Journal of Civil Engineering Beyond Limits (CEBEL)

Saad Issa Sarsam

The asphalt concrete behavior through the Visco-plastic and Visco-elastic stages of failure is highly susceptible to the variation in the temperature of the pavement. In the present work, slab samples of asphalt concrete were prepared with the optimum asphalt binder requirement with the aid of laboratory roller compactor. Beam specimens of asphalt concrete were obtained from the prepared slab samples and tested under the influence of dynamic flexural stresses for fatigue life at constant strain level of 750 microstrain. Specimens were tested at three testing temperatures of (30, 20, and 5) ℃. The variations of the behavior through Visco-plastic and Visco-elastic modes in terms of phase angle, permanent deformation, flexural stiffness, and cumulative dissipated energy with the testing temperatures have been assessed. It was verified that at the end of the Visco-plastic stage of failure, the energy dissipation of the mixture increased by (2, 1.5, and 29) folds at the testing temperatures of (5, 20, and 30) °C respectively as compared with the energy dissipation at the end of the Visco-elastic stage of failure. The phase angle declined to (55, 20, and 30) ° at (5, 20, and 30) °C testing temperatures respectively at the end of Visco-plastic stage of failure as compared with that at the end of Visco-elastic stage of failure. The flexural stiffness at the Visco-plastic stage of failure declined by (60, 80, and 92) % at (5, 20, and 30) °C testing temperature respectively as compared with the flexural stiffness at the Visco-elastic stage of failure.

https://doi.org/10.36937/cebel.2025.1993

Md Mahabub Rahman Md Abu Sayed Mst. Mariam Khatun Afsana Mimi

In geotechnical engineering and construction, the optimal moisture content (OMC) and maximum dry density (MDD) is crucial for determining the ideal conditions for soil strength and stability in infrastructure. Traditional laboratory techniques for calculating OMC and MDD are both costly and time-consuming. Machine learning offers a potential alternative for traditional empirical approaches by making it easier to create complex prediction models and algorithms that can improve the accuracy and efficacy of forecasts of compaction parameters. Machine learning-specifically Meta-heuristic optimization (MHO)-approaches are high-level problem-solving strategies that seek optimal or near-optimal solutions to difficult optimization problems, which are frequently non-linear, multi-modal, or non-differentiable. The Genetic Algorithm (GA), Generalized Population-Based Adaptive Search (GPAS), and Particle Swarm Optimization (PSO) are three powerful meta-heuristic optimization algorithms that are commonly employed to solve complex optimization issues. The goal of this project is to develop a framework that uses meta-heuristic optimization techniques to estimate OMC and MDD. Using MHO models, the study shows a substantial correlation between OMC and MDD, respectively, with significant soil factors such as specific gravity, Atterberg limits, and grain size distribution parameters. This study depicts three distinct models for the prediction of OMC and MDD named GA, PSO, and GPAS models. Among the models, the GA model demonstrated the highest accuracy in predicting OMC (R2 = 0.9999, MSE = 0.0001), while the PSO model was most effective for MDD prediction (R2 = 0.9660, MSE = 0.1871). These findings highlight the accuracy and dependability of the GA technique, which presents a viable method for precisely forecasting the MDD and OMC of soil stabilization mixtures in a range of engineering applications. Additionally, it reduces the negative effects that soil extraction and modification have on the environment.

https://doi.org/10.36937/cebel.2025.1995

Oğuzhan Çelebi Muhammet Mücahit Demir

The high concrete consumption in the construction industry contributes significantly to global CO2 emissions and energy consumption. Recently, geopolymer concretes have been studied as an alternative to traditional concrete. Studies have shown that geopolymer concretes can contribute to the formation of a sustainable construction environment by reducing energy consumption and C02 emissions. In this study, the use of geopolymer concrete mortars for both sustainable and earthquake-resistant structures was investigated. It is well established that, the first damaged element under the effect of an earthquake is the infill walls. It is thought that making infill walls more resistant to earthquakes will improve the performance of load-bearing elements such as columns and beams. It is known that connecting infill walls to columns with flexible joints will increase the relative story drift capacity of the structure. In the current study, it is aimed to investigate the effect of using traditional and geopolymer mortars on seismic behavior in infill walls connected to columns and beams with flexible joints. In this context, the effect of geopolymer mortars was investigated in the time and frequency domains with shaking table tests applied to two steel frame models produced in a laboratory environment. It was observed that the use of geopolymer mortars reduced both the peak displacement and peak acceleration values of the frame decreased and the dominant frequency values of the structure increased.

https://doi.org/10.36937/cebel.2025.1996

Hilal Çodur Çelebi Ahmet Atalay

Throughout history, transportation structures have played a vital role to ensure cultural and commercial interaction. Today, the continuation of this interaction and the transportation superstructure's ability to safely withstand vehicle and earthquake loads is critical. In the current study, the performance of the Kırkgözeler transportation superstructure, which is located in the Horasan district of Erzurum, Turkey and connects the villages in the region to the district and city center, has been evaluated. The performance of the Kırkgözeler bridge under vehicle and earthquake loads was evaluated using time history analyses. The results indicate that the bridge experiences limited damage under horizontal earthquake loading, but may sustain controllable damage, particularly in the main beams, under vertical earthquake loading. Reinforcement of the main beams is suggested to mitigate potential damage.

https://doi.org/10.36937/cebel.2025.1997

Md Abu Sayed Arabi Suraia Rose Md. Maruf Hasan Mst. Mariam Khatun Harun Ar Rashid

Predicting the Water Quality Index (WQI), which provides communities and policymakers a measurable indicator of water quality, is crucial for efficient environmental management. The purpose of this study is to investigate numerical models for predicting the WQI values using Minitab (regression analysis) and machine learning algorithms namely Decision Tree (DTR), Random Forest (RGR), Stochastic Gradient Decent (SGD), and Support Vector Machine (SVR). This is accomplished by collecting surface and ground water from 200 locations in the Paba Upazila, Rajshahi and doing laboratory tests to determine the pH, turbidity, total dissolved solids and total solids to create an extensive dataset that reflects the water conditions in the area. The WQI is then computed using the parameters from the Brown et al. (1972) technique. According to the analysis, Minitab and SVR perform better than the others, obtaining strong classification metrics (93% accuracy, 0.94 F1-score) and remarkable prediction accuracy (r2 = 0.9503 for Minitab; r2 = 0.9443 for SVR). The intricate interactions between the several water quality indices in the study area are well captured by these models. With a data-driven strategy to monitoring and forecasting water quality in Paba Upazila, the findings offer significant insights for local water resource management. The results of the evaluation can provide a scientific basis for the conservation of the local aquatic environment, and the model created in this study can be used as a guide for similar water quality assessment work. This study also highlights the potential of integrating machine learning algorithms with statistical software such as Minitab for environmental monitoring applications, and it helps design customized solutions for water quality evaluation in comparable regions of Bangladesh.

https://doi.org/10.36937/cebel.2025.1999