Research Article
Seismic Performance Comparison of Flat Slab and RC Frame Structures Using Pushover Analysis
Issue:
Volume 13, Issue 6, December 2025
Pages:
313-328
Received:
8 August 2025
Accepted:
20 August 2025
Published:
9 December 2025
DOI:
10.11648/j.ajce.20251306.11
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Abstract: This study presents an in-depth investigation of seismic performance between flat slab and reinforced concrete (RC) frame structural systems using nonlinear static pushover analysis. This nonlinear static analysis technique generates capacity curves that identify potential failure modes, assess displacement demands, and determine performance levels for each structural system. The goal is to offer better directional insight into the variations of the structural performance for stability, safety, economic efficiency and to flag the pros and cons of each system. This study is significant for addressing the cases of seismically active regions where structural integrity is a key factor and small perimeter change can create disaster. The investigation employs methodology to evaluate the seismic capacity and performance of both structural systems under earthquake loading conditions. This study also evaluates fixed base conditions and uses material properties based on BNBC and geometrical properties of typical mid-rise buildings as limiting conditions. It develops detailed three-dimensional finite element models to simulate the nonlinear behavior of both structures. Results show that flat slab structures are more flexible and vulnerable to earthquakes, while RC frame buildings offer greater strength and better resistance to seismic forces. These findings highlight the importance of structural system selection in improving earthquake resilience.
Abstract: This study presents an in-depth investigation of seismic performance between flat slab and reinforced concrete (RC) frame structural systems using nonlinear static pushover analysis. This nonlinear static analysis technique generates capacity curves that identify potential failure modes, assess displacement demands, and determine performance levels...
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Review Article
Evaluation of Design Techniques for Extra Heavy-duty Flexible Pavements and Other Critical Considerations
Boon Tiong Chua*
,
Kali Prasad Nepal
Issue:
Volume 13, Issue 6, December 2025
Pages:
329-349
Received:
27 October 2025
Accepted:
7 November 2025
Published:
9 December 2025
DOI:
10.11648/j.ajce.20251306.12
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Abstract: The design of heavy-duty flexible pavements for highways is well-established in the United States, Europe, and Australia. However, a standardised design methodology for extra heavy-duty flexible pavements–specifically tailored for ports and intermodal container terminals–remains lacking. These pavements present unique challenges due to significant variations in several load repetitions, load magnitudes, long-term static loads, tyre pressures, wheel and axle configurations, and loading characteristics, with axle loads reaching up to 120 tonnes. Existing design methods are often influenced by industry interests, such as concrete interlocking pavers, concrete, and asphalt, leaving pavement practitioners with limited tools to optimise designs for the extreme load conditions encountered over the pavement’s design life. Traditionally, extra heavy-duty pavements are considered high-risk areas due to their high failure rates and the substantial costs associated with such failures. This study provides a comprehensive review of existing design methodologies and software available internationally, critically compares these methods, and discusses other critical considerations to mitigate the risks of extra heavy-duty pavement failure. The literature review reveals that the development of develop unified design guidelines for extra heavy-duty flexible pavements intended to withstand severe axle loads up to 120 tonnes or more would require further research in this area.
Abstract: The design of heavy-duty flexible pavements for highways is well-established in the United States, Europe, and Australia. However, a standardised design methodology for extra heavy-duty flexible pavements–specifically tailored for ports and intermodal container terminals–remains lacking. These pavements present unique challenges due to significant ...
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Research Article
A Multi-Output Deep Learning Framework for Automated Reinforced Concrete (RC) Beam Design and Detailing
Junaid Khan,
Shalini Paka,
Wisam Bukaita*
Issue:
Volume 13, Issue 6, December 2025
Pages:
350-361
Received:
28 October 2025
Accepted:
7 November 2025
Published:
9 December 2025
DOI:
10.11648/j.ajce.20251306.13
Downloads:
Views:
Abstract: This study presents a machine learning framework for the automated design of reinforced concrete (RC) beams in compliance with ACI 318-19 code provisions. Traditional RC beam design requires iterative calculations to satisfy strength and serviceability criteria across a wide range of geometric, material, and loading parameters. To enhance efficiency and consistency, a dataset comprising 10,000 synthetically generated beam configurations was developed by varying span length, concrete compressive strength, steel yield stress, and applied loading. Each configuration was generated using ACI 318-19 design equations to ensure code compliance and structural validity. A deep neural network (DNN) regression model was trained to learn the nonlinear mapping between input parameters and corresponding design outputs, including required tensile reinforcement, bar diameters, stirrup spacing, and ultimate moment capacity. Model performance was evaluated using a quantitative error matrix reporting mean absolute error (MAE), root mean squared error (RMSE), and coefficient of determination (R2) for each output variable. The model achieved MAE values ranging from 0.35 to 10 units, RMSE values from 0.45 to 12 units, and R2 values between 0.95 and 0.99, demonstrating high predictive accuracy and strong agreement with ACI-based reference designs. These results confirm that the framework can automatically generate code-compliant RC beam designs with high fidelity to ACI 318-19 specifications. By providing consistent, rapid, and interpretable predictions, this approach establishes a foundation for AI-assisted structural engineering tools that reduce computational time, improve design accuracy, and support data-driven automation in structural design workflows.
Abstract: This study presents a machine learning framework for the automated design of reinforced concrete (RC) beams in compliance with ACI 318-19 code provisions. Traditional RC beam design requires iterative calculations to satisfy strength and serviceability criteria across a wide range of geometric, material, and loading parameters. To enhance efficienc...
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