Scouring is a major concern affecting the overall stability and safety of a bridge. The current research investigated the effectiveness of the various artificial intelligence (AI) techniques, such as artificial neural networks (ANNs), the adaptive neuro-fuzzy inference system (ANFIS), and random forest (RF), for scouring depth prediction around a bridge abutment. This study attempted to make a comparative analysis between these AI models and empirical equations developed by various researchers. The current research paper utilized a dataset of water depth (Y), flow velocity (V), discharge (Q), and sediment particle diameter (d50) from a controlled laboratory setting. An efficient optimization tool (MATLAB Optimization Tool (version R2023a)) was used to develop a scour estimation formula around bridge abutments. The findings of the current investigation demonstrated the superior performance of the AI models, especially the ANFIS model, over empirical equations by precisely capturing the non-linear and complex interactions between these parameters. Moreover, the result of the sensitivity analysis demonstrated flow velocity and discharge to be the most influencing parameters affecting the scouring depth around a bridge abutment. The results of the current research highlight the precise and accurate prediction of the scouring depth around a bridge abutment using AI models. However, the empirical equation (Equation 2) demonstrated better performance with a higher R-value of 0.90 and a lower MSE value of 0.0012 compared to other empirical equations. The findings revealed that ANFIS, when combined with neural networks and fuzzy logic systems, produced highly accurate and precise results compared to the ANN models.

Scouring around the bridge structure is a major concern of the globe. Therefore, a precise estimation of the scour depth is essential to minimize bridge failure and provide preventive measures. This review paper aims to analyze the critical review of various artificial intelligence (AI) techniques utilized in the literature to estimate bridge abutment scour depth including artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), gene expression programming (GEP), support vector machines (SVM), and extreme learning machines (ELM). The predictive power of each technique was assessed in terms of different performance indicators, such as correlation coefficient (R), mean square error (MSE), predicted values, Taylor's diagram, sensitivity analysis, and violin plot. This review paper highlights that by comparing different AI techniques, ELM and GEP techniques have superior performance, especially in predicting scour depth and dealing with complex and large datasets. However, various limitations and proposed solutions have been reported for techniques, such as ANN, ANFIS, SVM, and group method of data handling (GMDH). The main challenges in the ANN, ANFIS, SVM, and GMDH techniques were overfitting and hyperparameter tuning. Based on the performance of each technique, the current review paper found the satisfactory performance of the ELM technique because of its computation speed and precise estimation capability. Moreover, the proposed solutions would be helpful to researchers working in the field of hydraulics engineering, particularly scouring around the bridge abutment.

Scouring around bridge abutments is a crucial and complex process that sometimes may lead to the failure of the bridge abutment. Therefore, in the present research, scouring around bridge abutments under clear water conditions was examined without and with countermeasures for providing an economical solution. A total of forty-five experiments were performed under clear water conditions to find the maximum scour depth around the bridge abutment. Experiments were performed in two different phases. In the first phase, scour depth was investigated without any countermeasures. In the second phase, scour depth was investigated using marble and brick waste as a countermeasure. The results showed that the maximum scour depth around the bridge abutment (at a distance of 10 cm on the upstream side and 15 cm on the downstream side of the abutment) for the Froude’s number of 0.22 was 0.137 m without any countermeasure. The scouring depth increased up to 40% with an increase in the Froude’s number from 0.13 to 0.22. The maximum reduction of scour depth was observed to be 40% and 55% when brick and marble waste were used as a countermeasure, respectively, compared to without a countermeasure case. It was concluded that marble and brick waste not only reduced scour depth to a significant level but also provided an economical solution.

Flooding is the most common natural disaster throughout the world and requires efficient management. Therefore, the current investigation aimed to explore the impact of a composite defense system comprising dyke and vegetation on flow dynamics and velocity reduction. Experiments were conducted in an open channel setup with an adjustable bed slope and transparent sidewalls, and the vegetation model was replicated as real trees such as Eucalyptus trees. The study involved calculating several parameters, including flow velocity, reduction of fluid force index (RFI%), reduction in moment index (RMI%), and hydrostatic and hydrodynamic forces. These calculations were done by changing the channel bed slope and keeping the flow rate (discharge) constant while considering both subcritical and supercritical flow conditions. Moreover, regression analysis was performed for the prediction of RFI% and RMI% under various flow conditions. Also, statistical analyses were performed to assess the effectiveness of the defense system in reducing fluid force and moment indices. The result of the current investigation indicates that the highest values of RFI% and RMI% under subcritical flow conditions were 79% and 88%, while under supercritical flow conditions they were 94% and 78%, respectively. Moreover, a velocity reduction of 69% was observed under subcritical flow, while 84% was observed under supercritical flow conditions. Under subcritical flow conditions, RFI% and RMI% enhanced by enhancing Froude number (Fr) because of an increase in velocity reduction and hydraulic jump formation. Similar trends were observed under supercritical flow conditions, with effective mitigation of high-velocity flows by the composite system. The finding of current research helps in providing effective techniques for flood management.

Abutment scour is a major cause of bridge failures worldwide, leading to disruptions, economic losses, and loss of life. The present experimental study examines countermeasures against abutment scour using hooked-collar protections on vertical-wall and wing-wall abutments (at 45° and 60°) under different flow conditions. All 60 experiments were performed under sub-critical flow conditions by investigating scour around an abutment 20 cm long, 20 cm wide, and 25 cm tall. Two distinct values of the Froude number, 0.154 and 0.179, and a sediment particle diameter (d₅₀) of 0.88 mm were used throughout the experimental phase. The resulting equilibrium scour around the abutments was compared to those with collar and hooked-collar protections. It was determined that the maximum abutment scour depth reduction was 83.89% when hooked collars were placed on vertical wall abutments beneath the bed surface level, and for wing-wall abutments at 45° and 60°, it was 74.2% and 73.5%, respectively, at the bed surface level. Regression analysis was conducted to assess the non-dimensional scour depth (D_s/Y_f) and scour reduction (R_Ds/Y_f), with a high enough coefficient of determination (R² values of 0.96 and 0.93, respectively), indicating high confidence in the analysis. The sensitivity analysis findings demonstrate that the width of the collar (W_c) and L_a are the most influencing factors affecting D_s/Y_f and RD_s/Y_f.