Spectral Analysis of Parallel Seismic Method Data for Surveying Underground Structures

Shmurak, D. V.; Churkin, A. A.; Lozovsky, I. N.; Zhostkov, R. A.

doi:10.3103/s1062873822010221

Cited by 4 publications

(2 citation statements)

References 5 publications

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“…In non-destructive testing, there is a crucial requirement for regulatory control, prompting the development of formalized algorithms to analyze measurement results. To quantitatively characterize the calculated spectrum, we introduce a set of spectral attributes that have been previously proposed for the analysis of seismic microzonation data, ground-penetrating radar, slab impulse-response tests [25][26], low strain impact, and parallel seismic methods for pile integrity testing [27][28]. These attributes include spectrum area Sn, normalized spectrum area Sn_norm, weighted mean frequency fw, and maximum frequency fmax.…”

Section: Calculation Of Spectral Attributes For Csl Datamentioning

confidence: 99%

Spectral analysis of cross-hole sonic logging data for pile integrity assessment

Lozovsky,

Churkin

2024

Res. Eng. Struct. Mat.

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Ensuring the safety of foundations requires advanced non-destructive testing techniques. Cross-hole sonic logging (CSL) is a widely adopted method for evaluating the integrity of deep foundation elements, such as bored piles, barrettes, and diaphragm walls. This method involves analyzing the propagation time and relative energy of ultrasonic pulses transmitted and registered by probes inserted into pre-installed access tubes. However, in certain cases, standard analyses may not effectively distinguish anomalous signals arising from defects and other factors unrelated to concrete quality. Our study explores the potential of an alternative frequency-domain approach for CSL data analysis. We propose new attributes to quantify ultrasonic signal spectra, including spectrum area, normalized spectrum area, weighted mean frequency, and maximum frequency. These attributes are automatically calculated, eliminating the need for additional data processing time and minimizing the risk of human error. The proposed approach was applied to CSL data collected from two bored piles of 46 m length and successfully identified signals classified as anomalous through standard time-domain analysis. Further research is deemed necessary to fully explore the potential of the frequency-domain approach in enhancing the information content and reliability of CSL pile integrity testing

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Section: Calculation Of Spectral Attributes For Csl Datamentioning

confidence: 99%

Spectral analysis of cross-hole sonic logging data for pile integrity assessment

Lozovsky,

Churkin

2024

Res. Eng. Struct. Mat.

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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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Increasing the Reliability of the Low Strain Integrity Testing of Piles Under Existing Structures

Churkin¹,

Лосева²,

Lozovsky³

et al. 2022

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Inspection of piles located under the pile cap or an existing building is a common task in the practice of capital construction. The low strain impact testing allows us to estimate the pile length and the presence of defects in its body. This provides designers and builders with information on structures, which can be considered when planning the further operation of the foundation. Improving the reliability of test results for examining piles under cap is the issue of interest. On the example of field test results, the application of the multifrequency signal excitation during data collection and the wavelet analysis of signals during data processing are shown.

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Spectral Analysis of Parallel Seismic Method Data for Surveying Underground Structures

Cited by 4 publications

References 5 publications

Spectral analysis of cross-hole sonic logging data for pile integrity assessment

Spectral analysis of cross-hole sonic logging data for pile integrity assessment

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

Increasing the Reliability of the Low Strain Integrity Testing of Piles Under Existing Structures

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