Journal Article

Stiffness and Strength of Stabilized Organic Soils—Part II/II: Parametric Analysis and Modeling with Machine Learning

Medina-Cetina, Zenon, Yousefpour, Negin, Al-Tabbaa, Abir, Hernandez-Martinez, Francisco

2021

Overview

Predicting the range of achievable strength and stiffness from stabilized soil mixtures is critical for engineering design and construction, especially for organic soils, which are often considered “unsuitable” due to their high compressibility and the lack of knowledge about their mechanical behavior after stabilization. This study investigates the mechanical behavior of stabilized organic soils using machine learning (ML) methods. ML algorithms were developed and trained using a database from a comprehensive experimental study (see Part I), including more than one thousand unconfined compression tests on organic clay samples stabilized by wet soil mixing (WSM) technique. Three different ML methods were adopted and compared, including two artificial neural networks (ANN) and a linear regression method. ANN models proved reliable in the prediction of the stiffness and strength of stabilized organic soils, significantly outperforming linear regression models. Binder type, mixing ratio, soil organic and water content, sample size, aging, temperature, relative humidity, and carbonation were the control variables (input parameters) incorporated into the ML models. The impacts of these factors were evaluated through rigorous ANN-based parametric analyses. Additionally, the nonlinear relations of stiffness and strength with these parameters were developed, and their optimum ranges were identified through the ANN models. Overall, the robust ML approach presented in this paper can significantly improve the mixture design for organic soil stabilization and minimize the experimental cost for implementing WSM in engineering projects.

• Publisher: MDPI AG
• Subject: Ageing / Aging / Algorithms / Artificial neural networks / Carbon dioxide / Carbonation / Cement / Clay / Compressibility / Compression / Compression tests / Design engineering / Earth science / Humidity / Investigations / Learning algorithms / Learning behaviour / Learning theory / Machine learning / Mechanical properties / Methods / Mixing ratio / Moisture content / Neural networks / Neurons / Organic soils / Parameter identification / Parameters / Parametric analysis / Parametric statistics / regression / Regression analysis / Regression models / Relative humidity / Soil / Soil compressibility / Soil investigations / Soil mechanics / Soil mixing / Soil mixtures / Soil stabilization / Soil temperature / Soil water / Stabilization / stabilized organic soils / Stiffness / Strength / Support vector machines / Variables / Water content