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Open AccessArticle

Inverse Estimation Method of Material Randomness Using Observation

Department of Civil and Environmental Engineering, Sejong University, 209 Neungdong-ro, Gwangjin-gu, Seoul 05006, Korea
Building Materials and Construction Chemistry, Technische Universität Berlin, Gustav-Meyer-Allee 25, 13355 Berlin, Germany
Faculty of Civil Engineering and Architecture, West Pomeranian University of Technology Szczecin, Al. Piastow 50, 70-311 Szczecin, Poland
Author to whom correspondence should be addressed.
Crystals 2020, 10(6), 512;
Received: 16 April 2020 / Revised: 22 May 2020 / Accepted: 15 June 2020 / Published: 16 June 2020
This study proposes a method for inversely estimating the spatial distribution characteristic of a material’s elastic modulus using the measured value of the observation data and the distance between the measurement points. The structural factors in the structural system possess temporal and spatial randomness. One of the representative structural factors, the material’s elastic modulus, possesses temporal and spatial randomness in the stiffness of the plate structure. The structural factors with randomness are typically modeled as having a certain probability distribution (probability density function) and a probability characteristic (mean and standard deviation). However, this method does not consider spatial randomness. Even if considered, the existing method presents limitations because it does not know the randomness of the actual material. To overcome the limitations, we propose a method to numerically define the spatial randomness of the material’s elastic modulus and confirm factors such as response variability and response variance. View Full-Text
Keywords: Bayesian updating; spatial randomness; uncertainty; correlation distance; stochastic field Bayesian updating; spatial randomness; uncertainty; correlation distance; stochastic field
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Kim, D.-Y.; Sikora, P.; Araszkiewicz, K.; Chung, S.-Y. Inverse Estimation Method of Material Randomness Using Observation. Crystals 2020, 10, 512.

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