Using Backpropagation Neural Networks for the Prediction of Residual Shear Strength of Cohesive Soils

Author

Luke Detwiler, University of VermontFollow

Date of Completion

2014

Document Type

Honors College Thesis

Department

College of Engineering and Mathematical Sciences

First Advisor

Donna Rizzo

Second Advisor

Mandar Dewoolkar

Third Advisor

Alison Pechenick

Keywords

Artificial Neural Network, Backpropagation, ANN, BPNN, Residual Friction Angle, Predict

Abstract

A complex nonlinear relationship exists between indicative soil parameters (liquid limit, plastic limit, clay fraction, sand fraction, and normal stress loading) and drained secant residual friction angle (ø_r’). Academic literature provides various empirical models for the prediction of ø_r’, though their predictions generally suffer from insufficient representation of the nonlinear relationship between parameters. In this study, an artificial neural network was developed for the prediction of ø_r’. Artificial neural networks are computational learning algorithms that derive their structure and learning procedures from phenomena observed in biological nervous systems. Their complex, interconnected structures allow for successful mapping of nonlinear relationships between parameters. This motivated the development and application of a network known as a backpropagation neural network (BPNN) as an alternative predictive model for ø_r’.

The BPNN was trained to successfully map the relationship between indicative soil parameters and ø_r’ using a variety of soil datasets provided from academic literature and other sources. The BPNN’s performance was evaluated using a normalized root mean square error (RMSE) term. It was posited that the BPNN predictions could be improved by training individual networks on soil data subdivided by clay fraction ranges. Analysis showed that the division of data into subsets significantly reduced the BPNN’s predictive performance by limiting the amount of data available for individual network training.

Where other predictive models generally neglect sand fraction as a predictive parameter for ø_r’, this study attempted to evaluate its predictive value. Comparison between a BPNN that included sand fraction and one that did not proved inconclusive as the results from multiple RMSE analyses between the two models were not statistically different.

Correlation-based equations for the prediction of ø_r’ in Stark and Hussain (2013) are based on the subdivision of soil data by clay fraction ranges. A comparison of the BPNN predictive model to other empirical models was performed in order to evaluate the viability of a BPNN as an alternative to current predictive models. The BPNN outperformed a traditional, multivariate least-squares linear regression model as well as correlation-based equations from Stark and Hussain (2013). The normalized root mean square error for Stark and Hussain equation-based predictions of ø_r’ was 0.2270 in comparison to 0.1278 for the BPNN. Where Stark and Hussain’s equations performed well on the particular dataset used to create these empirical equations, it failed to accurately predict ø_r’ for other data sets provided in the literature. The BPNN model was more robust in its ability to predict ø_r’ over a variety of datasets in this study and suggests that the BPNN may provide a viable alternative to other predictive models for ø_r’.

Comments

The attached .pdf file contains a manuscript document, figures and tables document, and appendix.

Creative Commons License

This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License.

Recommended Citation

Detwiler, Luke, "Using Backpropagation Neural Networks for the Prediction of Residual Shear Strength of Cohesive Soils" (2014). UVM Patrick Leahy Honors College Senior Theses. 12.
https://scholarworks.uvm.edu/hcoltheses/12