Reliability of RC beams under chloride-ingress

Reliability-based assessment of the effect of climatic conditions on the corrosion of RC structures subject to chloride ingress

“…[3,4]) have been carried out in order to define the probabilistic distributions of the variables involved in the corrosion mechanism. Concerning the modeling of corrosion, some studies propose reliability models taking into account temporal variations of corrosion parameters [5,6] or the temporal and spatial variations of these parameters [7,8], while other studies investigate the coupled effect of reliability corrosion models and other degradation mechanisms such as fatigue [9] or biodeterioration [10].…”

Time-Dependent Probabilistic Capacity Assessment of a Prestressed Concrete Pile in a Spatially Varying Corrosive Marine Environment Using Detailed Finite Element Methods

“…However, for structures in marine environments, this assumption may not be acceptable as chlorides accumulate on the concrete surface over time. Assuming that C 0 increases with the square root of time, the following relationship was derived for the chloride content (Duprat 2007)…”

“…This implies that the value of C(x,t) must represent the critical chloride concentration at the cover depth required to begin corrosion, C(x,T i ). In order to enforce this condition, a value for critical concentration is probabilistically generated from a uniform distribution (Duprat 2007) assuming good quality concrete whose mean is 2 [kg/m 3 ]. The use of a uniform distribution is generally considered a good choice for critical concentration because of the parameter's heavy dependence on difficult to obtain chemical and mechanical properties within the concrete that are not readily available for sampling in the field.…”

A Bayesian network framework for statistical characterisation of model parameters from accelerated tests: application to chloride ingress into concrete

“…Tables Table 1 BN discretisation and a priori information of model parameters Table 2 Theoretical values for generating simulated chloride profiles Table 3 Description of the inspection schemes Table 4 Considered theoretical values of n D (DuraCrete, 2000b) Table 5 Studied cases with different inspection times Table 6 Theoretical values for generating simulated evidences (Bastidas-Arteaga, Bressolette, Chateauneuf, & Sánchez-Silva, 2009;Duprat, 2007) Table 7 A priori information and discretisation of model parameters Table 8 Times (in days) to take cores from slabs since the beginning of the exposure Table 9 Fitted mean values of D at each exposure time in normal and accelerated tests Table 10 Evolution of model parameters after the iterative procedure Case 2b T1=300 T2=600 T3=900 a Using different boundaries for child nodes according to (Tran et al, 2016a) …”