Accuracy of Non-Destructive Estimation of Length of Soil Nails

Author:

Wang Yonghong1,Jin Jiamin1,Zhang Qijun2,Zhang Ming3,Lin Xiwei2,Wang Xin4,Lin Peiyuan5ORCID

Affiliation:

1. School of Civil Engineering, Qingdao University of Technology, Qingdao 266520, China

2. Qingdao Yegao Construction Engineering Co., Ltd., Qingdao 266520, China

3. Sichuan Lutong Detection Technology Co., Ltd., Chengdu 610097, China

4. China Southwest Geotechnical Investigation & Design Institute Co., Ltd., Chengdu 610052, China

5. School of Civil Engineering, Sun Yat-Sen University, Zhuhai 519082, China

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.

Funder

Natural Science Foundation of Shandong Province of China

National Natural Science Foundation of China

Publisher

MDPI AG

Subject

Building and Construction,Civil and Structural Engineering,Architecture

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