Condition transfer between prestressed bridges using structural state translation for structural health monitoring

Author:

Luleci Furkan,Necati Catbas F.ORCID

Abstract

AbstractImplementing Structural Health Monitoring (SHM) systems with extensive sensing layouts on all civil structures is obviously expensive and unfeasible. Thus, estimating the state (condition) of dissimilar civil structures based on the information collected from other structures is regarded as a useful and essential way. For this purpose, Structural State Translation (SST) has been recently proposed to predict the response data of civil structures based on the information acquired from a dissimilar structure. This study uses the SST methodology to translate the state of one bridge (Bridge #1)to a new state based on the knowledge acquired from a structurally dissimilar bridge (Bridge #2). Specifically, the Domain-Generalized Cycle-Generative (DGCG) model is trained in the Domain Generalization learning approach on two distinct data domains obtained fromBridge #1; the bridges have two different conditions:State-HandState-D. Then, the model is used to generalize and transfer the knowledge onBridge #1toBridge #2. In doing so, DGCG translates the state ofBridge #2to the state that the model has learned after being trained. In one scenario,Bridge #2’s State-His translated toState-D; in another scenario,Bridge #2’s State-Dis translated toState-H. The translated bridge states are then compared with the real ones via modal identifiers and mean magnitude-squared coherence (MMSC), showing that the translated states are remarkably similar to the real ones. For instance, the modes of the translated and real bridge states are similar, with the maximum frequency difference of 1.12% and the minimum correlation of 0.923 in Modal Assurance Criterion values, as well as the minimum of 0.947 in Average MMSC values. In conclusion, this study demonstrates that SST is a promising methodology for research with data scarcity and population-based structural health monitoring (PBSHM). In addition, a critical discussion about the methodology adopted in this study is also offered to address some related concerns.

Funder

Division of Civil, Mechanical and Manufacturing Innovation

National Academy of Engineering

National Aeronautics and Space Administration

Publisher

Springer Science and Business Media LLC

Reference40 articles.

1. Albuquerque, I., Monteiro, J., Darvishi, M., et al. (2019). Generalizing to unseen domains via distribution matching. arXiv

2. Avci, O., Abdeljaber, O., Kiranyaz, S., Inman, D. (2017). Structural damage detection in real time: implementation of 1D convolutional neural networks for SHM applications. In: C. Niezrecki (Ed.), Structural Health Monitoring & Damage Detection, vol 7. Conference Proceedings of the Society for Experimental Mechanics Series. Springer: Cham. https://doi.org/10.1007/978-3-319-54109-9_6

3. Ben-David, J, Blitzer, J., Crammer, K., Pereira F. (2007). Analysis of representations for domain adaptation. in NIPS 19.

4. G. Blanchard, G. Lee, C. Scott (2011) Generalizing from several related classification tasks to a new unlabeled sample. in NeurIPS

5. Catbas, F.N., Ciloglu, S.K., Hasancebi, O., Aktan, A.E. (2002). Fleet Strategies for Condition Assessment and Its Application for Re-qualification of Pennsylvania’s Aged T-beam Bridges. Paper No. 02-3890. In Proceedings of the 80th Annual Meeting of Transportation Research Board, TRB, Washington DC

Cited by 8 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3