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

• Published in: AI in civil engineering Vol. 2; no. 1; p. 7
• Main Authors: Luleci, Furkan, Necati Catbas, F.
• Format: Journal Article
• Language: English
• Published: Singapore Springer Nature Singapore 01.12.2023; Springer Nature B.V
• Subjects: Artificial Intelligence; Civil Engineering; Datasets; Engineering; Original; Original Article; Generative adversarial networks; Structural state translation; Structural health monitoring; Population-based structural health monitoring; Domain generalization
• ISSN: 2730-5392; 2097-0943; 2730-5392
• DOI: 10.1007/s43503-023-00016-0

Implementing 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 from Bridge #1 ; the bridges have two different conditions: State-H and State-D . Then, the model is used to generalize and transfer the knowledge on Bridge #1 to Bridge #2 . In doing so, DGCG translates the state of Bridge #2 to the state that the model has learned after being trained. In one scenario, Bridge #2’s State-H is translated to State-D ; in another scenario, Bridge #2’s State-D is translated to State-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.