Key Takeaways & Executive Findings
- •• A novel damage detection method integrates model reduction and response reconstruction to address challenges in large-scale civil structures. • The two-step model updating framework (substructure-level localization and element-level detection) enhances damage localization and quantification accuracy. • The improved sensitivity algorithm incorporates reconstructed responses, reducing sensor requirements while maintaining detection reliability. • Numerical validation on a truss bridge demonstrates improved efficiency and accuracy over traditional methods, offering a practical solution for structural health monitoring.
Abstract
Structural damage detection is hard to conduct in large-scale civil structures due to enormous structural data and insufficient damage features. To improve this situation, a damage detection method based on model reduction and response reconstruction is presented. Based on the framework of two-step model updating including substructure-level localization and element-level detection, the response reconstruction strategy with an improved sensitivity algorithm is presented to conveniently complement modal information and promote the reliability of model updating. In the iteration process, the reconstructed response is involved in the sensitivity algorithm as a reconstruction-related item. Besides, model reduction is applied to reduce computational degrees of freedom (DOFs) in each detection step. A numerical truss bridge is modelled to vindicate the effectiveness and efficiency of the method. The results showed that the presented method reduces the requirement for installed sensors while improving efficiency and ensuring accuracy of damage detection compared to traditional methods.
1. Introduction
The performance of a civil structure declines with time in its operational stage, which significantly affects the operational durability and safety. If structural damage is left to develop, it is likely to lead to greater loss. Therefore, the structure status must be continuously monitored to ensure operational safety [1, 2]. Finite element (FE) model updating, which detects damage by minimizing the model discrepancy, has attracted enormous interest of research during decades owing to its capability to promptly determine damage conditions.
The sensitivity-based method is an effective algorithm to solve the minimization problem, which is popularly used to conduct damage detection based on the FE model. Researchers have developed several types of sensitivity-based methods [3 −9]. The first type uses modal parameters to construct the model discrepancy. At this point, YANG et al [10] established a modelling framework based on the elemental sensitivity of modal strain energy to identify damage in functionally graded beams; LIU et al [11] presented a strategy to locate and quantify damage corresponding to defects of stiffness and damping through model updating based on sensitivity analysis. The second type of sensitivity-based approaches refers to response sensitivity, in which the sensitivity information is sourced from the derivative of the collected response data or its variant with respect to the update parameters. For example, LAW et al [12] proposed to use wavelet packet as the damage feature and deduced its sensitivity with respect to updating parameters that could approach damage conditions rapidly with satisfactory resolution. LU et al [13] presented to detect damage in time domain with response sensitivity and iteratively calculated the updating matrix by solving the dynamic equation of first-order derivative with respect to updating factors. The study also demonstrated that acceleration signal was more sensitive to damage than displacement. Compared with those based on modal parameters, response-sensitivity-based methods provide more sensitive information for damage, and the amount of effective data is only subjected to measurement time, although the requirement of known excitation is a strict limitation for many cases of damage detection. Nonetheless, for that demanding high detection precision, response-sensitivity-based method might be a better choice, while the required excitation could be obtained by analysis of artificial loading such as vehicle-structure coupling [14, 15].
Applying response-sensitivity-based methods in complex civil structures would be difficult for two primary reasons. First, structural damage cannot be predicted in advance. Due to the limitations of economy and structural complexity, installed sensors are usually sparsely distributed compared with the large structural dimension so that cannot provide enough damage features to push damage parameters to be updated in the correct direction. The second aspect is mainly affected by a large damage-searching dimension and considerable computational DOFs involved in the process of damage detection would require massive calculation resource, while the actual damaged region and measurement-required DOFs are in small numbers compared to the
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ZOU Yun-feng, SU Yun-hui, LU Xuan-dong, HE Xu-hui, CAI Chen-zhi (2025). Structural damage detection based on model reduction and response reconstruction. Journal of Central South University. https://doi.org/10.1007/s11771-025-6105-1
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Frequently Asked Questions
What is the main challenge addressed by this damage detection method?
The method addresses the challenges of large-scale civil structures where enormous structural data and insufficient damage features make damage detection difficult, particularly with sparse sensor installations.
How does the proposed method improve damage detection efficiency?
It integrates model reduction to reduce computational degrees of freedom and response reconstruction to complement modal information, thereby improving efficiency and reducing sensor requirements while maintaining accuracy.
What is the two-step model updating framework?
The framework consists of substructure-level localization followed by element-level detection, which helps to narrow down the damage location and quantify the damage severity more accurately.
What are the key advantages of the response-sensitivity-based approach?
Response-sensitivity-based methods provide more sensitive damage information compared to modal-parameter-based methods, and the amount of effective data is only limited by measurement time, though they require known excitation.
How was the proposed method validated?
The method was validated using a numerical truss bridge model, demonstrating that it reduces sensor requirements while improving efficiency and ensuring accuracy compared to traditional methods.
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