Evaluation of earthquake-induced liquefaction potential is crucial in the design phase of construction projects. Although several machine learning models achieve good prediction accuracy on their particular datasets, they may not perform well in other liquefaction datasets. To address this issue, we proposed a novel hybrid classifier ensemble to improve generalizability by combining the predictions of seven base classifiers using the weighted voting method. The applied base classifiers include back propagation neural network, support vector machine, decision tree, k-nearest neighbours, logistic regression, multiple linear regression and naïve Bayes. The hyperparameters and weights of the base classifiers were tuned using the genetic algorithm. To verify the robustness of the classifier ensemble, its performance was tested on three datasets collected from previous published researches. The results show that the proposed classifier ensemble outperforms the base classifiers in terms of a variety of performance metrics including accuracy, Kappa, precision, recall, F1 score, AUC and ROC on the three datasets. In addition, the importance of influencing variables was achieved by the classifier ensemble on the three datasets to facilitate the future data collecting work. This robust ensemble method can be extended to solve other classification problems in civil engineering.

在建设项目的设计阶段，评估地震诱发的液化潜力至关重要。尽管一些机器学习模型在其特定数据集上实现了良好的预测精度，但它们在其他液化数据集中的表现可能不佳。为了解决这个问题，我们提出了一种新颖的混合分类器集合，通过使用加权投票方法结合七个基本分类器的预测来提高通用性。应用的基本分类器包括反向传播神经网络，支持向量机，决策树，k最近邻，逻辑回归，多元线性回归和朴素贝叶斯。使用遗传算法调整基本分类器的超参数和权重。为了验证分类器整体的鲁棒性，在从以前发表的研究中收集的三个数据集上测试了其性能。结果表明，在三个数据集的各种性能指标（包括准确性，Kappa，精度，召回率，F1得分，AUC和ROC）方面，拟议的分类器集合均优于基本分类器。此外，分类器集合对三个数据集实现了影响变量的重要性，以方便将来的数据收集工作。这种鲁棒的集成方法可以扩展为解决土木工程中的其他分类问题。影响变量的重要性是通过分类器集合对这三个数据集实现的，以方便将来的数据收集工作。这种鲁棒的集成方法可以扩展为解决土木工程中的其他分类问题。影响变量的重要性是通过分类器集合对三个数据集实现的，以方便将来的数据收集工作。这种鲁棒的集成方法可以扩展为解决土木工程中的其他分类问题。