CumInCAD is a Cumulative Index about publications in Computer Aided Architectural Design supported by the sibling associations ACADIA, CAADRIA, eCAADe, SIGraDi, ASCAAD and CAAD futures
Leu, S.-S., Chen, C.-N. and Chang, S.-L.
Data mining for tunnel support stability: neural network approach
Automation in Construction 10 (4) (2001) pp. 429-441
This paper presents a data mining approach to the prediction of tunnel support stability using artificial neural networks (ANN). The case data of a railway tunnel recently finished in Taiwan were used to establish the model. The main rock type was sedimentary rock. Rock mechanical and construction-related parameters with significant influences on support stability were filtered to train and test the ANN. Validation was also performed to show that the ANN outperformed the discriminant analysis and the multiple non-linear regression method in predicting tunnel support stability status.