Onset detection of ultrasonic signals for the testing of concrete foundation piles by coupled continuous wavelet transform and machine learning algorithms. (January 2020)

• Record Type: Journal Article
• Authors: Zhang, Mengxi; Li, Mingchao; Zhang, Jinrui; Liu, Le; Li, Heng
• Is Part Of: Advanced engineering informatics. Volume 43(2020)
• Publication Date: 2020-01
• Subjects: Concrete pile foundation -- Ultrasonic signals -- Onset detection -- Continuous wavelet transform -- Machine learning -- Error analysis; Computer-aided engineering -- Periodicals; Engineering -- Data processing -- Periodicals; 620.00285
• DOI: 10.1016/j.aei.2020.101034
• Languages: English
• ISSNs: 1474-0346

Abstract

Abstract: The construction of ultra-high-rise and long-span structures requires higher requirements for the integrity detection of piles. The acoustic signal detection has been verified an efficient and accurate nondestructive testing method. In fact, the integrity of piles is closely related to the onset time of signals. The accuracy of onset time directly affects the integrity evaluation of a pile. To achieve high-precision onset detection, continuous wavelet transform (CWT) preprocessing and machine learning algorithms were integrated into the software of high-sampling rate testing equipment. The distortion of waveforms, which could interfere with the accuracy of detection, was eliminated by CWT preprocessing. To make full use of the collected waveform data, three types of machine learning algorithms were used for classifying whether the data points are ambient or ultrasonic signals. The models involve a commonly used classifier (ELM), an individual classification tree model (DTC), an ensemble tree model (RFC) and a deep learning model (DBN). The classification accuracy of the ambient and ultrasonic signals of these models was compared by 5-fold validation. Results indicate that RFC performance is better than DBN and DTC after training. It is more suitable for the classification of points in waveforms. Then, a detection method of onset time based on classification results was therefore proposed to minimize the interference of classification errors on detection. In addition to the three data mining methods, the autocorrelation function method was selected as the control method to compare the proposed data mining based methods with the traditional one. The accuracy and error analysis of 300 waveforms proved the feasibility and stability of the proposed method. The RFC-based detection method is recommended because of the highest accuracy, lowest errors, and the most favorable error distribution among four onset detection methods. Successful applications demonstrate that it could provide a new way for ensuring the accurate testing of pile foundation integrity.