Salt damage is frequently found in coastal structures and is known to be one of the leading causes of concrete degradation. To ensure a concrete structure with adequate resistance against such a threat, chloride ion diffusion in concrete needs to be thoroughly investigated. Although chloride diffusion in concrete has attracted significant attention from researchers, the method to determine the chloride ion concentration remains the subject of debate. In this study, a series of tests was first conducted on cement mortars less than 168 d old. A model based on statistical learning theory and adopting the least-squares support vector machine was developed. Comparison with the experimental results revealed that the model could provide an accurate prediction of chloride ion concentration.Moreover, the proposed model, unlike the conventional Fick's law approach, is applicable to cement mortars of differing compositions and ages, thus contributing to its applicability to practical engineering. The test variables included age, depth (measured position), dimensions of diffusion and the presence of a reinforcement bar. All the specimens were mixed with fresh water and cured in salt water.

To further capture the influences of uncertain factors on river bridge safety evaluation, a probabilistic approach is adopted. Because this is a systematic and nonlinear problem, MPP-based reliability analyses are not suitable. A sampling approach such as a Monte Carlo simulation (MCS) or importance sampling is often adopted. To enhance the efficiency of the sampling approach, this study utilizes Bayesian least squares support vector machines to construct a response surface followed by an MCS, providing a more precise safety index. Although there are several factors impacting the flood-resistant reliability of a bridge, previous experiences and studies show that the reliability of the bridge itself plays a key role. Thus, the goal of this study is to analyze the system reliability of a selected bridge that includes five limit states. The random variables considered here include the water surface elevation, water velocity, local scour depth, soil property and wind load. Because the first three variables are deeply affected by river hydraulics, a probabilistic HEC-RAS-based simulation is performed to capture the uncertainties in those random variables. The accuracy and variation of our solutions are confirmed by a direct MCS to ensure the applicability of the proposed approach. The results of a numerical example indicate that the proposed approach can efficiently provide an accurate bridge safety evaluation and maintain satisfactory variation.

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