Machine Learning Methods to Predict and Analyse Unconfined Compressive Strength of Stabilised Soft Soil with Polypropylene Columns

Abstract

AbstractIn this study, several machine learning approaches are used for the prediction of the unconfined compressive strength (UCS) of polypropylene-stabilised soft soil. This research work generates new data and applies several machine learning algorithms for the analysis of UCS. Fifty-two samples are in our generated data. In our generated data, five input features are used: Column Reinforcement Type, Column Diameter, Area replacement ratio,Column Penetration Ratio and Max_Deviator Stress. On the other hand, the output consists of three target stress class. Our experimental result shows that Random Forest (RF) provides good prediction result of unconfined compressive test (UCT) and that is satisfied. RF model gets result of mean absolute error of 0.0625, mean square root error of 0.0625, root mean sqrt error of 0.2500, r2 value of 0.8942 and accuracy of 0.9375. In addition, the sequential model got training loss of 0.2535, training accuracy of 0.9024, validation loss of 0.4056 and validation accuracy: 0.9091. The results showed that the suggested RF and sequential model performs excellently in predicting the UCS of stabilised soft soil with polypropylene. Our technique is more practical and time-consuming than arduous laboratory work. In the future, we will do the experiment with various soft soil characteristics to develop high-performing machine and deep learning models.

Keywords

• Soft soil
• UCT
• Machine learning
• Unconfined compressive strength
• Prediction
• ANN