An improved artificial bee colony-random forest (IABC-RF) model for predicting the tunnel deformation due to an adjacent foundation pit excavation

Abstract

An improved artificial bee colony-random forest (IABC-RF) model is proposed for predicting the tunnel deformation due to the excavation of an adjacent foundation pit. A new search strategy of the artificial bee colony (ABC) algorithm is herein developed and incorporated, with the results showing that a much higher computational efficiency can be achieved with the new model, while high computational accuracy can also be maintained. The improved ABC algorithm is thereafter utilised and combined with the random forest (RF) model, where four important hyper-parameters are optimized, for a tunnel deformation prediction. Results are thoroughly compared with those of other prediction methods based on machine learning (ML), as well as the monitored data on the site. Via the comparisons, the validity and effectiveness of the proposed model are fully demonstrated, and a more promising perspective can be seen of the method for its potential wide applications in geotechnical engineering.

Keywords

• Tunnel deformation prediction
• Improved artificial bee colony algorithm
• Random forest
• Hyper-parametric optimization search