Study on a Method for Evaluating Accuracy and Reproducibility Based on Uncertainty Theory

DOI

10.2991/978-94-6463-564-5_10How to use a DOI?

Keywords

uncertainty; accuracy; reproducibility

Abstract

For engineering experiments such as marine trials where there is no explicit error function or it is difficult to establish a mathematical model for reproducibility, a computational method is proposed to assess their accuracy and reproducibility. First, calculate the combined uncertainty of various physical quantities in the experiment based on the experimental conditions. Utilize theoretical analysis to calculate the uncertainty of the theoretical values. Then, through repeated measurements during the experiment, obtain the mean and standard deviation of the measured values, thereby determining the uncertainty of the measured values. Then, based on probability methods such as the central limit theorem, determine the accuracy and reproducibility of the experiment. Finally, the aforementioned method was applied to a suspended tunnel experiment, achieving the assessment of accuracy and reproducibility for the trial.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Yang Liu
AU  - Chan Huang
AU  - Wei Lin
PY  - 2024
DA  - 2024/10/31
TI  - Study on a Method for Evaluating Accuracy and Reproducibility Based on Uncertainty Theory
BT  - Proceedings of the 2024 International Conference on Civil Engineering Structures and Concrete Materials (CESCM 2024)
PB  - Atlantis Press
SP  - 87
EP  - 95
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-564-5_10
DO  - 10.2991/978-94-6463-564-5_10
ID  - Liu2024
ER  -