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 (V_s) 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 V_s 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 V_s 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.

土壤液化潜力的评估是岩土工程设计中必不可少的步骤。本研究提出了一种使用锥入度测试 (CPT) 和剪切波速度测试 ( V _s ) 测量的新型土壤液化潜力评估系统。为此，开发了一种称为 PSO-KELM 模型的新混合机器学习模型，该模型将内核极限学习机 (KELM) 与粒子群优化 (PSO) 相结合，以评估土壤液化潜力。然后，采用基于 PSO-KELM 的搜索技术搜索 CRR 和 CPT 之间的非线性关系以及V _s测量。最后，基于加权最大似然估计，通过考虑模型的不确定性和抽样偏差，开发了一种新的概率模型。结果表明，PSO-KELM 模型的性能明显优于许多其他机器学习方法。循环应力比、等效洁净砂归一化锥尖阻力、归一化摩擦比、细粒含量和土壤行为类型是 PSO-KELM 模型的推荐输入变量。CPT和V _s测量的结合使用可以显着提高预测精度，因为它可以更全面地反映土壤液化现象。所提出的评估系统可以提高地震 CPT (SCPT) 在土壤液化潜力评估中的性能。