An Alternative Method for Determination of Liquefaction Susceptibility of Soil

Samui, Pijush; Jagan, J.; Rajadurai, Hariharan

doi:10.1007/s10706-015-9969-2

Cited by 25 publications

(15 citation statements)

References 20 publications

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“…The manual mapping of the surface was performed by Schiltz [17, 18] and reported by Wouters and Schiltz [19], and is used as a reference in this study. For the site-specific clustering, we use both the x -means and MCLUST algorithms with I c as the CPT variable.…”

Section: Methodsmentioning

confidence: 99%

“…More recent work in the framework of interpretation or classification of CPT data is mostly focussed on using Bayesian approaches [6, 7], fuzzy classification techniques [8, 9], hierarchical and k -means clustering [10–13], and the use of neural networks [14–17], both for supervised and unsupervised problems.…”

Section: Introductionmentioning

confidence: 99%

“…The classical interpretations of standard CPTs in geotechnical literature are performed by visual examination of the raw data or the use of empirical soil (or soil behaviour type—SBT) classification charts [ 4 , 5 , 1 ]. More recent work in the framework of interpretation or classification of CPT data is mostly focussed on using Bayesian approaches [ 6 , 7 ], fuzzy classification techniques [ 8 , 9 ], hierarchical and k -means clustering [ 10 – 13 ], and the use of neural networks [ 14 – 17 ], both for supervised and unsupervised problems.…”

Section: Introductionmentioning

confidence: 99%

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Model-based classification of CPT data and automated lithostratigraphic mapping for high-resolution characterization of a heterogeneous sedimentary aquifer

et al. 2017

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Cone penetration testing (CPT) is one of the most efficient and versatile methods currently available for geotechnical, lithostratigraphic and hydrogeological site characterization. Currently available methods for soil behaviour type classification (SBT) of CPT data however have severe limitations, often restricting their application to a local scale. For parameterization of regional groundwater flow or geotechnical models, and delineation of regional hydro- or lithostratigraphy, regional SBT classification would be very useful. This paper investigates the use of model-based clustering for SBT classification, and the influence of different clustering approaches on the properties and spatial distribution of the obtained soil classes. We additionally propose a methodology for automated lithostratigraphic mapping of regionally occurring sedimentary units using SBT classification. The methodology is applied to a large CPT dataset, covering a groundwater basin of ~60 km2 with predominantly unconsolidated sandy sediments in northern Belgium. Results show that the model-based approach is superior in detecting the true lithological classes when compared to more frequently applied unsupervised classification approaches or literature classification diagrams. We demonstrate that automated mapping of lithostratigraphic units using advanced SBT classification techniques can provide a large gain in efficiency, compared to more time-consuming manual approaches and yields at least equally accurate results.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Model-based classification of CPT data and automated lithostratigraphic mapping for high-resolution characterization of a heterogeneous sedimentary aquifer

et al. 2017

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“…Recently, supervised machine learning techniques have been proposed in the literature and provided superior performance in learning complex relationships while maintaining a reliable generalization ability. The most notable machine learning models used in seismic-induced liquefaction studies are Support Vector Machines (SVMs) 13 , Decision Trees (DTs) 14 , Artificial Neural Networks (ANNs) 1 and Extreme Learning Machines (ELMs) 15 .…”

Section: Introductionmentioning

confidence: 99%

Predicting seismic-induced liquefaction through ensemble learning frameworks

Alobaidi

Meguid

Chebana

2019

Sci Rep

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The regional nature of liquefaction records and limited information available for a certain set of explanatories motivate the development of complex prediction techniques. Indirect methods are commonly applied to incidentally derive a hyperplane to this binary classification problem. Machine learning approaches offer evolutionary prediction models which can be used as direct prediction methods to liquefaction occurrence. Ensemble learning is a recent advancement in this field. According to a predefined ensemble architecture, a number of learners are trained and their inferences are integrated to produce stable and improved generalization ability. However, there is a need to consider several aspects of the ensemble learning frameworks when exploiting them for a particular application; a comprehensive evaluation of an ensemble learner’s generalization ability is required but usually overlooked. Also, the literature falls short on work utilizing ensemble learning in liquefaction prediction. To this extent, this work examines useful ensemble learning approaches for seismic-induced liquefaction prediction. A comprehensive analysis of fifteen ensemble models is performed. The results show improved prediction performance and diminishing uncertainty of ensembles, compared with single machine learning models.

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“…Support vector machines have also been applied to geotechnical engineering problems-the study by Goh and Goh (2007) on seismic liquefaction data is one of the early applications of SVM in geotechnical engineering. Samui and Sitharam (2011) used SVM to classify expansive soils; Yu et al (2012) used SVM along with data assimilation techniques to predict soil moisture both at the surface and the root zone; Gill et al (2006) performed 4 and 7-day forecasts of soil moisture while exploiting the relationship between the soil moisture and metrological factors; Samui and Karthikeyan (2011) predicted the susceptibility to liquefaction of soils using features of cone resistance and cyclic stress ratio, in which the model was locally trained and tested and even further extended to a global data set with reasonable success. Lee and Chern (2013) used SVM to classify liquefied and non-liquefied soils.…”

Section: Introductionmentioning

confidence: 99%

Prediction of pore-water pressure response to rainfall using support vector regression

et al. 2016

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Nonlinear complex behavior of pore-water pressure responses to rainfall was modelled using support vector regression (SVR). Pore-water pressure can rise to disturbing levels that may result in slope failure during or after rainfall. Traditionally, monitoring slope pore-water pressure responses to rainfall is tedious and expensive, in that the slope must be instrumented with necessary monitors. Data on rainfall and corresponding responses of pore-water pressure were collected from such a monitoring program at a slope site in Malaysia and used to develop SVR models to predict pore-water pressure fluctuations. Three models, based on their different input configurations, were developed. SVR optimum metaparameters were obtained using k-fold cross validation and a grid search. Model type 3 was adjudged the best among the models and was used to predict three other points on the slope. For each point, lag intervals of 30 min, 1 h and 2 h were used to make the predictions. The SVR model predictions were compared with predictions made by an artificial neural network model; overall, the SVR model showed slightly better results. Uncertainty quantification analysis was also performed for further model assessment. The uncertainty components were found to be low and tolerable, with d-factor of 0.14 and 74 % of observed data falling within the 95 % confidence bound. The study demonstrated that the SVR model is effective in providing an accurate and quick means of obtaining pore-water pressure response, which may be vital in systems where response information is urgently needed.

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An Alternative Method for Determination of Liquefaction Susceptibility of Soil

Cited by 25 publications

References 20 publications

Model-based classification of CPT data and automated lithostratigraphic mapping for high-resolution characterization of a heterogeneous sedimentary aquifer

Model-based classification of CPT data and automated lithostratigraphic mapping for high-resolution characterization of a heterogeneous sedimentary aquifer

Predicting seismic-induced liquefaction through ensemble learning frameworks

Prediction of pore-water pressure response to rainfall using support vector regression

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