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

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.

[1]  Yashon O. Ouma,et al.  Wavelet-morphology based detection of incipient linear cracks in asphalt pavements from RGB camera imagery and classification using circular Radon transform , 2016, Adv. Eng. Informatics.

[2]  Mingchao Li,et al.  Prediction of Ultimate Axial Capacity of Square Concrete-Filled Steel Tubular Short Columns Using a Hybrid Intelligent Algorithm , 2019, Applied Sciences.

[3]  Xing Zhao,et al.  Spectral–Spatial Classification of Hyperspectral Data Based on Deep Belief Network , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[4]  Hsiao-Chuan Wang,et al.  Automatic estimation of voice onset time for word-initial stops by applying random forest to onset detection. , 2011, The Journal of the Acoustical Society of America.

[5]  Yifan Zhao,et al.  Comparison of alternatives to amplitude thresholding for onset detection of acoustic emission signals , 2017 .

[6]  Theodora Chaspari,et al.  Automated ergonomic risk monitoring using body-mounted sensors and machine learning , 2018, Adv. Eng. Informatics.

[7]  Frédéric Bosché,et al.  Terrestrial laser scanning and continuous wavelet transform for controlling surface flatness in construction - A first investigation , 2015, Adv. Eng. Informatics.

[8]  Marco Mattavelli,et al.  Music Onset Detection Based on Resonator Time Frequency Image , 2008, IEEE Transactions on Audio, Speech, and Language Processing.

[9]  Ming Sun,et al.  External Sulfate Attack to Reinforced Concrete Under Drying-Wetting Cycles and Loading Condition: Numerical Simulation and Experimental Validation by Ultrasonic Array Method , 2017 .

[10]  Karen Margaret Holford,et al.  Towards improved damage location using acoustic emission , 2012 .

[11]  Gangbing Song,et al.  Damage detection of concrete piles subject to typical damage types based on stress wave measurement using embedded smart aggregates transducers , 2016 .

[12]  Xue Wang,et al.  Damage assessment in structural steel subjected to tensile load using nonlinear and linear ultrasonic techniques , 2017, Applied Acoustics.

[13]  Andy Liaw,et al.  Classification and Regression by randomForest , 2007 .

[14]  Heng Li,et al.  Data mining approach to construction productivity prediction for cutter suction dredgers , 2019, Automation in Construction.

[15]  Shaoze Yan,et al.  A time-frequency analysis algorithm for ultrasonic waves generating from a debonding defect by using empirical wavelet transform , 2018 .

[16]  Hyun Woo Jeon,et al.  Estimation of the degree of hydration of concrete through automated machine learning based microstructure analysis - A study on effect of image magnification , 2019, Adv. Eng. Informatics.

[17]  Mark B. Sandler,et al.  A tutorial on onset detection in music signals , 2005, IEEE Transactions on Speech and Audio Processing.

[18]  Tobias Diehl,et al.  Automatic S-Wave Picker for Local Earthquake Tomography , 2009 .

[19]  S. Mostafa Mousavi,et al.  Automatic microseismic denoising and onset detection using the synchrosqueezed continuous wavelet transform , 2016 .

[20]  Qinghua Han,et al.  Effects of internally introduced sulfate on early age concrete properties: Active acoustic monitoring and molecular dynamics simulation , 2018, Construction and Building Materials.

[21]  Dick K. P. Yue,et al.  A fast multi-layer boundary element method for direct numerical simulation of sound propagation in shallow water environments , 2019, J. Comput. Phys..

[22]  T. Lokajíček,et al.  A first arrival identification system of acoustic emission (AE) signals by means of a high-order statistics approach , 2006 .

[23]  Mirko van der Baan,et al.  Spectral estimation—What is new? What is next? , 2014 .

[24]  J. J. McArthur,et al.  Machine learning and BIM visualization for maintenance issue classification and enhanced data collection , 2018, Adv. Eng. Informatics.

[25]  Herbert H. Einstein,et al.  Detection of Cracking Levels in Brittle Rocks by Parametric Analysis of the Acoustic Emission Signals , 2016, Rock Mechanics and Rock Engineering.

[26]  Zongjin Li,et al.  Steel corrosion in magnesia–phosphate cement concrete beams , 2017 .

[27]  Tao Zhang,et al.  Deep topology network: A framework based on feedback adjustment learning rate for image classification , 2019, Adv. Eng. Informatics.

[28]  Mirko van der Baan,et al.  Adaptive STA-LTA with Outlier Statistics , 2015 .

[29]  Wei Liu,et al.  Seismic Time–Frequency Analysis via Empirical Wavelet Transform , 2016, IEEE Geoscience and Remote Sensing Letters.

[30]  Jochen H Kurz,et al.  Strategies for reliable automatic onset time picking of acoustic emissions and of ultrasound signals in concrete. , 2005, Ultrasonics.

[31]  Fu Xiao,et al.  A short-term building cooling load prediction method using deep learning algorithms , 2017 .

[32]  Zongjin Li,et al.  Processing method and property study for cement-based piezoelectric composites and sensors , 2015 .

[33]  Khandakar M. Rashid,et al.  Times-series data augmentation and deep learning for construction equipment activity recognition , 2019, Adv. Eng. Informatics.

[34]  Petr Sedlák,et al.  Acoustic emission localization in thin multi-layer plates using first-arrival determination , 2013 .

[35]  Hongyan Ma,et al.  Defect detection and location in switch rails by acoustic emission and Lamb wave analysis: A feasibility study , 2016 .

[36]  Chuan Li,et al.  A generalized synchrosqueezing transform for enhancing signal time-frequency representation , 2012, Signal Process..

[37]  Nhat-Duc Hoang,et al.  Image processing based automatic recognition of asphalt pavement patch using a metaheuristic optimized machine learning approach , 2019, Adv. Eng. Informatics.

[38]  Hongyan Ma,et al.  Monitoring setting and hardening of concrete by active acoustic method: Effects of water-to-cement ratio and pozzolanic materials , 2015 .

[39]  Peter E. D. Love,et al.  A deep learning-based approach for mitigating falls from height with computer vision: Convolutional neural network , 2019, Adv. Eng. Informatics.

[40]  Mingchao Li,et al.  Multiple mechanical properties prediction of hydraulic concrete in the form of combined damming by experimental data mining , 2019, Construction and Building Materials.

[41]  Yang Shen,et al.  A new distributed time series evolution prediction model for dam deformation based on constituent elements , 2019, Adv. Eng. Informatics.

[42]  Hengchang Dai,et al.  Automatic picking of seismic arrivals in local earthquake data using an artificial neural network , 1995 .

[43]  Mirko van der Baan,et al.  Applications of the synchrosqueezing transform in seismic time-frequency analysis , 2014 .

[44]  Manfred Baer,et al.  An automatic phase picker for local and teleseismic events , 1987 .

[45]  Lukumon O. Oyedele,et al.  Big Data in the construction industry: A review of present status, opportunities, and future trends , 2016, Adv. Eng. Informatics.

[46]  Józef Jonak,et al.  Early fault detection in gearboxes based on support vector machines and multilayer perceptron with a continuous wavelet transform , 2015, Appl. Soft Comput..