Damage detection using substructure identification.

摘要

As civil infrastructure ages, occupants and users are placed at risk. Due to limited funding, agencies are required to push structures past their original design lifetime. This creates an imperative for the civil engineering community to develop robust and accurate methods for monitoring the health of civil structures and ensuring public safety. This goal is realized by developing methods to detect both long-term degradation and immediate post-event health assessment. New methods are required because current practice, based on subjective time- and labor-intensive visual inspection is unable to adequately meet these needs. This requires novel research to transform the current state-of-the-art of visual inspection into a new paradigm of continuous monitoring.;Substructure identification has emerged as a promising damage detection and long-term monitoring tool for civil structures. Substructure identification starts by applying a reduced order model to a portion of the structure --- analogous to a coarse finite element model --- and then forms an estimator of the reduced order behavior using response measurements from the global structure. Its benefits are increased sensitivity to common structural damage, decentralized data processing, improved statistical performance, and others. This work develops a generalized framework for formulating substructure estimators. Moreover, it develops two important predictors of estimator performance: model function curvature and an identification error analysis. This allows the analyst to develop an improved estimator and evaluate its performance.;These theoretical developments are applied to several simulations including uncertainty propagation, damage detection, and damage localization. These simulations demonstrate that substructure identification is well-suited for chain structures. Next, a controlled substructure identification procedure is described and the performance is evaluated. An active control law is developed using non-convex constrained optimization.;Experimental verification is provided by two studies. First, a two-story, bench-scale flexible structure is identified. Then, improved identification precision is provided by passive structural control. The second study uses a 12 ft, four-story, steel structure. This structure is identified and damage, caused by releasing a story-level's boundary condition, is detected. Moreover, second-floor identification is not achieved, which is correctly predicted by the identification error analysis developed herein.;Concluding remarks are provided and avenues for future work are detailed. Specifically, an active control experiment using the 12 ft structure is proposed. Semi-active control design is discussed and substructure identification estimators for frame and bridge structures are outlined.