Abstract
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.
Highlights
Improving living conditions and a global trend in migration of population from rural to urban centers result in increasing demand for civil infrastructure [1]
error-domain model falsification (EDMF) when compared with Bayesian model updating (BMU) and residual minimization has been shown to provide accurate identification due to its robustness to correlation assumptions and explicit estimation of model bias based on engineering heuristics [29,30,31,32]
EDMF is computationally more efficient than Bayesian model updating methodologies in an iterative data-interpretation framework, especially when grid sampling is used in combination with parallel computing
Summary
Improving living conditions and a global trend in migration of population from rural to urban centers result in increasing demand for civil infrastructure [1]. Zhang et al [35] developed a data-interpretation tool that employs BMU with the goal of assisting asset managers Except from these studies, most present day research in structural identification has focused on damage detection within a sequential framework [36]. No research is available that evaluates the usefulness of data-interpretation methodologies such as BMU and residual minimization within iterative frameworks to assist asset managers faced with real-world challenges. Adaptive-sampling strategies such as radial-basis functions [54] and probabilistic global search optimization [55] have been implemented to improve sampling efficiency for EDMF While use of these search methods decreases computational cost, their efficiency for use in an iterative framework for data-interpretation has not been studied. Comparisons have been made using two full-scale bridge case studies to evaluate applicability of these data-interpretation methodologies outside of well-controlled laboratory environments
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