Abstract
This paper presents the experimental database and corresponding statistical analysis (Part I), which serves as a basis to perform the corresponding parametric analysis and machine learning modelling (Part II) of a comprehensive study on organic soil strength and stiffness, stabilized via the wet soil mixing method. The experimental database includes unconfined compression tests performed under laboratory-controlled conditions to investigate the impact of soil type, the soil’s organic content, the soil’s initial natural water content, binder type, binder quantity, grout to soil ratio, water to binder ratio, curing time, temperature, curing relative humidity and carbon dioxide content on the stabilized organic specimens’ stiffness and strength. A descriptive statistical analysis complements the description of the experimental database, along with a qualitative study on the stabilization hydration process via scanning electron microscopy images. Results confirmed findings on the use of Portland cement alone and a mix of Portland cement with ground granulated blast furnace slag as suitable binders for soil stabilization. Findings on mixes including lime and magnesium oxide cements demonstrated minimal stabilization. Specimen size affected stiffness, but not the strength for mixes of peat and Portland cement. The experimental database, along with all produced data analyses, are available at the Texas Data Repository as indicated in the Data Availability Statement below, to allow for data reproducibility and promote the use of artificial intelligence and machine learning competing modelling techniques as the ones presented in Part II of this paper.
Highlights
The building of superstructures on organic soils has always been a great challenge to geotechnical engineers due to the high compressibility and low strength on these soil types
The key control variables included in the statistical analysis were the specimen’s diameter to height ratio (D/H), initial water content of the soil (w), organic content (OC), quantity of binder (QB), water to binder ratio (W/B), grout to soil ratio (G/S), curing time (t), curing temperature (T), curing relative humidity (RH), carbonation (CO2), ratio of binder for Portland cement (RB-PC), ratio of binder for ground granulated blast furnace slag (RB-Ground granulated blast furnace slag (GGBS)), ratio of binder for pulverized fuel ash (RB-PFA), ratio of binder for lime (RB-L), ratio of binder for magnesium oxide (RB-magnesium oxide cement (MgO-C)), and ratio of binder for gypsum (RB-G)
This work included a descriptive statistics analysis focusing on the effects produced by binder type, quantity of binder, specimen size and the effect of curing time on the soils’ strength and stiffness
Summary
The building of superstructures on organic soils has always been a great challenge to geotechnical engineers due to the high compressibility and low strength on these soil types. The main focus of this work is to set the basis to explore, via machine learning modelling (Part II), the effects of the control variables—soil type, the soil’s organic content, the soil’s initial natural water content, binder type, binder quantity, grout to soil ratio, water to binder ratio, curing time, temperature, curing relative humidity and carbon dioxide content—on the stiffness and strength of the resulting stabilized organic specimens. These variables were investigated experimentally by preparing mechanically different mixtures under a laboratory-controlled environment. All experimental investigations were included in a database, which served as the basis source of information for the generation of the present work [2,34,35,36,37,38]
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