The growing emphasis on sustainable construction materials has prompted extensive research on agricultural and industrial by-products as a partial replacement for traditional cement. This study examines the effect of rice husk ash on the mechanical and durability characteristics of M40 grade concrete. The aim is to determine the optimum rice husk ash content that improves strength, durability, and cost-efficiency. Concrete mixes were prepared with 0%, 5%, 7.5%, 10%, 12.5%, and 15% rice husk ash by weight of cement and evaluated for compressive, tensile, and flexural strength at 7 and 28 days. Durability was assessed through water absorption and acid resistance tests. 7.5% rice husk ash replacement showed the highest performance, with an increase of almost 12% in 28-day compressive strength and improved resistance to water and acid exposure. The setting time remained within satisfactory limits, and a 4.35% reduction in material cost was achieved. The study concludes that 7.5% rice husk ash is the optimal dosage for enhancing strength and durability while endorsing sustainability. Future investigations can explore the long-term performance of rice husk ash concrete in different environmental situations and its synergy with other waste materials.

The scour phenomenon is highly complex, and its precise prediction remains a considerable challenge for hydraulic researchers. Traditionally, most of the studies relay on empirical approaches for scour prediction. However, with the rapid development of infrastructure and the increasing number of bridges, these empirical models are inadequate in providing precise scour prediction across diverse field conditions. Consequently, there is a rising requirement to adopt advanced Machine Learning (ML) based methodologies to achieve more precise and computationally efficient scour prediction. The primary objective of this study is to propose an alternate model to traditional scour prediction methods by employing ML-based tools, specifically Artificial Neural Networks (ANN), Support Vector Machines (SVM), and the Ensemble Tree Method. These ML-based models are capable of handling complex and nonlinear problems. In addition to simulation work, a detailed experimental investigation has been carried out for a single cylindrical pier. The results of the present study, along with existing literature data, have been utilized for training and testing of ANN, SVM, and Ensemble Tree models. To evaluate the performance of the proposed models, an in-depth statistical analysis has been carried out. The findings of this study highlight that all three models significantly outperform traditional methods, demonstrating their effectiveness in improving scour depth predictions.

The scour around the bridge piers has been estimated using conventional empirical formulae; however, these formulae are unable to predict the scour depth precisely. The present study is conducted in two parts (a) experimental investigation for evaluating the behaviour of local scour around twin piers positioned in the transverse direction of flow and (b) an empirical equation to estimating scour depth is proposed utilising new evolutionary artificial intelligence technique gene expression programming (GEP). Experimental results for the present study demonstrate the influence of the rate of flow and clear spacing between the piers on the scour depth. Additionally the results of the soft computing technique GEP during testing and training of proposed modelling, fitness function root mean square error is observed as 0.00133 and 0.00113, with the coefficient of determination as 0.950 and 0.955, respectively. Furthermore, in order to find out the role of each variable for scour depth sensitivity, analysis has been conducted. The findings of the sensitivity analysis show that the pier spacing and rate of flow play the most significant role in scour depth estimation. Results of this study demonstrate a good agreement with the proposed GEP model and conclude that it is a better approach for forecasting scour depth.

In the case of multiple pier arrangements, commonly used manuals still suggest using methods for an isolated pier, irrespective of the spacing between the pier and the factors influencing the pairs of the pier. The present study is an experimental investigation for assessing maximum scour depth (MSD) characteristics around the twin piers installed in side-by-side (SbS) positioning. Twenty-seven experimental conditions were evaluated for three sets of the rate of flow. The Acoustic Doppler Velocimeter (ADV) was used to precisely measure flow intensity under the clear water condition. Center-to-center (C2C) spacing between the piers was varied from 1.5, 2, 2.5, 3, 3.5, 4, 4.5 to 5 times pier diameter (D). In the vortex region higher amount of turbulence is perceived owing to a stronger horseshoe vortex forming between the piers. This turbulence resulted from various factors influencing the MSD, and these parameters are assessed and formulated by dimensional analysis. The findings of the study demonstrate the effect of Froud number, the effect of dimensional parameters, the effect of sediment deposition, and the effect of C2C spacing between the piers on the pattern of MSD for the two piers. During the comparison of the experimental data with the semi-empirical equations, the best value root mean square and maximum absolute percentage error was observed as 0.97 and 1.5%, respectively, for the Larsen and Toch equation. Bridge pier constitutes numbers of piers, this study will impart new light on mutual interference between the two piers and its effect on the local scouring evolved in the propinquity of the pier, so that the current engineering equations can be modified for the accurate prediction of scouring.

Single pier characterization has been predominately considered by a majority of the recommended equations, although all bridge structures are laid on the group of piers. The behaviour of scour must be extensively examined in order to identify the parameters that primarily influence the bridge scour. The primary aim of the study is to investigate the effect of the densimetric Froude number, temporal scour development and pier spacing (Ps) on scour depth and propose a new equation for scour depth estimation using GEP (Gene Expression Programming). For profoundly understanding and continuous monitoring of scour evolution, SeaTek Ultrasonic ranging transducers have been used. The results exemplify that non-dimensional scour depth increases with the increase in the densimetric Froude number and velocity of flow. The effect of pier spacing shows that the scour depth increases as the pier spacing increases at a certain Ps range, then it decreases. The maximum scour depth at the front pier was observed 24% greater than the single pier, and 14% rear pier. The comparison of the proposed GEP equation with the well-known equation and Experimental results shows that the GEP is better prediction tool for scour depth.