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Published May 15, 2021 | public
Journal Article

Structural damage detection of space frame structures with semi-rigid connections

Abstract

The accurate modeling of connection joints for civil engineering structures is challenging. The conventional approaches generally assume that the joints are either pinned or rigid, while real joints are semi-rigid in practice. Most existing damage detection methods do not consider the semi-rigidity of the joints, and thus may lead to false or inaccurate identification results. Moreover, the joints are more vulnerable to damage compared to other members of the structure. However, only limited studies have focused on the detection of the joint damage, and few have studied the damage in structural members and joints simultaneously for space structures. In this regard, a two-step damage detection method for space frame structures with semi-rigid connections is developed in this study. Rotational springs with some flexibility are used to model the mechanical behavior of the joints, which enables identification of damage in the bar elements as well as in joints. The first step determines the existence and location of the damaged elements using the l1 regularization technique. In the second step, the damage locations and severities are then identified within the suspicious damaged elements only that are detected in the first step. This process progressively reduces the number of unknown parameters and thus alleviates the instability and divergence problems to improve the damage identification accuracy. An experimental 3D space structure is utilized to demonstrate the effectiveness of the proposed damage detection method. The results show that damage in structural elements and joints can be correctly located and quantified progressively. The numerical investigation shows that the sensitivity of the modal properties with respect to joint damage is much smaller than to damage in bar elements.

Additional Information

© 2021 Elsevier Ltd. Received 2 October 2020, Revised 14 January 2021, Accepted 3 February 2021, Available online 3 March 2021. This study was supported by the Research Grants Council-General Research Fund (RGC-GRF Project No. 15201920) and Key-Area R&D Program of Guangdong Province (Project No. 2019B111106001). CRediT authorship contribution statement: Rongrong Hou: Software, Methodology, Writing - original draft. James L. Beck: Writing - review & editing. Xiaoqing Zhou: Validation. Yong Xia: Conceptualization, Resources, Writing - review & editing, Funding acquisition. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional details

Created:
August 22, 2023
Modified:
October 23, 2023