A crucial factor in the efficient design of concrete sustainable buildings is the compressive strength (Cs) of eco-friendly concrete. In this work, a hybrid model of Gradient Boosting Regression Tree (GBRT) with grid search cross-validation (GridSearchCV) optimization technique was used to predict the compressive strength, which allowed us to increase the precision of the prediction models. In addition, to build the proposed models, 164 experiments on eco-friendly concrete compressive strength were gathered for previous researches. The dataset included the water/binder ratio (W/B), curing time (age), the recycled aggregate percentage from the total aggregate in the mixture (RA%), ground granulated blast-furnace slag (GGBFS) material percentage from the total binder used in the mixture (GGBFS%), and superplasticizer (kg). The root mean square error (RMSE) and coefficient of determination (R²) between the observed and forecast strengths were used to evaluate the accuracy of the predictive models. The obtained results indicated that—when compared to the default GBRT model—the GridSearchCV approach can capture more hyperparameters for the GBRT prediction model. Furthermore, the robustness and generalization of the GSC-GBRT model produced notable results, with RMSE and R² values (for the testing phase) of 2.3214 and 0.9612, respectively. The outcomes proved that the suggested GSC-GBRT model is advantageous. Additionally, the significance and contribution of the input factors that affect the compressive strength were explained using the Shapley additive explanation (SHAP) approach.

Traditional laboratory methods for estimating soil compaction parameters, such as the Proctor test, have been recognized as time-consuming and labor-intensive. Given the increasing need for the rapid and accurate estimation of soil compaction parameters for a range of geotechnical applications, the application of machine learning models offers a promising alternative. This study focuses on employing the multivariate adaptive regression splines (MARS) model algorithm, a machine learning method that presents a significant advantage over other models through generating human-understandable piecewise linear equations. The MARS model was trained and tested on a comprehensive dataset to predict essential soil compaction parameters, including optimum water content (w_opt) and maximum dry density (ρ_dmax). The performance of the model was evaluated using coefficient of determination (R²) and root mean square error (RMSE) values. Remarkably, the MARS models showed excellent predictive ability with high R² and low RMSE, MAE, and relative error values, indicating its robustness and reliability in predicting soil compaction parameters. Through rigorous five-fold cross-validation, the model’s predictions for w_opt returned an RMSE of 1.948%, an R² of 0.893, and an MAE of 1.498%. For ρ_dmax, the results showcased an RMSE of 0.064 Mg/m³, an R² of 0.899, and an MAE of 0.050 Mg/m³. When evaluated on unseen data, the model’s performance for w_opt prediction was marked with an MAE of 1.276%, RMSE of 1.577%, and R² of 0.948. Similarly, for ρ_dmax, the predictions were characterized by an MAE of 0.047 Mg/m³, RMSE of 0.062 Mg/m³, and R² of 0.919. The results also indicated that the MARS model outperformed previously developed machine learning models, suggesting its potential to replace conventional testing methods. The successful application of the MARS model could revolutionize the geotechnical field through providing quick and reliable predictions of soil compaction parameters, improving efficiency for construction projects. Lastly, a variable importance analysis was performed on the model to assess how input variables affect its outcomes. It was found that fine content (C_f) and plastic limit (PL) have the greatest impact on compaction parameters.

Reinforced concrete (RC) flat slabs are widely employed in modern construction, and accurately predicting their load-carrying capacity is crucial for ensuring safety and reliability. Existing design methods and empirical equations still exhibit discrepancies in determining the ultimate load capacity of flat slabs. This study aims to develop a robust machine learning model, specifically the M5P model tree, for predicting the punching shear capacity of a RC flat slab without shear reinforcement. A comprehensive dataset of 482 experimentally tested flat slabs without shear reinforcement was gathered through an extensive literature review and utilized for the development of the M5P model. The model takes into account influential parameters, such as slab thickness, longitudinal reinforcement ratios, and concrete strength. The performance of the proposed M5P model was compared with existing design codes and other empirical models. The comparison highlights that the developed M5P model tree provides a more accurate and reliable prediction of the punching shear capacity of RC flat slabs. This study contributes to the advancement of structural engineering knowledge and has the potential to improve the design and safety assessment of concrete flat slab structures.

Torsional strength is related with one of the most critical failure types for the design and assessment of reinforced concrete (RC) members due to the complexity of the associated stress state and low ductility. Previous studies have shown that reliable methods to predict the torsional strength of RC beams are still needed, namely for over-reinforced and high-strength RC beams. This research aims to offer a novel set of models to predict the torsional strength of RC beams with a wide range of design attributes and geometries by using advanced M5P tree and nonlinear regression models. For this, a broad database with 202 experimental tests is used to generate highly reliable and resilient models. To build the models, three independent variables related with the properties of the RC beams are considered: concrete cross-section area (area enclosed within the outer perimeter of the cross-section), concrete compressive strength, and torsional reinforcement factor (which accounts for the type—longitudinal or transverse—amount, and yielding strength of the torsional reinforcement). In contrast to multiple nonlinear regression approaches, the findings show that the M5P tree approach has the best estimation in terms of both accuracy and safety. Furthermore, M5P model predictions are far more accurate and safer than the most prevalent design equations. Finally, sensitivity and parametric studies are used to confirm the robustness of the presented models.

Stone columns have been extensively advocated as a traditional approach to increase the undrained bearing capacity and reduce the settlement of footings sitting on cohesive ground. However, due to the complex interaction between the soil and the stone columns, there currently needs to be a commonly acknowledged approach that can be used to precisely predict the undrained bearing capacity of the system. For this reason, the bearing capacity of a sandy bed reinforced with geogrid and sitting above a collection of geogrid-encased stone columns floating in soft clay was studied in this research. Using a white-box machine learning (ML) technique called Multivariate Polynomial Regression (MPR), this work aims to develop a model for predicting the bearing capacity of the referred foundation system. For this purpose, two hundred and forty-five experimental results were collected from the literature. In addition, the model was compared to two other ML models, namely, a black-box model known as Random Forest (RF) and a white-box ML model called Linear Regression (LR). In terms of R² (coefficient of determination) and RMSE (Root Mean Absolute Error) values, the newly proposed model outperforms the two other referred models and demonstrates robust estimation capabilities. In addition, a parametric analysis was carried out to determine the contribution of each input variable and its relative significance on the output.

This article presents a mathematical model developed using the M5P tree to predict the shear strength of steel-fiber-reinforced concrete (SFRC) for slender beams using soft computing techniques. This method is becoming increasingly popular for addressing complex technical problems. Other approaches, such as semi-empirical equations, can show known inaccuracies, and some soft computing methods may not produce predictive equations. The model was trained and tested using 332 samples from an experimental database found in the previous literature, and it takes into account independent variables such as the effective depth d, beam width b_w, longitudinal reinforcement ratio ρ, concrete compressive strength f_c, shear span to effective depth ratio a/d, and steel fiber factor F_sf. The predictive performance of the proposed M5P-based model was also compared with the one of existing models proposed in the previous literature. The evaluation revealed that the M5P-based model provided a more consistent and accurate prediction of the actual strength compared to the existing models, achieving an R² value of 0.969 and an RMSE value of 37.307 for the testing dataset. It was found to be a reliable and also straightforward model. The proposed model is likely to be highly helpful in assessing the shear capacity of SFRC beams during the pre-planning and pre-design stages and could also be useful to help for future revisions of design standards.