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 3 publications

(2 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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

View full text Add to dashboard Cite

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.

show abstract

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

View full text Add to dashboard Cite

show abstract

“…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

View full text Add to dashboard Cite

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.

show abstract

Theoretical Analysis of Dynamic Response of Pipe Pile with Multi-Defects

Zhong

Meng²

2023

JMSE

View full text Add to dashboard Cite

A mathematical model for an outer soil, multi-defects pipe pile, and inner soil dynamic interaction system is established to research the influences of multi-defects on the vibration of a pipe pile. The dynamic impedance of the pipe pile is deduced by applying a Laplace transformation method and an impedance recursive technique. Then, the velocity response at the pile head is further obtained using the inverse Fourier transform method. Moreover, parametric analyses are conducted to research the influences of the type, degree, distribution, length, and depth of multi-defects on the vibration of the pipe pile. The results indicate that the characteristics of multi-defects appear different, with amplitude differences and reflected signal features on the velocity admittance and velocity response curve, respectively. This means that the obtained analytical solutions and relevant results can be used to detect multi-defects of pipe piles using the different appearances of the velocity admittance and velocity response curve, as measured in engineering practice.

show abstract

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

Cited by 3 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

Theoretical Analysis of Dynamic Response of Pipe Pile with Multi-Defects

Contact Info

Product

Resources

About