Cui De-Mi, Yan Weizhong, Wang Xiao-Quan, Lu Lie-Min
Anhui and Huaihe River Institute of Hydraulic Research, No. 771 Zhihuai Road, Bengbu 233000, China.
GE Global Research Center, Niskayuna, New York, NY 12309, USA.
Sensors (Basel). 2017 Oct 25;17(11):2443. doi: 10.3390/s17112443.
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.
低应变桩身完整性检测（LSPIT）因其操作简单且成本低廉，是桩基施工中最常用的无损检测方法之一。虽然在现场进行低应变桩身完整性检测通常相当简单快捷，但通过分析和解读测试信号（反射波曲线）来确定受检桩的完整性仍然只是一个由经验丰富的专家执行的人工过程。对于待检测桩数量众多的基础施工现场，专家可能需要数天时间才能完成对所有桩的解读并出具完整性评估报告。能够自动解读测试信号、从而缩短低应变桩身完整性检测周转时间的技术具有巨大的商业价值，且需求迫切。受此需求推动，本文开发了一种计算机辅助反射波曲线解读（CARI）方法，该方法能够快速且一致地解读大量低应变桩身完整性检测信号。该方法基于先进的信号处理和机器学习技术构建，可用于协助专家对低应变桩身完整性检测信号进行定性和定量解读。具体而言，该方法可通过快速筛选所有受检桩并识别出少量可疑桩，以便专家进行人工深入解读，从而减轻专家的解读负担。我们使用从多个实际桩基础施工现场收集的低应变桩身完整性检测信号来证明该方法的有效性。所提出的方法有可能改进低应变桩身完整性检测，并使其在深基础施工质量控制中更加高效。
