Artificial neural network (ANN) modeling & validation to predict compression index of tropical soft soil

• Main Author: Yong, Shirley Xiao Wei
• Format: Final Year Project Report
• Language: English; English
• Published: Universiti Malaysia Sarawak, UNIMAS 2010
• Subjects: QC Physics; TA Engineering (General). Civil engineering (General)
• Online Access: http://ir.unimas.my/id/eprint/7678/1/Artificial%20neural%20network%20%28ANN%29%20modeling%20%26%20validation%20to%20predict%20compression%20index%20of%20tropical%20soft%20soil%20%2824pgs%29.pdf; http://ir.unimas.my/id/eprint/7678/8/Shirley%20%28full%20text%29.pdf; http://ir.unimas.my/id/eprint/7678/
• Institution: Universiti Malaysia Sarawak

Soft soil due to high settlement when it subjected to certain stress, caused pore water pressure increase and finally reduced the volume of the soil mass. Therefore, many settlement prediction methods have focused on correlations with in-situ tests, such as the Cone Penetration Test (CPT), Standard Penetration Test (SPT), Dilatometer Modulus Test (DMT), plate load test, and pressure-meter test. In current study, bore hole data was collected from JKR, Kuching to train and validate the neural network for the prediction of settlement rather than compression index (Cc). Artificial Neural Network is the potential software that suitable to perform a kind of function fitting by using multiple parameters on existing information and predict the possible relationship of compressibility characteristics for the soft soil, if the physical properties of soil e.g., moisture content, specific gravity, liquid limit etc. are known. This study demonstrates the comparison between the conventional estimation of Cc by using Terzaghi’s settlement equation and the predicted Cc from ANN. Therefore, a programming was written by using MATLAB 6.5 and train with eight different training algorithm, namely Resilient Backpropagation (rp), Conjugate Gradient Polak-Ribiére algorithm (cgp), Scale Conjugate Gradient (scg), Levenberg-Marquardt algorithm (lm), BFGS Quasi-Newton (bfg), Conjugate Gradient with Powell/Beale Restarts (cgb), Fletcher-Powell Conjugate Gradient (cgf), and One-step Secant (oss) have been compared for the best prediction of Cc. The result shows that the network trained with Resilient Backpropagation (rp) simulates the most accurate results of correlation coefficient, R and efficiency coefficient, E2.