Abstract The large-scale application of coal mine waste rock (CMWR) in concrete is limited by a porous interfacial transition zone (ITZ), which reduces mechanical performance and accelerates fluid transport. This study introduces a hierarchical reinforcement approach that combines nano-silica (NS)-induced matrix densification with basalt fiber (BF) bridging. NS refines the pore structure and strengthens the ITZ, creating a low-permeability matrix that enables effective fiber anchorage and crack arrest. The hybrid mixture containing 0.5% NS and 4% BF achieved a 30.5% higher compressive strength, a 26.0% greater flexural strength, and a 41.5% reduction in water absorption compared to the control. These findings demonstrate that nanoscale matrix densification is crucial for activating microscale fiber reinforcement, providing a viable route to developing waste-based cementitious composites with enhanced resistance to fluid ingress and improved transport properties.
As Vietnam's metropolitan centers transition toward rail-based urbanism, a distinct socio-spatial phenomenon - the emergence of the “Metro Elite”- is reshaping the urban hierarchy. This study evaluates whether new metro corridors in Hanoi and Ho Chi Minh City foster inclusive Transit-Oriented Development (TOD) or facilitate the formation of elite-oriented enclaves. To address this, the research investigates three critical dimensions: the economic accessibility of TOD housing for average citizens, the socio-economic profiles of current residents, and the balance between functional benefits and social costs. The study employs a multi-stage methodology. Initially, thematic analysis was applied to data from 64 semi-structured in-depth interviews with residents along major metro lines to extract core themes. These themes were subsequently synthesized through the Push-Pull-Mooring (PPM) framework to establish a causal model of residential relocation. Findings reveal a significant affordability gap, indicating that nascent property developments are largely inaccessible to the average income earner, primarily serving a “Metro Elite” driven by temporal sovereignty (pull factor) and institutional trust (mooring factor). Crucially, the analysis suggests that the tangible benefits of enhanced accessibility are currently being offset by the social costs of socio-spatial decoupling and the erosion of traditional community structures. The study concludes that without robust inclusionary zoning and equity-led planning, Vietnam's metro lines risk formalizing a geography of exclusion. This research provides a vital critique of market-led TOD and demonstrates the effectiveness of integrating thematic analysis with the PPM framework to understand complex urban transitions.
Let u and v be plurifine plurisubharmonic functions. In this paper, we investigate sufficient conditions on u and v under which they can be compared. (c) 2026 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
Determining the residual compressive strength (CS) of concrete after exposure to elevated temperatures poses a significant metrological challenge, as direct measurements are often hazardous and impractical. To address this gap, an interpretable indirect measurement system based on machine learning was developed and validated. Using a dataset of 207 experimental observations, five advanced boosting algorithms were systematically compared. A Gradient Boosting model optimized with the Slime Mould Algorithm (GB-SMA) was identified as the most effective, yielding the highest accuracy (test RMSE of 6.199 MPa). The focus on metrological reliability in this work constitutes a primary contribution. SHapley Additive exPlanations (SHAP) were used to provide model transparency by quantifying the influence of input parameters, such as temperature and aggregate content, on the final measurement. To transition the validated model into a practical tool, a graphical user interface (GUI) was developed. This interface allows engineers to perform efficient, code-free estimations of post-fire concrete strength. The outcome is therefore not simply a predictive model but an interpretable and validated measurement system for a fundamental parameter in civil engineering.
The use of recycled materials as internal curing (IC) agents offers substantial benefits to the concrete industry by improving performance and enhancing environmental sustainability. However, the design of IC concrete has grown intricate due to the nonlinear interactions among many input variables. Previous research on IC is mostly experimental, with only a few studies focusing on predicting the compressive strength (CS) of IC concrete. In particular, machine learning has not been applied to quantify the effect of roof-tile waste (RTW) on the CS of IC concrete. This research presents an innovative hybrid model that combines random forest and particle swarm optimization (RF-PSO) to predict the CS of IC concrete using RTW as an IC aggregate. Before model building, a comparative analysis of potential methodologies was conducted, highlighting the key characteristics, benefits, and drawbacks. RF-PSO was then chosen, achieving enhanced accuracy with a coefficient of determination (R2) of 0.961, a root mean square error (RMSE) of 5.361 MPa, and a mean absolute error (MAE) of 4.001 MPa. The RF-PSO model improved prediction accuracy by increasing R2 from 0.906 to 0.961 and reducing statistical errors by nearly 30% compared with conventional machine learning models. A Shapley Additive exPlanations (SHAP) analysis was performed to interpret the model results. The analysis identified the water-to-cement ratio and curing age as the dominant predictors, while IC water contributed a secondary, age-dependent effect. The proposed framework makes contributions: it integrates SHAP-based interpretability into a high-accuracy RF-PSO model and provides a viable tool for reducing empirical trial mixes in sustainable design workflows. Despite the limited dataset, the findings provide a reproducible baseline for future expansion and highlight the potential of combining RTW with IC to improve early and long-term strength.
