The performance of both non-spliced and spliced steel bars significantly affects the overall performance of structural reinforced concrete elements. In this context, the mechanical properties of reinforcement bars (i.e., their ultimate strength and strain) should be determined in order to evaluate their reliability prior to the construction procedure. In this study, the application of Tree-Based machine learning techniques is implemented to analyze the ultimate strain of non-spliced and spliced steel reinforcements. In this regard, a database containing the results of 225 experimental tests was collected based on the research investigations available in peer-reviewed international publications. The database included the mechanical properties of both non-spliced and mechanically spliced bars. For better accuracy, the databases of other splicing methods such as lap and welded-spliced methods were excluded from this research. The database was categorized as two sub-databases: training (85%) and testing (15%) of the developed models. Various effective parameters such as splice technique, steel grade of the bar, diameter of the steel bar, coupler geometry—including length and outer diameter along with the testing temperatures—were defined as the input variables for analyzing the ultimate strain using tree-based approaches including Decision Trees and Random Forest. The predicted outcomes were compared to the actual values and the precision of the prediction models was assessed via performance metrics, along with a Taylor diagram. Based on the reported results, the reliability of the proposed ML-based methods was acceptable (with an R² ≥ 85%) and they were time-saving and cost-effective compared to more complicated, time-consuming, and expensive experimental examinations. More importantly, the models proposed in this study can be further considered as a part of a comprehensive prediction model for estimating the stress-strain behavior of steel bars.

One of the practical ways to overcome the adverse environmental effects of plastic bottle waste is to implement bottles into concrete, one of the most widely used materials in the construction industry. Plastic bottles are mainly made of polyethylene terephthalate (PET) and can be used as a fiber to reinforce concrete. In recent years, PET fiber-reinforced concrete (PFRC) has attracted researcher attention, and several experimental studies have been conducted. This paper aims to present the benefits of using PET fiber as a reinforcing element in concrete using a machine learning approach. By considering the effect of PET fibers in concrete, engineers and stakeholders may be encouraged to further use these recycled materials. The proposed network was successfully able to capture the response of PFRC with high accuracy (mean squared error (MSE) of 7.11 MPa and R coefficient of 98%). The results of the proposed network show that the amount of PET fiber usage in concrete has a significant effect on the compressive strength of PFRC. Moreover, the PFRC’s response considering the variation of mechanical and geometrical properties of PET fiber mainly depends on the fiber’s shape. The most effective shapes of PET fiber are shapes with deformation, followed by embossed and irregular shapes.