Objective Site Characterization Using Clustering of Piezocone Data

Hegazy, Yasser A.; Mayne, Paul W.

doi:10.1061/(asce)1090-0241(2002)128:12(986)

Cited by 60 publications

(21 citation statements)

References 7 publications

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“…In the clustering method (Hegazy and Mayne 2002;Liao and Mayne 2007) adopted here, only the (Q tn , F r ) parameters were considered. Numbers of clusters of n c = 40, 80, and 200 were considered, and Figures 18e-g show the corresponding analysis results.…”

Section: Among All Methods the Bayesian Methods (Wang Et Al 2013) Anmentioning

confidence: 99%

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Cone penetration test (CPT)-based stratigraphic profiling using the wavelet transform modulus maxima method

et al. 2015

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In this paper, a stratigraphic profiling approach is proposed based on the soil behavior type index I c obtained from the cone penetration test (CPT). The basic idea of this approach is simple: the layer boundaries can be identified as the points at which a change occurs in the I c profile. It is shown that these change points can be easily identified using the wavelet transform modulus maxima (WTMM) method. This method is able to accurately pinpoint the locations of change points in the I c profile and to produce graphs and plots that fit well with engineers' methods of visualization and intuition. Moreover, by virtue of the Fast Fourier Transform (FFT), the computation is very fast. Case studies show that the WTMM method is effective for the detection of change points in the I c profile. It is also capable of detecting thin soil layers.

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Section: Among All Methods the Bayesian Methods (Wang Et Al 2013) Anmentioning

confidence: 99%

“…The clustering method adopted here is not exactly identical to those adopted in Hegazy and Mayne (2002) and Liao and Mayne (2007). In the latter two studies, the CPTU pore pressure coefficient B q was considered.…”

Section: The Nges At Texas Aandm Universitymentioning

confidence: 99%

Cone penetration test (CPT)-based stratigraphic profiling using the wavelet transform modulus maxima method

et al. 2015

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“…Das and Basudhar (2009) used an unsupervised learning network (a SOM) for the clustering of CPT data for soil stratification. The soil stratification based on SOM is found to similar to that obtained using fuzzy-C clustering, but different from commonly used hierarchical clustering (Hegazy and Mayne, 2002). Ferentinou and Sakellariou (2007) used SOM for the slope stability analysis problem.…”

Section: The Basic Ann Architecturementioning

confidence: 70%

Artificial Neural Networks in Geotechnical Engineering

Das

2013

Metaheuristics in Water, Geotechnical and Transport Engineering

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“…In these methods based on statistical approaches, the uncertainties are attributed to both the soil mechanical behavior and the soil composition. Hegazy et al (2002) proposed a statistical analysis (cluster analysis) and Kurup and Griffin (2006) explored the capabilities of regression-based artificial neural network (ANN) model in predicting soil composition from CPT data. Das and Basudhar (2009) proposed self-organizing maps and fuzzy clustering techniques to isolate different layers in stratified soil based on available piezocone penetration test results.…”

Section: Contents Lists Available At Sciencedirectmentioning

confidence: 99%