Building structural health monitoring: a tool for building collapse mitigation
Author(s): |
S. O. Ongbali
S. A. Afolalu S. Oladipupo S. Akra K. A. Bello |
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Medium: | journal article |
Language(s): | English |
Published in: | IOP Conference Series: Materials Science and Engineering, 1 March 2021, n. 1, v. 1036 |
Page(s): | 012028 |
DOI: | 10.1088/1757-899x/1036/1/012028 |
Abstract: |
Building collapse occurs recurrently worldwide on account of a myriad of variables associated with man or nature whose consequences are loss of life and properties that are sometimes incalculable. This paper aims to appraise the current advances in the topic area to identify and provide a holistic document on building structural health monitoring approaches that are fragmented in the literature to enable practitioners, stakeholders, and inspectors gain insight into the modern and cost-effective methods of building structural health monitoring to alleviate the incessant problem. It appeared that most of the approaches are interwoven and dependent on one another to obtain reliable information on building structural integrity. Apart from the traditional methods of visual inspection and non-destructive assessment of building health monitoring, it appeared the predominant and recent approaches to building health monitoring are analytics or statistics-based, statistics-sensor based, fiber optic framework, fiber optic-sensor network, and sensor-based. Arguments in the literature suggest that the analytics or statistics approach may not provide accurate information on account of the outlier and data corruption. The fiber optic approach is expensive and time consuming compared with the fiber-sensor network method. Finally, the sensor-based building health monitoring is less expensive over other approaches and provides reliable and accurate information on building integrity. Hence, we suggest future research direction should focus on the development and integration of fiber optic-sensor network and sensor-based methods of building health monitoring to improve the accuracy of the methods. |
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data sheet - Reference-ID
10674752 - Published on:
18/06/2022 - Last updated on:
18/06/2022