Superiority of artificial neural networks over statistical methods in prediction of the optimal length of rock bolts

Abstract Rock bolting is one of the most important support systems used for rock structures. Rock bolts are widely used in underground excavations as they are suitable for a wide range of geological conditions and allow using progressive design methods; besides, they help economising in the use of materials and manpower. Thus, to provide the most effective support at minimum cost by means of rock bolting, it is essential to optimise the elements contributing to bolt design, including their length, as well as bolt density and tension during installation. This paper considers the length of bolts for optimisation of the design phase, which is one of the most important parameters impacting the entire design procedure. Presenting and comparing results of some statistical models, neural network modeling is introduced as powerful means in prediction of the optimal length of rock bolts. Subsequent to training and testing of a large number of 1-layer and 2-layer backpropagation neural networks, it was reported tha...

[1]  Yingjie Yang,et al.  A hierarchical analysis for rock engineering using artificial neural networks , 1997 .

[2]  Edmundas Kazimieras Zavadskas,et al.  Multiple Criteria Assessment of Pile-Columns Alternatives , 2011 .

[3]  P. Ziembicki,et al.  Analysis of district heating network monitoring by neural networks classification , 2006 .

[4]  Henrikas Sivilevičius,et al.  Experimental study on technological indicators of pile-columns at a construction site , 2012 .

[5]  K. S. Wong,et al.  Estimation of lateral wall movements in braced excavations using neural networks , 1995 .

[6]  Farzad Khosrowshahi,et al.  Innovation in artificial neural network learning: Learn-On-Demand methodology , 2011 .

[7]  Ken-ichi Funahashi,et al.  On the approximate realization of continuous mappings by neural networks , 1989, Neural Networks.

[8]  Jurgis Medzvieckas,et al.  Evaluating Elastic-Plastic Behaviour of Rock Materials Using Hoek–Brown Failure Criterion , 2012 .

[9]  JunLu Luo.  A New Rock Bolt Design Criterion and Knowlwdge-based Expert System for Stratified Roof , 1999 .

[10]  M. Monjezi,et al.  Simultaneous prediction of fragmentation and flyrock in blasting operation using artificial neural networks , 2010 .

[11]  Saulius Valentinavičius,et al.  MULTILEVEL OPTIMIZATION OF GRILLAGES , 2002 .

[12]  W. Pitts,et al.  A Logical Calculus of the Ideas Immanent in Nervous Activity (1943) , 2021, Ideas That Created the Future.

[13]  M. Grima,et al.  Application of neural networks for the prediction of the unconfined compressive strength (UCS) from Equotip hardness , 1999 .

[14]  Yujing Jiang,et al.  An analytical model to predict axial load in grouted rock bolt for soft rock tunnelling , 2004 .

[15]  M. A. A. Kiefa.  GENERAL REGRESSION NEURAL NETWORKS FOR DRIVEN PILES IN COHESIONLESS SOILS , 1998 .

[16]  Warren S. Sarle,et al.  Neural Networks and Statistical Models , 1994 .

[17]  K. S. Kim,et al.  Optimum Grillage Structure Design Under a Worst Point Load Using Real-coded Micro-Genetic Algorithm , 2005 .

[18]  Patrick K. Simpson,et al.  Artificial Neural Systems: Foundations, Paradigms, Applications, and Implementations , 1990 .

[19]  Dmitrij Šešok,et al.  Global optimization of grillages using simulated annealing and high performance computing , 2010 .

[20]  Damodar Maity,et al.  Damage assessment in structure from changes in static parameter using neural networks , 2004 .

[21]  Daiva Zilioniene,et al.  Evaluation of Soil Shear Strength Parameters via Triaxial Testing by Height Versus Diameter Ratio of Sample , 2009 .

[22]  Koon Meng Chua,et al.  A numerical study of the effectiveness of mechanical rock bolts in an underground opening excavated by blasting , 1992 .

[23]  Tetsuro Esaki,et al.  A rock bolt and rock mass interaction model , 2004 .

[24]  Juozas Atkočiūnas,et al.  Saosys toolbox as Matlab implementation in the elastic‐plastic analysis and optimal design of steel frame structures , 2010 .

[25]  Jerzy Zbigniew Piotrowski,et al.  Neural model of residential building air infiltration process , 2006 .

[26]  Murat Sönmez,et al.  An Artificial Neural Networks Model for the Estimation of Formwork Labour , 2011 .

[27]  Gintaris Kaklauskas,et al.  Investigation of Shrinkage of Concrete Mixtures Used for Bridge Construction in Lithuania , 2011 .

[28]  Kim Young-Su,et al.  Use of Artificial Neural Networks in the Prediction of Liquefaction Resistance of Sands , 2006 .

[29]  Krzysztof Schabowicz,et al.  Application of artificial neural networks in predicting earthmoving machinery effectiveness ratios , 2008 .

[30]  Kastytis Dundulis,et al.  Problems of Correlation between Dynamic Probing Test (DPSH) and Cone Penetration Test (CPT) for Cohesive Soils of Lithuania , 2010 .

[31]  F ROSENBLATT,et al.  The perceptron: a probabilistic model for information storage and organization in the brain. , 1958, Psychological review.

[32]  T. Singh,et al.  Evaluation of blast-induced ground vibration predictors , 2007 .

[33]  Nick Barton,et al.  Engineering classification of rock masses for the design of tunnel support , 1974 .

[34]  Amir Hossein Alavi,et al.  A Radial Basis Function Neural Network Approach for Compressive Strength Prediction of Stabilized Soil , 2009 .

[35]  Edmundas Kazimieras Zavadskas,et al.  Multiple criteria analysis of foundation instalment alternatives by applying Additive Ratio Assessment (ARAS) method , 2010 .

[36]  Tahir Çelik,et al.  An integrated web-based data warehouse and artificial neural networks system for unit price analysis with inflation adjustment , 2011 .

[37]  Herbert A. Mang,et al.  Hilltop buckling as the A and O in sensitivity analysis of the initial postbuckling behavior of elastic structures , 2009 .

[38]  K. M. Neaupane,et al.  Prediction of tunneling-induced ground movement with the multi-layer perceptron , 2006 .

[39]  Syd S. Peng,et al.  Roof bolting in underground mining: a state-of-the-art review , 1984 .

[40]  Yingjie Yang,et al.  The artificial neural network as a tool for assessing geotechnical properties , 2002 .

[41]  S. Yasrebi,et al.  Application of Artificial Neural Networks ( ANNs ) in prediction and Interpretation of Pressuremeter Test Results , 2008 .

[42]  Šarūnas Skuodis,et al.  Grunto Stiprio, Kintančio Išilgai Polio, Įtaka Smūgio Bangos Sklidimui Polyje , 2011 .