A novel PSO-KELM based soil liquefaction potential evaluation system using CPT and Vs measurements

Evaluation of soil liquefaction potential is an essential step in geotechnical engineering design. This study presents a novel soil liquefaction potential evaluation system using cone penetration test (CPT) and shear wave velocity test (Vs) measurements. To this end, a new hybrid machine learning model called PSO-KELM model that combines the kernel extreme learning machine (KELM) with particle swarm optimization (PSO) is developed to assess soil liquefaction potential. Then, the PSO-KELM based searching technique is adopted to search the nonlinear relationship between CRR and CPT along with Vs measurements. Finally, a new probabilistic model is developed by considering the model uncertainty and sampling bias based on weighted maximum likelihood estimation. Results demonstrate that the performance of PSO-KELM model is significantly better than that of many other machine learning methods. The cyclic stress ratio, equivalent clean sand normalized cone tip resistance, normalized friction ratio, fines content, and soil behavior type are the recommended input variables for PSO-KELM model. The combined use of CPT and Vs measurements can significantly improve the prediction accuracy since it can more fully reflect soil liquefaction phenomenon. The proposed evaluation system can improve the performance of seismic CPT (SCPT) in soil liquefaction potential evaluation.

Automated summary

This study proposes a novel soil liquefaction potential evaluation system combining CPT and Vs measurements, using the machine learning model PSO-KELM to assess soil liquefaction potential, and develops a new probabilistic model to improve prediction accuracy.