Interpretable artificial intelligence approach for understanding shear strength in stabilized clay soils using real field soil samples

Abstract

Deep mixing, also known as deep stabilization, is a widely used ground improvement method in Nordic countries, particularly in urban and infrastructural projects, aiming to enhance the properties of soft, sensitive clays. Understanding the shear strength of stabilized soils and identifying key influencing factors are essential for ensuring the structural stability and durability of engineering structures. This study introduces a novel explainable artificial intelligence framework to investigate critical soil properties affecting shear strength, utilizing a data set derived from stabilization tests conducted on laboratory samples from the 1990s. The proposed framework investigates the statistical variability and distribution of crucial parameters affecting shear strength within the collected data set. Subsequently, machine learning models are trained and tested to predict soil shear strength based on input features such as water/binder ratio and water content, etc. Global model analysis using feature importance and Shapley additive explanations is conducted to understand the influence of soil input features on shear strength. Further exploration is carried out using partial dependence plots, individual conditional expectation plots, and accumulated local effects to uncover the degree of dependency and important thresholds between key stabilized soil parameters and shear strength. Heat map and feature interaction analysis techniques are then utilized to investigate soil properties interactions and correlations. Lastly, a more specific investigation is conducted on particular soil samples to highlight the most influential soil properties locally, employing the local interpretable model-agnostic explanations technique. The validation of the framework involves analyzing laboratory test results obtained from uniaxial compression tests. The framework demonstrates an ability to predict the shear strength of stabilized soil samples with an accuracy surpassing 90%. Importantly, the explainability results underscore the substantial impact of water content and the water/binder ratio on shear strength.

Graphical abstract

Keywords

explainable artificial intelligence / Shapley additive explanations / local interpretable model-agnostic explanations / partial dependence plots / stabilized soil / water/binder ratio / water content / shear strength

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