Hydrologic Behavior of Residual Soil Slopes in Singapore

Rezaur, R. B.; Rahardjo, Harianto; Leong, Eng Choon; Lee, T. T.

doi:10.1061/(asce)1084-0699(2003)8:3(133)

Cited by 32 publications

(31 citation statements)

References 12 publications

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“…ANN model with appropriate network architecture can be used for predicting time series of pore-water pressure responses to variations in rainfall pattern. It also appears that time series of pore-water pressure responses to rainfall which is known to be a function of many factors such as rainfall, evaporation, soil properties, antecedent conditions (Rahardjo, et al, 2008;Rezaur, et al, 2003) could be predicted using ANN from the knowledge of a relatively few factors only.…”

Section: Resultsmentioning

confidence: 99%

“…The data was collected through a field instrumentation program of a residual soil slope in Yishun, Singapore (Rahardjo, Leong, & Rezaur, 2008;Rezaur, Rahardjo, Leong, & Lee, 2003). The three and a half month time series data consists of 1450 pore-water pressure measurements varying between both positive and negative values with a maximum negative PWP of magnitude 15 kPa.…”

Section: Data Source and Study Areamentioning

confidence: 99%

“…The three and a half month time series data consists of 1450 pore-water pressure measurements varying between both positive and negative values with a maximum negative PWP of magnitude 15 kPa. The entire monitoring period spanned over three years and included time series of pore-water pressure and rainfall measurements at 4 different slopes in 2 major geological formations (Bukit Timah Granitic Formation and Sedimentary Jurong Formation) and at soil depths 0.5, 1, 2, and 3 m. The data was primarily collected with a view to understand rainfall-induced slope failure mechanism (Rahardjo, et al, 2008) and hydrological responses of slopes under tropical climate (Rezaur, et al, 2003). Climate at the study area is hot and humid (equatorial) with no marked dry season.…”

Section: Data Source and Study Areamentioning

confidence: 99%

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Effect of Antecedent Conditions on Prediction of Pore-Water Pressure using Artificial Neural Networks

Mustafa¹,

Bhuiyan²,

Isa³

et al. 2012

MAS

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The effect of antecedent conditions on the prediction of soil pore-water pressure (PWP) using Artificial Neural Network (ANN) was evaluated using a multilayer feed forward (MLFF) type ANN model. The Scaled Conjugate Gradient (SCG) training algorithm was used for training the ANN. Time series data of rainfall and PWP was used for training and testing the ANN model. In the training stage, time series of rainfall was used as input data in one model whereas, rainfall and pore water pressure with some antecedent conditions was used in second model and corresponding time series of PWP was used as the target output. In the testing stage, data from a different time period was used as input and the corresponding time series of pore-water pressure was predicted. The performance of the model was evaluated using statistical measures of root mean square error (RMSE) and coefficient of determination (R 2 ). The results of the model prediction revealed that when antecedent conditions (past rainfall and past pore-water pressures) are included in the model input data, the prediction accuracy improves significantly.

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Section: Resultsmentioning

confidence: 99%

Section: Data Source and Study Areamentioning

confidence: 99%

Section: Data Source and Study Areamentioning

confidence: 99%

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Effect of Antecedent Conditions on Prediction of Pore-Water Pressure using Artificial Neural Networks

Mustafa¹,

Bhuiyan²,

Isa³

et al. 2012

MAS

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“…A high correlation between the increase in pore-water pressure and the daily rainfall may provide a convenient estimate of the increase in pore-water pressure due to the daily rainfall. [6] Jaehong et al (2004) studied Shallow failures of slopes in weathered soil were caused by infiltration due to prolonged rainfall. an increase in factor of safety due to the wetting front suction was small, it make either stability or failure for slopes with factors of safety only slightly greater than unity at any field condition.…”

Section: Literature Reviewmentioning

confidence: 99%

An On-Situ Study of Stability Analysis on Slopes Using Undrained Shear Strength Parameters

2013

IOSR-JMCE

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“…Lim et al (1996) and Lee et al (2009) showed that the PWP response to rainfall depends largely on the antecedent PWP. Rezaur et al (2003) concluded that over 80 % of the increased PWP can be accounted for by rainfall. Mustafa et al (2012) showed that five antecedent PWP records are required for good PWP prediction models, while Rahardjo et al (2008) argued that five antecedent rainfall records are needed to achieve worst PWP.…”

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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Hydrologic Behavior of Residual Soil Slopes in Singapore

Cited by 32 publications

References 12 publications

Effect of Antecedent Conditions on Prediction of Pore-Water Pressure using Artificial Neural Networks

Effect of Antecedent Conditions on Prediction of Pore-Water Pressure using Artificial Neural Networks

An On-Situ Study of Stability Analysis on Slopes Using Undrained Shear Strength Parameters

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

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