
ISSN: 2960-2025 (Print)
ISSN: 2960-2033 (Online)
CODEN: SCABAK
CiteScore 2025: 1.5
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As a core material in modern construction, the early-age properties of concrete have a decisive impact on the safety and durability of civil engineering structures. However, systematic research on the mechanical properties of early-age concrete remains limited, particularly regarding the combined influence of cohesive and frictional properties on the material’s macroscopic mechanical behavior, which has not been thoroughly explored. To address this gap, this paper employs a decoupling method for testing the cohesion-friction mechanical properties of concrete, as proposed in previous work. This method successfully separates the cohesive and frictional properties of early-age concrete, validating its applicability under early-age conditions and obtaining typical failure modes following material performance degradation. Furthermore, by analyzing the evolution patterns of cohesive and frictional properties during deformation and strength development, the synergistic mechanism of cohesion-friction mechanical properties in early-age concrete was revealed. The results indicate that the responses of cohesive and frictional properties to hydrostatic pressure in early-age concrete exhibit significant differences. The reduction in macroscopic shear strength and stiffness is fundamentally attributed to the irreversible dissipation of cohesive strength. Ultimately, the mechanical behavior of early-age concrete gradually approaches that of granular materials without cohesion.
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.
Structural computational analysis in civil engineering increasingly demands efficient, robust, and physics-aware methodologies capable of addressing non-Euclidean geometries, history-dependent behaviors, and multi-scale problems that remain challenging for conventional numerical approaches. Recent advances in frontier artificial intelligence (AI) techniques have shown promising potential to overcome these limitations. This paper presents a comprehensive review of frontier AI applications in computational structural analysis from 2020 to 2025, focusing on graph neural networks (GNNs), sequence-to-sequence (Seq2Seq) and Transformer-based architectures, and physics-informed methods. We synthesize fundamental concepts, typical model variants, and representative applications across diverse tasks, including constitutive modeling, static and dynamic structural analysis, data reconstruction, and parameter inversion. Furthermore, we identify critical research gaps and discuss potential future directions within each model family. A quantitative analysis of the reviewed studies is conducted, categorizing them by publication year, application task, and adopted model type. Common challenges regarding benchmarking, empirical–physics trade-offs, scalability and generalizability are summarized. Finally, we highlight several promising techniques for advancing intelligent structural computation and promoting practical engineering deployment.