Soil Classification from Piezocone Penetration Test Using Fuzzy Clustering and Neuro-Fuzzy Theory
The advantage of the piezocone penetration test is a guarantee of continuous data, which are a source of reliable interpretation of the target soil layer. Much research has been carried out for several decades, and several classification charts have been developed to classify in situ soil from the c...
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| Format: | Article |
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MDPI AG
2022-04-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/8/4023 |
| _version_ | 1797436962979708928 |
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| author | Joon-Shik Moon Chan-Hong Kim Young-Sang Kim |
| author_facet | Joon-Shik Moon Chan-Hong Kim Young-Sang Kim |
| author_sort | Joon-Shik Moon |
| collection | DOAJ |
| description | The advantage of the piezocone penetration test is a guarantee of continuous data, which are a source of reliable interpretation of the target soil layer. Much research has been carried out for several decades, and several classification charts have been developed to classify in situ soil from the cone penetration test result. Even though most present classification charts or methods were developed on the basis of data which were compiled over many countries, they should be verified to be feasible for local country. However, unfortunately, revision of those charts is quite difficult or almost impossible even though a chart provides misclassified soil class. In this research, a new method for developing soil classification model is proposed by using soft computing theory—fuzzy C-mean clustering and neuro-fuzzy theory—as a function of 5173 piezocone penetration test (PCPT) results and soil boring logs compiled from 17 local sites around Korea. Feasibility of the proposed soil classification model was verified from the viewpoint of accuracy of the classification result by comparing the classification results not only for data which were used for developing the model but also new data, which were not included in developing the model with real boring logs, other fuzzy computing classification models, and Robertson’s charts. The biggest advantage of the proposed method is that it is easy to make the piezocone soil classification system more accurate by updating new data. |
| first_indexed | 2024-03-09T11:11:09Z |
| format | Article |
| id | doaj.art-873f7e1c4aea418387a6ff521aba1eb1 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T11:11:09Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-873f7e1c4aea418387a6ff521aba1eb12023-12-01T00:43:36ZengMDPI AGApplied Sciences2076-34172022-04-01128402310.3390/app12084023Soil Classification from Piezocone Penetration Test Using Fuzzy Clustering and Neuro-Fuzzy TheoryJoon-Shik Moon0Chan-Hong Kim1Young-Sang Kim2Department of Civil Engineering, Kyungpook National University, Daegu 41566, KoreaKorea Mine Rehabilitation and Mineral Resources Corporation, Wonju 26464, KoreaDepartment of Civil Engineering, Chonnam National University, Gwangju 61186, KoreaThe advantage of the piezocone penetration test is a guarantee of continuous data, which are a source of reliable interpretation of the target soil layer. Much research has been carried out for several decades, and several classification charts have been developed to classify in situ soil from the cone penetration test result. Even though most present classification charts or methods were developed on the basis of data which were compiled over many countries, they should be verified to be feasible for local country. However, unfortunately, revision of those charts is quite difficult or almost impossible even though a chart provides misclassified soil class. In this research, a new method for developing soil classification model is proposed by using soft computing theory—fuzzy C-mean clustering and neuro-fuzzy theory—as a function of 5173 piezocone penetration test (PCPT) results and soil boring logs compiled from 17 local sites around Korea. Feasibility of the proposed soil classification model was verified from the viewpoint of accuracy of the classification result by comparing the classification results not only for data which were used for developing the model but also new data, which were not included in developing the model with real boring logs, other fuzzy computing classification models, and Robertson’s charts. The biggest advantage of the proposed method is that it is easy to make the piezocone soil classification system more accurate by updating new data.https://www.mdpi.com/2076-3417/12/8/4023piezoconesoil classificationfuzzy C-means clusteringneuro-fuzzy |
| spellingShingle | Joon-Shik Moon Chan-Hong Kim Young-Sang Kim Soil Classification from Piezocone Penetration Test Using Fuzzy Clustering and Neuro-Fuzzy Theory Applied Sciences piezocone soil classification fuzzy C-means clustering neuro-fuzzy |
| title | Soil Classification from Piezocone Penetration Test Using Fuzzy Clustering and Neuro-Fuzzy Theory |
| title_full | Soil Classification from Piezocone Penetration Test Using Fuzzy Clustering and Neuro-Fuzzy Theory |
| title_fullStr | Soil Classification from Piezocone Penetration Test Using Fuzzy Clustering and Neuro-Fuzzy Theory |
| title_full_unstemmed | Soil Classification from Piezocone Penetration Test Using Fuzzy Clustering and Neuro-Fuzzy Theory |
| title_short | Soil Classification from Piezocone Penetration Test Using Fuzzy Clustering and Neuro-Fuzzy Theory |
| title_sort | soil classification from piezocone penetration test using fuzzy clustering and neuro fuzzy theory |
| topic | piezocone soil classification fuzzy C-means clustering neuro-fuzzy |
| url | https://www.mdpi.com/2076-3417/12/8/4023 |
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