Abstract
We use Bayesian logic in reproducing how a rational agent, called Ernest in the paper, analyses monitoring data and infers structural condition. The case study is Adige Bridge, a 260 m-long statically indeterminate structure with a deck supported by 12 stay cables. Bridge structural redundancy, possible relaxation losses and an as-built condition differing from design suggest that long-term load redistribution between cables can be expected. Therefore, the bridge owner installed a monitoring system, including fiberoptic sensors that allow measurement of deformation with an accuracy of a few microstrains. After 1 year of system operation, which included maintenance of the interrogation unit, the data analysis showed an apparent contraction of the cable lengths. This result is in contrast with the expected behavior. We analyze how a rational agent analyzes the observed response, and, in particular, we discuss to what extent he is prone to accept the sensor response as a result of the real mechanical behavior of the bridge versus a mere malfunction of the interrogation unit. In this analysis, we consider four psychological profiles, which vary based on their personal trust in the reliability of the instrumentation and on their knowledge of the structural behavior of the bridge. Using Bayesian logic as a tool to combine prior belief with sensor data, we explore how the extent of prior knowledge can alter the final engineering perception of the current state of the bridge and we demonstrate how the engineer’s posterior judgment is predictable with a mathematical model. Formal reproduction of the human decision-making process can have strong impact in the field of structural health monitoring, as it may enable: (1) quantification of probabilities that engineers attribute to various events based on their subjective experience (which is currently an important challenge); (2) better understanding and improvement of the decision-making process itself; (3) embedding of decision making into structural health-monitoring methods for the full benefit of the latter.
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Acknowledgments
The monitoring project presented in this paper was funded by the Department of Public Works of the Autonomous Province of Trento; the authors wish to thank specifically L. Martorano, S. Rivis, A. Bertò, M. Pravda, P. Nicolussi Paolaz and E. Pedrotti. Although based on real facts and information, the story reported in the introduction is mostly fictionalized, simplified and does not necessarily reflect the actual timeline and events that occurred. The character of Ernest is inspired by Paolo Esposito, who worked at the University of Trento between 2010 and 2012, and who is greatly acknowledged by the authors. However, facts and thoughts credited to the character do not necessarily reflect Esposito’s real life or thoughts. Special thanks are also addressed to Dr. Nicola Tondini, University of Trento, who plays the role of Ernest in Fig. 5. Finally, the authors wish to thank Daniele Inaudi, Smartec SA, Daniele Posenato, Smartec SA and Saulo Maestranzi, formerly with the University of Trento.
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Cappello, C., Zonta, D., Pozzi, M. et al. Impact of prior perception on bridge health diagnosis. J Civil Struct Health Monit 5, 509–525 (2015). https://doi.org/10.1007/s13349-015-0120-0
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DOI: https://doi.org/10.1007/s13349-015-0120-0