---

Chloride ion ingress in concrete is the main reason of concrete corrosion. In real world both uncertainty and stochasticity are

main attributes of almost all measurements including testing and modeling of chloride content profile in concrete. Regarding

these facts new models should be able to represent at least some of the uncertainties in the predictions. In this paper after

inspiration from classical physics related to diffusion and random walk concepts a stochastic partial differential equation (SPDE)

of diffusion is introduced to show a more realistic modeling/calibration scheme for construction of stochastic chloride content

profile in concrete. Diffusion SPDE provides a consistent quantitative way of relating uncertainty in inputs to uncertainty in

outputs. Although it is possible to run sensitivity analysis to get some statistical results from deterministic models but the nature

of diffusion is inherently stochastic. Brownian motion process (Wiener process) is used in SPDE to simulate the random nature

of the diffusion in heterogeneous media or random fields like concrete. The proposed method can be used to calibrate/model the

chloride ion profile in concrete by only some limited data for a given depth. Then the stochastic chloride ion diffusion can be

simulated by langevin equation. Results of the method are compared with data from some references and all show good

agreements.

---

Concrete bridge deck damage detection by measurement and monitoring variables related to vibration signatures is one of the main tasks of any Bridge Health Monitoring System (BHMS). Generally damage puts some detectable/discoverable signs in the parameters of bridge vibration behavior. However, differences between frequency and mode shape before and after damage are not remarkable as vibration signatures. Therefore most of the introduced methods of damage detection cannot be used practically. Among many methods it seems that models based on artificial intelligence which apply soft computing methods are more attractive for specific structures. In this paper an Adaptive Neuro-Fuzzy Inference System (ANFIS) is used to detect the damage location in a concrete bridge deck modeled by finite element method. Some damage scenarios are simulated in different locations of the deck and accelerations as representatives of response at some specific points are calculated. Excitement is done by applying an impact load at the center of the deck. In the proposed ANFIS damage detection model accelerations are inputs and location of the damage is output. Trained model by simulated data can show the location of the damage very well with a few training data and scenarios which are not used in training stage. This system is capable to be included in real-time damage detection systems as well.