Predicting Geotechnical Parameters from Seismic Wave Velocity Using Artificial Neural Networks
Geotechnical investigation plays an indispensable role in site characterization and provides necessary data for various construction projects. However, geotechnical measurements are time-consuming, point-based, and invasive. Non-destructive geophysical measurements (seismic wave velocity) can comple...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/24/12815 |
| _version_ | 1797461645829603328 |
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| author | Fatema Tuz Johora Craig J. Hickey Hakan Yasarer |
| author_facet | Fatema Tuz Johora Craig J. Hickey Hakan Yasarer |
| author_sort | Fatema Tuz Johora |
| collection | DOAJ |
| description | Geotechnical investigation plays an indispensable role in site characterization and provides necessary data for various construction projects. However, geotechnical measurements are time-consuming, point-based, and invasive. Non-destructive geophysical measurements (seismic wave velocity) can complement geotechnical measurements to save project money and time. However, correlations between geotechnical and seismic wave velocity are crucial in order to maximize the benefit of geophysical information. In this work, artificial neural networks (ANNs) models are developed to forecast geotechnical parameters from seismic wave velocity. Specifically, published seismic wave velocity, liquid limit, plastic limit, water content, and dry density from field and laboratory measurements are used to develop ANN models. Due to the small number of data, models are developed with and without the validation step in order to use more data for training. The results indicate that the performance of the models is improved by using more data for training. For example, predicting seismic wave velocity using more data for training improves the R<sup>2</sup> value from 0.50 to 0.78 and reduces the ASE from 0.0174 to 0.0075, and MARE from 30.75 to 18.53. The benefit of adding velocity as an input parameter for predicting water content and dry density is assessed by comparing models with and without velocity. Models incorporating the velocity information show better predictability in most cases. For example, predicting water content using field data including the velocity improves the R<sup>2</sup> from 0.75 to 0.85 and reduces the ASE from 0.0087 to 0.0051, and MARE from 10.68 to 7.78. A comparison indicates that ANN models outperformed multilinear regression models. For example, predicting seismic wave velocity using field plus lab data has an ANN derived R<sup>2</sup> value that is 81.39% higher than regression model. |
| first_indexed | 2024-03-09T17:22:22Z |
| format | Article |
| id | doaj.art-ad3dadb387b3465281fa541181a898ea |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T17:22:22Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-ad3dadb387b3465281fa541181a898ea2023-11-24T13:05:14ZengMDPI AGApplied Sciences2076-34172022-12-0112241281510.3390/app122412815Predicting Geotechnical Parameters from Seismic Wave Velocity Using Artificial Neural NetworksFatema Tuz Johora0Craig J. Hickey1Hakan Yasarer2Civil Engineering Department, University of Mississippi, Oxford, MS 38677, USANational Center for Physical Acoustics, Oxford, MS 38677, USACivil Engineering Department, University of Mississippi, Oxford, MS 38677, USAGeotechnical investigation plays an indispensable role in site characterization and provides necessary data for various construction projects. However, geotechnical measurements are time-consuming, point-based, and invasive. Non-destructive geophysical measurements (seismic wave velocity) can complement geotechnical measurements to save project money and time. However, correlations between geotechnical and seismic wave velocity are crucial in order to maximize the benefit of geophysical information. In this work, artificial neural networks (ANNs) models are developed to forecast geotechnical parameters from seismic wave velocity. Specifically, published seismic wave velocity, liquid limit, plastic limit, water content, and dry density from field and laboratory measurements are used to develop ANN models. Due to the small number of data, models are developed with and without the validation step in order to use more data for training. The results indicate that the performance of the models is improved by using more data for training. For example, predicting seismic wave velocity using more data for training improves the R<sup>2</sup> value from 0.50 to 0.78 and reduces the ASE from 0.0174 to 0.0075, and MARE from 30.75 to 18.53. The benefit of adding velocity as an input parameter for predicting water content and dry density is assessed by comparing models with and without velocity. Models incorporating the velocity information show better predictability in most cases. For example, predicting water content using field data including the velocity improves the R<sup>2</sup> from 0.75 to 0.85 and reduces the ASE from 0.0087 to 0.0051, and MARE from 10.68 to 7.78. A comparison indicates that ANN models outperformed multilinear regression models. For example, predicting seismic wave velocity using field plus lab data has an ANN derived R<sup>2</sup> value that is 81.39% higher than regression model.https://www.mdpi.com/2076-3417/12/24/12815field measurementlaboratory measurementmultilinear regression analysisartificial neural networks |
| spellingShingle | Fatema Tuz Johora Craig J. Hickey Hakan Yasarer Predicting Geotechnical Parameters from Seismic Wave Velocity Using Artificial Neural Networks Applied Sciences field measurement laboratory measurement multilinear regression analysis artificial neural networks |
| title | Predicting Geotechnical Parameters from Seismic Wave Velocity Using Artificial Neural Networks |
| title_full | Predicting Geotechnical Parameters from Seismic Wave Velocity Using Artificial Neural Networks |
| title_fullStr | Predicting Geotechnical Parameters from Seismic Wave Velocity Using Artificial Neural Networks |
| title_full_unstemmed | Predicting Geotechnical Parameters from Seismic Wave Velocity Using Artificial Neural Networks |
| title_short | Predicting Geotechnical Parameters from Seismic Wave Velocity Using Artificial Neural Networks |
| title_sort | predicting geotechnical parameters from seismic wave velocity using artificial neural networks |
| topic | field measurement laboratory measurement multilinear regression analysis artificial neural networks |
| url | https://www.mdpi.com/2076-3417/12/24/12815 |
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