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

Abstract

In 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

Public Interest Statement

This research work generates new data and applies several machine learning algorithms for the prediction of the unconfined compressive strength (UCS) of polypropylene-stabilised soft soil. Fifty-two samples are in our generated data. In our data, five input features are used: ‘Type’, ‘Column Diameter’, ‘Column Penetration Ratio’ and ‘Max_Deviator Stress’. On the other hand, the output consists of target stress class. The results showed that the suggested RF and sequential model performs excellently in predicting the UCS. This method is more practical and time-consuming than arduous laboratory work. Our method will be applied in real world for the following purpose: (1) increase the bearing capacity of soft soil; (2) improve the stability of soft soil; (3) minimise the settlement and lateral deformation of soft soil; (4) solve the problem of building infrastructure on soft soil and (V) reduce the expense of soft soil.

Acknowledgments

The authors acknowledge Universiti Malaysia Pahang (UMP) for financing this research through the Research Grant Scheme, Project Number RDU 223309. The authors thank the staff of the Geotechnical Engineering Laboratory at UMP for providing facilities and cooperative behaviour during the tests.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Notes on contributors

Md. Ikramul Hoque

Md Ikramul Hoque is working as an Associate Professor at Khulna University of Engineering and Technology(KUET), Now he is studying as a Ph.D. student at Civil Engineering, at University Malaysia Pahang (UMP). He is an active researcher in the field of Civil Engineering. He has Published 26 articles in different high-impact facto journals. His research focus is in Geotechnical Engineering, Construction engineering and management.