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Mathematics
Volume 13
Issue 23
10.3390/math13233834
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

A Novel Deep Hybrid Learning Framework for Structural Reliability Under Civil and Mechanical Constraints

by
Qasim Aljamal
1,
Mahmoud AlJamal
2,
Mohammad Q. Al-Jamal
3,
Zaid Jawasreh
4,
Ayoub Alsarhan
5,
Sami Aziz Alshammari
6,*,
Nayef H. Alshammari
7 and
Rahaf R. Alshammari
8
1
Department of Civil Engineering, Faculty of Architecture and Civil Engineering, Technical University Dortmund, 44227 Dortmund, Germany
2
Department of Cybersecurity, Science and Information Technology, Irbid National University, Irbid 21110, Jordan
3
Department of Renewable Energy, Technical Faculty, Jadara University, Irbid 21110, Jordan
4
Department of Artificial Intelligence, Science and Information Technology, Irbid National University, Irbid 21110, Jordan
5
Department of Data Science and Artificial Intelligence, Faculty of Information Technology, Al-Ahliyya Amman University, Amman 19328, Jordan
6
Department of Information Technology, Faculty of Computing and Information Technology, Northern Border University, Rafha 91911, Saudi Arabia
7
Department of Computer Science, Faculty of Computers and Information Technology, University of Tabuk, Tabuk 71491, Saudi Arabia
8
Department of Computer Science, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh 11671, Saudi Arabia
*
Author to whom correspondence should be addressed.
Mathematics 2025, 13(23), 3834; https://doi.org/10.3390/math13233834 (registering DOI)
Submission received: 21 October 2025 / Revised: 11 November 2025 / Accepted: 18 November 2025 / Published: 29 November 2025
(This article belongs to the Special Issue Evolutionary Computation for Feature Selection and Dimensionality Reduction)
Versions Notes

Abstract

This study presents an AI-based framework that unifies civil and mechanical engineering principles to optimize the structural performance of steel frameworks. Unlike traditional methods that analyze material behavior, load-bearing capacity, and dynamic response separately, the proposed model integrates these factors into a single hybrid feature space combining material properties, geometric descriptors, and load-response characteristics. A deep learning model enhanced with physics-informed reliability constraints is developed to predict both safety states and optimal design configurations. Using AISC steel datasets and experimental records, the framework achieves 99.91% accuracy in distinguishing safe from unsafe designs, with mean absolute errors below 0.05 and percentage errors under 2% for reliability and load-bearing predictions. The system also demonstrates high computational efficiency, achieving inference latency below 3 ms, which supports real-time deployment in design and monitoring environments. the proposed framework provides a scalable, interpretable, and code-compliant approach for optimizing steel structures, advancing data-driven reliability assessment in both civil and mechanical engineering.
Keywords: AI-assisted structural optimization; physics-informed machine learning; structural reliability assessment; hybrid civil–mechanical framework; steel material properties AI-assisted structural optimization; physics-informed machine learning; structural reliability assessment; hybrid civil–mechanical framework; steel material properties

Share and Cite

MDPI and ACS Style

Aljamal, Q.; AlJamal, M.; Al-Jamal, M.Q.; Jawasreh, Z.; Alsarhan, A.; Alshammari, S.A.; Alshammari, N.H.; Alshammari, R.R. A Novel Deep Hybrid Learning Framework for Structural Reliability Under Civil and Mechanical Constraints. Mathematics 2025, 13, 3834. https://doi.org/10.3390/math13233834

AMA Style

Aljamal Q, AlJamal M, Al-Jamal MQ, Jawasreh Z, Alsarhan A, Alshammari SA, Alshammari NH, Alshammari RR. A Novel Deep Hybrid Learning Framework for Structural Reliability Under Civil and Mechanical Constraints. Mathematics. 2025; 13(23):3834. https://doi.org/10.3390/math13233834

Chicago/Turabian Style

Aljamal, Qasim, Mahmoud AlJamal, Mohammad Q. Al-Jamal, Zaid Jawasreh, Ayoub Alsarhan, Sami Aziz Alshammari, Nayef H. Alshammari, and Rahaf R. Alshammari. 2025. "A Novel Deep Hybrid Learning Framework for Structural Reliability Under Civil and Mechanical Constraints" Mathematics 13, no. 23: 3834. https://doi.org/10.3390/math13233834

APA Style

Aljamal, Q., AlJamal, M., Al-Jamal, M. Q., Jawasreh, Z., Alsarhan, A., Alshammari, S. A., Alshammari, N. H., & Alshammari, R. R. (2025). A Novel Deep Hybrid Learning Framework for Structural Reliability Under Civil and Mechanical Constraints. Mathematics, 13(23), 3834. https://doi.org/10.3390/math13233834

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