The instability of mining waste dumps poses significant environmental hazards, including loss of life, damage to infrastructure, and ecological degradation. The complex interdependence of Thermal, Hydraulic, and Mechanical (THM) processes has been increasingly recognised as a critical factor influencing slope stability. In this study, a coupled THM numerical model was developed using the finite element method (FEM) to evaluate slope stability in a coal mine waste dump in Maamba, Zambia. Key parameters, including stress distribution, displacement, pore water pressure, and temperature variations, were incorporated to achieve a comprehensive assessment of slope failure mechanisms. Field data and geotechnical investigations were integrated with advanced computational simulations to ensure realistic modelling. The findings demonstrated that conventional limit equilibrium methods (LEM) underestimated the impact of coupled processes on slope failure. The safety factor was observed to decrease by more than 30% due to THM interactions, with thermal gradients and hydro-mechanical (H-M) responses identified as primary contributors to slope instability. The results underscore the necessity of incorporating THM coupling in slope stability assessments, particularly in geotechnically sensitive mining environments. The proposed framework provides a scientifically grounded methodology for evaluating and mitigating landslide risks in mining waste dumps, offering valuable insights applicable to regions with similar geotechnical and climatic conditions. The findings contribute to the refinement of slope stability management strategies and provide a basis for the development of risk mitigation measures in vulnerable mining areas.
The effects of polycarboxylate superplasticizer (PCE) on the rheological properties and workability of cement-based composites were investigated by testing parameters such as static yield stress, dynamic yield stress, plastic viscosity, slump flow, bleeding rate, and penetration depth. The correlation between the dosage of PCE and the rheological parameters of fresh cement-based composites was analyzed. The results indicated that with an increase in the PCE dosage, the static yield stress, dynamic yield stress, and plastic viscosity of fresh cement-based composites decreased, demonstrating that PCE can improve the rheological properties of these composites. As the PCE dosage increased, the slump flow and bleeding rate of fresh cement-based composites also increased, but the rate of change decreased at higher dosages. Additionally, with an increase in PCE dosage, the penetration depth gradually increased, while the penetration depth difference ($\Delta {H}$) decreased. Furthermore, the compressive strength of cement-based composite cubes slightly decreased with an increase in PCE dosage.
The accurate estimation of the longitudinal dispersion coefficient is crucial for predicting solute transport in natural water bodies. In this study, an analytical (integral) method based on first principles is compared with Fischer’s widely used empirical approach, which is implemented in hydraulic modeling software such as the Hydrologic Engineering Center-River Analysis System (HEC-RAS). The primary objective is to evaluate the accuracy, applicability, and limitations of both methods under varying hydraulic conditions. A key advantage of the analytical approach is its ability to estimate the dispersion coefficient using velocity data alone, eliminating the need for high-cost tracer experiments that rely on solute concentration measurements. The determination index suggests an acceptable level of agreement between the two methods; however, the empirical approach systematically overestimates dispersion coefficients. Furthermore, a clear inverse relationship is observed between the slope of the channel and the magnitude of the dispersion coefficient, which is attributed to the increasing influence of shear velocity on the diffusion process. As slope values increase, solute separation time decreases, and concentration gradients become steeper. Conversely, at lower slopes, solute dispersion occurs over a broader time frame, resulting in lower concentration peaks. These findings indicate that while Fischer’s method provides a robust empirical framework, it should be supplemented with field measurements to improve reliability. In contrast, the analytical method offers a more theoretically grounded alternative that may enhance predictive accuracy in solute transport modeling. The implications of these results extend to water quality management, contaminant transport studies, and hydraulic engineering applications, where the selection of an appropriate dispersion estimation method significantly influences predictive outcomes.
Traditional tensioning monitoring techniques for prestressed concrete structures often exhibit limitations in real-time performance, accuracy, and adaptability to complex stress distributions. To address these challenges, an intelligent monitoring framework is developed based on a Radial Basis Function (RBF) neural network. Using the Dongjiacun aqueduct as a case study, a comprehensive methodology is established, integrating numerical simulation, Machine Learning (ML), and real-time data processing. Initially, Finite Element Analysis (FEA) is conducted to simulate stress distribution during the tensioning process, allowing for the extraction of critical stress data at key structural locations. These data serve as the foundation for training the RBF neural network, which functions as a high-fidelity surrogate model capable of efficiently predicting stress variations with enhanced accuracy. By leveraging the network's strong generalization capabilities, the proposed framework ensures rapid and precise estimation of stress evolution throughout the tensioning process. Furthermore, an intelligent monitoring platform is designed, incorporating real-time data acquisition, automated stress prediction, and visualization functionalities. The platform facilitates prestress control and structural health assessment, contributing to the long-term safety and durability of prestressed concrete structures. Additionally, an interactive user interface is prototyped using Mock Plus to enhance usability and facilitate intuitive interpretation of stress-related insights. The proposed approach not only advances the automation and intelligence of tensioning monitoring but also provides a robust technical foundation for optimizing prestress management in large-scale infrastructure applications.
Detailed Understanding of Roman concrete requires context from Roman military and civil engineering. The Romans prioritized durable infrastructure due to the impracticality of maintaining temporary wooden structures across their vast empire. This led to the development of long-lasting roads, bridges, and fortifications, many of which still exist today. Roman construction techniques, including concrete use, evolved significantly over time. Although Vitruvius documented early methods in the 1st century BC, later advancements—such as “hot mixing”—were not included in his texts. Roman concrete’s durability, especially in late Empire formulations, contributed to its longevity and continued use through the medieval period. In modern times, concrete construction shifted towards heavily reinforced structures, often without adequate protection. This has led to durability issues, highlighted by events like the collapse of the Morandi Bridge. In contrast, Roman concrete demonstrates superior longevity and self-healing properties despite being unreinforced. The study of Roman concrete offers valuable insights for modern construction, suggesting that minimally reinforced or unreinforced methods inspired by Roman practices could enhance durability and sustainability.
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