Statistical Damage Prognosis for In-Service Civil Structures against Hazards: Formulations and Applications

Abstract

This paper proposes an expectation-maximization (EM) algorithm embedded statistical damage prognosis paradigm for in-service civil structures against natural hazards. Being within the scope of structural health monitoring, damage prognosis examines the future safety performance of existing structures given relevant damage diagnosis results that reveal the current health condition of the structures under investigation. The damage diagnosis results, which constitute a prerequisite for a reasonably accurate damage prognosis, may in reality turn out to be incomplete owing to various on-site or off-site operation issues. Instead of working on measures to help preclude any data missingness event, this study focuses on exploring an innovative approach, namely for the purpose of damage prognosis striving to make the most of the damage diagnosis results that have become incomplete. The proposed EM algorithm embedded damage prognosis paradigm comprises two sets of procedures, i.e., prognosis validation and prognosis implementation, and each set of the procedures takes into account both the time invariant and time variant damage prognoses. The paradigm is first illustrated by using some meticulously constructed generic performance functions and then applied to some typical situations where existing civil structures are subjected to natural hazards.