Stiffness and strength of stabilized organic soils—part i/ii: Experimental database and statistical description for machine learning modelling

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Hernandez-Martinez, FG 
Al-Tabbaa, A 
Medina-Cetina, Z 
Yousefpour, N 

jats:pThis 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.</jats:p>

stabilized organic soils, wet soil mixing methods, strength, stiffness, descriptive statistics, unconfined elastic tangent modulus, unconfined compression strength, soil microstructure imaging, database, machine learning
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Geosciences (Switzerland)
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Texas Aamp (Zachry Career Development Professorship II at Texas AM University)