A main reason behind reinforced concrete structural deterioration is chlorideinduced corrosion. Once the critical chloride concentration is exceeded at the rebar level, the structure becomes susceptible to corrosion initiation. Corrosion propagates progressively, degrades the resistance capacity of the structure and decreases the design safety margin. To mitigate this risk, a stochastic sequential data assimilation technique based on chloride concentration measurements and the Polynomial Chaos Kalman Filter (PCKF) is presented. The Power of PCKF lies in its sampling free scheme and polynomial structure to represent uncertainty. In modeling chloride ingress mechanism, different independent sources of uncertainty should be incorporated in the system including, mathematical model simplification errors, parametric errors, boundary condition errors, and time independent sensors errors. In such circumstances, the curse of dimensionality hinders the efficiency and the applicability of PCKF, due to the exponential growth of the required bases to account for the added uncertainties. This study presents a practical framework to maintain an acceptable accuracy of PCKF without scarifying the computational efficiency of the filter. A one dimensional synthetic numerical example is presented to verify the efficiency of the proposed implementation scheme.
The polynomial chaos Kalman filter (PCKF) has been gaining popularity as a computationally efficient and robust alternative to sampling methods in sequential data assimilation settings. The PCKF's sampling free scheme and attractive structure to represent non-Gaussian uncertainties makes it a promising approach for data filtering techniques in nonlinear and non-Gaussian frameworks. However, the accuracy of PCKF is dependent on the dimension and order of the polynomial chaos expansion used to represent all sources of uncertainty in the system. Thus, when independent sources of errors, like process noise and time independent sensors' errors are incorporated in the system, the curse of dimensionality hinders the efficiency and the applicability of PCKF. This study sheds light on this issue and presents a practical framework to maintain an acceptable accuracy of PCKF without scarifying the computational efficiency of the filter. The robustness and efficiency of the presented implementation is demonstrated on 3 typical numerical examples to illustrate its ability to achieve considerable accuracy at a low computational tax.
Recently, there has been a growing interest in identifying suitable routes for the disposal of pharmaceutical wastes. This study investigates the potential of matrix materials composed of recycled polyethylene/polypropylene reclaimed from municipal solid wastes at immobilizing pharmaceutical solid wastes. Diclofenac (DF) drug product was embedded in boards of recycled plastic material, and leaching in water was assessed at various temperatures. DF concentrations were determined by high-performance liquid chromatography and revealed a maximum leachable fraction of 4% under accelerated conditions of 70°C, and less than 0.3% following 39 days of exposure at 20°C. The Ensemble Kalman Filter was employed to characterize the leaching behavior of DF. The filter verified the occurrence of leaching through diffusion, and was successful in predicting the leaching behavior of DF at 50°C and 70°C.
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