Multivariate adaptive regression splines for analysis of geotechnical engineering systems
With the rapid increases in processing speed and memory of low-cost computers, it is not surprising that various advanced computational learning tools such as neural networks have been increasingly used for analyzing or modeling highly nonlinear multivariate engineering problems. These algorithms ar...
| Main Authors: | , |
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| Format: | Journal Article |
| Language: | English |
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2013
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| Online Access: | https://hdl.handle.net/10356/96646 http://hdl.handle.net/10220/9949 |
| _version_ | 1811690611201277952 |
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| author | Zhang, Wengang Goh, Anthony Teck Chee |
| author2 | School of Civil and Environmental Engineering |
| author_facet | School of Civil and Environmental Engineering Zhang, Wengang Goh, Anthony Teck Chee |
| author_sort | Zhang, Wengang |
| collection | NTU |
| description | With the rapid increases in processing speed and memory of low-cost computers, it is not surprising that various advanced computational learning tools such as neural networks have been increasingly used for analyzing or modeling highly nonlinear multivariate engineering problems. These algorithms are useful for analyzing many geotechnical problems, particularly those that lack a precise analytical theory or understanding of the phenomena involved. In situations where measured or numerical data are available, neural networks have been shown to offer great promise for mapping the nonlinear interactions (dependency) between the system’s inputs and outputs. Unlike most computational tools, in neural networks no predefined mathematical relationship between the dependent and independent variables is required. However, neural networks have been criticized for its long training process since the optimal configuration is not known a priori. This paper explores the use of a fairly simple nonparametric regression algorithm known as multivariate adaptive regression splines (MARS) which has the ability to approximate the relationship between the inputs and outputs, and express the relationship mathematically. The main advantages of MARS are its capacity to produce simple, easy-to-interpret models, its ability to estimate the contributions of the input variables, and its computational efficiency. First the MARS algorithm is described. A number of examples are then presented that explore the generalization capabilities and accuracy of this approach in comparison to the back-propagation neural network algorithm. |
| first_indexed | 2024-10-01T06:06:45Z |
| format | Journal Article |
| id | ntu-10356/96646 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:06:45Z |
| publishDate | 2013 |
| record_format | dspace |
| spelling | ntu-10356/966462020-03-07T11:43:44Z Multivariate adaptive regression splines for analysis of geotechnical engineering systems Zhang, Wengang Goh, Anthony Teck Chee School of Civil and Environmental Engineering DRNTU::Engineering::Civil engineering::Geotechnical With the rapid increases in processing speed and memory of low-cost computers, it is not surprising that various advanced computational learning tools such as neural networks have been increasingly used for analyzing or modeling highly nonlinear multivariate engineering problems. These algorithms are useful for analyzing many geotechnical problems, particularly those that lack a precise analytical theory or understanding of the phenomena involved. In situations where measured or numerical data are available, neural networks have been shown to offer great promise for mapping the nonlinear interactions (dependency) between the system’s inputs and outputs. Unlike most computational tools, in neural networks no predefined mathematical relationship between the dependent and independent variables is required. However, neural networks have been criticized for its long training process since the optimal configuration is not known a priori. This paper explores the use of a fairly simple nonparametric regression algorithm known as multivariate adaptive regression splines (MARS) which has the ability to approximate the relationship between the inputs and outputs, and express the relationship mathematically. The main advantages of MARS are its capacity to produce simple, easy-to-interpret models, its ability to estimate the contributions of the input variables, and its computational efficiency. First the MARS algorithm is described. A number of examples are then presented that explore the generalization capabilities and accuracy of this approach in comparison to the back-propagation neural network algorithm. Accepted version 2013-05-21T06:10:17Z 2019-12-06T19:33:22Z 2013-05-21T06:10:17Z 2019-12-06T19:33:22Z 2012 2012 Journal Article Zhang, W. G., & Goh, A. T. C. (2012). Multivariate adaptive regression splines for analysis of geotechnical engineering systems. Computers and Geotechnics, 48, 82-95. 0266-352X https://hdl.handle.net/10356/96646 http://hdl.handle.net/10220/9949 10.1016/j.compgeo.2012.09.016 171104 en Computers and geotechnics © 2012 Elsevier Ltd. This is the author created version of a work that has been peer reviewed and accepted for publication by Computers and Geotechnics, Elsevier Ltd. It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document. The published version is available at: [DOI: http://dx.doi.org/10.1016/j.compgeo.2012.09.016]. application/pdf |
| spellingShingle | DRNTU::Engineering::Civil engineering::Geotechnical Zhang, Wengang Goh, Anthony Teck Chee Multivariate adaptive regression splines for analysis of geotechnical engineering systems |
| title | Multivariate adaptive regression splines for analysis of geotechnical engineering systems |
| title_full | Multivariate adaptive regression splines for analysis of geotechnical engineering systems |
| title_fullStr | Multivariate adaptive regression splines for analysis of geotechnical engineering systems |
| title_full_unstemmed | Multivariate adaptive regression splines for analysis of geotechnical engineering systems |
| title_short | Multivariate adaptive regression splines for analysis of geotechnical engineering systems |
| title_sort | multivariate adaptive regression splines for analysis of geotechnical engineering systems |
| topic | DRNTU::Engineering::Civil engineering::Geotechnical |
| url | https://hdl.handle.net/10356/96646 http://hdl.handle.net/10220/9949 |
| work_keys_str_mv | AT zhangwengang multivariateadaptiveregressionsplinesforanalysisofgeotechnicalengineeringsystems AT gohanthonyteckchee multivariateadaptiveregressionsplinesforanalysisofgeotechnicalengineeringsystems |