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Sensors 2017, 17(11), 2443; https://doi.org/10.3390/s17112443

Towards Intelligent Interpretation of Low Strain Pile Integrity Testing Results Using Machine Learning Techniques

1
Anhui and Huaihe River Institute of Hydraulic Research, No. 771 Zhihuai Road, Bengbu 233000, China
2
GE Global Research Center, Niskayuna, New York, NY 12309, USA
*
Author to whom correspondence should be addressed.
Received: 4 August 2017 / Revised: 10 October 2017 / Accepted: 13 October 2017 / Published: 25 October 2017
(This article belongs to the Special Issue Intelligent Sensing Technologies for Nondestructive Evaluation)
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Abstract

Low strain pile integrity testing (LSPIT), due to its simplicity and low cost, is one of the most popular NDE methods used in pile foundation construction. While performing LSPIT in the field is generally quite simple and quick, determining the integrity of the test piles by analyzing and interpreting the test signals (reflectograms) is still a manual process performed by experienced experts only. For foundation construction sites where the number of piles to be tested is large, it may take days before the expert can complete interpreting all of the piles and delivering the integrity assessment report. Techniques that can automate test signal interpretation, thus shortening the LSPIT’s turnaround time, are of great business value and are in great need. Motivated by this need, in this paper, we develop a computer-aided reflectogram interpretation (CARI) methodology that can interpret a large number of LSPIT signals quickly and consistently. The methodology, built on advanced signal processing and machine learning technologies, can be used to assist the experts in performing both qualitative and quantitative interpretation of LSPIT signals. Specifically, the methodology can ease experts’ interpretation burden by screening all test piles quickly and identifying a small number of suspected piles for experts to perform manual, in-depth interpretation. We demonstrate the methodology’s effectiveness using the LSPIT signals collected from a number of real-world pile construction sites. The proposed methodology can potentially enhance LSPIT and make it even more efficient and effective in quality control of deep foundation construction. View Full-Text
Keywords: deep foundation; defect detection; extreme learning machine; neural network; non-destructive evaluation; pile integrity testing; wavelet decomposition deep foundation; defect detection; extreme learning machine; neural network; non-destructive evaluation; pile integrity testing; wavelet decomposition
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Cui, D.-M.; Yan, W.; Wang, X.-Q.; Lu, L.-M. Towards Intelligent Interpretation of Low Strain Pile Integrity Testing Results Using Machine Learning Techniques. Sensors 2017, 17, 2443.

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