Basalt fibers are a type of reinforcing fiber that can be added to concrete to improve its strength, durability, resistance to cracking, and overall performance. The addition of basalt fibers with high tensile strength has a particularly favorable impact on the splitting tensile strength of concrete. The current study presents a data set of experimental results of splitting tests curated from the literature. Some of the best-performing ensemble learning techniques such as Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Random Forest, and Categorical Boosting (CatBoost) have been applied to the prediction of the splitting tensile strength of concrete reinforced with basalt fibers. State-of-the-art performance metrics such as the root mean squared error, mean absolute error and the coefficient of determination have been used for measuring the accuracy of the prediction. The impact of each input feature on the model prediction has been visualized using the Shapley Additive Explanations (SHAP) algorithm and individual conditional expectation (ICE) plots. A coefficient of determination greater than 0.9 could be achieved by the XGBoost algorithm in the prediction of the splitting tensile strength.

In the design of reliable structures, the soil classification process is the first step, which involves costly and time-consuming work including laboratory tests. Machine learning (ML), which has wide use in many scientific fields, can be utilized for facilitating soil classification. This study aims to provide a concrete example of the use of ML for soil classification. The dataset of the study comprises 805 soil samples based on the soil drillings of the new Gayrettepe–Istanbul Airport metro line construction. The dataset has both missing data and class imbalance. In the data preprocessing stage, first, data imputation techniques were applied to deal with the missing data. Two different imputation techniques were tested, and finally, the data were imputed with the KNN imputer. Later, a balance was achieved with the synthetic minority oversampling technique (SMOTE). After the preprocessing, a series of ML algorithms were tested with 10-fold cross-validation. Unlike the studies conducted in previous research, new gradient-boosting methods such as XGBoost, LightGBM, and CatBoost were tested, high classification accuracy rates of up to +90% were observed, and a significant improvement in the accuracy of prediction (when compared with previous research) was achieved.

The performance of ultra-high-performance concrete (UHPC) allows for the design and creation of thinner elements with superior overall durability. The compressive strength of UHPC is a value that can be reached after a certain period of time through a series of tests and cures. However, this value can be estimated by machine-learning methods. In this study, multilayer perceptron (MLP) and Stacking Regressor, an ensemble machine-learning models, is used to predict the compressive strength of high-performance concrete. Then, the ML model’s performance is explained with a feature importance analysis and Shapley additive explanations (SHAPs), and the developed models are interpreted. The effect of using different random splits for the training and test sets has been investigated. It was observed that the stacking regressor, which combined the outputs of Extreme Gradient Boosting (XGBoost), Category Boosting (CatBoost), Light Gradient Boosting Machine (LightGBM), and Extra Trees regressors using random forest as the final estimator, performed significantly better than the MLP regressor. It was shown that the compressive strength was predicted by the stacking regressor with an average R² score of 0.971 on the test set. On the other hand, the average R² score of the MLP model was 0.909. The results of the SHAP analysis showed that the age of concrete and the amounts of silica fume, fiber, superplasticizer, cement, aggregate, and water have the greatest impact on the model predictions.

Cooling load refers to the amount of energy to be removed from a space (or consumed) to bring that space to an acceptable temperature or to maintain the temperature of a space at an acceptable range. The study aimed to develop a series of models and determine the most accurate ones in the prediction of the cooling load of low-rise tropical buildings based on their basic architectural and structural characteristics. In this context, a series of machine learning (regression) algorithms were tested during the research to determine the most accurate/efficient prediction model. In this regard, a data set consisting of ten features indicating the basic characteristics of the building (floor area, aspect ratio, ceiling height, window material, external wall material, roof material, window wall ratio north faced, window wall ratio south faced, horizontal shading, orientation) were used to predict the cooling load of a low-rise tropical building. The dataset was generated utilizing a set of generative and algorithmic design tools. Following the dataset generation, a series of regression models were tested to find the most accurate model to predict the cooling load. The results of the tests with different algorithms revealed that the relationship between the predictor variables and cooling load could be efficiently modeled through Histogram Gradient Boosting and Stacking models.

CO₂ emission is one of the biggest environmental problems and contributes to global warming. The climatic changes due to the damage to nature is triggering a climate crisis globally. To prevent a possible climate crisis, this research proposes an engineering design solution to reduce CO₂ emissions. This research proposes an optimization-machine learning pipeline and a set of models trained for the prediction of the design variables of an ecofriendly concrete column. In this research, the harmony search algorithm was used as the optimization algorithm, and different regression models were used as predictive models. Multioutput regression is applied to predict the design variables such as section width, height, and reinforcement area. The results indicated that the random forest algorithm performed better than all other machine learning algorithms that have also achieved high accuracy.

Due to environmental impacts and the need for energy efficiency, the cement industry aims to make more durable and sustainable materials with less energy requirements without compromising mechanical properties based on UN Sustainable Development Goals 9 and 11. Carbon dioxide (CO₂) emission into the atmosphere is mostly the result of human-induced activities and causes dangerous environmental impacts by increasing the average temperature of the earth. Since the production of ordinary Portland cement (PC) is a major contributor to CO₂ emissions, this study proposes alkali-activated binders as an alternative to reduce the environmental impact of ordinary Portland cement production. The dataset required for the training processes of these algorithms was created using Mendeley as a data-gathering instrument. Some of the most efficient state-of-the-art meta-heuristic optimization algorithms were applied to obtain the optimal neural network architecture with the highest performance. These neural network models were applied in the prediction of carbon emissions. The accuracy of these models was measured using statistical measures such as the mean squared error (MSE) and coefficient of determination (R²). The results show that carbon emissions associated with the production of alkali-activated concrete can be predicted with high accuracy using state-of-the-art machine learning techniques. In this study, in which the binders produced by the alkali activation method were evaluated for their usability as a binder material to replace Portland cement, it is concluded that the most successful hyperparameter optimization algorithm for this study is the genetic algorithm (GA) with accurate mean squared error (MSE = 161.17) and coefficient of determination (R² = 0.90) values in the datasets.