Damage Localization and Severity Identification in Truss Structures Using Dynamic Response and Differential Evolution Algorithm

Authors

  • Amirmorteza Panahi Executive Engineer, Parsian Sazeh Abnieh Sazan Company, Tonekabon, Iran.
  • Morteza Biklaryan * Department of Civil Engineering, Chalous Branch, Islamic Azad University, Chalous, Iran. https://orcid.org/0000-0001-6665-5550

https://doi.org/10.48314/ijrceai.v2i2.37

Abstract

Structural damage detection is a critical concern in civil engineering, enabling timely maintenance and preventing catastrophic failures. This study presents an efficient method for identifying the location and severity of damage in truss structures by combining dynamic response analysis with an optimization algorithm. Damage is modeled as a reduction in the elastic modulus of individual members. The problem is formulated as an optimization task where an Effective Correlation-Based Index (ECBI) serves as the objective function, minimizing the difference between the dynamic responses of healthy and damaged structures. The Differential Evolution (DE) algorithm is employed to solve the optimization problem. Numerical validations are conducted on three planar trusses (10, 23, and 31 members) under single and multiple damage scenarios, both with and without 0.15% measurement noise. Results demonstrate that the proposed method accurately identifies damage locations and severities, even in the presence of noise, showing high robustness and precision.

Keywords:

Damage detection, Dynamic response, Differential evolution, Truss structure, Optimization algorithm

References

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Published

2026-03-15

Issue

Vol. 3 No. 1 (2026): International Journal of Researches on Civil Engineering with Artificial Intelligence (IJRCEAI)

Section

Articles

How to Cite

Panahi, A., & Biklaryan, M. (2026). Damage Localization and Severity Identification in Truss Structures Using Dynamic Response and Differential Evolution Algorithm. International Journal of Researches on Civil Engineering With Artificial Intelligence , 3(1), 1-8. https://doi.org/10.48314/ijrceai.v2i2.37

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