In cemented paste backfill (CPB), fly ash (FA) can reduce cement costs. However, the chemical compositions of FA and tailings used in the CPB can vary significantly, affecting the strength values of CPBs, which can be determined through laboratory tests and play a crucial role in design operations. Therefore, developing a predictive model would be advantageous in terms of time and cost. The most critical aspect of this study is that machine learning (ML) models demonstrate high accuracy in the performance of strength prediction in experimental studies, especially in nonlinear and complex data structures, and even in the presence of uncertainty in geochemical and geophysical parameters. Among the ML algorithms, random forest (RF), artificial neural network (ANN), linear regression (LR), voting, and extreme gradient boosting (XGBoost) algorithms were used in this study. According to the results obtained, the XGBoost model exhibited the most robust predictive performance, evidenced by the highest correlation coefficient (R) (0.922) and the lowest mean absolute error (0.666). XGBoost also demonstrated its durability and stability by achieving the lowest relative absolute error (18.81%) and root mean square error (41.10%). Therefore, it has been understood that significant time and resource savings can be achieved in important projects by eliminating the need for experimental tests.

Thermal treatment is one of the well-known methods used before froth flotation to remove the polar groups from coal surfaces to make them more hydrophobic. In this study, the effect of conventional and microwave thermal treatments on the wettability of low and high-rank lignite was investigated using flotation experiments, zeta potential, and bubble-particle attachment time measurements, as well as hydrophilicity index analyses. The results showed that both of the thermal treatments increased hydrophobicity, and therefore floatability of the coal samples. The optimum results were achieved with conventional and microwave thermal treatment for the low and high-rank coals, respectively.

Concrete compressive strength is traditionally modeled as a function of mixture composition, with cement dosage often assumed to produce proportional strength gains. However, such interpretations are typically correlational and do not quantify the causal effectiveness of cement additions under varying mixture conditions. This study introduces an interpretable causal machine learning (ICML) framework to estimate the marginal causal effect of cement dosage on compressive strength using an R-learner-based approach. Cement content is treated as a continuous intervention variable, and heterogeneous treatment effects are estimated conditionally on mixture composition and curing age. The estimated average marginal effect of cement dosage is 0.136 MPa per kg/m³ (95% bootstrap confidence interval: [0.1055, 0.1433]). However, substantial heterogeneity is observed, with individual marginal effects ranging from −0.027 to 0.370 MPa (5th–95th percentile). Near-zero and, in limited regimes, negative marginal effects emerge under high water content and unfavorable mixture conditions, indicating inefficient cement utilization. Robustness checks across alternative cross-fitting schemes and trimming procedures confirm the stability of the estimated causal effects. Unlike conventional machine learning models that explain predicted strength values, the proposed framework applies explainability directly to the estimated causal effect function. Local SHAP-based explanations reveal the mixture configurations under which cement additions are effective or inefficient. By explicitly identifying mixture conditions under which cement additions are effective or inefficient, the proposed framework supports more rational cement use, reducing unnecessary material consumption, lowering construction costs, and easing the decision-making burden on designers in practical concrete mix design.

In this study, coal properties and environmental characteristics were investigated, and the most suitable coal drying method was determined for four different regions (Armutcuk, Afsin, Can, and Soma) where Türkiye's significant coal reserves are located, using the AHP method (Analytical Hierarchy Process). Installation and operating cost, drying rate, environmental risk, and land area parameters were used as selection criteria in the AHP method. After analyzing the physical and chemical properties of coals from four different regions, changes in moisture content and drying costs were calculated according to various drying methods (rotary, microwave, solar, and air). The results indicated that the solar energy data of the regions were determined to be 1315, 1610, 1400, and 1444 kWh/m²-year for Armutcuk, Afsin, Can, and Soma, respectively. Accordingly, solar energy drying systems are expected to be preferred in regions with high solar energy. However, with the effect of other selection criteria, it was determined that although the Can region receives less solar energy than the Soma region, the microwave drying method should be used for the coal of the Soma region, and the solar energy drying method should be used for the coal of Can region. Microwave and solar energy methods should be preferred for drying the coals of the Armutcuk and Afsin regions, respectively. As in many sectors, multi-criteria decision-making methods are necessary for optimum equipment/method selection in the mining sector. In conclusion, this study showed that the AHP method can be used when appropriate criteria are thoroughly determined.