REGRESSION AND ARTIFICIAL NEURAL NETWORK MODELS FOR RELATIONSHIP BETWEEN VARIOUS SOIL PROPERTIES AND ELECTRICAL RESISTIVITY

• Main Author: SIDDIQUI, FAHAD IRFAN
• Format: Thesis
• Language: English
• Published: 2012
• Subjects: TA Engineering (General). Civil engineering (General)
• Online Access: http://utpedia.utp.edu.my/21192/1/2012-CIVIL-REGRESSION%20AND%20ARTIFICIAL%20NEURAL%20NETWORK%20MODELS%20FOR%20RELATIONSHIP%20BETWEEN%20VARIOUS%20SOIL%20PROPERTIES%20AND%20ELECTRICAL%20RESISTIVITY-FAHAD%20IRFAN%20SIDDIQUI.pdf; http://utpedia.utp.edu.my/21192/

The primary objective of the current research work is to investigate the relationship between electrical resistivity and various soil parameters of naturally occurring soils around Universiti Teknologi, Petronas, Malaysia. The research work consists of four major phases; field resistivity surveys, soil boring, laboratory resistivity measurements, and soil characterization tests. Field survey includes ID vertical electrical sounding (VES) and 2D resistivity imaging. Total 79 soil samples (46 Silty-sand and 36 Sandy soils samples) were obtained from ten C1CT) boreholes fBH-01 to BH-10) brought to geotechnical laboratory for various soil characterization tests. Moisture content of soil samples ranges from 6.11% to 52.42%. Plasticity index ranges from 0% to 26.27%. Direct shear test results indicates that cohesion ranges from 0.00 to 68.23 KPa. The friction angle values for all soil samples ranges between 5.36° to 42.51°. The correlations between electrical resistivity and various properties of soil samples were evaluated using least-squares regression method. Relationship between moisture content and resistivity values shows a good power correlation with regression co-efficient R =0.56. Unit weight has poor relationship with resistivity (R =0.10) for all soil samples. Results indicates a good correlation between plasticity index and resistivity with regression coefficients R2=0.42, R2=0.19 and R2=0.24 for all soil samples, silty-sand soils, and sandy soils. Cohesion indicated a weaker relationship with resistivity for all types ofsoil. Friction angle and resistivity indicates increasing logarithmic trend with R =0.29 for all soil samples. Artificial neural network modeling was also performed using LM and SCG learning rule upto 10 hidden neurons. Best network with particular learning algorithm and optimum number of neuron in hidden layer presenting lowest root mean square error RMSE was selected for prediction of various soil properties. ANN models showed higher prediction accuracy for all soil properties.