Measurement uncertainty of shear wave velocity: A case study of thirteen alluvium test sites in Taipei Basin

Wang, Jiun-Shiang; Hwang, Jin-Hung; Lu, Chih-Chieh; Deng, Yuan-Chang

doi:10.1016/j.soildyn.2022.107195

Cited by 10 publications

(4 citation statements)

References 33 publications

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“…In general, Vs can be determined using either laboratory or in situ testing methods. Some commonly used methods for the in situ measurement of Vs include seismic refraction measurements, surface waves, and cross-hole and downhole techniques [2][3][4]. Owing to the favourable characteristics of machine learning (ML), many researchers in the literature have suggested the use of ML approaches for determining geotechnical parameters [5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Models for Predicting Shear Wave Velocity of Soils

Mousavi,

Bayat,

Feng

2024

IOP Conf. Ser.: Earth Environ. Sci.

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As regards regions prone to seismic activity, shear wave velocity (Vs) is a design parameter for geotechnical systems exposed to dynamic loads. Evaluating Vs for geomaterials involves on-site and laboratory assessments; however, its availability is often limited in projects owing to resource and time constraints. Various mathematical and empirical models have been proposed to predict Vs for cohesive or granular soils; however, a majority of these models are specific to certain soil types and loading conditions. In this study, machine learning techniques were used for Vs prediction. These models encompass factors such as grading attributes, void ratio (e), mean effective confining pressure (σ’m), consolidation stress ratio (KC), and specimen preparation methods. To achieve this, a series of bender element tests was performed on various sand and gravel mixtures supplemented with culled data from earlier investigations. This study facilitated the development of three machine learning models aimed at predicting the Vs for granular soils: artificial neural networks (ANN), support vector regression (SVR), and gradient boosting regression (GBR), aimed at predicting Vs for granular soils. The findings of the study demonstrated that the ANN model exhibited enhanced precision and reduced error compared with the other models.

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Section: Introductionmentioning

confidence: 99%

Machine Learning Models for Predicting Shear Wave Velocity of Soils

Mousavi,

Bayat,

Feng

2024

IOP Conf. Ser.: Earth Environ. Sci.

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“…Seismic geophysical tests are widely used to determine P-and S-wave propagation velocity profiles and soil properties that are essential for evaluating the dynamic behavior of soil deposits, the geotechnical design of foundations, and the seismic response analysis of geotechnical structures [1][2][3]. These tests require the installation of seismic sensors (geophones or receivers) and an energy source for generating seismic waves (a hammer or explosive charge).…”

Section: Introductionmentioning

confidence: 99%

Interpretation Methods for Seismic Downhole Test in Inclined Boreholes

Bautista,

Aguilar

2023

Civ Eng J

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Geotechnical investigations often involve inclined boreholes, which can be used for downhole (DH) seismic surveys. However, as there is no interpretation method for downhole tests in inclined boreholes (IDH), this study proposes alternative interpretation methods based on the direct method (DM), interval method (IM), modified interval method (MIM), and refracted ray path method (RRM). We have named the proposed methods, adding an I to the original name to indicate that they are performed in an inclined well, i.e., DMI, IMI, MIMI, and RRMI. To analyze the applicability of the proposed methods, eight simple models with horizontal layers and four 2D models were used to obtain the P- and S-wave velocity profiles. Among all the proposed methods, the RRMI method showed the best fit between the calculated S-wave velocity (Vs) profile and the real models, providing good reliability. To test the equations and hypotheses, new interpretation steps were developed based on Snell's law and a modification of the numerical bisection method, which showed that the error increased slightly as the dip angle of the well decreased. The next step was to test the accuracy of the RRMI method in the field and develop downhole test processing software for vertical and inclined boreholes. Doi: 10.28991/CEJ-2023-09-10-016 Full Text: PDF

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“…To gure out and quantify the model uncertainties of SPT-N, CPT-q c , and V S -based methods, the datasets of SPT, CPTU, and in-situ seismic methods at 13 alluvium sites in the Taipei Basin have been compiled (Wang et al 2022) as a case study. In this paper, the measurements associated with sandy soils are exploited to rst examine the consistencies between the SPT-N methods described in the 1998 NCEER/NSF workshop (hereinafter denoted NCEER-SPT methods) and some popular CPT-q c and V S methods, and to simultaneously calibrate the corresponding model uncertainties.…”

Section: Introductionmentioning

confidence: 99%

“…To ensure data quality, the test operations were supervised by professional engineers from the National Center for Research on Earthquake Engineering throughout the whole process, and experienced geotechnical experts intermittently inspected the implementation procedures. The procedures for implementing the seismic tests, V S data acquisition, and supervision have been elaborated on by Wang et al (2022).…”

Section: Introductionmentioning

confidence: 99%

Model uncertainties of SPT, CPT, and VS-based simplified methods for soil liquefaction assessment

Wang

Hwang

Deng

et al. 2023

Bull Eng Geol Environ

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Simpli ed methods for assessing soil liquefaction potential based on the standard penetration test (SPT) are prevalent in practice and widely accepted by several seismic design codes. When encountering sites that have not been investigated using SPT, such as offshore sites or sites with a high level of gravel content, engineers can only substitute the methods based on piezocone penetration test data (CPT-q c methods) or shear wave velocity measurements (V S -based methods); however, these two approaches perform inconsistently with methods based on SPT data (SPT-N methods). As a result, this paper exploits the datasets consisting of SPT, CPTU, and in-situ seismic test measurements from 13 alluvium sites in the Taipei Basin to compare the performance of prevalent SPT-N, CPT-q c , and V S -based methods. The discrepancies (uncertainties) of these methods are characterized as Gaussian distribution models, which is believed to be a feasible strategy for predicting equivalent results for SPT-N methods when SPT data are not available. Finally, the application of the proposed models to liquefaction potential index evaluation is demonstrated using a real case study.

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Measurement uncertainty of shear wave velocity: A case study of thirteen alluvium test sites in Taipei Basin

Cited by 10 publications

References 33 publications

Machine Learning Models for Predicting Shear Wave Velocity of Soils

Machine Learning Models for Predicting Shear Wave Velocity of Soils

Interpretation Methods for Seismic Downhole Test in Inclined Boreholes

Model uncertainties of SPT, CPT, and VS-based simplified methods for soil liquefaction assessment

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