Government Engineering College, Siwan

The goal of this study is to replicate the short-term aging that takes place during the production and placement of asphalt mixtures in the field by examining the short-term oven aging (STOA) of asphalt mixtures under carefully monitored laboratory terms. The objective is to determine appropriate STOA durations for Asphalt Concrete (AC) and Stone Mastic Asphalt (SMA) wearing course mixtures by evaluating and comparing the binder and mixture characteristics of both field-aged and laboratory-aged specimens. Laboratory-fabricated specimens were subjected to aging at 135 ^∘ C for various durations (0, 2, 3, and 4 hours). The extracted binders were analyzed for their rheological behavior, including master curves of the complex modulus, complex shear modulus ( G^* ), non-recoverable creep compliance ( J_nr ) and percent recovery (R ^∘ C offers a trustworthy simulation for SMA, whereas a 3-hour STOA works well for AC.

This study developed an integrated machine learning framework for predicting the factor of safety (FOS) of soil slopes using Extreme Gradient Boosting (XGB) optimized with three metaheuristic algorithms: Particle Swarm Optimization (PSO), Tiki Taka Optimization Algorithm (TTOA), and Whale Optimization Algorithm (WOA). A dataset comprising six influential parameters, namely cohesion, unit weight, friction angle, slope angle, slope height, and pore pressure ratio, was employed for model development. The dataset was normalized and divided into training and independent testing subsets, while five-fold cross validation was adopted during model optimization to improve robustness and reduce overfitting. Model performance was assessed using multiple statistical indicators, scatter plots, Regression Error Characteristic curves, and Taylor diagrams. The results demonstrate that all optimized XGB models effectively captured the nonlinear relationship between the input parameters and FOS, while the proposed framework provides an accurate, interpretable, and computationally efficient tool. To improve model transparency, SHAP, LIME, and permutation feature importance analyses were incorporated. The explainability results identified slope angle, cohesion, and slope height as the dominant predictors, consistent with their governing roles in slope geometry and shear strength. The proposed framework provides an accurate, interpretable, and computationally efficient tool for data driven slope stability assessment.

Abstract The recent improvements of current RGB-D salient object detection models have achieved better results by leveraging the depth modality in a convolutional neural network. Most existing approaches use a multi-stream architecture with enhanced feature fusion to subsequently determine saliency. The inconsistency of salient regions may result from the limitations of the acquisition devices and the low-depth images. Multi-horizon feature characteristics and complementary modalities are essential for resolving these problems. Even in cluttered backgrounds, complementary and multi-horizon features across several horizons must be taken into consideration since they provide structural and contextual information about the salient object. To maximize important features in low-depth images, the proposed model addresses these constraints. The multi-horizon features are optimized from the RGB and depth streams by using the proposed backbone network with the feature aggregation and optimization (FAO) module. The FAO module, which optimizes feature extraction by utilizing complementary features from the depth stream, is used to enhance the RGB stream. The proposed model investigates multi-horizon features between multi-resolution and multi-stage complementary features using a multi-horizon reconstruction module. To forecast optimal saliency, the FAO module combines data at multiple levels. A guiding framework for maximizing salient regions and reducing non-salient ones throughout the fusion process is provided by attention maps at various phases. Current evaluation metrics from the six publicly accessible difficult RGB-D datasets are used in the experimental investigation. Furthermore, we compare our findings with those of 18 other cutting-edge methods that have demonstrated encouraging performance on the above challenges.

Ultra-high performance concrete exhibits excellent mechanical properties, such as high compressive strength, good durability, and structural reliability. However, its application is currently hampered by very high cement consumption and a considerable amount of embodied carbon, which is significantly dependent on the energy intensive use of steel fibers. The study conducts a technical and bibliometric review of sustainable UHPC systems that employ hybrid fiber reinforcement and supplementary cementitious materials. A systematic literature review on SCMs and UHPC has been analysed through VOSviewer to identify trends of research, collaboration networks, and thematic clusters of UHPC research. The findings show an increasing trend towards sustainability-oriented mix design in the field of UHPC, characterized by the use of high volume SCMs, optimization of particle packing, and hybrid fiber systems that combine steel fibers with polypropylene, basalt, or glass fibers. Sustainability assessments also indicate that cement production and steel fibers are the largest contributors to embodied carbon in UHPC, and that SCM substitution and hybrid fiber optimization can provide a reduction of up to 40-50 % in global warming potential of UHPC. Although encouraging laboratory scale performances were obtained, large scale implementations are still hindered by material variability, fiber dispersion, cost issues, and lack of standardization.

A novel single-stage single-phase (SSSP) 5-level switched-capacitor (SC)-based multilevel inverter (MLI) is presented in this paper. The suggested topology is structured to produce 5-level from a single DC source. The proposed design (PD) has various advantages, such as boosting ability, self-balanced switched capacitor voltages and reduced voltage stress. In addition, the PD does not use an H-bridge at the end of the inverter and all switching components tolerate voltage stress equal to the amount of input supply DC source. This indication of a significant decrease in total standing voltage (TSV). PD description, operation principle, pulse width modulation (PWM), comparative study and losses analysis are presented in detail. The simulation and experimental results have validated the viability of the inverter design.