Chloride-induced corrosion and load induced concrete cracking affect the serviceability and safety of reinforced concrete (RC) structures. Once these phenomena occur simultaneously, the prediction of RC structures' lifetimes becomes a major challenge. The objective of this paper is to propose a methodology to evaluate the effect of loading and cracking on the mechanism of chloride ion penetration in concrete. The proposed methodology will be based on Bayesian networks. Bayesian networks are useful to update the lifetime assessment based on experimental data as well as to characterize the uncertainties of the input parameters of a chlorination model including a chloride diffusion acceleration factor. The proposed methodology is illustrated with experimental data coming from tests on RC beams subjected to static and cyclic loading before being in contact with a solution containing chloride ions. The characterized parameters are then used to evaluate the effect of these two loading conditions (static and cyclic) on the corrosion initiation time and the corrosion initiation probability. The results obtained indicate that the proposed methodology is capable of integrating loading and chlorination test data for the determination of the probabilistic parameters of a model in a comprehensive way.