Data-Interpretation Methodologies for Practical Asset-Management

• Published in: Journal of sensor and actuator networks Vol. 8; no. 2; p. 36
• Main Authors: Pai, Sai G. S., Reuland, Yves, Smith, Ian F. C.
• Format: Journal Article
• Language: English
• Published: MDPI AG 22.06.2019
• Subjects: Bayesian model updating; computation time; error-domain model falsification; iterative asset-management; practical applicability; probabilistic data-interpretation
• ISSN: 2224-2708; 2224-2708
• DOI: 10.3390/jsan8020036

Monitoring and interpreting structural response using structural-identification methodologies improves understanding of civil-infrastructure behavior. New sensing devices and inexpensive computation has made model-based data interpretation feasible in engineering practice. Many data-interpretation methodologies, such as Bayesian model updating and residual minimization, involve strong assumptions regarding uncertainty conditions. While much research has been conducted on the scientific development of these methodologies and some research has evaluated the applicability of underlying assumptions, little research is available on the suitability of these methodologies to satisfy practical engineering challenges. For use in practice, data-interpretation methodologies need to be able, for example, to respond to changes in a transparent manner and provide accurate model updating at minimal additional cost. This facilitates incremental and iterative increases in understanding of structural behavior as more information becomes available. In this paper, three data-interpretation methodologies, Bayesian model updating, residual minimization and error-domain model falsification, are compared based on their ability to provide robust, accurate, engineer-friendly and computationally inexpensive model updating. Comparisons are made using two full-scale case studies for which multiple scenarios are considered, including incremental acquisition of information through measurements. Evaluation of these scenarios suggests that, compared with other data-interpretation methodologies, error-domain model falsification is able to incorporate, iteratively and transparently, incremental information gain to provide accurate model updating at low additional computational cost.