Accuracy of Non-Destructive Estimation of Length of Soil Nails
Author(s): |
Yonghong Wang
Jiamin Jin Qijun Zhang Ming Zhang Xiwei Lin Xin Wang Peiyuan Lin |
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Medium: | journal article |
Language(s): | English |
Published in: | Buildings, 28 June 2023, n. 7, v. 13 |
Page(s): | 1699 |
DOI: | 10.3390/buildings13071699 |
Abstract: |
The effective length of soil nails is one of the critical parameters ensuring the reinforcing effect, and its accurate estimation is of great significance for the safety of the slope and deep foundation pit supporting projects. Traditional quality insurance methods, such as nail pullout tests, suffer from a series of drawbacks including being destructive, high cost, and time-consuming. In contrast, non-destructive testing (NDT) has been increasingly applied in various engineering fields in the past decades given its advantages of not damaging the material and easy operation. However, the current application of NDT in soil nail length measurement is relatively limited, and its accuracy and reliability are yet to be quantitatively evaluated. This paper introduces three methods for estimating soil nail length based on guided wave theory and collects 116 sets of NDT data for nail length. The accuracy of the NDT in soil nail prediction is statistically analyzed using the model bias method. The results show that those methods can accurately predict the nail length with an average error of less than 3% and a low dispersion of 12%. The accuracy of the NDT methods is not affected by the hammer type or estimation method. Furthermore, this paper proposes a model calibration to the original NDT method, which improves the model’s average accuracy by 3% and reduces dispersion by 4% without increasing computational complexity. Finally, the probability distributions of the model biases are characterized. This study can provide practical tools for fast estimation of in situ nail length, which is of high significance to supporting slopes and deep foundation pits. |
Copyright: | © 2023 by the authors; licensee MDPI, Basel, Switzerland. |
License: | This creative work has been published under the Creative Commons Attribution 4.0 International (CC-BY 4.0) license which allows copying, and redistribution as well as adaptation of the original work provided appropriate credit is given to the original author and the conditions of the license are met. |
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data sheet - Reference-ID
10737071 - Published on:
03/09/2023 - Last updated on:
14/09/2023