Accurate discharge gauging and controlled energy release are recurring requirements in irrigation, drainage and hydraulic control structures. Compound weirs serve both, yet the pairing of a Cipolletti notch with a rectangular crest has not been characterized. This study presents the first clear-water dimensionless characterization of this compound sharp-crested weir in two orientations: a Cipolletti base with a rectangular crest (Weir-1) and the inverted arrangement (Weir-2). Tests covered bed slopes 0 to 4° and upstream heads H1 in a 5.00 m × 0.30 m tilting flume, with calibrated instruments, triplicate readings and a stabilisation criterion. Discharge followed the velocity-area method with Pitot verification, and the governing dimensionless groups (H1/P, H1/L, Re, We, S) came from the Buckingham π theorem. Weir-1 gave a stable rating, Cd = 0.63 ± 0.06 (CoV = 9.8%, 95% CI 0.61 to 0.66), exceeding Weir-2 (Cd = 0.52, CoV = 9.6%) by 20.3% (Welch t = 5.60, p < 0.001). The Cd model (RMSE = 0.052, MAE = 0.042; Shapiro-Wilk p = 0.83; VIF = 1.01) and the energy-dissipation relation (R2 = 0.83) were validated. Downstream Frd reached 6.47 and 11.66. A hydraulic performance index ranked Weir-1 above Weir-2 (0.74 versus 0.66), recommending it for dependable gauging. In practical terms, the study delivers a validated and transferable rating for a novel compound geometry that measures discharge with the accuracy of a purpose-built single weir while retaining low-flow sensitivity, and it provides a quantitative decision framework, including a hydraulic performance index, for selecting between gauging-oriented and dissipation-oriented configurations in canals, drainage systems and small hydraulic control structures.

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.

This study aims to analyse the effect of different shapes of pores on the damping properties of spherical particle-filled composites. A two-step stochastic homogenization approach is developed to evaluate the effective elastic properties within the micromechanical framework. Modified Mori-Tanaka homogenization approach considering the differential scheme for iteratively addition of pores as well as fillers is incorporated in the present model. From the results, the saturated ratio of the stiffness of fillers to the matrix is found to exist beyond which further increase in the stiffness of the fillers does not add in the increase in the effective elastic properties. It is further noted that the presence of voids decreases the elastic properties significantly. The results obtained from the proposed model are found to be in good agreement with that obtained from the computational study.

Autonomous wireless sensing for structural-health monitoring and Internet-of-Things applications increasingly demands battery-free power sources, motivating the conversion of ambient mechanical vibration into usable electrical energy. This study introduces an interpretable machine-learning framework for designing variable-stiffness piezoelectric composite vibration energy harvesters, in which curvilinear (tow-steered) fibers provide a continuous means of tailoring both the resonant frequency and in conjunction with the electrode layout, the harvested power. A coupled electromechanical first-order shear-deformation finite-element model, using the MITC4 formulation to suppress shear locking, is developed and verified against analytical plate solutions and an established distributed-parameter harvester benchmark. A 1200-sample Latin-hypercube dataset spanning the fiber path, plate geometry and electrical load is used to train four surrogates Gaussian process regression, random forest, gradient boosting and a multilayer perceptron for the fundamental frequency, peak power and volumetric power density. Gaussian process regression is the most accurate (R2 ≈ 0.998). A SHAP analysis, cross-validated by permutation importance, identifies the governing variables, and a surrogate-driven Pareto optimization balances power density against resonance matching. Through a fully controlled comparison at fixed geometry, mass and resonance, fiber steering is shown to provide essentially no benefit with a full-coverage electrode but to increase peak harvested power by up to about 10% once the electrode is localized near the clamped root, by concentrating the modal strain where the charge is collected; all designs are revalidated by direct finite-element analysis to within 3%. Fiber steering and electrode geometry must therefore be co-designed—an interpretable guideline for variable-stiffness piezoelectric-based vibration energy harvesting.

The present study examines the utilization of machine learning (ML) methodologies for predicting groundwater levels (GWLs) in the Bina River basin of Madhya Pradesh, India. The study incorporates several ML techniques, such as regression trees (RT), support vector machines (SVM), and ensemble tree models, to predict groundwater dynamics using an extensive array of hydrogeological, meteorological, and anthropogenic factors. The dataset was partitioned into training and testing subsets, and models were assessed employing conventional statistical measures including R2, root mean square error, mean absolute error, and Akaike information criteria. Of the evaluated models, the fine RT had the best accuracy (R2 = 0.976), illustrating its proficiency in capturing intricate non-linear interactions. The SVM models, namely the linear and medium Gaussian variations, demonstrated competitive performance. The ensemble boosted trees model exhibited poor performance, perhaps attributable to overfitting. The research highlights the significance of data-driven models in groundwater management, particularly in areas with restricted access to physical monitoring facilities. The results indicate that ML-based forecasting offers a dependable alternative to conventional approaches and may substantially improve decision-making for sustainable groundwater resource management. This study presents a unique regional application and establishes a benchmark for scalable, AI-driven hydrological modeling in comparable catchments.

The transition towards sustainable structural power sources motivates vibration energy harvesters that avoid both lead-based piezoceramics and petroleum-derived substrates. This work presents an interpretable machine-learning framework for a resonant harvester formed from a natural-fibre (flax/epoxy) biocomposite host plate instrumented with a lead-free piezoelectric patch, benchmarked against conventional PZT-5A. A coupled first-order shear-deformation finite-element model of the harvester is developed and validated against analytical and published solutions spanning static deflection, free vibration, piezoelectric actuation and the electromechanically coupled power response. A 1500-sample Latin-hypercube dataset over eight geometric, material and circuit variables and three transducers (PZT-5A, BaTiO3, BZT-BCT) trains four surrogate models; a Gaussian-process regressor is the most accurate (test R2 = 0.95) for the predicted power density. These results are model-based predictions that await experimental confirmation. Shapley additive (SHAP) analysis reveals that proximity to resonance and the load resistance dominate the response, whereas the material contribution is governed by the transduction figure of merit d312/ϵ33T rather than the piezoelectric strain coefficient alone. Notably, BZT-BCT underperforms PZT-5A despite its larger |d31|, because of its high permittivity, giving the ranking PZT-5A > BZT-BCT > BaTiO3 consistently across the figures of merit, the simulated power density and the data-driven importances. The framework provides a transferable, physics-consistent route to sustainability-aware co-design of biocomposite vibration energy harvesters.

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.

This study investigates the flow hydrodynamics around two cylinders by comparing large eddy simulation (LES) and Reynolds averaged Navier-Stokes (RANS) models. The computational flow domain includes streamwise, spanwise, and vertical directions, defined over a 26D × 20D × 3D space. Numerical simulations are carried out for three gap ratios (S/D = 2, 3, and 4) and four different Reynolds numbers (Re = 253, 12,674, 25,348, and 126,746). The effects of Reynolds number and gap spacing on the formation of streamlines, wake structures, and horseshoe vortices are examined. At low Reynolds numbers and narrow gaps, interactions between the flow and cylinders result in significant deviations in flow behavior compared to that around a single isolated cylinder. In contrast, at higher Reynolds numbers and wider gaps, the influence of cylinder spacing diminishes, and the flow characteristics become largely uniform. Overall, LES provides a more accurate representation of flow hydrodynamics compared to RANS, especially in capturing complex vortex structures.

This study numerically investigates arsenic (As) transport in a homogeneous steady-state earth dam founded on an impermeable base using finite element modeling (SEEP/W and CTRAN/W, GeoStudio 2020). Locally sourced silty sand (SM) was characterized through laboratory tests, yielding a maximum dry density of 18.05 kN/m³, optimum moisture content of 13.1%, cohesion of 19 kPa, friction angle of 33°, and hydraulic conductivity of 6.79 × 10⁻⁶ m/s. These parameters were used as model inputs for eight dam geometries with varying slopes (34°–59°), heights (15–20 m), base widths (52–56 m), and longitudinal slopes (0–2.28%), subjected to upstream As concentrations of 10–40 mg/m³. Results show that As migration occurs primarily below the phreatic line, with negligible flux above it due to the assumption of saturated transport; unsaturated diffusion was intentionally excluded to isolate geometric effects. The solute flux increased from 3.86 × 10⁻¹² to 1.14 × 10⁻¹¹ kg/s·m² as concentration rose from 10 to 40 mg/m³, while reducing dam height from 20 m to 15 m decreased flux by over 70%. Incorporating retardation (R = 12.6) and adsorption effects produced realistic attenuation consistent with the soil’s moderate permeability. The findings demonstrate that dam geometry exerts a stronger control on As transport than concentration magnitude. The novelty of this work lies in quantifying the combined influence of geometry, concentration, and hydraulic–sorption parameters on As migration, offering design-level insights for safer dam and embankment construction in arsenic-prone regions.

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.

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.

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.

The most common source of catastrophic bridge failures around the world is scour; it is a phenomenon that occurs due to soil-structures interaction. Scour events generally occur without provocation and are extremely difficult to monitor during floods. These events are becoming more frequent due to an increase in flood frequency and magnitude, in the light of current climatic changes. To ensure the safe functioning of the aging bridge structures, scour monitoring is paramount. This study provides a complete overview of contemporary scour monitoring techniques and devices designed with their benefits and drawbacks. These devices are summarized on the basis of their uses in the field and laboratory. In addition, scour monitoring devices utilized for observing the changes in structural dynamics are also included. The intent of the study is to serve as a resource for scientists and professionals working on bridge pier scour.