Sustainable Earth Trends

Sustainable Earth Trends

Identifying Damage in 3D Skeletal Structures Using a Two-Stage Probabilistic Approach by the MTLBO Optimization Algorithm

Document Type : Original Research Article

Authors
Sina Dehyadegari
Ali Keyhani
Faculty of Civil Engineering, Shahrood University of Technology, Shahrood, Iran
10.48308/set.2025.241268.1153
Abstract
This paper introduces an advanced structural health monitoring (SHM) framework that integrates Bayesian data fusion with a Modified Teaching–Learning-Based Optimization (MTLBO) algorithm to improve damage identification in space frames and truss structures. The proposed method utilizes multiple vibration-based damage indices, including Modal Strain Energy (MSE), Frequency Response Function Strain Energy (FRFSE), Flexibility Strain Energy (FSE), and Residual Force-Based (RFB) indicators, derived from natural frequencies and mode shapes. These indices collectively enhance damage localization accuracy while reducing the number of false or redundant damaged elements, thereby improving computational efficiency. Through the application of Bayesian data fusion, the approach establishes a robust probabilistic framework that effectively integrates different damage indicators to refine detection results. The incorporation of the MTLBO algorithm further enhances the convergence speed and accuracy of the optimization process, expediting the identification of damage across complex structural systems. Moreover, by employing a probabilistic objective function, the framework demonstrates notable resilience to measurement noise, ensuring reliable performance under uncertain conditions. A key innovation of this study lies in the simultaneous probabilistic treatment of both damage localization and damage severity estimation, enabling more precise, noise-resistant, and computationally efficient damage detection. This hybrid methodology represents a significant advancement in SHM, offering a robust and practical solution for real-world structural assessment and maintenance applications.
Keywords
Bayesian fusion
Damage detection
Modify teaching learning-based
Multi-damage detection
Structural health monitoring
Subjects
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