Formal analysis of critical infrastructures by structural identification using constraint programming paradigm

Abstract

Structural identification of civil infrastructures, using measured modal properties, remains a promising research field with many applications in performance-based civil engineering and structural health monitoring. In particular, either computationally swift or direct methods for identifying structural models from partially described and incomplete modal parameter estimates are of foremost interest to facilitate near real-time and reliable structural performance assessment and diagnostics. This paper proposes modeling structural systems as Constraint Satisfaction Problems (CSPs) for structural identification to solve for uncertain parameters in structural models. Consistent with measurement data, modal parameter estimates are treated as truncated both in terms of the number of modes measured and the number of measured degrees of freedom relative to the analytical model, which yields a challenging nonlinear inverse eigenvalue problem. Using nonlinear constraints and parameter bounds, the Constraint Programming approach is demonstrated to be capable of properly reconstructing estimates of both uncertain structural parameters and unmeasured modal parameters for a truss model with only a limited number of measured degrees of freedom.