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Research Article
Improved Correlation Models for Optimum Moisture Content Based on Atterberg Limits
Mahmuda Khanom*
,
Md. Abdul Alim
Issue:
Volume 13, Issue 5, October 2025
Pages:
257-264
Received:
11 August 2025
Accepted:
25 August 2025
Published:
10 October 2025
Abstract: The compaction characteristics of soil are fundamental to the stability and performance of a wide range of geotechnical engineering applications. Compaction, a mechanical process involving the application of energy to increase soil density, fulfills multiple essential purposes: minimizing structural settlement under load, reducing soil permeability to mitigate liquefaction risks, and enhancing shear strength. It is particularly vital in hydraulic structures such as dams, where water retention is essential. However, conducting standard laboratory compaction tests, such as the Proctor test, is often expensive and time-consuming. In contrast, the determination of Atterberg limits, namely Liquid Limit (LL), Plastic Limit (PL), and Plasticity Index (PI) is relatively quick, simple, and cost-effective. Establishing correlations between these Atterberg limits and compaction characteristics, particularly Optimum Moisture Content (OMC), may offer a practical alternative for predicting compaction behavior. This study investigates such correlations using five types of fine-grained clay soils collected from various locations within the Rajshahi Division of Bangladesh. Through regression analysis, four predictive relationships between OMC and the Atterberg limits are proposed, highlighting the potential to estimate OMC without relying solely on traditional compaction tests.
Abstract: The compaction characteristics of soil are fundamental to the stability and performance of a wide range of geotechnical engineering applications. Compaction, a mechanical process involving the application of energy to increase soil density, fulfills multiple essential purposes: minimizing structural settlement under load, reducing soil permeability...
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Research Article
Numerical Investigation on Seismic Strengthening of Composite Two Way Beam- Column Joint
Issue:
Volume 13, Issue 5, October 2025
Pages:
265-274
Received:
9 May 2025
Accepted:
26 September 2025
Published:
22 October 2025
Abstract: Beam-column joints are crucial structural components to ensure the overall stability of composite framed structures subjected to seismic loads. Many more research efforts have been dedicated to enhance the seismic behavior of beam-column joints. Due to limited research on composite beam-column joint, the effect of some parameters is not well documented on the current building codes. A total of eleven specimens including a control specimen were simulated by considering the effects of doubler plate thickness, haunch plate thickness and haunch configuration in a numerical research conducted using ABAQUS/Standard to investigate the performance of composite steel beam column joint under cyclic loading. Experimental results from other researchers validated the accuracy of the numerical model. Finite-element analysis results showed use of higher thickness doubler plate with better haunch thickness increased the load carrying capacity by 45.02% and 34.02% respectively. Moreover, using appropriate haunch configuration along the flange and beam column with haunch support improved the load carrying capacity and seismic resistance of the joint. With use of higher doubler plate and haunch thickness the stiffness and energy dissipation capacity of the joint showed improved result. These results verified that the composite beam column joint with the aid of doubler plate thickness, haunch plate thickness, better haunch configuration and supporting the joint with haunch helps the beam column joint to withstand the seismic action better.
Abstract: Beam-column joints are crucial structural components to ensure the overall stability of composite framed structures subjected to seismic loads. Many more research efforts have been dedicated to enhance the seismic behavior of beam-column joints. Due to limited research on composite beam-column joint, the effect of some parameters is not well docume...
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Research Article
A Shannon Entropy Approach for Quantitative Evaluation of Resilience and Sustainability for Transport Infrastructures
Radu Andrei*
Issue:
Volume 13, Issue 5, October 2025
Pages:
275-283
Received:
13 September 2025
Accepted:
24 September 2025
Published:
30 October 2025
DOI:
10.11648/j.ajce.20251305.13
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Views:
Abstract: This paper presents an original methodology for quantitative evaluation of resilience and sustainability in transport infrastructure projects, specifically focusing on highways and earthworks construction. The methodology employs Shannon entropy theory to assess five key resilience criteria: technical, socio-economic, environmental, climate change adaptation, and strategic aspects. A probabilistic approach is used to calculate resilience indices, enabling comparative analysis between traditional and innovative construction technologies. According to this approach, in resilience studies conducted for a new project it is recommended to investigate at least two alternatives, a classical/standard one in parallel with the new proposed one. The resilience value obtained for each investigated project is compared with the resilience of an ideal project, so that to be possible decide which alternative is more near the ideal solution and thus may better satisfy, the designed / desired resilience requirements This proposed methodology is validated through a case study of the Tarhuna-Beni Walid road project in Libya, where dry compaction technology was implemented to address water scarcity challenges. Results demonstrate that the proposed approach achieved a resilience index of 2.16 bits compared to 3.98 bits for conventional methods, indicating superior resilience performance. The methodology provides practitioners with a quantitative framework for infrastructure decision-making and risk assessment.
Abstract: This paper presents an original methodology for quantitative evaluation of resilience and sustainability in transport infrastructure projects, specifically focusing on highways and earthworks construction. The methodology employs Shannon entropy theory to assess five key resilience criteria: technical, socio-economic, environmental, climate change ...
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Research Article
Earthquake Prediction and Synthetic Seismogram Generation Using Hybrid CNN – LSTM Model
Issue:
Volume 13, Issue 5, October 2025
Pages:
284-303
Received:
24 September 2025
Accepted:
7 October 2025
Published:
30 October 2025
DOI:
10.11648/j.ajce.20251305.14
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Views:
Abstract: This study addresses the critical challenge of earthquake prediction and synthetic seismogram generation through the application of deep learning. A hybrid Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) framework is proposed to capture both spatial and temporal characteristics of seismic data, enabling robust forecasting of seismic activity and the generation of realistic waveform simulations. Historical seismographic records and metadata, including magnitude, depth, and epicentral location, were sourced from the United States Geological Survey (USGS). Key predictive features such as amplitude variation, temporal intervals, epicentral distances, and regional spatial attributes were extracted to train and validate the model. Model development and experimentation were conducted in Python using TensorFlow, Keras, NumPy, Pandas, Scikit-learn, and Imbalanced-learn. The CNN component was employed to extract spatial representations from seismograms, while the LSTM component modeled sequential dependencies inherent in seismic waveforms. The final model achieved an accuracy of 84%, with notable improvements across precision, recall, and loss metrics. Statistical evaluations further validated the reliability of the results. The findings demonstrate the potential of hybrid deep learning architectures to enhance early earthquake warning systems and hazard assessment strategies. By integrating prediction with synthetic seismogram generation, this research advances data-driven seismology and provides a scalable foundation for future applications in disaster preparedness and risk mitigation.
Abstract: This study addresses the critical challenge of earthquake prediction and synthetic seismogram generation through the application of deep learning. A hybrid Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) framework is proposed to capture both spatial and temporal characteristics of seismic data, enabling robust forecasting of seismic ...
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Research Article
Benchmarking Numerical Solvers for Damped Single-Degree-of-Freedom Vibration Systems: Technical Evaluation and Educational Insights
John Cannon,
Tadeh Zirakian*
Issue:
Volume 13, Issue 5, October 2025
Pages:
304-312
Received:
4 October 2025
Accepted:
14 October 2025
Published:
30 October 2025
DOI:
10.11648/j.ajce.20251305.15
Downloads:
Views:
Abstract: This study presents a systematic benchmarking of numerical methods for solving ordinary differential equations (ODEs) applied to damped single-degree-of-freedom (SDOF) vibration systems. Ten solvers—including Runge–Kutta variants, Adams–Bashforth–Moulton, Rosenbrock, and Backward Differentiation Formula (BDF)—were evaluated under both non-stiff and stiff conditions by varying mass, damping, and stiffness parameters. Analytical solutions were used as references to quantify global error, convergence behavior, and computational efficiency. High-order adaptive solvers, such as Verner’s and Runge–Kutta 7/8, consistently achieved the highest accuracy, reducing global error by up to 15% compared with the classical Runge–Kutta (ODE45) method. Implicit methods, including Rosenbrock and BDF, demonstrated superior stability in stiff and highly damped cases. In contrast, low-order approaches, particularly the Trapezoidal rule, exhibited the largest errors, exceeding 30% in oscillatory regimes. The results confirm that solver performance is problem-dependent, emphasizing that no single algorithm is universally optimal. Beyond the technical contributions, this study introduces a pedagogical framework that allows engineering students to visualize solver trade-offs, quantify numerical accuracy, and interpret computational efficiency. The educational integration strengthens conceptual understanding of numerical methods and supports data-driven solver selection in vibration analysis and related engineering applications.
Abstract: This study presents a systematic benchmarking of numerical methods for solving ordinary differential equations (ODEs) applied to damped single-degree-of-freedom (SDOF) vibration systems. Ten solvers—including Runge–Kutta variants, Adams–Bashforth–Moulton, Rosenbrock, and Backward Differentiation Formula (BDF)—were evaluated under both non-stiff and...
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