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Soil mixing is a ground improvement method that consists of mixing cementitious binders with soil in-situ to create soilcrete. A key parameter in the design and construction of this method is the Unconfined Compressive Strength (UCS) of the soilcrete after a given curing time. This paper explores the intersection of Machine Learning (ML) with geotechnical engineering and soilcrete applications. A database of soilcrete UCS and site/soil/means/methods metadata is compiled from recent projects in the western United States and leveraged to explore UCS prediction with the eXtreme Gradient Boosting (XGBoost) ML algorithm which resulted in a ML model with a R2 value of 88%. To achieve insights from the ML model, the Explainable ML model SHapley Additive exPlanations (SHAP) was then applied to the XGBoost model to explain variable importances and influences for the final UCS prediction value. From this ML application, a blueprint of how to scaffold, feature engineer, and prepare soilcrete data for ML is showcased. Furthermore, the insights obtained from the SHAP model can be further pursued in traditional geotechnical research approaches to expand soil mixing knowledge. 

The abundance of post-earthquake data from the Canterbury, New Zealand (NZ), area can be leveraged for exploring machine learning (ML) opportunities for geotechnical earthquake engineering. Herein, random forest (RF) is chosen as the ML model to be utilized as it is a powerful non-parametric classification model that can also calculate global feature importance post-model building. The results and procedure are presented of building a multiclass liquefaction manifestation classification RF model with features engineered to preserve special relationships. The RF model hyperparameters are optimized with a two-step fivefold crossvalidation grid search to avoid overfitting. The overall model accuracy is 96% over six ordinal categories predicting over the Canterbury earthquake sequence measurements from 2010, 2011, and 2016. The resultant RF model can serve as a blueprint for incorporation of other sources of physical data such as geological maps to widen the bounds of model usability. 

The abundant post-earthquake data from the Canterbury, New Zealand (NZ) area is poised for use with machine learning (ML) to further advance our ability to better predict and understand the effects of liquefaction. Liquefaction manifestation is one of the identifiable effects of liquefaction, a nonlinear phenomenon that is still not well understood. ML algorithms are often termed as “black-box” models that have little to no explainability for the resultant predictions, making them difficult for use in practice. With the SHapley Additive exPlanations (SHAP) algorithm wrapper, mathematically backed explanations can be fit to the model to track input feature influences on the final prediction. In this paper, Random Forest (RF) is chosen as the ML model to be utilized as it is a powerful non-parametric classification model, then SHAP is applied to calculate explanations for the predictions at a global and local feature scale. The RF model hyperparameters are optimized with a two-step grid search and a five-fold cross-validation to avoid overfitting. The overall model accuracy is 71% over six ordinal categories predicting the Canterbury Earthquake Sequence measurements from 2010, 2011, and 2016. Insights from the SHAP application onto the RF model include the influences of PGA, GWT depths, and SBTs for each ordinal class prediction. This preliminary exploration using SHAP can pave the way for both reinforcing the performance of current ML models by comparing to previous knowledge and using it as a discovery tool for identifying which research areas are pertinent to unlocking more understanding of liquefaction mechanics. 

The rise of data capture and storage capabilities have led to greater data granularity and sharing of data sets in geotechnical earthquake engineering. This broader shift to big data requires ways to process and extract value from it and is aided by the progress in methodologies from the computer science domain and advancements in computer hardware capabilities. General machine learning (ML) models typically receive a set of input parameters and run them through an algorithm to gain outputs with no constraints on the parameters or algorithm process. Three topic areas of ML applications in geotechnical earthquake engineering are reviewed and summarized in this paper: seismic response, liquefaction triggering analysis, and performance-based assessments (lateral displacements and settlement analysis). The current progress of ML is summarized, while the challenges and potential in adopting such approaches are addressed.