Advancements and Applications of Artificial Neural Networks in Structural Engineering A Comprehensive Review

Authors

  • Habtamu Alemayehu Tadesse Faculty Civil and Water Resources Engineering, Bahir Dar Institute of Technology-Bahir Dar University, Bahir Dar, P.O.Box 26, Ethiopia
  • Dawit Wagnebachew Nega Faculty Civil and Water Resources Engineering, Bahir Dar Institute of Technology-Bahir Dar University, Bahir Dar, P.O.Box 26, Ethiopia
  • Wasihun Moges Fikadie Faculty Civil and Water Resources Engineering, Bahir Dar Institute of Technology-Bahir Dar University, Bahir Dar, P.O.Box 26, Ethiopia
  • Naveen Bhari Onkareswara Faculty Civil and Water Resources Engineering, Bahir Dar Institute of Technology-Bahir Dar University, Bahir Dar, P.O.Box 26, Ethiopia
  • Belete Molla Berihun Faculty Technology, Department of Civil Engineering, Debre Tabor University, Ethiopia

DOI:

https://doi.org/10.20372/pjet.v3i2.1850

Keywords:

Artificial Neural Networks (ANNs), Civil Engineering, Deep Feedforward Neu-ral Network (FFNN), Nonlinear Finite Element (NLFE), Load Capacity Predic-tion, Structural Analysis, Crack Detection, Hybrid Methodology, Machine Learn-ing, Ethiopia, Infrastructure Development.

Abstract

Artificial Neural Networks (ANNs) have revolutionized the field of civil engineering by offering efficient and accurate solutions for complex material behavior predictions. This paper reviews the applications of ANNs in civil engineering, emphasizing their role in predicting the load capacities of structural components under various conditions. The study highlights the development and application of a deep feed forward neural network (FFNN) for predicting the load capacities of post-installed adhesive anchors in cracked concrete. Additionally, it explores a hybrid methodology combining nonlinear finite element (NLFE) techniques with FFNN to enhance prediction accuracy and reduce computational effort. The research demonstrates the significant potential of ANNs in diverse civil engineering applications, including crack detection, structural analysis, design optimization, and strength estimation. Despite challenges such as data quality, computational resources, model interpretability, and generalization, the opportunities for enhanced prediction accuracy, reduced computational effort, and adaptability to various applications are substantial. The study particularly emphasizes the potential for ANN adoption and development in Ethiopia, presenting opportunities for capacity building and infrastructure improvement. The findings underscore the robustness and efficiency of ANNs, particularly deep FFNNs, as a vital tool in advancing structural engineering practices.

Author Biographies

Habtamu Alemayehu Tadesse, Faculty Civil and Water Resources Engineering, Bahir Dar Institute of Technology-Bahir Dar University, Bahir Dar, P.O.Box 26, Ethiopia

Faculty Civil and Water Resources Engineering, Department of Structural Engineering, Lecturer

Dawit Wagnebachew Nega, Faculty Civil and Water Resources Engineering, Bahir Dar Institute of Technology-Bahir Dar University, Bahir Dar, P.O.Box 26, Ethiopia

Faculty of Civil and Water Resources Engineering, Department of Structural Engineering, Lecturer 

Wasihun Moges Fikadie, Faculty Civil and Water Resources Engineering, Bahir Dar Institute of Technology-Bahir Dar University, Bahir Dar, P.O.Box 26, Ethiopia

Faculty of Civil and Water Resources Engineering, Department of Structural Engineering, Lecturer  

Naveen Bhari Onkareswara, Faculty Civil and Water Resources Engineering, Bahir Dar Institute of Technology-Bahir Dar University, Bahir Dar, P.O.Box 26, Ethiopia

Faculty Civil and Water Resources Engineering, Department of Structural Engineering, Assistant Professor 

Belete Molla Berihun, Faculty Technology, Department of Civil Engineering, Debre Tabor University, Ethiopia

Faculty Technology, Department of Civil Engineering, Lecturer

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Published

2025-07-31

Issue

Vol. 3 No. 2 (2025): Volume 3 Issue 2

Section

Resilient Infrastructure and Transportation