Large-scale model test for detecting pile defects using the parallel seismic method

Yang, Jun; Sun, Xiaoyan; Bian, Decun; Ji-xi, SHAO

doi:10.1016/j.soildyn.2020.106300

Cited by 5 publications

(2 citation statements)

References 8 publications

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“…Therefore, it is necessary to evaluate the integrity and quality of pile foundations and thus ensure the safety of supported superstructures. To detect the integrity of reinforced concrete (RC) piles, a variety of non-destructive testing techniques have been proposed successively, such as the low-strain pile integrity test (PIT) [ 1 , 2 , 3 , 4 ], sonic cross-hole logging (SCL) [ 5 , 6 , 7 , 8 ] and the parallel seismic (PS) method [ 9 , 10 , 11 , 12 , 13 , 14 ]. The existence of potential pile defects and unknown pile lengths can be detected by one method or a combination of multiple methods.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Convolutional and Recurrent Neural Network for Multi-Sensor Pile Damage Detection with Time Series

Wu,

El Naggar,

Wang

2024

Sensors

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Machine learning (ML) algorithms are increasingly applied to structure health monitoring (SHM) problems. However, their application to pile damage detection (PDD) is hindered by the complexity of the problem. A novel multi-sensor pile damage detection (MSPDD) method is proposed in this paper to extend the application of ML algorithms in the automatic identification of PDD. The time-series signals collected by multiple sensors during the pile integrity test are first processed by the traveling wave decomposition (TWD) theory and are then input into a hybrid one-dimensional (1D) convolutional and recurrent neural network. The hybrid neural network can achieve the automatic multi-task identification of pile damage detection based on the time series of MSPDD results. Finally, the analytical solution-based sample set is utilized to evaluate the performance of the proposed hybrid model. The outputs of the multi-task learning framework can provide a detailed description of the actual pile quality and provide strong support for the classification of pile quality as well.

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Section: Introductionmentioning

confidence: 99%

A Hybrid Convolutional and Recurrent Neural Network for Multi-Sensor Pile Damage Detection with Time Series

Wu,

El Naggar,

Wang

2024

Sensors

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“…Using a lateral horizontal impact has been suggested as a more feasible approach for assessing pile foundations of superstructures [24,25]. Although PS tests have been used to determine the length and integrity of piles using the rst arrival time and waveforms [19,26], this method is limited by complex soil parameters, and a borehole must be drilled to place the sensors.…”

Section: Introductionmentioning

confidence: 99%

Integrity Testing of a Platform‐Pile System Using a Sensor Array and Wavenumber Domain Analysis

Lin

2022

Advances in Civil Engineering

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Appropriate impact and sensor locations must be chosen in pile integrity tests to prevent three-dimensional effects caused by the torsional and flexural modes. The three-dimensional characteristics cause high-frequency interference, especially in bridge and wharf piles. A method is required to minimize the high-frequency interference without reducing the accuracy of the pile integrity test. A multivelocity integrity test method is proposed based on a sensor array and frequency-wavenumber (FK) domain analysis to eliminate high-frequency interference and reduce the errors in the output of integrity tests of platform-pile systems. FK filtering is performed to eliminate the spatial alias frequency and separate the upward and downward wavefield and the vibration modes in an integrity test of a platform-pile system. The optimum sensor location to minimize the influence of interference signals is at the bending plane relative to the impact location. Using a sensor array reduces the influence of the sensor location on the test results and minimizes the requirements for determining the location of the excitation point and sensors in the traditional low-strain integrity testing (LST) method, thereby improving the applicability of this method.

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Identification of bored pile defects utilizing torsional low strain integrity test: Theoretical basis and numerical analysis

Zhang,

Ji,

Zhang

et al. 2024

Journal of Rock Mechanics and Geotechnical Engineering

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Large-scale model test for detecting pile defects using the parallel seismic method

Cited by 5 publications

References 8 publications

A Hybrid Convolutional and Recurrent Neural Network for Multi-Sensor Pile Damage Detection with Time Series

A Hybrid Convolutional and Recurrent Neural Network for Multi-Sensor Pile Damage Detection with Time Series

Integrity Testing of a Platform‐Pile System Using a Sensor Array and Wavenumber Domain Analysis

Identification of bored pile defects utilizing torsional low strain integrity test: Theoretical basis and numerical analysis

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