Bayesian Network Framework for Statistical Characterisation of Model Parameters from Accelerated Tests: Application to Chloride Ingress Into Concrete

摘要: This paper addresses the topic of long-term characterisation and probabilistic modelling chloride ingress into reinforced concrete (RC) structures. Since corrosion initiation stage may cover various decades, normal tests which simulate penetration in laboratory conditions as same natural conditions, will require significant experimental times. Hence, lifetime assessment RC structures under attack remains still a challenge. In practice this problem is solved through use accelerated speed up rate provide valuable mid-and information on process. Nevertheless, cannot be directly used for parameter statistical if equivalent times required to reach concentrations are unknown. Consequently, study proposes novel iterative approach based Bayesian network updating estimate model parameters from data obtained conditions. The Network structure first tested with numerical evidences. Thereafter, complete proposed methodology verified results real measurements. indicate that combining significantly reduces error parameters.