Computerization of Rock Engineering Systems Using Neural Networks with an Expert System

Summary With the Rock Engineering Systems (RES) methodology, rock mechanics and rock engineering problems are studied systematically using a total systems approach, incorporating rock mass properties, interrelated parameters, complex interaction mechanisms and dynamic behavioral modes. In this paper, a method of implementation and computerization of RES is considered, using neural networks together with an expert system. The computerized RES starts with the data processing of rock mass properties and boundary conditions and data base management of rock engineering case records. This step is followed by building and operating parameter interaction matrices with the combined use of backpropagation networks and an expert system. Finally, a simulator for modelling the dynamic process of rock engineering systems using the Hopfield network is incorporated. With the aid of neural networks' learning capability and expert system's symbol-reasoning capability, the RES approach is implemented in an “intelligent” mode.

[1]  William G. Wee,et al.  A knowledge based neural network approach for waste water treatment system , 1990, 1990 IJCNN International Joint Conference on Neural Networks.

[2]  John A. Hudson,et al.  ROCK ENGINEERING SYSTEMS. THEORY AND PRACTICE , 1992 .

[3]  Qing Zhang,et al.  An expert system for prediction of Karst disaster in excavation of tunnels or underground structures through a carbonate rock area , 1993 .

[4]  James H. Garrett,et al.  Knowledge-Based Modeling of Material Behavior with Neural Networks , 1992 .

[5]  Larry R. Medsker,et al.  Hybrid Neural Network and Expert Systems , 1994, Springer US.

[6]  John A. Hudson,et al.  Using Neural Networks In Rock Engineering Systems For Cavern Performance Auditing , 1996 .

[7]  Christopher R. Thewalt,et al.  Neural Network Approaches in Structural Mechanics Computations , 1989 .

[8]  Richard E. Goodman,et al.  Methods of Geological Engineering in Discontinuous Rocks , 1975 .

[9]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[10]  J J Hopfield,et al.  Neural networks and physical systems with emergent collective computational abilities. , 1982, Proceedings of the National Academy of Sciences of the United States of America.

[11]  Yuan-bin Mo,et al.  An Expert System For Classification Of Rock Masses , 1988 .

[12]  Qing Zhang,et al.  Application of neural network models to rock mechanics and rock engineering , 1991 .

[13]  L. C. Rabelo,et al.  Using hybrid neural networks/expert systems for intelligent scheduling in flexible manufacturing systems , 1989, International 1989 Joint Conference on Neural Networks.

[14]  Walter C. Merrill,et al.  A real time neural net estimator of fatigue life , 1990, 1990 IJCNN International Joint Conference on Neural Networks.

[15]  James H. Garrett,et al.  Use of neural networks in detection of structural damage , 1992 .

[16]  John A. Hudson,et al.  The fully-coupled model for rock engineering systems , 1995 .

[17]  Xiping Wu.  Neural network-based material modeling , 1992 .

[18]  A. G. Butler,et al.  Classex: An Expert System For Rock Mass Classification , 1990 .

[19]  John A. Hudson,et al.  Rock Mechanics Principles in Engineering Practice , 1989 .

[20]  Dean L. Millar,et al.  On The Investigation Of A Multilayer Feedforward Neural Network Model Of Rock Deformability Behaviour , 1995 .

[21]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[22]  Norman Abramson,et al.  Information theory and coding , 1963 .

[23]  Chul-Wook Lee,et al.  Artificial Neural Network Integrated With Expert System For Preliminary Design Of Tunnels And Slopes , 1995 .

[24]  John A. Hudson,et al.  A new approach to studying complete rock engineering problems , 1992, Quarterly Journal of Engineering Geology.

[25]  Y. Yamamoto,et al.  Integrated approach using neural networks for fault detection and diagnosis , 1990, 1990 IJCNN International Joint Conference on Neural Networks.

[26]  R. L. Sterling,et al.  IDENTIFYING PROBABLE FAILURE MODES FOR UNDERGROUND OPENINGS USING A NEURAL NETWORK , 1992 .