Probabilistic structural identification and condition assessment of prestressed concrete bridges based on Bayesian inference using deflection measurements

Abstract

This paper presents a model-based probabilistic structural identification that uses the deflection of prestressed concrete bridges (PSCBs) as observational information to perform Bayesian inference on the state variables associated with creep, structural rigidity, dead loads, shrinkage and prestress. The creep development is modeled as a stochastic process, and the structural rigidity is modeled as a stochastic field using the Karhunen–Loève transform. By incorporating the stochastic process/field into the inference frame, detailed information on structural states can be extracted. Considering the high dimension of the state variables, their posterior distributions are derived by the Hamiltonian Markov chain Monte Carlo (HMCMC) algorithm. As an illustrative example, two sets of deflection measurement of a case bridge are used to update the state variables in a sequential manner. Bayesian inference can calibrate the state variables, while the uncertainties associated with the state variables can be reduced. A K-means analysis can reveal the typical modes in the joint posterior distribution of the state variables, corresponding to the typical failure modes in the attributive analysis of the excessive deflection. The updated state variables are used in the probabilistic condition assessment associated with the deflection evolution.