Modeling nonlinear elastic behavior of reinforced soil using artificial neural networks |
| |
| Authors: | Shouling He Jiang Li |
| |
| Affiliation: | 1. Department of Electrical and Computer Engineering, Penn State Erie, The Behrend College, PA 16563, USA;2. Department of Civil Engineering, Morgan State University, Baltimore, MD 21251, USA;1. Geotechnical Research Institute, College of Civil and Transportation Engineering, Hohai University, Nanjing, China;2. Department of Civil and Environmental Engineering, Hong Kong University of Science and Technology, Clearwater Bay, Kowloon, Hong Kong;3. State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan, China;1. Department of Engineering, Virginia State University, P.O. Box 9032, Petersburg, VA 23806, United States;2. Texas Transportation Institution, The Texas A&M University System, College Station, TX 77843, United States;1. Department of Civil and Environmental Engineering, Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China;2. School of Civil Engineering & Built Environment, Science and Engineering Faculty, Queensland University of Technology (QUT), Brisbane, Qld 4001, Australia |
| |
| Abstract: | This paper presents the application using a multilayer neural network to model nonlinear elastic behavior of composite soil reinforced with fiber and stabilized with lime. First, shear modulus of the reinforced soil was assumed to be a nonlinear function of multiple variables such as contents of short fiber and lime powder, confining pressure, sample-aging period as well as shear strain. Secondly, a multilayer neural network was designed to map the highly nonlinear relationship between shear stress and strain. Thirdly, conventional triaxial shearing tests have been conducted for 34 sets of soil samples to provide experimental data for training and validating the neural network model. Finally, the neural network-based parameter sensitivities have been analyzed. The results of sensitivity analysis indicate that the lime content and the sample curing time play more significant roles than the fiber content in improving soil mechanical properties. It is the first attempt to apply the neural network to modeling of elastic behavior of composite soils, and has been found that modeling of reinforced soil using a multilayer neural network can provide more quality information on the performance of reinforced soil for better decision-making and continuous improvement of construction material designs. |
| |
| Keywords: | |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
