Login | Register

Stability assessment of homogeneous slopes loaded with mobile tracked cranes—An artificial neural network approach

Title:

Stability assessment of homogeneous slopes loaded with mobile tracked cranes—An artificial neural network approach

Ai, Xin and Zsaki, Attila Michael (2017) Stability assessment of homogeneous slopes loaded with mobile tracked cranes—An artificial neural network approach. Cogent Engineering, 4 (1). pp. 1-13. ISSN 2331-1916

[img]
Preview
Text (application/pdf)
Zsaki-cogent-engineering-2017.pdf - Published Version
Available under License Spectrum Terms of Access.
1MB

Official URL: http://dx.doi.org/10.1080/23311916.2017.1360236

Abstract

Construction projects often involve the use of mobile crawler cranes to excavate, backfill, dredge or move material and equipment on or near slopes. Crane manufacturers often only provide guidelines for the safe operation of cranes with respect to over tipping. However, the complex interaction of many variables such as the crane, its load, the slope geometry and its geotechnical properties can create slope instability. In this study, an artificial neural network was developed to predict the stability of these slopes loaded by mobile cranes. The neural network was built and trained using a set of slope stability models that were constructed using the above parameters via Monte Carlo sampling. The trained network was capable of predicting the factor of safety of a loaded slope and the location of the critical failure surface with relatively low error. In addition, the quality of the network’s output was investigated using multiple metrics, such as the correlation ratio or the mean squared error and quite high correlation was achieved. Thus, the predicting capabilities of the network can be used with confidence to aid the positioning of mobile cranes on slopes without a need to perform slope stability analysis for each scenario.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Building, Civil and Environmental Engineering
Item Type:Article
Refereed:Yes
Authors:Ai, Xin and Zsaki, Attila Michael
Journal or Publication:Cogent Engineering
Date:31 July 2017
Funders:
  • Concordia Open Access Author Fund
Digital Object Identifier (DOI):10.1080/23311916.2017.1360236
Keywords:slope stability, crawler crane, artificial neural network
ID Code:982892
Deposited By: DANIELLE DENNIE
Deposited On:30 Aug 2017 15:02
Last Modified:18 Jan 2018 17:55

References:

Abdalla, J. A., Attom, M., & Hawileh, R. (2012). Artificial neural network prediction of factor of safety of slope stability of soils. In Proceedings of the 14th International Conference on Computing in Civil and Building Engineering, Moscow, Russia, June 27–29.

Ai, X. (2016). Stability assessment of homogeneous slopes loaded with mobile tracked cranes— An artificial neural network approach (M.A.Sc. thesis. Department of Building, Civil and Environmental Engineering, Concordia University, Montreal, Quebec, Canada.

Caniani, D., Pascale, S., Sdao, F., & Sole, A. (2007). Neural networks and landslide susceptibility: A case study of the urban area of Potenza. Natural Hazards, 45, 55–72.

Chang, T.-C., & Chao, R.-J. (2006). Application of backpropagation networks in debris flow prediction. Engineering Geology, 85, 270–280. https://doi.org/10.1016/j.enggeo.2006.02.007

Cheng, Y. M. (2007). Global optimization analysis of slope stability by simulated annealing with dynamic bounds and dirac function. Engineering Optimization, 39, 17–32. https://doi.org/10.1080/03052150600916294

Choobbasti, A. J., Farrokhzad, F., & Barari, A. (2009). Prediction of slope stability using artificial neural network (case study: Noabad, Mazandaran, Iran). Arabian Journal of Geosciences, 2, 311–319.

Das, B. M. (2007). Fundamentals of geotechnical engineering. (3rd ed.). Begumpet: CL-engineering. DeGroot, M. H. (1989). Probability and statistics (2nd ed.). Reading, MA: Addison-Wesley Publishing Company.

Figueiredo, E., Park, G., Farrar, C. R., Worden, K., & Figueiras, J. (2010). Machine learning algorithms to damage detection under operational and environmental variability. In Proceedings of SPIE 7650, Health Monitoring of Structural and Biological Systems 2010, San Diego, USA.

Goh, A. T. C., & Kulhawy, F. H. (2004). Reliability assessment of serviceability performance of braced retaining walls using a neural network approach. International Journal for Numerical and Analytical Methods in Geomechanics, 29, 627–642.

Goh, A. T. C., Wong, K. S., & Broms, B. B. (1995). Estimation of lateral wall movements in braced excavations using neural networks. Canadian Geotechnical Journal, 32, 1059–1064. https://doi.org/10.1139/t95-103

Gomez, H., & Kavzoglu, T. (2004). Assessment of shallow landslide susceptibility using artificial neural networks in Jabonosa River Basin, Venezuela. Engineering Geology, 78, 11–27.

Gurney, K. (1997). An introduction to neural networks. London, UK: UCL Press. https://doi.org/10.4324/9780203451519

Hammah, R. E., Curran, J. H., Yacoub, T. E., & Corkum, B. (2004). Stability analysis of rock slopes using the finite element method. In Proceedings of the ISRM Regional Symposium EUROCK 2004 and the 53rd Geomechanics Conference, Salzburg, Austria.

Kung, G. T. C., Hsiao, E. C. L., Schuster, M., & Juang, C. H. (2007). A neural network approach to estimating deflection of diaphragm walls caused by excavation in clays. Computers and Geotechnics, 34, 385–396. https://doi.org/10.1016/j.compgeo.2007.05.007

Lin, H.-M., Chang, S.-K., Wu, J.-H., & Juang, C. H. (2009). Neural network-based model for assessing failure potential of highway slopes in the Alishan, Taiwan area: Pre- and post-earthquake investigation. Engineering Geology, 104, 280–289.

Liu, X., Chan, D. H., & Gerbrandt, B. (2008). Bearing capacity of soils for crawler cranes. Canadian Geotechnical Journal, 45, 1282–1302. https://doi.org/10.1139/T08-056

MATLAB and Neural Network Toolbox. (2012). Natick, MA: The MathWorks Inc. Mehryar, M., Afshin, R., & Ameet, T. (2012). Foundations of machine learning. Cambridge, MA: The MIT Press.

Nash, D. (1987). A comparative review of limit equilibrium methods of stability analysis. In M. G. Anderson & K. S. Richards (Eds.), Slope stability (pp. 11–75). New York, NY: John Wiley and Sons.

Rumelhart, D., & McClelland, J. (1986). Parallel distributed processing. Cambridge, MA: MIT Press.

Salehi, H., Das, S., Chakrabartty, S., Biswas, S., & Burgueño, R. (2015). Structural assessment and damage identification algorithms using binary data. In Proceedings of the ASME 2015 Conference on Smart Materials, Adaptive Structures and Intelligent Systems. Colorado Springs; ASME Press.

Salehi, H., Burgueño, R., Das, S., Biswas, S., & Chakrabartty, S. (2016). Structural health monitoring from discrete binary data through pattern recognition. In Alphose Zingoni (Ed.), Insights and Innovations in Structural Engineering, Mechanics and Computation, (pp. 1840–1845). Boca Raton, FL: Taylor & Francis Group. https://doi.org/10.1201/9781315641645

SCX500 Hydraulic Crawler Crane Specifications. (n.d.). Retrieved Hitachi Sumitomo Heavy Industries Construction Crane Co. Ltd.: from https://www.hsc-crane.com/e/

Siddappa, G., & Shanthakumar, M. C. (2014). Stability analysis of homogeneous earth slopes. In Proceedings of the 2014 International Conference on Geological and Civil Engineering IPCBEE. Singapore: IACSIT Press.

Silva, M., Santos, A., Figueiredo, E., Santos, R., Sales, C., & Costa, J. C. W. A. (2016). A novel unsupervised approach based on a genetic algorithm for structural damage detection in bridges. Engineering Applications of Artificial Intelligence, 52, 168–180. https://doi.org/10.1016/j.engappai.2016.03.002

Slide User’s Manual. (2015). Rocscience Inc. Retrieved from www.rocscience.com

Sonmez, H., Gokceoglu, C., Nefeslioglu, H. A., & Kayabasi, A. (2006). Estimation of rock modulus: For intact rocks with an artificial neural network and for rock masses with a new empirical equation. International Journal of Rock Mechanics & Mining Sciences, 43, 224–235. https://doi.org/10.1016/j.ijrmms.2005.06.007

Xiao, S., Li, K., Ding, X., & Liu, T. (2015). Numerical computation of homogeneous slope stability. Computational Intelligence and Neuroscience, 2015, 12. ID: 802835.

Zoomlion Crawler Crane Operator’s Manual. (n.d.). Retrieved from Zoomlion Heavy Industry Science and Technology Inc.: https://www.zoomlion.com

Zsaki, A. M. (1999). Non-circular slope stability analysis using the generalized wedge method with modifications and extensions for application in rock engineering (M.A.Sc. thesis). Department of Civil Engineering, University of Toronto, Toronto, Canada.
All items in Spectrum are protected by copyright, with all rights reserved. The use of items is governed by Spectrum's terms of access.

Repository Staff Only: item control page

Downloads per month over past year

Back to top Back to top