In civil engineering, ultra-high-strength concrete (UHSC) is a useful and efficient building material. To save money and time in the construction sector, soft computing approaches have been used to estimate concrete properties. As a result, the current work used sophisticated soft computing techniques to estimate the compressive strength of UHSC. In this study, XGBoost, AdaBoost, and Bagging were the employed soft computing techniques. The variables taken into account included cement content, fly ash, silica fume and silicate content, sand and water content, superplasticizer content, steel fiber, steel fiber aspect ratio, and curing time. The algorithm performance was evaluated using statistical metrics, such as the mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R²). The model’s performance was then evaluated statistically. The XGBoost soft computing technique, with a higher R² (0.90) and low errors, was more accurate than the other algorithms, which had a lower R². The compressive strength of UHSC can be predicted using the XGBoost soft computing technique. The SHapley Additive exPlanations (SHAP) analysis showed that curing time had the highest positive influence on UHSC compressive strength. Thus, scholars will be able to quickly and effectively determine the compressive strength of UHSC using this study’s findings.

Recently, research has centered on developing new approaches, such as supervised machine learning techniques, that can compute the mechanical characteristics of materials without investing much effort, time, or money in experimentation. To predict the 28-day compressive strength of steel fiber–reinforced concrete (SFRC), machine learning techniques, i.e., individual and ensemble models, were considered. For this study, two ensemble approaches (SVR AdaBoost and SVR bagging) and one individual technique (support vector regression (SVR)) were used. Coefficient of determination (R²), statistical assessment, and k-fold cross validation were carried out to scrutinize the efficiency of each approach used. In addition, a sensitivity technique was used to assess the influence of parameters on the prediction results. It was discovered that all of the approaches used performed better in terms of forecasting the outcomes. The SVR AdaBoost method was the most precise, with R² = 0.96, as opposed to SVR bagging and support vector regression, which had R² values of 0.87 and 0.81, respectively. Furthermore, based on the lowered error values (MAE = 4.4 MPa, RMSE = 8 MPa), statistical and k-fold cross validation tests verified the optimum performance of SVR AdaBoost. The forecast performance of the SVR bagging models, on the other hand, was equally satisfactory. In order to predict the mechanical characteristics of other construction materials, these ensemble machine learning approaches can be applied.

The several government subsidies available in Poland contributed to an increased interest in PV installations. Installed PV capacity increased from 100 MW in 2016 up to 2682.7 MW in July 2020. In 2019 alone, 104,000 microinstallations (up to 50 kWp) were installed in Poland. The paper determines the energy gain and the associated reduction of CO₂ emissions for two types of solar installation located in Poland. The monofacial solar modules with a power of 5.04 kWp (located in Leki) and bifacial solar modules with a power of 6.1 kWp (located in Bydgoszcz). Both installations use mono-crystalline Si-based 1st generation PV cells. With comparable insolation, a bifacial installation produces approx. 10% (for high insolation) to 28% (for low insolation) more energy than a monofacial PV installation. Avoided annual CO₂ emission in relation to the installation capacity ranges from 0.58 to 0.64 Mg/kWp for monofacial and from 0.68 to 0.74 Mg/kWp for bifacial and is on average approx. 16% higher for bifacial installations. Cost-benefit analyses were made. For different electricity prices, the NPV for monofacial and bifacial was determined.

Researchers and engineers are presently focusing on efficient waste material utilization in the construction sector to reduce waste. Waste marble dust has been added to concrete to minimize pollution and landfills problems. Therefore, marble dust was utilized in concrete, and its prediction was made via an artificial intelligence approach to give an easier way to scholars for sustainable construction. Various blends of concrete having 40 mixes were made as partial substitutes for waste marble dust. The ultrasonic pulse velocity of waste marble dust concrete (WMDC) was compared to a control mix without marble dust. Additionally, this research used standalone (multiple-layer perceptron neural network) and supervised machine learning methods (Bagging, AdaBoost, and Random Forest) to predict the ultrasonic pulse velocity of waste marble dust concrete. The models’ performances were assessed using R², RMSE, and MAE. Then, the models’ performances were validated using k-fold cross-validation. Furthermore, the effect of raw ingredients and their interactions using SHAP analysis was evaluated. The Random Forest model, with an R² of 0.98, outperforms the MLPNN, Bagging, and AdaBoost models. Compared to all the other models (individual and ensemble), the Random Forest model with greater R² and lower error (RMSE, MAE) has a superior performance. SHAP analysis revealed that marble dust content has a positive and direct influence on and relationship to the ultrasonic pulse velocity of concrete. Using machine learning to forecast concrete properties saves time, resources, and effort for scholars in the engineering sector.

High quality coke is a key raw material for the metallurgical industry. The characteristics of the coal have a significant influence on the parameters of the coke produced and, consequently, on the valuation of coal deposits and the economic assessment of mining projects. Predicting the quality of coking coal allows for the optimisation of production processes, including the planning and management of operations and the early detection of quality problems. In this study, using the principles of a smart mine, it is proposed to determine the quality of coal based on the combination of mining and geological conditions of mineral deposits and its quality indicators. Possible interrelationships between the quality of the coal in the deposit and the characteristics of the final product have been identified. A neural network is used to determine the priority of individual indicators that have a significant impact on the quality of coking coal. An important part of the research is its practical implementation in the conditions of the Jastrzębska Spółka Węglowa SA. Qualitative and quantitative parameters of coking coals were obtained for each mine of the region by the method of sampling and statistical processing of data such as: degree of metamorphism, thickness, deviation of volatile substances, presence of phosphorus, ash content, etc. For their evaluation, the Group Method of Data Handling was used to compare the factors of quality indicators depending on the priority of influence on the final characteristics of the coking coal. Based on the results obtained, it is shown that not all coal quality indicators have a significant impact on the quality of the final product. The study shows that it is possible to predict the main indicators (CRI – Coke Reactivity Index, CSR – Coke Strength after Reaction) of coke quality using neural networks based on a larger number of coal quality parameters and to eliminate parameters that have virtually no influence on the value of the final product. This method can also be used to improve the results of economic valuation of a deposit and to better plan exploration and mining operations.