APPLYING BIOGEOGRAPHY-BASED MULTI-LAYER PERCEPTRON NEURAL NETWORK TO PREDICT CALIFORNIA BEARING CAPACITY VALUE OF STABILIZED POND ASH WITH LIME AND LIME SLUDGE

Applying Neural Network to Predict California bearing capacity Value of Stabilized Pond Ash with Lime and Lime Sludge

Authors

  • Jundong Wu China Airport Construction Group Co.,Ltd. Northwest Branch, Xi'an, Shaanxi, 710000, China
  • Jiaman Li 1. Shaanxi College of Communication Technology, Xi'an, Shaanxi, 710000, China
  • Wei Hu 3. Shaanxi provincial transport planning design and research institute, Xi'an, Shaanxi, 710000, China

DOI:

https://doi.org/10.14311/CEJ.2022.02.0026

Keywords:

California bearing capacity, Pond Ash Stabilized, Lime, Lime Sludge, Hybrid Biogeography-Based Multi-Layer Perceptron Neural Network

Abstract

In this study, a hybrid biogeography-based multi-layer perceptron neural network (BBO-MLP) with different number of hidden layers (one up to three) was developed for predicting the California bearing capacity (CBR) value of pond ash stabilized with lime and lime sludge. To this aim, model had five variables named maximum dry density, optimum moisture content, lime percentage, lime sludge percentage and curing period as inputs, and CBR as output variable. Regarding BBO-MLP models, BBO-MLP1 has the best results, which its R2 stood at 0.9977, RMSE at 0.7397, MAE at 0.476, and PI at 0.0104. In all three developed models, the estimated CBR values specify acceptable agreement with experimental results, which represents the workability of proposed models for predicting the CBR values with high accuracy. Comparison of three developed models supply that BBO-MLP1 outperform others. Therefore, BBO-MLP1 could be recognized as proposed model.

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References

. Esmaeili-Falak, M., Sarkhani Benemaran, R. and Seifi, R. (2020a), “Improvement of the mechanical and durability parameters of construction concrete of the Qotursuyi Spa”, Concrete Res., 13(2), 81-90.

. Bera AK, Ghosh A, Ghosh A (2007) Compaction characteristics of pond ash. J Mater Civ Eng 19(4):349–357

. Suthar M, Aggarwal P (2017) Analysis of heavy metals in pond ash samples from Haryana. In Proceedings of 29th research world international conference, Las Vegas, USA, 16–17 March 2017, ISBN: 978-93-86291-88-2

. Parsa J, Munson-McGee SH, Steiner R (1996) Stabilization/ solidification of hazardous wastes using fly ash. J Environ Eng 122(10):935–940

. Ghosh A, Subbarao C (2006) Tensile strength bearing ratio and slake durability of class F fly ash stabilized with lime and gypsum. J Mater Civ Eng 18(1):18–27

. Ghosh A (1996) Environmental and engineering characteristics of stabilized low lime fly ash. PhD dissertation, Indian Institute of Technology, Kharagpur, India

. Ghosh A, Subbarao C (2007) Strength characteristics of class F fly ash modified with lime and gypsum. J Geotech Geoenviron Eng 1337:757–766

. Pandian NS (2004) Fly ash characterization with reference to geotechnical applications. J Indian Inst Sci 184:189–216

. Suthar M, Aggarwal P (2015) Class-F pond ash a potential highway construction material—a review. Ind Highways IRC 43(8):23–32

. Sahu V, Gayathri V (2014) The use of fly ash and lime sludge as partial replacement of cement in mortar. Int J Eng Technol Innov 4(1):30–37

. Battaglia A, Calace N, Nardi E, Petronio BM, Pietroletti M (2007) Reduction of Pb and Zn bioavailable forms in metal polluted soils due to paper mill sludge addition. Effects on Pb

and Zn transferability to barley. Biores Technol 98:2993–2999

. Calacea N, Campisib T, Iacondinib A, Leonia M, Petronioa BM, Pietroletti M (2005) Metal-contaminated soil remediation by means of paper mill sludges addition: chemical and ecotoxicological evaluation. Environ Poll 136:485–492

. Mahmood T, Elliot A (2006) A review of secondary sludge reduction technology for the pulp and paper industry. Water Res 40(11):2093–2112

. Medhi UJ, Talukdar AK, Deka S (2005) Physicochemical characteristics of lime sludge waste of paper mill and its impact on growth and production of rice. J Ind Pollut Contr 21(1):51–58

. Talukdar DK (2015) A study of paper mill lime sludge for stabilization of village road sub-base. Int J Emerg Technol Adv Eng 5(2):389–393

. Singh M, Garg M (2008) Utilization of waste lime sludge as building materials. J Sci Ind Res 67:161–166

. Ghosh A (2010) Compaction characteristics and bearing ratio of pond ash stabilized with lime and phosphogypsum. J Mater Civ Eng 22(4):343–351

. Day WR (2001) Soil testing manual procedures, classification data, and sampling practices. McGraw Hill, New York, p 619

. Benemaran, R. S., & Esmaeili-Falak, M. (2020). Optimization of cost and mechanical properties of concrete with admixtures using MARS and PSO. Computers and Concrete, 26(4), 309-316.

. Kin MW (2006) California bearing ratio correlation with soil index properties master of engineering (civil-geotechnics). Thesis, Faculty of Civil Engineering, University Teknology Malaysia

. Yorulmaz, A., Sivrikaya, O., & Uysal, F. (2021). Evaluation of the bearing capacity of poor subgrade soils stabilized with waste marble powder according to curing time and freeze-thaw cycles. Arabian Journal of Geosciences, 14(5), 1-10.

. Caglar N, Arman H (2007) The applicability of neural networks in the determination of soil profiles. Bull Eng Geol Environ 66(3):295–301

. Sarkhani Benemaran, R., Esmaeili-Falak, M., & Katebi, H. (2020). Physical and numerical modelling of pile-stabilised saturated layered slopes. Proceedings of the Institution of Civil Engineers-Geotechnical Engineering, 1-16.

. Esmaeili-Falak, M., Katebi, H., Vadiati, M., & Adamowski, J. (2019). Predicting triaxial compressive strength and Young’s modulus of frozen sand using artificial intelligence methods. Journal of Cold Regions Engineering, 33(3), 04019007.

. Nassr, A., Esmaeili-Falak, M., Katebi, H., & Javadi, A. (2018). A new approach to modeling the behavior of frozen soils. Engineering Geology, 246, 82-90.

. Das SK, Basudhar PK (2006) Undrained lateral load capacity of piles in clay using artificial neural network. Comput Geotech 33(8):454–459

. Park HI, Cho CH (2010) Neural network model for predicting the resistance of driven piles. Mar Georesour Geotech 28(4):324–344 30. Cho SE (2009) Probabilistic stability analyses of slopes using the ANN-based response surface. Comput Geotech 36:787–797

. Erzin Y, Cetin T (2013) The prediction of the critical factor of safety of homogeneous finite slopes using neural networks and multiple regressions. Comput Geosci 51:305–313

. Erzin Y, Cetin T (2014) The prediction of the critical factor of safety of homogeneous finite slopes subjected to earthquake forces using neural networks and multiple regressions. Int J Geomech Eng 6(1):1–15

. Zhao HB (2008) Slope reliability analysis using a support vector machine. Comput Geotech 35:459–467

. Shi JJ (2000) Reduction prediction error by transforming input data for neural networks. J Comput Civil Eng 14(2):109–116

. Yoo C, Kim JM (2007) Tunneling performance prediction using an integrated GIS and neural network. Comput Geotech 34:19–30

. Yildirim B, Gunaydin O (2011) Estimation of California bearing ratio by using soft computing systems. Expert Syst Appl 38:6381–6391

. Sabat AK (2013) Prediction of California bearing ratio of a soil -stabilized with lime and quarry dust using artificial neural network. Electron J Geotech Eng 18:3261–3272

Suthar, M., & Aggarwal, P. (2019, June). Modeling CBR value using RF and M5P techniques. In MENDEL (Vol. 25, No. 1, pp. 73-78).

. Raja, M. N. A., Shukla, S. K., & Khan, M. U. A. (2021). An intelligent approach for predicting the strength of geosynthetic-reinforced subgrade soil. International Journal of Pavement Engineering, 1-17.

. Suthar, M., & Aggarwal, P. (2018). Predicting CBR value of stabilized pond ash with lime and lime sludge using ANN and MR models. International Journal of Geosynthetics and Ground Engineering, 4(1), 1-7.

D. Simon, Biogeography-Based Optimization, IEEE Transactions on Evolutionary 505 Computation. 12 (2008) 702–713. doi:10.1109/TEVC.2008.919004.

Basheer, Imad A, and Maha Hajmeer. 2000. Artificial Neural Networks: Fundamentals, Computing, Design, and Application. Journal of microbiological methods 43(1): 3–31.

Gordan, Behrouz et al. 2019. Estimating and Optimizing Safety Factors of Retaining Wall through Neural Network and Bee Colony Techniques. Engineering with Computers 35(3): 945–54.

Hasanipanah, Mahdi et al. 2018. A Risk-Based Technique to Analyze Flyrock Results through Rock Engineering System. Geotechnical and Geological Engineering 36(4): 2247–60.

Koopialipoor, Mohammadreza, Danial Jahed Armaghani, Mojtaba Haghighi, and Ebrahim Noroozi Ghaleini. 2019. A Neuro-Genetic Predictive Model to Approximate Overbreak Induced by Drilling and Blasting Operation in Tunnels. Bulletin of Engineering Geology and the Environment 78(2): 981–90.

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Published

2022-07-31

How to Cite

Wu, J., Li, J. ., & Hu, W. . (2022). APPLYING BIOGEOGRAPHY-BASED MULTI-LAYER PERCEPTRON NEURAL NETWORK TO PREDICT CALIFORNIA BEARING CAPACITY VALUE OF STABILIZED POND ASH WITH LIME AND LIME SLUDGE: Applying Neural Network to Predict California bearing capacity Value of Stabilized Pond Ash with Lime and Lime Sludge. Stavební Obzor - Civil Engineering Journal, 31(2), 349–359. https://doi.org/10.14311/CEJ.2022.02.0026

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