Hybrid Formulation of Resilient Modulus for Cohesive Subgrade Soils Utilizing CPT Test Parameters
Publication: Journal of Materials in Civil Engineering
Volume 32, Issue 9
Abstract
In the present study, a novel model is introduced for the prediction of a resilient modulus () of cohesive subgrade soils considering cone-penetration test parameters to establish correlations with the . A reliable previously published database composed of 124 datasets was utilized for the development of the proposed model, which incorporates both cone penetration test (CPT) parameters and laboratory indices. In order to generate the predictive model, a hybrid algorithm combining a firefly algorithm with a multilayer perceptron neural network (FA-MLP) is proposed. The FA algorithm is employed in the MLP network structure to adjust the weights and the bias of the network and, hence, improve the overall performance of the network. The proposed FA-MLP formulation was found to have the capacity to predict, satisfactorily, the of cohesive subgrade soils using the results of the CPT test.
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Data Availability Statement
Some or all data, models, or code generated or used during the study are available from the corresponding author by request. All data shown in figures and tables can be provided on request.
Acknowledgments
This research was supported under the Australian Research Council’s Linkage Projects funding scheme (Project No. LP170100072). The second and fourth authors would like to acknowledge the support from the National Science and Technology Development Agency (NSTDA), Thailand, under the Chair Professor program (P-19-52303).
References
Cybenko, G. 1989. “Approximations by superpositions of a sigmoidal function.” Math. Control Signals Syst. 2 (4): 303–314. https://doi.org/10.1007/BF02551274.
Garson, D. G. 1991. “Interpreting neural-network connection weights.” AI Expert. 6 (4): 46–51.
Ghorbani, B., E. Sadrossadat, J. B. Bazaz, and P. R. Oskooei. 2018a. “Numerical ANFIS-based formulation for prediction of the ultimate axial load bearing capacity of piles through CPT data.” Geotech. Geol. Eng. 36 (4): 2057–2076. https://doi.org/10.1007/s10706-018-0445-7.
Ghorbani, M., R. C. Deo, Z. M. Yaseen, M. H. Kashani, and B. Mohammadi. 2018b. “Pan evaporation prediction using a hybrid multilayer perceptron-firefly algorithm (MLP-FFA) model: Case study in North Iran.” Theor. Appl. Climatol. 133 (3–4): 1119–1131. https://doi.org/10.1007/s00704-017-2244-0.
Huang, Y. H. 1993. Pavement analysis and design. Upper Saddle River, NJ: Pearson Education.
Kiani, B., A. H. Gandomi, S. Sajedi, and R. Y. Liang. 2016. “New formulation of compressive strength of preformed-foam cellular concrete: An evolutionary approach.” J. Mater. Civ. Eng. 28 (10): 04016092. https://doi.org/10.1061/(ASCE)MT.1943-5533.0001602.
Kim, S.-H., J. Yang, and J.-H. Jeong. 2014. “Prediction of subgrade resilient modulus using artificial neural network.” KSCE J. Civ. Eng. 18 (5): 1372–1379. https://doi.org/10.1007/s12205-014-0316-6.
Koopialipoor, M., A. Fahimifar, E. N. Ghaleini, M. Momenzadeh, and D. J. Armaghani. 2019. “Development of a new hybrid ANN for solving a geotechnical problem related to tunnel boring machine performance.” Eng. Comput. 36 (1): 1–13. https://doi.org/10.1007/s00366-019-00701-8.
Liu, J., K. Yan, J. Liu, and X. Zhao. 2018. “Using artificial neural networks to predict the dynamic modulus of asphalt mixtures containing recycled asphalt shingles.” J. Mater. Civ. Eng. 30 (4): 0401805. https://doi.org/10.1061/(ASCE)MT.1943-5533.0002242.
Liu, S., H. Zou, G. Cai, T. V. Bheemasetti, A. J. Puppala, and J. Lin. 2016. “Multivariate correlation among resilient modulus and cone penetration test parameters of cohesive subgrade soils.” Eng. Geol. 209 (Jul): 128–142. https://doi.org/10.1016/j.enggeo.2016.05.018.
Mathworks. 2017. MATLAB and statistics toolbox release 2017b. Natick, MA: Mathworks.
Mishra, P. N., S. Surendran, V. K. Gadi, R. A. Joseph, and D. N. Arnepalli. 2017. “Generalized approach for determination of thermal conductivity of buffer materials.” J. Hazard. Toxic Radioactive Waste. 21 (4): 04017005. https://doi.org/10.1061/(ASCE)HZ.2153-5515.0000357.
Mohammad, L. N., H. H. Titi, and A. Herath. 2002. Effect of moisture content and dry unit weight on the resilient modulus of subgrade soils predicted by cone penetration test. Baton Rouge, LA: Louisiana Transportation Research Center.
Rostami, M. F., E. Sadrossadat, B. Ghorbani, and S. M. Kazemi. 2018. “New empirical formulations for indirect estimation of peak-confined compressive strength and strain of circular RC columns using LGP method.” Eng. Comput. 34 (4): 865–880. https://doi.org/10.1007/s00366-018-0577-7.
Sadrossadat, E., B. Ghorbani, R. Oskooei, and M. Kaboutari. 2018a. “Use of adaptive neuro-fuzzy inference system and gene expression programming methods for estimation of the bearing capacity of rock foundations.” Eng. Comput. 35 (5): 2078–2106. https://doi.org/10.1108/EC-07-2017-0258.
Sadrossadat, E., B. Ghorbani, B. Zohourian, M. Kaboutari, and P. Rahimzadeh Oskooei. 2020. “Predictive modelling of the of subgrade cohesive soils incorporating CPT-related parameters through a soft-computing approach.” Road Mater. Pavement Des. 21 (3): 701–719. https://doi.org/10.1080/14680629.2018.1527241.
Sadrossadat, E., A. Heidaripanah, and B. Ghorbani. 2018b. “Towards application of linear genetic programming for indirect estimation of the resilient modulus of pavements subgrade soils.” Road Mater. Pavement Des. 19 (1): 139–153. https://doi.org/10.1080/14680629.2016.1250665.
Singh, D., M. Zaman, and S. Commuri. 2013. “Artificial neural network modeling for dynamic modulus of hot mix asphalt using aggregate shape properties.” J. Mater. Civ. Eng. 25 (1): 54–62. https://doi.org/10.1061/(ASCE)MT.1943-5533.0000548.
Yang, X.-S. 2009. “Firefly algorithms for multimodal optimization.” In Proc., Int. Symp. on Stochastic Algorithms, 169–178. Berlin: Springer.
Yang, X.-S. 2010. Nature-inspired metaheuristic algorithms. Cambridge, UK: Luniver Press.
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Information
Published In
Journal of Materials in Civil Engineering
Volume 32 • Issue 9 • September 2020
Copyright
© 2020 American Society of Civil Engineers.
History
Received: Jun 14, 2019
Accepted: Feb 24, 2020
Published online: Jul 9, 2020
Published in print: Sep 1, 2020
Discussion open until: Dec 9, 2020
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