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.

In this research, an assessment of scour depth prediction in meandering channels with spur dikes is made employing machine learning approaches. Efficient determination of the scour depth is therefore vital in the prediction of morphologic aspects and structural stability. The input parameters include sinuosity (S), spur dike locations (Ld), and porosity (P) with experimental data from sinusoidal flumes. Four machine learning models; Extreme Gradient Boosting (XGBoost) with Particle Swarm Optimization (PSO) XGBoost-PSO, Random Forest (RF), k-Nearest Neighbors (k-NN), and Decision Tree-Neural Network (DT-NN) were used and compared. The findings demonstrate an R-value of 0.995 in the case of RF model while XGBoost-PSO gave second-best accuracy with R = 0.988. The results of the SHAP analysis illustrated that porosity and sinuosity are significant factors affecting scour depth (Ds/Yn, Ds: scour depth, Yn: water depth) and had moderate importance assigned to spur dike location. Kernel density plots further supported the RF model regarding error distribution consistency. Even though, both XGBoost-PSO yielded better results because of hyperparameter tuning, k-NN and DT-NN had less precise outcomes specifically predicted for progressive hydraulic procedures. Taylor's diagram even revealed greater accuracy of prediction by RF. Hence, a proper selection of appropriate machine learning models remains the first step in estimating scour depth sufficiently for flood and erosion control.

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.

This study investigates the role of emerged vegetation in enhancing the performance of submerged spur dikes for better flow control and bank protection in river systems. The research utilizes a numerical model based on Computational Fluid Dynamics (CFD), validated with experimental data. After validation, the study explores various configurations of vegetated spur dike, adjusting the submergence heights of the impermeable spur dike to achieve porosity ranging from fully impermeable to highly porous. Porosity levels of 24%, 48%, and 72% were chosen based on the spur dike height and the effectiveness of staggered vegetation arrangements in maximizing drag and reducing velocity. This approach aims to determine their impact on flow structure, velocity, and turbulence characteristics. The results reveal that impermeable dikes create strong recirculation zones downstream, increasing the potential for bank erosion. Introducing vegetation on the dike, particularly at moderate porosities (24% and 48%), effectively disrupts this behavior by reducing downstream velocity and mitigating mass and momentum exchange concentration between the spur dike field and the mainstream. However, the highest porosity case (72%) offered reduced flow resistance, limiting its protective effectiveness. Analysis of velocity and stress distributions showed that vegetation porosity significantly impacts normal and shear stresses, influencing flow stability at critical points around the spur dike. The findings signify the potential of integrating vegetation into spur dike designs to achieve a balance between effective flow conveyance and erosion control even in the spur dike submergence condition. This approach can outperform conventional emerged impermeable spur dikes, as supported by previous studies that demonstrate the effectiveness of porous and vegetated structures in reducing flow resistance, minimizing stagnation zones, and improving sediment deposition compared to impermeable designs.

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.