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.

This study demonstrates the conversion of agricultural and industrial waste into construction materials by developing ultra-high-performance concrete using cold-bonded sesame ash and waste glass aggregates. The primary focus of this study was sustainability and waste valorization in self-curing concrete systems. This study focuses on many aspects of producing cementless concrete with superior short- and long-term properties, incorporating an innovative artificial aggregate premanufactured using sesame ash and waste glass. Prepacking technology of casting was used. A self-curing additive is used to reduce the energy required for curing. In cold-bonded aggregates (CBAs), the aggregate content ranged from 10 to 50% of the total sand volume. Polyethylene glycol was used as an internal curing agent to evaluate the mechanical properties of the concrete, including the compressive strength and tensile strength at different ages. The durability characteristics of the concrete were also analyzed in terms of its resistance to sulfates, chloride ion penetration, and performance at elevated temperatures of 300 and 600 °C. Microscopic analyses were conducted by scanning electron microscopy (SEM), thermogravimetric analysis (TGA), Fourier transform infrared spectroscopy (FTIR), and Differential Scanning Calorimetry (DSC). The results showed a significant improvement in the mechanical and durability performance, especially at 30%, which resulted in the highest compressive strength of 147.2 MPa at 90 days. This is an 11.93% increase compared with that of the reference mix. The tensile strength was also improved by 14.5% at the same replacement ratio. The mix containing 30% manufactured aggregate demonstrated the best thermal resistance, retaining the highest percentage of residual strength at both 300 °C and 600 °C, as well as superior sulfate impact resistance, with a strength reduction factor of 39.5%. When the replacement ratio was increased to 50%, the chloride penetration resistance improved significantly by 41% compared with that of the reference mix. FTIR, TGA, and DSC analyses also demonstrated enhanced silicate polymerization and increased carbonate formation, contributing to the improved chemical stability and density of the concrete matrix.

Machine learning (ML) models in material science and construction engineering have significantly improved predictive accuracy and decision making. However, the practical implementation of these models often requires technical expertise, limiting their accessibility for engineers and practitioners. A user-friendly graphical user interface (GUI) can be an essential tool to bridge this gap. In this study, a sustainable approach to improve the compressive strength (C.S) of plastic-based mortar mixes (PMMs) by replacing cement with industrial waste materials was investigated using ML models such as support vector machine, AdaBoost regressor, and extreme gradient boosting. The significance of key mix parameters was further analyzed using SHapley Additive exPlanations (SHAPs) to interpret the influence of input variables on model predictions. To enhance the usability and real-world application of these ML models, a GUI was developed to provide an accessible platform for predicting the C.S of PMMs based on input material proportions. The ML models demonstrated strong correlations with experimental results, and the insights from SHAP analysis further support data-driven mix design strategies. The developed GUI serves as a practical and scalable decision support system, encouraging the adoption of ML-based approaches in sustainable construction engineering.