Measurement plan targeting the accuracy of calibrated chloride ingress model for concrete structures in marine environment

Surface chloride concentration (CS) and chloride diffusion coefficient (DCl) are key parameters for durability assessment of concrete structures in marine environment; they are time-varying and highly dependent on the exposure condition. To reasonably model their behaviors at a specific location, durability measurement data are often needed to calibrate the apparent chloride ingress model based on Fick's second law. In view of the significant variability of measurements and the bias of chloride ingress model, it remains unaddressed how to formulate a measurement plan to make the calibrated model achieve the required accuracy. This paper first establishes the probabilistic time-dependent models of CS and DCl with both sample variance and model bias considered, and then introduces the Bayesian method to update the two models using measurement data. By assuming realistic models of CS and DCl and comparing them with updated ones, the effectiveness of Bayesian updating method is demonstrated, and the key factors affecting the updated model accuracy are discussed, including prior estimate of parameters, model bias and measuring times. On this basis, a determination method of measurement plan targeting the calibrated model accuracy is proposed, which works for both Bayesian updating and linear fitting for model calibration. And finally numerical examples are presented to show the validity of the proposed method. The sample size obtained by the proposed method is exact for linear fitting and slightly more than required for Bayesian updating.

Automated summary

This paper proposes a method to determine a measurement plan for durability assessment of concrete structures, by calibrating the models using Bayesian updating and linear fitting in order to achieve the required accuracy. The paper establishes probabilistic time-dependent models for surface chloride concentration and chloride diffusion coefficient, and discusses the key factors affecting the accuracy of the models.