Within the last decade, structural identification(St-Id) has enjoyed increasing significance as a core and enabling concept for structural health monitoring (SHM), performance-based engineering (PBE), and asset management (AM) ? three paradigms that are now recognized by the US Congress, Federal Government Agencies and the ASCE as critical for improving infrastructure performance. The goal of St- Id is to infer un-measurable attributes of structural systems (e.g. boundary and continuity conditions, material stiffness, damage, etc) through the correlation of mathematical models and experimental response data. The underlying assumption is that there is a sufficient correlation between the measurable responses of the structure (e.g. displacement, strain, acceleration, etc.) and the desired un-measurable attributes, such that they can be inferred reliably. Although widely accepted, our present validation of this key assumption is provided nearly exclusively by anecdotal evidence and is neither comprehensive nor quantifiable (e.g. mode shapes are more sensitive than frequencies to damage). This knowledge gap both sustains a heavy reliance on user intuition and heuristics, and precludes reliable estimates of the total uncertainty (random and bias) associated with applications of St-Id.
To address this knowledge gap, the proposed research is cast as a multivariate calibration in which the ability of various St-Id approaches to reliably infer changes in desired un-measurable attributes will be examined and quantified through the use of a probability-based ?identifiability index?, Id (analogous to the reliability index). This calibration will be carried out on a physical model in the laboratory at the Drexel Intelligent Infrastructure Institute (DI3), which was designed and constructed with similitude to common slab-on-girder bridges. To enable the control needed for this calibration, the model was fabricated with the ability to simulate several common boundary conditions, damage scenarios, and sources of structural complexities (e.g. local nonlinearities) as well as the ability to be configured with multiple levels of redundancies and irregularities (such as skew). Although the primary fundamental objective of this study is to determine the ?identifiability? of physics-based response indices (e.g. modal parameters) and non-physics- based models (e.g. neural networks), a secondary fundamental objective of identifying the ability of model updating procedures to infer absolute values of un-measurable attributes will also be satisfied.
Intellectual Merit: The intellectual merit of this study lies in its aim to quantitatively establish the causal relationship between response indices/identified models and un-measurable attributes of constructed systems ? a relationship that serves as the underpinning for the St-Id process. While St-Id applications to date have provided some insight into this relationship, these insights are neither comprehensive nor quantitative in nature, which precludes their generalization. The authors believe that this knowledge gap represents a fundamental barrier to the extraction of reliable and widely applicable performance measures from constructed facilities, which hinders meaningful applications of SHM, PBE, and AM. The proposed effort will be the first attempt to rigorously investigate this relationship for constructed systems and represents a necessary first-step towards a more reliable and scientific form of St-Id.
Broader Impacts: The proposed research will engage the St-Id community at large in the first damage detection benchmark study that explicitly represents realistic aspects of constructed facilities (e.g. structural complexities) as well as realistic approaches to acquiring data (e.g. ambient monitoring and crawl-speed truck testing). The results of this research will be broadly disseminated through newsletters, conferences and journals as well as through the development of an interactive website. The integrated educational component of this study will incorporate the physical model in the DI3 laboratory into undergraduate structural analysis courses at both Drexel and Carnegie Mellon Universities (DU and CMU). The objective of this effort will be to demonstrate less-understood but realistic aspects of the structural behavior of constructed systems and provide concrete experiences that illustrate the role uncertainty plays in the analysis of real structures. Students at DU will visit the model periodically to examine the impact of structural complexities on the application of traditional structural analysis topics such as influence lines, determination of elastic deflections, etc. Students at CMU will examine similar phenomena; however, their interaction with the model will be facilitated through the interactive website.
Douglas A. Foutch, Program Director, Structural Systems and Hazard Mitigation of Structures
