An Adaptive Neuro Fuzzy Inference System to Model the Uniaxial Compressive Strength of Cemented Hydraulic Backfill

H. Basarir¹, H. Bin², A. Fourie¹, A. Karrech¹, M. Elchalakani¹

¹The University of Western Australia, Perth, Australia

²University of Science and Technology Beijing, Beijing, China

Min. miner. depos. 2018, 12(2):1-12

https://doi.org/10.15407/mining12.02.001

Full text (PDF)

ABSTRACT

Purpose. The purpose of this paper is to develop the models for predicting the uniaxial compressive strength (UCS) of cemented hydraulic backfill (CHB), a widely used technique for filling underground voids created by mining operations as it provides the high strength required for safe and economical working environment and allows the use of waste rock from mining operations as well as tailings from mineral processing plants as ingredients.

Methods. In this study, different modelling techniques such as conventional linear, nonlinear multiple regression and one of the evolving soft computing methods, adaptive neuro fuzzy inference system (ANFIS), were used for the prediction of UCS, the main criterion used to design backfill recipe.

Findings. Statistical performance indices used to evaluate the efficiency of the developed models indicated that the ANFIS model can effectively be implemented for designing CHB with desired UCS. As proved by the performance indicators ANFIS model gives more compatible results with the expert opinion and current literature than conventional modelling techniques.

Originality. In order to construct the models a very large database, containing more than 1600 UCS test results, was used. In addition to widely used conventional regression based modelling techniques, one of the evolving soft computing methods, ANFIS was employed. Numerical examples showing the implementation of constructed models were provided.

Practical implications. As proved by the statistical performance indicators, the developed models can be used for a reliable prediction of the UCS of CHB. However, more accurate results can be achieved by expanding the database and by constructing improved models using the algorithm presented in this paper.

Keywords: cemented hydraulic backfill, adaptive neuro fuzzy inference system, multiple regression model, underground mining

REFERENCES

Akkurt, S., Tayfur, G., & Can, S. (2004). Fuzzy Logic Model for the Prediction of Cement Compressive Strength. Cement and Concrete Research, 34(8), 1429-1433.
https://doi.org/10.1016/j.cemconres.2004.01.020

Asrari, A.A., Shahriar, K., & Ataeepour, M. (2013). The Performance of ANFIS Model for Prediction of Deformation Modulus of Rock Mass. Arabian Journal of Geosciences, 8(1), 357-365.
https://doi.org/10.1007/s12517-013-1097-9

Basarir, H., & Dincer, T. (2017). Prediction of Rock Mass P Wave Velocity Using Blasthole Drilling Information. International Journal of Mining, Reclamation and Environment, 1-14.
https://doi.org/10.1080/17480930.2017.1354960

Basarir, H., Wesseloo, J., Karrech, A., Paternak, E., & Dyskin, A. (2017). The Use of Soft Computing Methods for the Prediction of Rock Properties Based on Measurement While Drilling Data. In 8th International Conference on Deep and High Stress Mining (pp. 537-551). Perth, Australia: Australian Centre for Geomechanics.

Belem, T., Benzaazoua, M., & Bussiere, B. (2000). Mechanical Behaniour of Cemented Paste Backfill in Geotechnical Engineering at the Dawn of the 3rd Millennium. In Procee-ding of the 53rd Canadian Geotechnical Conference and 1st Joint IAH-CNC/CSG Conference. Ottawa, Ontario, Canada: Canadian Geotechnical Society.

Belem, T., & Benzaazoua, M. (2007). Design and Application of Underground Mine Paste Backfill Technology. Geotechnical and Geological Engineering, 26(2), 147-174.
https://doi.org/10.1007/s10706-007-9154-3

Bilgehan, M., & Kurtoğlu, A.E. (2015). ANFIS-Based Prediction of Moment Capacity of Reinforced Concrete Slabs Exposed to Fire. Neural Computing and Applications, 27(4), 869-881.
https://doi.org/10.1007/s00521-015-1902-3

Choudhary, B.S., & Kumar, S. (2013). Underground Void Filling by Cemented Mill Tailings. International Journal of Mining Science and Technology, 23(6), 893-900.
https://doi.org/10.1016/j.ijmst.2013.11.003

Clark, I.H. (1988). Evaluation of Cemented Tailings Backfill. In Backfill in South African Mines (pp. 77-89). Johannesburg, South Africa: Southern African Institute of Mining and Metallurgy.

De Siqueira Tango, C.E. (1998). An Extrapolation Method for Compressive Strength Prediction of Hydraulic Cement Products. Cement and Concrete Research, 28(7), 969-983.
https://doi.org/10.1016/s0008-8846(98)00074-x

Elchalakani, M., Basarir, H., & Karrech, A. (2017). Green Concrete with High-Volume Fly Ash and Slag with Recycled Aggregate and Recycled Water to Build Future Sustainable Cities. Journal of Materials in Civil Engineering, 29(2), 04016219.
https://doi.org/10.1061/(asce)mt.1943-5533.0001748

Fattahi, H. (2016). Indirect Estimation of Deformation Modulus of an In-Situ Rock Mass: An ANFIS Model Based on Grid Partitioning, Fuzzy C-Means Clustering and Subtractive Clustering. Geosciences Journal, 20(5), 681-690.
https://doi.org/10.1007/s12303-015-0065-7

Jang, J.-S.R. (1993). ANFIS: Adaptive-Network-Based Fuzzy Inference System. IEEE Transactions on Systems, Man, and Cybernetics, 23(3), 665-685.
https://doi.org/10.1109/21.256541

Jang, J.S.R., Sun, C.T., & Mizutani, E. (1997). Neuro-Fuzzy and Soft Computing. New Jersey, USA: Prentice-Hall, Upper Saddle River.

Kheder, G.F. (2003). Mathematical Model for the Prediction of Cement Compressive Strength at the Ages of 7 and 28 Days Within 24 hours. Materials and Structures, 36(264), 693-701.
https://doi.org/10.1617/13878

Lee, S.-C. (2003). Prediction of Concrete Strength Using Artificial Neural Networks. Engineering Structures, 25(7), 849-857.
https://doi.org/10.1016/s0141-0296(03)00004-x

MATLAB. (2011). Software for Technical Computing and Model-Based Design. Massachusetts, USA: The MathWorks.

Nayak, P., Sudheer, K., Rangan, D., & Ramasastri, K. (2004). A Neuro-Fuzzy Computing Technique for Modeling Hydrological Time Series. Journal of Hydrology, 291(1-2), 52-66.
https://doi.org/10.1016/j.jhydrol.2003.12.010

Özcan, F., Atiş, C.D., Karahan, O., Uncuoğlu, E., & Tanyildizi, H. (2009). Comparison of Artificial Neural Network and Fuzzy Logic Models for Prediction of Long-Term Compressive Strength of Silica Fume Concrete. Advances in Engineering Software, 40(9), 856-863.
https://doi.org/10.1016/j.advengsoft.2009.01.005

Pallant, J. (2010). SPSS Survival Manual: A Step by Step Guide to Data Analysis Using SPSS. Open University Press.

Potvin, Y., Thomas, E., & Fourie, A. (2005). Handbook on Mine Fill. Perth, Australia: Australian Centre for Geomechanics.

SPP20 IBM. (2011). SPSS Statistics for Windows. New York, USA: IBM Corp.

Sugeno, M., & Kang, G. (1988). Structure Identification of Fuzzy Model. Fuzzy Sets and Systems, 28(1), 15-33.
https://doi.org/10.1016/0165-0114(88)90113-3

Tsivilis, S., & Parissakis, G. (1995). A Mathematical Model for the Prediction of Cement Strength. Cement and Concrete Research, 25(1), 9-14.
https://doi.org/10.1016/0008-8846(94)00106-9

Wang, C., & Villaescusa, E. (2000). Backfill Research at the Western Australian School of Mines. In MassMin 2000 Proceedings (pp. 1-15). Brisbane, Australia: Australian Institute of Mining and Metallurgy.