic particles were detected in the gastrointestinal tract of 74% of individuals and were also present in muscle tissue in 41% of the analyzed specimens. Fibers were the dominant particle type, representing approximately 60% of the identified particles, followed by fragments at 40%. The majority of particles were micro-sized (<5 mm), although meso- and macro-sized particles were also recorded. Black-colored particles predominated, accounting for approximately 53% of the total. No significant differences in anthropogenic particle abundance were observed between sexes, and no consistent seasonal patterns were detected, except for higher occurrence in early autumn compared to winter, although this result should be interpreted with caution due to uneven sample sizes among sampling periods. No statistically significant correlations were found between anthropogenic particle abundance in the gastrointestinal tract or muscle tissue and fish size, condition factor, or stomach fullness. Overall, the findings highlight this species as a suitable bioindicator for monitoring anthropogenic particle pollution and provide baseline information for future assessments in the Saronikos Gulf. Particle identification was based on visual characterization without spectroscopic confirmation; therefore, the detected particles are considered anthropogenic and their polymer composition could not be definitively confirmed. Full article

This study investigates the macro–meso shear characteristics of the geogrid–silty clay interface under cyclic loading through a combination of laboratory cyclic direct shear tests and numerical simulations. The effects of geogrid roughness, soil moisture content, shear displacement amplitude, and normal stress on the interface behavior are systematically analyzed. The results show that the interface shear strength and shear stiffness exhibit a three-stage evolution with increasing cycle numbers. This evolution is characterized by rapid attenuation in the early stage, gradual change in the middle stage, and stabilization in the later stage. The main degradation occurs within the first 1–10 cycles, while the interface response tends to stabilize after approximately 25 cycles. Increasing geogrid roughness and normal stress significantly enhances the interface shear strength and restrains cyclic degradation. In contrast, the shear strength reaches a maximum at the optimum moisture content level of 13%. The damping ratio shows an opposite trend to stiffness, increasing with cycle number and gradually approaching stability. Numerical simulation results are in good agreement with the experimental data, with relative errors within 5%. At the mesoscopic level, shear stress is mainly concentrated at the intersections of geogrid ribs, and the soil zone within 0–20 mm above the interface is identified as the primary region of shear deformation. Full article

The nominal maximum aggregate size (NMAS) plays a critical role in determining the mechanical performance of cement-stabilized macadam (CSM), yet its meso-mechanical influence mechanism remains insufficiently understood. In this study, three skeleton-dense CSM mixtures with different NMAS values were designed, and a combined experimental–numerical approach was adopted to investigate the macro- and meso-scale mechanical behavior. Uniaxial compression tests and aggregate crushing value tests were conducted to evaluate strength development and load-transfer characteristics, while a three-dimensional discrete element method (DEM) model incorporating realistic aggregate morphology was established to analyze the evolution of contact forces and crack propagation. The results show that increasing NMAS significantly improves the mechanical performance of CSM. Compared with CSM-30, the 7-day compressive strength of CSM-40 and CSM-50 increased by approximately 10.3% and 37.3%, respectively. The stress–strain response indicates that mixtures with larger NMAS exhibit higher stiffness and a higher strain. At the meso-scale, a larger NMAS promotes the formation of a more efficient force-chain network dominated by coarse aggregates. Strong contacts were predominantly carried by aggregates larger than 9.5 mm, and in CSM-50, the proportion of strong contacts in the 37.5–53 mm fraction exceeded 90%, indicating that the largest particles likely form the primary load-bearing skeleton. In addition, increasing NMAS delayed crack initiation, reduced crack propagation rate, and decreased the total number of cracks at failure. These findings demonstrate that macroscopic strength improvement is closely associated with meso-scale optimization of the aggregate skeleton and enhanced load-transfer efficiency. This study provides a mechanistic basis for NMAS selection and gradation optimization in semi-rigid base materials. Full article

Functional microbial inoculation is widely applied in soil restoration; however, its effects on aggregate-scale nutrient cycling remain unclear. Based on ecological stoichiometry theory, we conducted 1-year and 3-year pot experiments using Bacillus thuringiensis (NL-11) and Gongronella butleri (NL-15) under plant-present and plant-absent conditions, with only NL-11 applied in the 1-year experiment. Aggregate size distribution, mean weight diameter (MWD), soil nutrients, microbial biomass, and enzyme activities were evaluated across aggregate classes. The results demonstrated that microbial effects were dependent on both time and plant presence. Under 3-year plant-present conditions, NL-11 and NL-15 significantly increased macroaggregate proportions and MWD, thereby enhancing aggregate stability. Under 3-year no-plant conditions, NL-15 increased organic carbon and total nitrogen in macro- and meso-aggregates by 55–59% and elevated soil C/P and N/P ratios, whereas NL-11 primarily enhanced total nitrogen. In 1-year no-plant macroaggregates, NL-11 increased microbial biomass phosphorus and reduced microbial biomass C/P and N/P ratios. Both inoculants enhanced invertase activity under plant-absent conditions, whereas plant presence stimulated acid phosphatase activity, with NAG activity increasing only under NL-15. Overall, microbial inoculation altered nutrient availability and microbial metabolic characteristics, promoted coordinated C–N–P stoichiometry, and facilitated the recovery of aggregate-scale nutrient cycling. Full article

In the context of accelerating urbanization, university students face mounting academic stress and increasingly severe psychological health challenges. University blue-green spaces are critical environments for fostering restorative experiences. They highlight the urgent need for targeted strategies to enhance their restorative potential. This study used three universities in Guangzhou as case studies, based on image collection and deep learning-based semantic segmentation methods, and employed the Perceived Restorativeness Scale (PRS) and Restoration Outcome Scale (ROS) to explore the hypothesized pathways and threshold characteristics through which visual elements of blue-green spaces are associated with university students’ psychological restoration within everyday campus environments. The results indicate: (1) the restorative effects of different space types follow a clear gradient: waterfront spaces > planar vegetation spaces > linear vegetation spaces > point vegetation spaces; (2) perceived restorativeness acts as a key mediator between visual elements and psychological restoration. The mediating pathways vary across space types. Waterfront spaces show polarized effects. Planar vegetation spaces rely on a dual pathway of being away and compatibility, supplemented by a secondary role of fascination. Linear vegetation spaces exhibit complex pathway patterns in which multidimensional positive support coexists with both positive and negative influences; (3) several visual elements display nonlinear threshold effects. This study deepens the understanding of the “environment–perception–psychology” pathway in the context of sustainable campus environments. It also proposes a three-level optimization framework (macro–meso–micro) that provides empirical references for evidence-informed planning and design of university blue-green spaces, with potential implications for sustainable campus environments and student well-being. Full article

Against the dual backdrop of the rural revitalization strategy and the pursuit of high-quality, balanced urban–rural education, optimizing rural campus spaces has emerged as an important lever for addressing educational resource disparities and improving pedagogical quality. However, conventional evaluation of campus space optimization faces two systemic dilemmas. First, top-down decision-making often neglects the authentic needs of diverse stakeholders and place-based knowledge, resulting in spatial interventions that lose regional distinctiveness. Second, routine public participation is constrained by geographical barriers, time costs, and sample-size limitations, which can amplify professional cognitive bias and impede comprehensive feedback formation. The compounded effect of these challenges contributes to a disconnect between spatial optimization outcomes and perceived needs, thereby constraining the distinctive development of rural educational spaces. To address these constraints, this study proposes a novel method that integrates regional spatial feature recognition with digital media-based public perception assessment. At the data collection and ethical governance level, the study strictly adheres to platform compliance and academic ethics. A total of 12,800 preliminary comments were scraped from major social media platforms (e.g., Douyin, Dianping, and Xiaohongshu) and processed through a three-stage screening workflow—keyword screening–rule-based filtering–manual verification—to yield 8616 valid records covering diverse public groups across China. All user-identifying information was fully anonymized to ensure lawful use and privacy protection. At the analytical modeling level, we develop a Transformer-based deep learning system that leverages multi-head attention mechanisms to capture implicit spatial-sentiment features and metaphorical expressions embedded in review texts. Evaluation on an independent test set indicates a classification accuracy of 89.2%, aligning with balanced and stable scoring performance. Robustness is further strengthened by introducing an equal-weight alternative strategy and conducting stability checks to indicate the consistency of model outputs across weighting assumptions. At the scenario interpretation level, we combine grounded-theory coding with semantic network analysis to establish a three-tier spatial analysis framework—macro (landscape pattern/hydro-topological patterns), meso (architectural interface), and micro (teaching scenes/pedagogical scenarios)—and incorporate an interpretive stakeholder typology (tourists, residents, parents, and professional groups) to systematically identify and quantify key features shaping public spatial perception. Findings show that, at the macro level, naturally integrated scenarios—such as “campus–farmland integration” and “mountain–water embeddedness”—exhibit high affective association, aligning with the “mountain-water-field-village” spatial sequence logic and suggesting broad public endorsement of ecological campus concepts, whereas vernacular settlement-pattern scenarios receive relatively low attention due to cognitive discontinuities. At the meso level, innovative corridor strategies (e.g., framed vistas and expanded corridor spaces) strengthen the building–nature interaction and suggest latent value in stimulating exploratory spatial experience. At the micro level, place-based practice-oriented teaching scenes (e.g., intangible cultural heritage handcraft and creative workshops) achieve higher scores, aligning with the compatibility of vernacular education’s “differential esthetics,” while urban convergence-oriented interdisciplinary curriculum scenes suggest an interpretive gap relative to public expectations. These results indicate an embedded relationship between public perception and regional spatial features, which is further shaped by a multi-actor governance process—characterized by “Government + Influencers + Field Study”—that mediates how rural educational spaces are produced, communicated, and interpreted in digital environments. The study’s innovative value lies in integrating sociological theories (e.g., embeddedness) with deep learning techniques to fill the regional and multi-actor perspective gap in rural campus POE and to promote a methodological shift from “experience-based induction” toward a “data-theory” dual-drive model. The findings provide inferential evidence for rural campus renewal and optimization; the methodological pipeline is transferable to small-scale rural primary schools with media exposure and salient regional ecological characteristics, and it offers a new pathway for incorporating digital media-driven public perception feedback into planning and design practice. The research methodology of this study consists of four sequential stages, which are implemented in a systematic and progressive manner: First, data collection was conducted: Python and the Octopus Collector were used to crawl online comment data related to Fuwen Township Central Primary School, strictly complying with the user agreements of the Douyin, Dianping, and Xiaohongshu platforms. Second, semantic preprocessing was performed: The evaluation content was segmented to generate word frequency statistics and semantic networks; qualitative analysis was conducted using Origin software, and quantitative translation was realized via Sankey diagrams. Third, spatial scene coding was carried out: Combined with a spatial characteristic identification system, a macro–meso–micro three-tier classification system for spatial scene characteristics was constructed to encode and quantitatively express the textual content. Finally, sentiment quantification and correlation analysis was implemented: A deep learning model based on the Transformer framework was employed to perform sentiment quantification scoring for each comment; Sankey diagrams were used to quantitatively correlate spatial scenes with sentiment tendencies, thereby exploring the public’s perceptual associations with the architectural spatial environment of rural campuses. Full article

Based on true triaxial loading experiments and particle flow numerical simulations (PFC3D), this study systematically analyzes the mechanical behavior and failure mechanisms of granite under the influence of stress difference (deviatoric stress). The experimental results indicate that increasing deviatoric stress reduces peak strength, axial strain, and lateral strain, promoting rock failure with less deformation and dilatancy. An energy analysis reveals that higher deviatoric stress suppresses peak energy accumulation, with a greater proportion of energy being dissipated through crack initiation and propagation. Macroscopic observations show that failure surfaces develop combined tensile-shear cracks, evolving into distinct “V” shapes as deviatoric stresses increase. Numerical simulations demonstrate that intermediate principal stress plays a dual role, initially facilitating, then inhibiting, and finally promoting rock failure with its continuous increase. Microscopically, tensile cracks dominate during pre-peak stages, while rapid crack coalescence in the post-peak stage leads to the formation of throughgoing V-shaped failure zones. Particle displacement analysis reveals that deformation concentrates along the minimum principal stress direction, with the displacement vectors ultimately forming a V-shaped boundary that delineates the failure zone. The research provides comprehensive insights into the macro-meso failure characteristics of hard rock under true triaxial conditions, offering valuable guidance for stability prediction and control in underground rock engineering projects such as water-sealed storage caverns. Full article

To investigate the effects of repeated drying and wetting on the mechanical properties and meso-fabric of metal tailings, lead-zinc tailings from Hunan Province were studied. A self-developed apparatus was used to simulate the cyclic drying-wetting processes. Combined with triaxial shear tests and stereomicroscopic image analysis, the changes in macroscopic mechanical properties and meso-fabric, as well as their correlation mechanisms, were investigated. The results indicate that the wet-dry cycles did not alter the strain-softening characteristics of the tailings’ stress-strain curves; however, they significantly intensified the degree of softening during the later stages of cycling (4–6 cycles). The static strength exhibited a trend characterized by “initial gradual degradation → temporary recovery → further deterioration” with an increasing number of cycles. After six cycles, the strength was significantly reduced compared to the initial state. The effective cohesion (c′) fluctuated markedly, with an amplitude of 31.1%, while the variation in the effective internal friction angle (φ′) was only 6.02%, indicating that dry-wet cycles have a more pronounced effect on the cohesion of tailings. At the microscopic level, the dry-wet cycling process promoted the upward migration of fine particles ranging from 0 to 60 µm, resulting in a decrease in the proportion of smaller particles in the lower layer. The porosity increased overall, with the lower layer rising from 44.06% to 54.26%. Pore evolution was dominated by the enlargement of pores larger than 150 µm. The macro-meso correlation analysis revealed that “fine particle migration → expansion of pores → loss of cementitious material” was the core driving factor for the deterioration of macroscopic mechanics, and the macroscopic mechanical response was the external manifestation of the adjustment of the microscopic structure. This research can provide certain theoretical support for the long-term safe operation and stability improvement of tailings dams. Full article

Traditional settlements exhibit remarkable climatic adaptability, representing a form of “Morphological Intelligence” developed over centuries. However, this inherent, physics-based wisdom remains underutilized in contemporary urban planning and design. This systematic review aims to decode such intelligence by analyzing the relationship between the morphological characteristics of traditional settlements and their thermal performance. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol, literature retrieval and evaluation were conducted via the databases of Web of Science, Scopus, and China National Knowledge Infrastructure (CNKI) for articles published during 2004~2024. A total of 82 related articles with available full texts were selected from 1227 records for in-depth analysis, including peer-reviewed journal articles and reputable conference publications. This study first presents an overview of bibliometric and methodological landscapes, revealing that research is increasingly concentrated in Asia’s tropical and subtropical climates, predominantly employing case studies and computational simulations. Secondly, we synthesize a few key climate-adaptive morphological features across macro- (e.g., settlement layout), meso- (e.g., street canyon geometry), and microscales (e.g., courtyards). The findings illustrate a reliance on methods and metrics developed for modern urban contexts, which could not fully capture the specific morphological characteristics of traditional settlements. Most importantly, this study summarizes four core principles of “Morphological Intelligence” in traditional settlements, i.e., strategic solar control, facilitated natural ventilation, use of thermal mass, and integration of natural elements and creation of thermal buffer zones. By identifying the limitations of existing investigations, this study highlights a few directions for future studies, including conducting more systematic multi-scalar integrated analysis, focusing on the development of dedicated quantitative metrics and analytical frameworks, delving into more mechanism-oriented investigation, assessing morphological resilience under urbanization, and translating principles into contemporary design guidelines. This study provides a foundational framework for translating the “Morphological Intelligence” of traditional settlements into actionable, evidence-based strategies for resilient and energy-efficient urban planning and design amidst climate change. Full article

Mountainous traditional villages represent unique socio-ecological systems that have evolved through centuries of adaptation to complex topographies and multi-hazard environments. Understanding their terrain–resilience coupling mechanisms is essential for risk-sensitive planning and heritage preservation in mountainous regions. This study integrates multi-source remote sensing data and GIS spatial analysis to investigate 57 national-level traditional villages in the southern Qinba Mountains, China. Using kernel density estimation (KDE), nearest neighbor index (NNI), and Geodetector modeling, we identify the spatial distribution characteristics and topographic driving forces that shape settlement patterns across macro-meso-micro scales. Results reveal that 83% of the villages are clustered in low-mountain and hilly zones (550–1200 m elevation), preferring slopes below 15° and south-facing aspects. Elevation exerts the strongest influence (q = 0.46), followed by slope (q = 0.32) and aspect (q = 0.29), forming a multi-level adaptation framework of “macro-elevation differentiation, meso-slope constraint, and micro-aspect optimization.” Morphological Spatial Pattern Analysis (MSPA) further indicates that traditional villages achieve ecological balance and disaster avoidance through adaptive spatial strategies such as terrace-based flood prevention, convex-bank stabilization, and platform-based hazard avoidance. These strategies are not merely topographic preferences but natural adaptation mechanisms formed by long-term responses to multi-hazard environments—dynamic adaptation processes that reduce disaster exposure and optimize resource use efficiency through active adjustment of site selection and spatial transformation (the disaster density in the 100m core zone buffer is 0.077 events/km², significantly lower than 0.290 events/km² in peripheral areas). These findings demonstrate that remote sensing techniques can effectively reveal the terrain–resilience coupling of traditional villages, providing quantitative evidence for integrating spatial resilience into cultural landscape conservation, ecological security assessment, and rural revitalization planning. The proposed multi-scale analytical framework offers a transferable approach for evaluating settlement adaptability and resilience in other mountainous cultural heritage regions worldwide. Full article

Fine particulate matter (PM_2.5) can have considerable negative effects on human health. An increasing number of scholars are finding that green space can not only decrease PM_2.5 levels but also exacerbate PM_2.5 levels. Few scholars have provided comprehensive reviews on this subject. This study reviews research from 1995 to 2024, including 118 studies based on a search of three databases (Web of Science, Engineering Village, and ResearchGate). We found that at the macro (e.g., city-wide) and meso (e.g., high-density built-up areas) scales, most studies report that green space can play a positive role in mitigating PM_2.5 concentrations. However, at the micro-scale under specific temporal conditions, green spaces may increase PM_2.5 concentrations in some micro-environments. Whether vegetation reduces or elevates local PM_2.5 levels, these processes are influenced by various factors, including green space configuration, microclimatic conditions, built-environment characteristics, and emission source distributions. Mechanistically, vegetation can both decrease ambient PM_2.5 levels through deposition, adsorption, and absorption and block its dispersion. In the process of exploring and optimizing the effect of greening on PM_2.5, we should not only consider these factors in isolation but also account for the environmental factors that can significantly change the effect. Based on our review of a myriad of studies from different disciplinary backgrounds and scales, we propose an optimization strategy consisting of promoting ventilation through weakening sources and strengthening sinks. Full article

This study quantifies the effects of freeze–thaw (FT) cycling and dynamic water scouring, and establishes links between mesoscale pore evolution and macroscale strength degradation in cement-stabilized macadam (CSM) bases. The objective is to provide quantitative indicators for durability design and non-destructive evaluation of CSM bases. First, laboratory tests were conducted to simulate alpine service conditions: CSM cylindrical specimens (Ø150 × 150 mm) with 4.5% cement content, cured for 28 days, were exposed to 0, 5, or 20 FT cycles (−18 °C for 16 h ↔ +25 °C for 8 h), followed by dynamic water scouring (0.5 MPa, 10 Hz) for 15, 30, or 60 min. Second, the resulting damage was tracked at two scales. Acoustic emission (AE) sensors monitored internal damage during subsequent splitting tests, while industrial computed tomography (CT) was used to scan selected specimens and quantify porosity, pore number, and average pore diameter. Third, gray relational analysis correlated pore structure parameters with strength loss. The results indicate that under 30 min of scouring, increasing FT cycles from 0 to 20 increased mass loss from 0.33% to 1.27% and reduced splitting strength by 28.8%. AE cumulative ringing count and energy decreased by 97.9% and 98.4%, respectively, indicating severe internal degradation. CT scans revealed porosity and pore count increased monotonically with FT cycles, while average pore diameter decreased (dominated by microcrack formation). Frost-heave pressure and cyclic suction enlarged edge pores and interconnected internal voids, accelerating erosion of cement paste. FT cycles compromise the cement–aggregate interfacial bond, thereby predisposing the matrix to accelerated deterioration under dynamic scouring; the ensuing evolution of pore structure emerges as the pivotal mechanism governing strength degradation. Average pore diameter exhibited the strongest correlation with splitting strength (r = 0.763), and its change was the primary driver of strength loss (r = 0.774). These findings facilitate optimizing cement dosage, validating non-destructive evaluation models for in-service base courses, and erosion durability of road base materials in permafrost regions. Full article