Strengthening of reinforced concrete (RC) beams subjected to significant torsion is an ongoing area of research. In addition, fiber-reinforced polymer (FRP) is the most popular choice as a strengthening material due to its superior properties. Moreover, machine learning models have successfully modeled complex behavior affected by many parameters. This study will introduce a machine learning model for calculating the ultimate torsion strength of concrete beams strengthened using externally bonded (EB) FRP. An experimental dataset from published literature was collected. Available models were outlined. Several machine learning models were developed and evaluated. The best model was the wide neural network, which had the most accurate results with a coefficient of determination, root mean square error, mean average error, an average safety factor, and coefficient of variation values of 0.93, 1.66, 0.98, 1.11, and 45%. It was selected and further compared with the models from the existing literature. The model showed an improved agreement and consistency with the experimental results compared to the available models from the literature. In addition, the effect of each parameter on the strength was identified and discussed. The most dominant input parameter is effective depth, followed by FRP-reinforcement ratio and strengthening scheme, while fiber orientation has proven to have the least effect on the prediction output accuracy.

Slab–column connections with FRPs fail suddenly without warning. Machine learning (ML) models can model the behavior with high precision and reliability. Nineteen ML algorithms were examined and compared. The comparisons showed that the ensembled boosted tree model showed the best, most precise prediction with the highest coefficient of determination (R2) (0.98), the lowest Root Mean Square Error (RMSE) (44.12 kN), and the lowest Mean Absolute Error (MAE) (35.95 kN). The ensembled boosted model had an average of 0.99, a coefficient of variation of 12%, and a lower 95% of 0.97, respectively, in terms of the measured strength. Thus, it was found to be more accurate and consistent compared to all implemented machine learning models and selected traditional models. In addition, the significance of various parameters with respect to the predicted strength was identified, where the effective depth was the most significant by a factor of 0.9, and the concrete compressive strength was the lowest by a factor of 0.3.

Over the past three decades, more than 8000 pedestrians have been killed in Australia due to vehicular crashes. There is a general assumption that pedestrians are often the most vulnerable to crashes. Sustainable transportation goals are at odds with the high risk of pedestrian fatalities and injuries in car crashes. It is imperative that the reasons for pedestrian injuries be identified if we are to improve the safety of this group of road users who are particularly susceptible. These results were obtained mostly through the use of well-established statistical approaches. A lack of flexibility in managing outliers, incomplete, or inconsistent data, as well as rigid pre-assumptions, have been criticized in these models. This study employed three well-known machine learning models to predict road-crash-related pedestrian fatalities (RCPF). These models included support vector machines (SVM), ensemble decision trees (EDT), and k-nearest neighbors (KNN). These models were hybridized with a Bayesian optimization (BO) algorithm to find the optimum values of their hyperparameters, which are extremely important to accurately predict the RCPF. The findings of this study show that all the three models’ performance was improved using the BO. The KNN model had the highest improvement in accuracy (+11%) after the BO was applied to it. However, the ultimate accuracy of the SVM and EDT models was higher than that of the KNN model. This study establishes the framework for employing optimized machine learning techniques to reduce pedestrian fatalities in traffic accidents.

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