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To effectively tackle the challenges posed by climate change, it is crucial to enhance the accuracy of rainfall-runoff models to ensure reliability amidst changing climatic conditions. Neural networks, renowned for their ability to capture complex patterns and relationships within uncertain input and output data, offer valuable tools in this pursuit. This study aims to evaluate the efficacy of two neural network (NN) models: the Radial Basis Function Neural Network (RBFNN) and the Model Tree M5 Neural Network (MTM5NN). These models are assessed both individually and in combination with the Wavelet (WT) data processing technique for rainfall-runoff modeling in the Kolar River watershed located in Madhya Pradesh, India. Fifteen models were developed employing four algorithms: RBFNN models, WRBFNN (RBF model integrating wavelet components of rainfall as inputs), MTM5NN, and WMTM5NN (MT model incorporating wavelet components of rainfall as inputs). Initially, rainfall and runoff data underwent normalization and were applied to RBFNN and MTM5NN networks. Subsequently, time series data for both rainfall and runoff were decomposed using wavelet transforms, resulting in various sub-time series signals such as approximations and decompositions. These derived signals were then utilized as input data for RBFNN and MTM5NN, specifically designated as WRBFNN and WMTM5NN. The most effective model identified was Model 8 of WMTM5NN, which demonstrated R2 values close to 0.97, outperforming the other models. These results underscore the superior performance of the WMTM5NN model, highlighting its effectiveness in achieving heightened accuracy in predicting runoff for this specific watershed.

In this paper, the influence of the Coefficient of Thermal Expansion (CTE) on the thermal stress analysis of laminated composite plates is explored. By introducing the undetermined integral terms in the displacement field, a new simple and efficient higher-order shear deformation theory is formulated for the thermo-mechanical behavior of thick laminated composite plates. This formulation aims to reduce the number of generated unknowns. Typically, a reduced order of the governing partial differential equations is expressed using the principle of virtual displacements. By using Navier’s technique, closed-form solutions are derived for laminated composite plates under thermal and/or mechanical loading. Unfortunately, several traditional research investigations significantly depend on the rule of the mixture to determine reliable CTE for composites. This paper offers and examines a variety of analytical micromechanics-based models for estimating CTE in laminated composite materials, incorporating into consideration different considerations. The obtained results are compared to those given by other alternative plate theories, and the efficiency and accuracy of the present theory are demonstrated for the thermomechanical behavior of laminated composite plates. This study reviews and applies several micromechanics-based models, contrary to previous investigations. Laminated composite plates could delaminate or crack due to the matrix material's longitudinal CTE, affecting fiber volume fraction and stacking sequence. Micromechanics-based approaches are important when arbitrary thermo-mechanical characteristics can generate inaccuracies. Interestingly, micromechanics-based models can estimate effective CTE. Schapery, Chamberlain, and Chamis provide models with identical longitudinal CTE. For increasing fiber volume fractions, Chamberlain's model is more sensitive to increasing fiber volume fractions. Mechanical stress changes laminated plate behavior more than thermal loading. Although all presented micromechanical-based models have simplified representations, this research attempts to provide a standard for future investigations. The use of detailed micromechanical-based models stimulates further progress in understanding and utilizing complex composite plates.

This research paper presents a precise three-dimensional (3D) pavement model designed to accurately account for the detrimental effects of the asphalt layer while representing various layers within a pavement structure. The primary objectives of this study include the development of an accurate 3D pavement model, the integration of asphalt concrete's viscoelastic properties into the analysis, and the improvement of pavement distress prediction accuracy. To achieve these goals, a comprehensive approach was undertaken, involving a stress relaxation test to assess the viscoelastic properties of asphalt concrete. The study incorporates Prony series coefficients into the analysis, employing Finite Element Modeling Software Abaqus for an accurate representation of asphalt concrete behavior. The realistic geometry of asphalt concrete is captured using the Simpleware application, enhancing precision compared to Abaqus mesh geometry. Additionally, a user-defined material subroutine (UMAT) based on the Kelvin viscoelastic model is integrated into the finite element analysis within Abaqus. This approach significantly enhances predictions of vertical displacements and horizontal strains associated with common pavement distresses, surpassing conventional linear pavement modeling methods. Moreover, it offers improved accuracy in representing the actual geometry of asphalt concrete and enhances the prediction of wearing course performance, especially in high-temperature regions. By customizing the UMAT subroutine to simulate flexible pavement behavior, this research contributes to the advancement of pavement analysis and design.

The Narmada River basin, a significant water resource in central India, plays a crucial role in supporting agricultural, industrial, and domestic water supply. Effective management of this basin requires accurate streamflow forecasting, which has become increasingly important. This study delves into streamflow forecasting using historical data from five major river stations, covering the upper reaches in the East and middle sections. The dataset spans from 1978 to 2020 and undergoes rigorous screening and preparation, including normalization using StandardScaler. The research adopts a comprehensive approach, developing models for training on 70 % of historical data, validation on the most current 15 %, and testing against future targets with another 15 % of the data. To achieve precise predictions, a suite of machine learning models is employed, including CatBoost, LGBM (Light Gradient Boosting Machine), Random Forest, and XGBoost. Performance evaluation of these models relies on key indices such as mean squared error (MSE), mean absolute error (MAE), root mean square error (RMSE), root mean square percent error (RMSPE), normalized root mean squared error (NRMSE), and R-squared (R2). Notably, among these models, Random Forest emerges as the most robust for streamflow prediction, showcasing its effectiveness in handling the complexities of hydrological forecasting. This research contributes significantly to the field of streamflow forecasting in the Narmada River basin by providing insights into the performance of various machine learning models. The findings not only enhance our understanding of watershed dynamics but also highlight the pivotal role that machine learning can play in improving hydrological forecasting for sustainable watershed management.

Rapid urbanization is a global trend that significantly alters Land Use and Land Cover Change (LULCC) patterns. Dhaka, a megacity, and Chittagong, the country’s second-largest city, have experienced substantial shifts in LULCC patterns over the past three decades. This study aims to examine the LULCC in Dhaka and Chittagong districts of Bangladesh from 1991 to 2021, focusing on identifying patterns and quantifying the effects of population growth and urbanization. Changes in LULCC patterns were identified by analyzing Landsat satellite imagery using remote sensing and geospatial techniques. The LULCC classes were classified using the Random Forest model and an inconsistency diagram, demonstrating high precision with an overall accuracy rate exceeding 85 %. The study’s findings reveal significant transformations in LULCC patterns across Dhaka and Chittagong districts from 1991 to 2021. A notable increase in built-up areas was observed in both districts, with Dhaka division witnessing a 129 % increase in built-up areas while Chittagong district saw a 515 % increase. This was due to transforming water bodies and barren land into built-up areas. However, the study also highlighted a promising increase in vegetation cover in both districts, indicating successful ecological restoration efforts. Chittagong and Dhaka saw increments of 597.95 km2 and 252.15 km2 in vegetation cover, respectively. Despite these positive changes, the study identified decreased surface water bodies in both regions. The results show a significant positive correlation between population growth/density and the built-up class, while a significant negative correlation with the barren land was observed in both districts. The findings could potentially guide policymakers in making informed decisions regarding land use, land cover, and urban sprawl management from an ecological restoration perspective. This could contribute to achieving sustainable development goals by promoting resilience ecological restoration practices.

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.