An Ensemble Method for Soil Parameter Prediction Based on Multisource Data Fusion
Site investigation is crucial in geotechnical engineering. The cone penetration test (CPT) and the multichannel analysis of surface waves (MASWs) are widely used as geotechnical and geophysical methods, respectively. CPT offers high precision but requires a high cost and only provides soil informati...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-01-01
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| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/adce/6623874 |
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| _version_ | 1846150221322518528 |
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| author | Mingyuan Wang Shaoxiang Zeng Zuguo Zhang Songting Chen Jun Wang |
| author_facet | Mingyuan Wang Shaoxiang Zeng Zuguo Zhang Songting Chen Jun Wang |
| author_sort | Mingyuan Wang |
| collection | DOAJ |
| description | Site investigation is crucial in geotechnical engineering. The cone penetration test (CPT) and the multichannel analysis of surface waves (MASWs) are widely used as geotechnical and geophysical methods, respectively. CPT offers high precision but requires a high cost and only provides soil information at limited locations. In contrast, MASW covers a broad range of soil information but has less accuracy compared to CPT. This study proposes a novel ensemble prediction method that fuses both CPT and MASW data to overcome the limitations of using either dataset alone. The method employs random forest (RF) and gradient boosting decision tree (GBDT) to achieve the transformation between the shear velocity and cone tip resistance (Vs–qc) and the prediction of qc at unknown locations. Unlike traditional empirical regression models, this method provides more accurate and reliable predictions by leveraging the complementary strengths of CPT and MASW. The proposed RF-GBDT ensemble model is validated using data from the New Zealand Geotechnical Database. The results show that the established RF-GBDT ensemble model outperforms simple empirical regression models and various popular machine learning models in the Vs–qc transformation and in predicting qc at unknown locations. Specifically, integrating MASW data increases the R2 at location CPT3 from 0.477 to 0.758, demonstrating that the proposed method can improve the predictions of soil parameters in areas with sparse data. |
| format | Article |
| id | doaj-art-1748e6dbc29848c8b12a15adc2a3dcb3 |
| institution | Kabale University |
| issn | 1687-8094 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Civil Engineering |
| spelling | doaj-art-1748e6dbc29848c8b12a15adc2a3dcb32024-11-29T05:00:06ZengWileyAdvances in Civil Engineering1687-80942024-01-01202410.1155/adce/6623874An Ensemble Method for Soil Parameter Prediction Based on Multisource Data FusionMingyuan Wang0Shaoxiang Zeng1Zuguo Zhang2Songting Chen3Jun Wang4Technical Advisory CenterCollege of Civil EngineeringEngineering Exploration and Survey DepartmentCollege of Civil EngineeringTechnical Advisory CenterSite investigation is crucial in geotechnical engineering. The cone penetration test (CPT) and the multichannel analysis of surface waves (MASWs) are widely used as geotechnical and geophysical methods, respectively. CPT offers high precision but requires a high cost and only provides soil information at limited locations. In contrast, MASW covers a broad range of soil information but has less accuracy compared to CPT. This study proposes a novel ensemble prediction method that fuses both CPT and MASW data to overcome the limitations of using either dataset alone. The method employs random forest (RF) and gradient boosting decision tree (GBDT) to achieve the transformation between the shear velocity and cone tip resistance (Vs–qc) and the prediction of qc at unknown locations. Unlike traditional empirical regression models, this method provides more accurate and reliable predictions by leveraging the complementary strengths of CPT and MASW. The proposed RF-GBDT ensemble model is validated using data from the New Zealand Geotechnical Database. The results show that the established RF-GBDT ensemble model outperforms simple empirical regression models and various popular machine learning models in the Vs–qc transformation and in predicting qc at unknown locations. Specifically, integrating MASW data increases the R2 at location CPT3 from 0.477 to 0.758, demonstrating that the proposed method can improve the predictions of soil parameters in areas with sparse data.http://dx.doi.org/10.1155/adce/6623874 |
| spellingShingle | Mingyuan Wang Shaoxiang Zeng Zuguo Zhang Songting Chen Jun Wang An Ensemble Method for Soil Parameter Prediction Based on Multisource Data Fusion Advances in Civil Engineering |
| title | An Ensemble Method for Soil Parameter Prediction Based on Multisource Data Fusion |
| title_full | An Ensemble Method for Soil Parameter Prediction Based on Multisource Data Fusion |
| title_fullStr | An Ensemble Method for Soil Parameter Prediction Based on Multisource Data Fusion |
| title_full_unstemmed | An Ensemble Method for Soil Parameter Prediction Based on Multisource Data Fusion |
| title_short | An Ensemble Method for Soil Parameter Prediction Based on Multisource Data Fusion |
| title_sort | ensemble method for soil parameter prediction based on multisource data fusion |
| url | http://dx.doi.org/10.1155/adce/6623874 |
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