Please use this identifier to cite or link to this item:
http://hdl.handle.net/20.500.11861/10586
Title: | Life-cycle performance modeling for sustainable and resilient structures under structural degradation: A systematic review |
Authors: | Anwar, Ghazanfar Ali Akber, Muhammad Zeshan Ahmed, Hafiz Asfandyar Hussain, Mudasir Dr. NAWAZ Mehmood Anwar, Jehanzaib Chan, Wai-Kit Lee, Hiu-Hung |
Issue Date: | 2024 |
Source: | Building, 2024, vol. 14(10), article no. 3053. |
Journal: | Buildings |
Abstract: | The performance of structures degrades during their service life due to deterioration and extreme events, compromising the social development and economic growth of structure and infrastructure systems. Buildings and bridges play a vital role in the socioeconomic development of the built environment. Hence, it is essential to understand existing tools and methodologies to efficiently model the performance of these structures during their life cycle. In this context, this paper aims to explore the existing literature on the life-cycle performance modeling, assessment, enhancement, and decision making of buildings and bridge infrastructure systems under deterioration and extreme events for a sustainable and resilient built environment. The main objectives are to (1) systematically review the existing literature on life-cycle performance modeling of buildings and bridges based on the PRISMA methodology, (2) provide a bibliometric analysis of the systematically assessed journal articles, (3) perform an analysis of the included articles based on the identified components of life-cycle performance modeling, and (4) provide a discussion on the utilized tools, techniques, methodologies, and frameworks for buildings and bridge infrastructure systems in the life-cycle context. The provided systematic literature review and subsequent discussions could provide an overview to the reader regarding the individual components and existing methodologies of life-cycle performance management under deterioration and extreme events. |
Type: | Peer Reviewed Journal Article |
URI: | http://hdl.handle.net/20.500.11861/10586 |
ISSN: | 2075-5309 |
DOI: | 10.3390/buildings14103053 |
Appears in Collections: | Applied Data Science - Publication |
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