Predicting tunnel squeezing using a hybrid classifier ensemble with incomplete data

Junfei Zhang, Dong Li, Yuhang Wang

Research output: Contribution to journalArticlepeer-review

29 Citations (Scopus)


Tunnel squeezing occurs when time-dependent rock creep produces large tunnel convergence. The occurrence of tunnel squeezing may result in buget increase and time waste during tunnel construction. The aim of this study was to propose a robust classifier ensemble to predict squeezing conditions in rock tunnels. Seven individual machine learning classifiers were aggregated using weighted voting methods to establish the classifier ensemble. The weight and hyperparameters of each individual classifier were tuned using the firefly algorithm. The classifier ensemble was trained and tested on a dataset collected from published literature. Missing values in the database were replaced by various imputation methods. The results indicate that the proposed classifier ensemble achieved an accuracy of 96%, higher than that of the traditionally used individual classifiers. This robust ensemble method can be applied to other classification problems in civil engineering.

Original languageEnglish
Pages (from-to)3245-3256
Number of pages12
JournalBulletin of Engineering Geology and the Environment
Issue number6
Publication statusPublished - 1 Aug 2020


Dive into the research topics of 'Predicting tunnel squeezing using a hybrid classifier ensemble with incomplete data'. Together they form a unique fingerprint.

Cite this