Abstract
Investigators publish new report on Machine Learning. According to news reporting originating in Tamil Nadu, India, by NewsRx journalists, research stated, "Majority of the presently available machine learning (ML) models employed to assess the liquefaction potential of soils are for sands or sands containing silt fraction. In the current study, an explainable ML (EML) model has been developed using the updated liquefaction database of gravelly soils." Financial support for this research came from Ministry of Education, Govt. of India. The news reporters obtained a quote from the research from the Indian Institute of Technology (IIT) Madras, "The Chinese dynamic cone penetration test (DPT) and shear wave velocity test results of gravelly soils are used in the analysis. A new empirical correlation between these two in-situ tests' results is developed using the final processed database. The light gradient boosting machine (LightGBM) is trained using this processed dataset and further tuned using Fast and Lightweight AutoML library (FLAML). The final tuned model shows relatively better deterministic and probabilistic predictive performance for the test sites as compared to the conventional method. An EML technique, SHapley Additive exPlanations (SHAP) is applied to provide further comprehension into the predictions. The developed LightGBM-SHAP model has achieved a right balance between explainability and accuracy. The obtained SHAP plots are consistent with almost all the existing domain knowledge (DK) in gravelly soil liquefaction."
NETL
NSTL
Elsevier