Shear Wave Velocity Prediction with Hyperparameter Optimization
Shear wave velocity (V<sub>s</sub>) is an important soil parameter to be known for earthquake-resistant structural design and an important parameter for determining the dynamic properties of soils such as modulus of elasticity and shear modulus. Different V<sub>s</sub> measur...
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2025-01-01
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| author | Gebrail Bekdaş Yaren Aydın Umit Işıkdağ Sinan Melih Nigdeli Dara Hajebi Tae-Hyung Kim Zong Woo Geem |
| author_facet | Gebrail Bekdaş Yaren Aydın Umit Işıkdağ Sinan Melih Nigdeli Dara Hajebi Tae-Hyung Kim Zong Woo Geem |
| author_sort | Gebrail Bekdaş |
| collection | DOAJ |
| description | Shear wave velocity (V<sub>s</sub>) is an important soil parameter to be known for earthquake-resistant structural design and an important parameter for determining the dynamic properties of soils such as modulus of elasticity and shear modulus. Different V<sub>s</sub> measurement methods are available. However, these methods, which are costly and labor intensive, have led to the search for new methods for determining the V<sub>s</sub>. This study aims to predict shear wave velocity (V<sub>s</sub> (m/s)) using depth (m), cone resistance (q<sub>c</sub>) (MPa), sleeve friction (f<sub>s</sub>) (kPa), pore water pressure (u<sub>2</sub>) (kPa), N, and unit weight (kN/m<sup>3</sup>). Since shear wave velocity varies with depth, regression studies were performed at depths up to 30 m in this study. The dataset used in this study is an open-source dataset, and the soil data are from the Taipei Basin. This dataset was extracted, and a 494-line dataset was created. In this study, using HyperNetExplorer 2024V1, V<sub>s</sub> prediction based on depth (m), cone resistance (q<sub>c</sub>) (MPa), shell friction (f<sub>s</sub>), pore water pressure (u<sub>2</sub>) (kPa), N, and unit weight (kN/m<sup>3</sup>) values could be performed with satisfactory results (R<sup>2</sup> = 0.78, MSE = 596.43). Satisfactory results were obtained in this study, in which Explainable Artificial Intelligence (XAI) models were also used. |
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| institution | Kabale University |
| issn | 2078-2489 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
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| spelling | doaj-art-009ea80a28ba42e38af5df20a1d0549e2025-01-24T13:35:19ZengMDPI AGInformation2078-24892025-01-011616010.3390/info16010060Shear Wave Velocity Prediction with Hyperparameter OptimizationGebrail Bekdaş0Yaren Aydın1Umit Işıkdağ2Sinan Melih Nigdeli3Dara Hajebi4Tae-Hyung Kim5Zong Woo Geem6Department of Civil Engineering, Istanbul University-Cerrahpaşa, 34320 Istanbul, TurkeyDepartment of Civil Engineering, Istanbul University-Cerrahpaşa, 34320 Istanbul, TurkeyDepartment of Architecture, Mimar Sinan Fine Arts University, 34427 Istanbul, TurkeyDepartment of Civil Engineering, Istanbul University-Cerrahpaşa, 34320 Istanbul, TurkeyDepartment of Civil Engineering, Istanbul University-Cerrahpaşa, 34320 Istanbul, TurkeyDepartment of Civil Engineering, Korea Maritime and Ocean University, Busan 49112, Republic of KoreaDepartment of Smart City, Gachon University, Seongnam 13120, Republic of KoreaShear wave velocity (V<sub>s</sub>) is an important soil parameter to be known for earthquake-resistant structural design and an important parameter for determining the dynamic properties of soils such as modulus of elasticity and shear modulus. Different V<sub>s</sub> measurement methods are available. However, these methods, which are costly and labor intensive, have led to the search for new methods for determining the V<sub>s</sub>. This study aims to predict shear wave velocity (V<sub>s</sub> (m/s)) using depth (m), cone resistance (q<sub>c</sub>) (MPa), sleeve friction (f<sub>s</sub>) (kPa), pore water pressure (u<sub>2</sub>) (kPa), N, and unit weight (kN/m<sup>3</sup>). Since shear wave velocity varies with depth, regression studies were performed at depths up to 30 m in this study. The dataset used in this study is an open-source dataset, and the soil data are from the Taipei Basin. This dataset was extracted, and a 494-line dataset was created. In this study, using HyperNetExplorer 2024V1, V<sub>s</sub> prediction based on depth (m), cone resistance (q<sub>c</sub>) (MPa), shell friction (f<sub>s</sub>), pore water pressure (u<sub>2</sub>) (kPa), N, and unit weight (kN/m<sup>3</sup>) values could be performed with satisfactory results (R<sup>2</sup> = 0.78, MSE = 596.43). Satisfactory results were obtained in this study, in which Explainable Artificial Intelligence (XAI) models were also used.https://www.mdpi.com/2078-2489/16/1/60shear wave velocityhyperparameter optimizationpredictionexplainable artificial intelligence |
| spellingShingle | Gebrail Bekdaş Yaren Aydın Umit Işıkdağ Sinan Melih Nigdeli Dara Hajebi Tae-Hyung Kim Zong Woo Geem Shear Wave Velocity Prediction with Hyperparameter Optimization Information shear wave velocity hyperparameter optimization prediction explainable artificial intelligence |
| title | Shear Wave Velocity Prediction with Hyperparameter Optimization |
| title_full | Shear Wave Velocity Prediction with Hyperparameter Optimization |
| title_fullStr | Shear Wave Velocity Prediction with Hyperparameter Optimization |
| title_full_unstemmed | Shear Wave Velocity Prediction with Hyperparameter Optimization |
| title_short | Shear Wave Velocity Prediction with Hyperparameter Optimization |
| title_sort | shear wave velocity prediction with hyperparameter optimization |
| topic | shear wave velocity hyperparameter optimization prediction explainable artificial intelligence |
| url | https://www.mdpi.com/2078-2489/16/1/60 |
| work_keys_str_mv | AT gebrailbekdas shearwavevelocitypredictionwithhyperparameteroptimization AT yarenaydın shearwavevelocitypredictionwithhyperparameteroptimization AT umitisıkdag shearwavevelocitypredictionwithhyperparameteroptimization AT sinanmelihnigdeli shearwavevelocitypredictionwithhyperparameteroptimization AT darahajebi shearwavevelocitypredictionwithhyperparameteroptimization AT taehyungkim shearwavevelocitypredictionwithhyperparameteroptimization AT zongwoogeem shearwavevelocitypredictionwithhyperparameteroptimization |