{"title":"基于自组织图法的土壤导电性随机估计","authors":"Kyeongmo Koo, Hyunki Kim","doi":"10.1016/j.sandf.2025.101601","DOIUrl":null,"url":null,"abstract":"<div><div>This study proposes an unsupervised Self-Organizing Map (SOM) approach to enhance saturated hydraulic conductivity (<em>k<sub>sat</sub></em>) estimation. Using the extensive FLSOIL database of 6,487 soil samples from Florida, the SOM-based <em>k<sub>sat</sub></em> estimation model is optimized based on map size and feature selection, then compared with seven empirical equations and three supervised machine learning models. Unlike the other methods, the SOM-based approach provides a probabilistic distribution of <em>k<sub>sat</sub></em>, enabling reliability-based design-value determination. Moreover, refining input features particularly by including specific surface area and Kozeny–Carman derived formulas improves accuracy and mitigates bias by the model features, especially in fine-grained soils.</div></div>","PeriodicalId":21857,"journal":{"name":"Soils and Foundations","volume":"65 3","pages":"Article 101601"},"PeriodicalIF":3.3000,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Stochastic estimation of soil hydraulic conductivity utilizing self-organizing map method\",\"authors\":\"Kyeongmo Koo, Hyunki Kim\",\"doi\":\"10.1016/j.sandf.2025.101601\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>This study proposes an unsupervised Self-Organizing Map (SOM) approach to enhance saturated hydraulic conductivity (<em>k<sub>sat</sub></em>) estimation. Using the extensive FLSOIL database of 6,487 soil samples from Florida, the SOM-based <em>k<sub>sat</sub></em> estimation model is optimized based on map size and feature selection, then compared with seven empirical equations and three supervised machine learning models. Unlike the other methods, the SOM-based approach provides a probabilistic distribution of <em>k<sub>sat</sub></em>, enabling reliability-based design-value determination. Moreover, refining input features particularly by including specific surface area and Kozeny–Carman derived formulas improves accuracy and mitigates bias by the model features, especially in fine-grained soils.</div></div>\",\"PeriodicalId\":21857,\"journal\":{\"name\":\"Soils and Foundations\",\"volume\":\"65 3\",\"pages\":\"Article 101601\"},\"PeriodicalIF\":3.3000,\"publicationDate\":\"2025-05-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Soils and Foundations\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0038080625000356\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, GEOLOGICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Soils and Foundations","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0038080625000356","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, GEOLOGICAL","Score":null,"Total":0}
Stochastic estimation of soil hydraulic conductivity utilizing self-organizing map method
This study proposes an unsupervised Self-Organizing Map (SOM) approach to enhance saturated hydraulic conductivity (ksat) estimation. Using the extensive FLSOIL database of 6,487 soil samples from Florida, the SOM-based ksat estimation model is optimized based on map size and feature selection, then compared with seven empirical equations and three supervised machine learning models. Unlike the other methods, the SOM-based approach provides a probabilistic distribution of ksat, enabling reliability-based design-value determination. Moreover, refining input features particularly by including specific surface area and Kozeny–Carman derived formulas improves accuracy and mitigates bias by the model features, especially in fine-grained soils.
期刊介绍:
Soils and Foundations is one of the leading journals in the field of soil mechanics and geotechnical engineering. It is the official journal of the Japanese Geotechnical Society (JGS)., The journal publishes a variety of original research paper, technical reports, technical notes, as well as the state-of-the-art reports upon invitation by the Editor, in the fields of soil and rock mechanics, geotechnical engineering, and environmental geotechnics. Since the publication of Volume 1, No.1 issue in June 1960, Soils and Foundations will celebrate the 60th anniversary in the year of 2020.
Soils and Foundations welcomes theoretical as well as practical work associated with the aforementioned field(s). Case studies that describe the original and interdisciplinary work applicable to geotechnical engineering are particularly encouraged. Discussions to each of the published articles are also welcomed in order to provide an avenue in which opinions of peers may be fed back or exchanged. In providing latest expertise on a specific topic, one issue out of six per year on average was allocated to include selected papers from the International Symposia which were held in Japan as well as overseas.