Estimating Liquefaction Susceptibility Using Machine Learning Algorithms with a Case of Metro Manila, Philippines
Soil liquefaction is a phenomenon that can occur when soil loses strength and behaves like a liquid during an earthquake. A site investigation is essential for determining a site’s susceptibility to liquefaction, and these investigations frequently generate project-specific geotechnical reports. How...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/11/6549 |
| _version_ | 1797597894848544768 |
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| author | Joenel Galupino Jonathan Dungca |
| author_facet | Joenel Galupino Jonathan Dungca |
| author_sort | Joenel Galupino |
| collection | DOAJ |
| description | Soil liquefaction is a phenomenon that can occur when soil loses strength and behaves like a liquid during an earthquake. A site investigation is essential for determining a site’s susceptibility to liquefaction, and these investigations frequently generate project-specific geotechnical reports. However, many of these reports are frequently stored unused after construction projects are completed. This study suggests that when these unused reports are consolidated and integrated, they can provide valuable information for identifying potential challenges, such as liquefaction. The study evaluates the susceptibility of liquefaction by considering several geotechnical factors modeled by machine learning algorithms. The study estimated site-specific characteristics, such as ground elevation, groundwater table elevation, SPT N-value, soil type, and fines content. Using a calibrated model represented by an equation, the investigation determined several soil properties, including the unit weight and peak ground acceleration (PGA). The study estimated PGA using a linear model, which revealed a significant positive correlation (R<sup>2</sup> = 0.89) between PGA, earthquake magnitude, and distance from the seismic source. On the Marikina West Valley Fault, the study also assessed the liquefaction hazard for an anticipated 7.5 M and delineated a map that was validated by prior studies. |
| first_indexed | 2024-03-11T03:11:50Z |
| format | Article |
| id | doaj.art-c8c919540c994b48819952721405201c |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T03:11:50Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-c8c919540c994b48819952721405201c2023-11-18T07:33:37ZengMDPI AGApplied Sciences2076-34172023-05-011311654910.3390/app13116549Estimating Liquefaction Susceptibility Using Machine Learning Algorithms with a Case of Metro Manila, PhilippinesJoenel Galupino0Jonathan Dungca1Department of Civil Engineering, De La Salle University, Manila 1004, PhilippinesDepartment of Civil Engineering, De La Salle University, Manila 1004, PhilippinesSoil liquefaction is a phenomenon that can occur when soil loses strength and behaves like a liquid during an earthquake. A site investigation is essential for determining a site’s susceptibility to liquefaction, and these investigations frequently generate project-specific geotechnical reports. However, many of these reports are frequently stored unused after construction projects are completed. This study suggests that when these unused reports are consolidated and integrated, they can provide valuable information for identifying potential challenges, such as liquefaction. The study evaluates the susceptibility of liquefaction by considering several geotechnical factors modeled by machine learning algorithms. The study estimated site-specific characteristics, such as ground elevation, groundwater table elevation, SPT N-value, soil type, and fines content. Using a calibrated model represented by an equation, the investigation determined several soil properties, including the unit weight and peak ground acceleration (PGA). The study estimated PGA using a linear model, which revealed a significant positive correlation (R<sup>2</sup> = 0.89) between PGA, earthquake magnitude, and distance from the seismic source. On the Marikina West Valley Fault, the study also assessed the liquefaction hazard for an anticipated 7.5 M and delineated a map that was validated by prior studies.https://www.mdpi.com/2076-3417/13/11/6549unit weightmachine learningSPTliquefactionPhilippines |
| spellingShingle | Joenel Galupino Jonathan Dungca Estimating Liquefaction Susceptibility Using Machine Learning Algorithms with a Case of Metro Manila, Philippines Applied Sciences unit weight machine learning SPT liquefaction Philippines |
| title | Estimating Liquefaction Susceptibility Using Machine Learning Algorithms with a Case of Metro Manila, Philippines |
| title_full | Estimating Liquefaction Susceptibility Using Machine Learning Algorithms with a Case of Metro Manila, Philippines |
| title_fullStr | Estimating Liquefaction Susceptibility Using Machine Learning Algorithms with a Case of Metro Manila, Philippines |
| title_full_unstemmed | Estimating Liquefaction Susceptibility Using Machine Learning Algorithms with a Case of Metro Manila, Philippines |
| title_short | Estimating Liquefaction Susceptibility Using Machine Learning Algorithms with a Case of Metro Manila, Philippines |
| title_sort | estimating liquefaction susceptibility using machine learning algorithms with a case of metro manila philippines |
| topic | unit weight machine learning SPT liquefaction Philippines |
| url | https://www.mdpi.com/2076-3417/13/11/6549 |
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