R. B. Rezaur

Effectiveness of horizontal drains for slope stability

Horizontal drains have been commonly used in stabilising unsaturated residual soil slopes. This study examines the effectiveness of horizontal drains in stabilising residual soil slopes against rainfall-induced slope failures under a tropical climate. The study includes field instrumentation at two residual soil slopes complemented with a parametric study relating to drain position. Field monitoring results indicate that rainfall infiltration is limited to a certain depth below which infiltration becomes insignificant. This zone tends to be unsuitable for horizontal drains. Horizontal drains were found to be most effective when located at the base of a slope. The parametric study indicated conditions under which horizontal drains are effective or ineffective in improving the stability of a slope. It was also found that horizontal drains have little role in minimising infiltration in an unsaturated residual soil slope. Benefits of using horizontal drains can be obtained through the lowering of the water table.

A Flume for Assessing Flux Boundary Characteristics in Rainfall-Induced Slope Failure Studies

Rainfall-induced slope failure studies and slope hydrological behavior studies often require an assessment of the flux boundary characteristics of a slope. However, quantification of the flux boundary conditions across a slope surface with respect to rainfall, runoff, and infiltration is difficult. This paper introduces the design and field installation of a flume for high resolution monitoring of runoff data from a small catchment, particularly for rainfall-induced slope failure studies. The features of the flume include reliable and continuous runoff measurement at high resolution, options for accommodating various flow rates as dictated by the slope size, and portability. The flume can be used to quantify the flux boundary conditions required for seepage analyses associated with rainfall-induced slope failure studies.

Artificial Neural Network Modeling for Spatial and Temporal Variations of Pore-Water Pressure Responses to Rainfall

Knowledge of spatial and temporal variations of soil pore-water pressure in a slope is vital in hydrogeological and hillslope related processes (i.e., slope failure, slope stability analysis, etc.). Measurements of soil pore-water pressure data are challenging, expensive, time consuming, and difficult task. This paper evaluates the applicability of artificial neural network (ANN) technique for modeling soil pore-water pressure variations at multiple soil depths from the knowledge of rainfall patterns. A multilayer perceptron neural network model was constructed using Levenberg-Marquardt training algorithm for prediction of soil pore-water pressure variations. Time series records of rainfall and pore-water pressures at soil depth of 0.5 m were used to develop the ANN model. To investigate applicability of the model for prediction of spatial and temporal variations of pore-water pressure, the model was tested for the time series data of pore-water pressure at multiple soil depths (i.e., 0.5 m, 1.1 m, 1.7 m, 2.3 m, and 2.9 m). The performance of the ANN model was evaluated by root mean square error, mean absolute error, coefficient of correlation, and coefficient of efficiency. The results revealed that the ANN performed satisfactorily implying that the model can be used to examine the spatial and temporal behavior of time series of pore-water pressures with respect to multiple soil depths from knowledge of rainfall patterns and pore-water pressure with some antecedent conditions.

Estimation of soil pore-water pressure variations using a thin plate spline basis function

Information of soil pore-water pressure changes due to climatic effect is an integral part for studies associated with hill slope analysis. Soil pore-water pressure variations in a soil slope due to rainfall were predicted using Artificial Neural Network (ANN) technique with Thin Plate Spline (TPS) radial basis function. A radial basis function (RBF) neural network with network architecture of 8-36-1 (input-hidden-output) was selected to develop RBF model. Number of hidden neurons was selected using trial and error procedure whereas spread of the basis function was established using normalization method. Time series data of rainfall and pore-water pressure was used for training and testing the RBF model. The performance of the model was evaluated using root mean square error, coefficient of correlation and coefficient of efficiency. The results of the model prediction revealed that the model produced promising results indicating that TPS basis function is able to predict time series of pore-water pressure responses to rainfall. Comparison with other studies showed that the RBF model using TPS basis function can be used as alternate of Gaussian basis function for prediction of soil pore-water pressure variations.

Data-driven Modelling for Pore Water Pressure Variation Responses to Rainfall

Measurement of soil pore water pressure is always a tedious, time consuming and expensive exercise. Moreover, unavailability of any physical based or mathematical relationship to get information of pore water pressure leads researchers to perform data-driven modelling. This study presents a data-driven modelling approach to predict soil pore water pressure variations in a slope. Point measurements based time series data of soil pore water pressure variations and corresponding rainfall was used to develop the data-driven model. The model was developed using radial basis function neural network with Multi-quadric basis function. The inputs of the model consist of 5 antecedent pore water pressure, two antecedent rainfall and one current rainfall values. Trial and error procedure was adopted to obtain the appropriate number of neurons in the hidden layer. Normalization method was used to determine the spread of the basis function. Mean absolute error (MAE) and coefficient of determination (R² ) as statistical measures were used to evaluate the performance of the model. The results revealed that the data-driven model predicted the pore water pressure values close to the observed values. The minimum value of MAE during test stage was observed as 0.327 with a coefficient of determination R² = 0.975. Multi-quadric basis function was found to be suitable for the prediction of soil pore water pressure variations.