Stochastic and recursive estimation of the hygro-thermo-chemical-mechanical parameters of concrete through Monte Carlo analysis and extended Kalman filter

Abstract

Hygro-thermo-chemical-mechanical models, used to determine the variations over time of temperature, relative humidity and shrinkage induced deformations in concrete components, are characterised by the presence of a large number of input parameters. Some of these parameters can be evaluated on the basis of the concrete mix specifications or from literature data, while the others present a large variability and, in some cases, do not have a precise physical meaning and, for this reason, require the implementation of proper identification strategies. The experimental work involved for this characterisation can be time-consuming and costly because based on the long-term monitoring of the time evolution of the field quantities in specific positions within concrete components. The aim of this paper is to propose and validate recursive identification strategies that exploit, in a step by step fashion, the information coming from the experimentation for the identification of the model input parameters. The influence of different exposure conditions and of different concrete thicknesses are investigated and, for each scenario considered, the expected identification error of each parameter is estimated, within a stochastic context implemented through Monte Carlo analyses and Kalman Filter, as a function of the monitored time.