Health monitoring of a steel moment-resisting frame subjected to seismic loads. (January 2018)

• Record Type: Journal Article
• Authors: Mosallam, Ayman; Zirakian, Tadeh; Abdelaal, Ayman; Bayraktar, Alemdar
• Is Part Of: Journal of constructional steel research. Volume 140(2018)
• Page Start: 34
• Page End: 46
• Publication Date: 2018-01
• Subjects: Health monitoring -- Damage identification -- Stiffness degradation -- Modal parameters -- Modal Assurance Criterion -- Steel moment-resisting frame; Steel, Structural -- Periodicals; Building, Iron and steel -- Periodicals; Acier de construction -- Périodiques; Construction métallique -- Périodiques; 624.1821
• DOI: 10.1016/j.jcsr.2017.10.023
• Languages: English
• ISSNs: 0143-974X

Abstract

Abstract: Structural health monitoring (SHM) offers the potential to evaluate the safety and integrity of the civil infrastructure. By obtaining accurate information about the condition of the structure, appropriate preventive measures can be taken to prolong the service life and prevent the catastrophic failure of the structure. Application of effective damage detection strategies can reduce the life-cycle costs as well. Damage reduces the stiffness and modifies the modal properties of a structure. Therefore, changes in modal properties can be used to detect damage in the structure. Although extensive research has been conducted on structural diagnosis by measuring the vibrational signals of structures, more research is still needed for development of reliable and effective damage detection techniques. This paper presents a study on damage detection of a 3-story steel moment-resisting frame structure instrumented by a network of wireless sensors and cable-based accelerometers. Experimental data from shake table testing and numerical results from finite element simulation were used for damage identification through two approaches. In the first approach, the finite element model of the structure was calibrated and used to locate and quantify the elemental stiffness loss on the basis of the experimentally-identified modal parameters. Moreover, a direct search algorithm was used for minimization of an objective function representing the difference between predicted and measured dynamic parameters of the structure. In the second approach, damage identification was performed through application of the Modal Assurance Criterion (MAC) and detection of the changes between undamaged and damaged conditions. Results of this study are indicative of capability and effectiveness of both approaches in identification of damage. Highlights: Two approaches were used for damage detection of a steel moment frame structure. Shake-table test data and numerical results were used for damage identification. Wireless sensors exhibited good performance in identifying the dynamic parameters. A direct search algorithm was successfully used for identification of local damage. Damage identification was performed by employing the Modal Assurance Criterion.