{"title":"水泥处理粘土强度发展的统计与预测分析","authors":"A. Abdallah, G. Russo, O. Cuisinier","doi":"10.3390/geotechnics3020026","DOIUrl":null,"url":null,"abstract":"The mechanical efficiency of soil stabilization with cement is mainly controlled by various parameters, namely, the amount of binder, the compaction soil state and the curing conditions. The strength of the treated soil is the result of a complex combination of several factors that condition the physicochemical processes involved in cement hydration, which are difficult to monitor. The objective of this study is to identify the relevant parameters governing the bonding in cement-treated soil and suggest a predictive model for strength evolution using these parameters as input. To this purpose, an extensive testing program is presented to assess the impact of the initial water content (11–18%) and dry density (1.6–1.87 Mg/m3) as well as cement dosage (3 and 6%) in sealed curing conditions for 0, 7, 28 and 90 days. The water content variation, the total suction and the compressive strength were measured after different curing durations. The experimental results are first discussed in the parameters’ space, and then through a principal components analysis to overcome the complexity due to the parameters’ interdependency. The PCA revealed that the cement dosage explained 40% of the dataset variance, the remaining 60% being related to a combination of the initial state and curing time. Finally, a predictive model based on an artificial neural network was developed and tested. The predicted results were excellent, with an R2 of +0.99 with the training data and +0.93 with the testing data. These results should be improved by extending the dataset to include different soils and additional compaction conditions.","PeriodicalId":11823,"journal":{"name":"Environmental geotechnics","volume":"45 1","pages":""},"PeriodicalIF":2.2000,"publicationDate":"2023-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Statistical and Predictive Analyses of the Strength Development of a Cement-Treated Clayey Soil\",\"authors\":\"A. Abdallah, G. Russo, O. Cuisinier\",\"doi\":\"10.3390/geotechnics3020026\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The mechanical efficiency of soil stabilization with cement is mainly controlled by various parameters, namely, the amount of binder, the compaction soil state and the curing conditions. The strength of the treated soil is the result of a complex combination of several factors that condition the physicochemical processes involved in cement hydration, which are difficult to monitor. The objective of this study is to identify the relevant parameters governing the bonding in cement-treated soil and suggest a predictive model for strength evolution using these parameters as input. To this purpose, an extensive testing program is presented to assess the impact of the initial water content (11–18%) and dry density (1.6–1.87 Mg/m3) as well as cement dosage (3 and 6%) in sealed curing conditions for 0, 7, 28 and 90 days. The water content variation, the total suction and the compressive strength were measured after different curing durations. The experimental results are first discussed in the parameters’ space, and then through a principal components analysis to overcome the complexity due to the parameters’ interdependency. The PCA revealed that the cement dosage explained 40% of the dataset variance, the remaining 60% being related to a combination of the initial state and curing time. Finally, a predictive model based on an artificial neural network was developed and tested. The predicted results were excellent, with an R2 of +0.99 with the training data and +0.93 with the testing data. These results should be improved by extending the dataset to include different soils and additional compaction conditions.\",\"PeriodicalId\":11823,\"journal\":{\"name\":\"Environmental geotechnics\",\"volume\":\"45 1\",\"pages\":\"\"},\"PeriodicalIF\":2.2000,\"publicationDate\":\"2023-06-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Environmental geotechnics\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.3390/geotechnics3020026\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENGINEERING, GEOLOGICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Environmental geotechnics","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.3390/geotechnics3020026","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, GEOLOGICAL","Score":null,"Total":0}
Statistical and Predictive Analyses of the Strength Development of a Cement-Treated Clayey Soil
The mechanical efficiency of soil stabilization with cement is mainly controlled by various parameters, namely, the amount of binder, the compaction soil state and the curing conditions. The strength of the treated soil is the result of a complex combination of several factors that condition the physicochemical processes involved in cement hydration, which are difficult to monitor. The objective of this study is to identify the relevant parameters governing the bonding in cement-treated soil and suggest a predictive model for strength evolution using these parameters as input. To this purpose, an extensive testing program is presented to assess the impact of the initial water content (11–18%) and dry density (1.6–1.87 Mg/m3) as well as cement dosage (3 and 6%) in sealed curing conditions for 0, 7, 28 and 90 days. The water content variation, the total suction and the compressive strength were measured after different curing durations. The experimental results are first discussed in the parameters’ space, and then through a principal components analysis to overcome the complexity due to the parameters’ interdependency. The PCA revealed that the cement dosage explained 40% of the dataset variance, the remaining 60% being related to a combination of the initial state and curing time. Finally, a predictive model based on an artificial neural network was developed and tested. The predicted results were excellent, with an R2 of +0.99 with the training data and +0.93 with the testing data. These results should be improved by extending the dataset to include different soils and additional compaction conditions.
期刊介绍:
In 21st century living, engineers and researchers need to deal with growing problems related to climate change, oil and water storage, handling, storage and disposal of toxic and hazardous wastes, remediation of contaminated sites, sustainable development and energy derived from the ground.
Environmental Geotechnics aims to disseminate knowledge and provides a fresh perspective regarding the basic concepts, theory, techniques and field applicability of innovative testing and analysis methodologies and engineering practices in geoenvironmental engineering.
The journal''s Editor in Chief is a Member of the Committee on Publication Ethics.
All relevant papers are carefully considered, vetted by a distinguished team of international experts and rapidly published. Full research papers, short communications and comprehensive review articles are published under the following broad subject categories:
geochemistry and geohydrology,
soil and rock physics, biological processes in soil, soil-atmosphere interaction,
electrical, electromagnetic and thermal characteristics of porous media,
waste management, utilization of wastes, multiphase science, landslide wasting,
soil and water conservation,
sensor development and applications,
the impact of climatic changes on geoenvironmental, geothermal/ground-source energy, carbon sequestration, oil and gas extraction techniques,
uncertainty, reliability and risk, monitoring and forensic geotechnics.