Paper Info
Title
Intelligent prediction of rock mass deformation modulus through three optimized cascaded forward neural network models
Abstract
Rock mass deformation modulus (Em) is a key parameter that is needed to be determined when designing surface or underground rock engineering constructions. It is not easy to determine the deformability level of jointed rock mass at the laboratory; thus, researchers have suggested different in-situ test methods. Today, they are the best methods; though, they have their own problems: they are too costly and time-consuming. Addressing such difficulties, the present study offers three advanced and efficient machine-learning methods for the prediction of Em. The proposed models were based on three optimized cascaded forward neural network (CFNN) using the Levenberg–Marquardt algorithm (LMA), Bayesian regularization (BR), and scaled conjugate gradient (SCG). The performance of the proposed models was evaluated through statistical criteria including coefficient of determination (R2) and root mean square error (RMSE). The computational results showed that the developed CFNN-LMA model produced better results than other CFNN-SCG and CFNN-BR models in predicting the Em. In this regard, the R2 and RMSE values obtained from CFNN-LMA, CFNN-SCG, and CFNN-BR models were equal to (0.984 and 1.927), (0.945 and 2.717), and (0.904 and 3.635), respectively. In addition, a sensitivity analysis was performed through the relevancy factor and according to its results, the uniaxial compressive strength (UCS) was the most impacting parameters on Em.
Year
DOI
Venue
2022
10.1007/s12145-022-00823-6
Earth Science Informatics
Keywords
DocType
Volume
Rock mass deformation modulus, Cascaded forward neural network, Prediction models, Optimization
Journal
15
Issue
ISSN
Citations 
3
1865-0473
0
PageRank 
References 
Authors
0.34
10
6
Name
Order
Citations
PageRank
Mahdi Hasanipanah131.79
Mehdi Jamei200.34
Ahmed Salih Mohammed331.08
Menad Nait Amar400.34
Ouaer Hocine510.69
Khaled Mohamed Khedher600.68