PREDICTION OF COMPRESSION INDEX USING ARTIFICIAL NEURAL NETWORKS

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1 IGC 2009, Guntur, INDIA PREDICTION OF COMPRESSION INDEX USING ARTIFICIAL NEURAL NETWORKS V.G. Mutalik Desai Professor, Department of Civil Engineering, K.L.S. Gogte Institute of Technology, Belgaum , India. Veena Desai & D.H. Rao Department of E&C, K.L.S. Gogte Institute of Technology, Belgaum , India. ABSTRACT: Compression index, slope e-log p plot, is used in predicting settlement of footings on clays. It is correlated to easily measurable parameters like liquid limit. Computational methods such as Artificial Neural Networks (s) are employed in prediction of various soil parameters. This paper aims at prediction of the compression index (Cc) of two clays, using s. The network was tested for the published data and then used for testing the generated data. The data generated comprises of liquid limit and compression index for different concentrations of a salt solution. It was observed that predicted liquid limit values, for different concentrations, very closely with a regression coefficient of Taking these liquid limit values as input compression index values were predicted using and are compared with experimentally observed values. predicted compression index values closely approximate the experimental values for both clays with a correlation coefficient between of INTRODUCTION Prediction of settlement of footings forms an important aspect in foundation engineering. Primary consolidation settlements are estimated using compression index, Cc, determined as the slope of e-log p plot. Labour of conducting consolidation test is overcome to certain extent by using empirical relations in predicting Cc. However advanced computational method like Artificial Neural Networks (s), Fuzzy computing, Probabilistic computing, Hybrid methods, Data mining, Chaos theory etc, can also be effectively used in the prediction of soil behaviour (Jeng, 2003 & Mohammad, 2001). The onset of a contaminant brings about significant changes in the properties of the soil. Therefore it is necessary to investigate the efficacy of the prediction methods under the changed soil environments. This paper aims at developing and validating a for prediction of compression index using liquid limit and use it in prediction Cc of two soils contaminated by an electrolyte solution. 2. LITERATURE REVIEW Review of the literature reveals that s have been used successfully in pile capacity prediction, modeling soil behavior, site characterization, earth retaining structures, settlement of structures, slope stability, design of tunnels and underground openings, liquefaction, soil permeability and hydraulic conductivity, soil compaction, soil swelling and classification of soils. Mini & Pandian (2005) have reported the feasibility of using for estimating the optimum moisture content and maximum dry density the values for different types of soil subjected to different compactive efforts. The impact of loose and saturated sands and seismic variables on the liquefaction potential of soil is investigated through neural networks by Ural et al. (1998). Niwari et al. (1996) have used AI techniques for design and analysis of deep foundations. The classification of the soils as per the Bureau of Indian standards was reported by Rajashekaran et al. (1996). The application of s in prediction the compression behavior of contaminated soils appears to be under investigation. Therefore this paper aims at the determination of compression index using artificial neural networks. 3. PRESENT INVESTIGATION The objective of the present investigation is to predict the compression index of soils contaminated by a known electrolyte solution. The concentration of the electrolyte solution influences the liquid limit () of the soil and hence compression index. The methodology comprises of developing an artificial neural network for prediction of compression index using liquid limit. The data published in the literature is used to train and test the network. This tested network is then used for predicting liquid limit and compression index from different concentrations of electrolyte solution. 614

2 3.1 Testing for Published Data The experimental compression index values for eleven soils, taken from Nagaraj & Srinivashnamuthy (1986), are adopted for validating the performance of. The range of values in the data base varied for liquid limit from to 36.2% and Compression index from to The liquid limit values used as input parameter were divided by 100 before analysis in networks. The experimental values were normalized in compliance with activation function used. In this study tansigmoidal transfer function was used for input and hidden layer and purelinear transfer function was used for output layer. The normalized training pattern helps to obtain better convergence Configuration of the Network The configuration starts with the selection of number of hidden layers to be used. In the present investigation, based on experiment, one hidden layer was chosen. Considering the number of neuron in input (1) and output (1) layers, a network with five neurons in the hidden layer was selected, leading to architecture of as illustrated in the figure 1. Table 1: Training Data Sets Cc Cc Testing the Network The network, after being trained using the above eight sets, is tested with three data sets. The data sets used for testing the network and are shown in the Table 2. Table 2: Data Sets Used for Testing Cc (-) Test Results The predicated values of Cc are compared with experimental values from the data base. Further the experimental data was compared with the Cc values predicted using Terazagi and Skempton s empirical relations. Table 3 shows comparison of published experimental data with predicted Cc, and Table 4 shows the Terazagi, Skempton and experimental predictions. Table 3: Comparison of Experimental and Predicted Cc Values Compression Index, Cc Experimental Predicted Fig. 1: Configuration of Network Considering the number of cycles required for convergence together with the accepted accuracy in the training and testing patterns, the error tolerance was chosen as For the chosen architecture of 1-5-1, the learning rates, momentum constant, learning increment factor and maximum number of iterations were taken as of 0.5, 0.85, 0.8 and 1000 epochs respectively Training the Network Eight data sets from database used for training the network, are given in the Table 1. Table 4: Comparison of Experimental and Predicted Cc Values Compression Index, Cc Terazagi Skempton Testing the for Generated data The liquid limit of a soil changes if the pore fluid is other than water. It also changes with the change in concentration of pore fluid and hence the compression index also varies. Therefore the prediction of Cc, using, for the soils with pore fluid other than water are presented using the network already tested for the published data. This is achieved in two stages. In the first stage the liquid limit corresponding to the known concentration of the pore fluid is predicted. Taking 615

3 the liquid limit value, thus predicted, as input in the second stage, the compression index is predicted. In the present study, two soil samples, hereafter referred as (= 60%) and ( = 80%) are tested for liquid limit, in Casagrande apparatus, with nine different concentrations of NaCl. Later the consolidation tests with these nine different concentrations of NaCl were conducted to determine the compression index values. The experimentally obtained liquid limit and compression index values are used for training and testing the network for prediction of compression index Prediction of for Known Concentration of Pore Fluid Network Configuration For each soil type and, out of nine sets of results, five are used for training the network and remaining four are used for testing. Input parameter is concentration of the pore fluid and output parameter is corresponding liquid limit. The range of concentrations varies from zero, for distilled water, to 1.5N (Normality) NaCl solution. To facilitate training the actual values were normalized in compliance with activation function used. Tansigmoidal & purelinear activation function were used. The values were normalized between 0 to 1. In the configuration one hidden layer was used. Considering the number of neurons in the input (1) and output (1) layers, a network with eight neurons in hidden layers was selected leading to architecture of Figure 2 shows the network configuration for &. maximum number of iterations were taken as of 0.5, 0.85, 0.8 and 1000 Epochs respectively Training of Network for and The training data set for and are listed in Table 5. TRAINLM method was adopted because of the set performance limits obtained in less number of epochs. The performance limit set was Table 5: The Training Sets for Soils and Concentration Liquid limit (N) Testing of Network for and The network after being trained using five data sets is tested with four data sets. The data sets used for testing the network for two soils and predicted are shown in Table 6. Table 6: Testing Data and Test Results for and Con. (N) Pred. Pred Concentration + NaCl/ + NaCl Liquid limit Prediction of Compression Index from Liquid limit The result obtained from first stage of prediction was taken as the input parameter and compression index was taken as output parameter. The network was trained using six sets of data and tested for four sets. The range of concentrations varied from zero N to 1N NaCl. Fig. 2: Network Configuration for both Soils and Considering the number of cycles required for convergence together with the accepted accuracy in the training and testing patterns, the error tolerance was chosen as For the chosen architecture of 1-8-1, learning rates, momentum constant, learning increment factor and Network Configuration The net work configuration was same as mentioned in , except that the number of neurons in the input (1) and output (1) layers, a network with five neurons in hidden layers were selected leading in architecture of Training the Network The network was trained for sample and independently. Table 7 shows the training data for sample and respectively. 616

4 Table 7: The Training Data of Soils & Con. (N) Cc Cc Testing the Network The network after being trained is tested with four data sets. The data sets used for testing the network along with predicted values of Cc and the error in prediction for and are presented in Table 8. Table 8: Testing Results of Soils and Con. (N) Cc Pred. Cc Cc Pred. Cc DISCUSSION ON TEST RESULTS The results presented in Table 3 indicate that the predicted values are closer to the published experimental Cc values. The maximum error was to the tune of 3.23%. Table 4 indicates a maximum error to tune of 25.22% for Terzaghi s and 21.45% for Skempton s empirical equations in Cc prediction when compared with the experimental values. This leads to a conclusion that the prediction is more reliable. Table 6 presents the predicted liquid limit values for different concentrations of pore fluid for both and. In general it is observed that the error in prediction is small and hence network predicts liquid limit values satisfactorily, for a given concentration of pore fluid. Correlation coefficient between experimental and predicted data for is and for it is From Table 8 it is observed that the predicted value of Cc for each concentration is in close approximation with experimental data for both and. Correlation coefficient between experimental and predicted data was for and for. 5. LIMITATIONS Despite their good performance in many situations, s suffer from a number of shortcomings, notably, the lack of theory to help with their development, the fact that success in finding a good solution is not always guaranteed and their limited ability to explain the way they use the available information to arrive a solution. In this case the prediction could be done for only one soil type at a time i.e. two different soil types could not be trained and tested at a time. Moreover a large number of data is needed to increase the efficiency of the networks. There is also a need for more research to give a comprehensive explanation of how s arrive at a prediction. The success of neural network implementation is dependent on quality of data used for training, type of structure of the neural network adopted, the method of training and the way in which both input and output data are structured and interpreted. 6. CONCLUSIONS Based on the results of the study presented in this paper, it is evident that s perform better compared to the empirical methods used for prediction of Cc. In contrast, s are based on the data alone in which the model can be trained on input-output data pairs to determine the structure and parameters of the model. In this case, there is no need to neither simplify the problem nor incorporate any assumptions. Moreover, s can always be updated to obtain better results by presenting new training examples as new data become available. It is concluded that neural networks can be a efficient model in prediction of compression index. REFERENCES Jeng, G.S., Cha, D.H. and Blumenstein, M. (2003). Application Neural Networks in civil engg. Problems, Asian Journal of Information Technology, vol. 2, no. 3, pp Mini, K.M. and Pandian N.S. (2005). Assessment of Compaction Behavior of Soils Using, Proc. Indian Geotechnical Conference, Dec 17-19, Ahmedabad, India, pp Mohammed A. Shahin, Jaksa, B.M. and Maeir, R.H., (2001). Applications in Geotechnical Engg, Australian Geomechanics, pp Nawari, N.O., Liang, R. and Nusairat, J. (1999). Artificial Intelligence Techniques for the Design and Analysis of Deep Foundations, Electronic J. Geotech. Engrg. geotech.civeng.okstate.edu/ejge/ppr9909/index.htm. Rajashekaran, S. and Vijayalaxmi Pai, Neural Networks, Fuzzy Logic, and Genetic Algorithms, Synthesis and Applications, published by Ashok K. Gosh, Prentice Hall of India private limited, New Delhi. T.S. Nagraj and B.R. Srinivasa Murthy, (1986). A Critical Reappraisal of Compression Index Equations, Geotechnique, 36, No. 1, pp Ural, D.N. and Saka, H. (1998). Liquefaction Assessment by Artificial Neural Network, Electronic journal of geotechnicalengg.http//geotech.civen.okstate.edu/ejige/ppr 9803/ index.html. 617

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