Abstract
A fuzzy logic (FL)–based modeling approach is employed for geogrid-reinforced subgrade soil of unpaved roads. A review of the literature reveals that fuzzy logic has not been used for predicting the behavior of geogrid-reinforced subgrade. This paper presents FL-based two models with fuzzy Triangular and Gaussian membership functions for input and output variables. It consists of eight input parameters/factors, namely, reinforced/unreinforced section, depth of reinforcement, liquid limit, plastic limit, plasticity index, optimum moisture content, maximum dry unit weight, and soaked/unsoaked condition, and California bearing ratio (CBR) as an output parameter. The fuzzy rules are deduced from the experimental data. The laboratory CBR tests were performed on the subgrade soil reinforced with geogrid. The precision of models was examined by comparing the predicted CBR values with the experimental CBR values for Triangular and Gaussian membership functions. The sensitivity analysis reflects a set of dominant parameters. The results indicated a significant improvement in the CBR value of geogrid-reinforced subgrade soil due to the inclusion of geogrid. The range for optimal depth of geogrid reinforcement is found to be 36 to 60% of the thickness of the soil layer. The potentialities of FL were found to be satisfactory.
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All data used to generate the models during the present study appear in the submitted article.
References
Bagdatli, M.E.C.: Fuzzy logic-based life-cycle cost analysis of road pavements. J. Transp. Eng., Part B: Pavements. 144(4), 04018050 (2018). https://doi.org/10.1061/JPEODX.0000081
Bhatt, S., Jain, P.K., Pradesh, M.: Prediction of California bearing ratio of soils using artificial neural network. American International Journal of Research in Science, Technology, Engineering & Mathematics. 8(2), 156–161 (2014)
Black, W.P.M.: A method of estimating the CBR of cohesive soils from plasticity data. Geotechnique. 12, 271–272 (1962)
Cabalar, A.F., Cevik, A., Gokceoglu, C.: Some applications of adaptive neuro-fuzzy inference system (ANFIS) in geotechnical engineering. Comput. Geotech. 40, 14–33 (2012). https://doi.org/10.1016/j.compgeo.2011.09.008
Cuelho, E.V., Perkins, S.W.: Geosynthetic subgrade stabilization—field testing and design method calibration. Transp Geotech. 10, 22–34 (2017). https://doi.org/10.1016/j.trgeo.2016.10.002
Duncan-Williams, E., Attoh-Okine, N.O.: Effect of geogrid in granular base strength—an experimental investigation. Constr. Build. Mater. 22(11), 2180–2184 (2008). https://doi.org/10.1016/j.conbuildmat.2007.08.008
Giroud, J.P., Han, J.: Design method for geogrid-reinforced unpaved roads. I. Development of design method. J. Geotech. Geoenviron. 130(8), 775–786 (2004). https://doi.org/10.1061/(ASCE)1090-0241(2004)130:8(775)
Gopal, M.: Digital control and state variable methods: conventional and intelligent control systems, 3rd edn. Tata McGraw-Hill Education Pvt. Ltd, Singapore (2009)
Günaydin, O.: Estimation of soil compaction parameters by using statistical analyses and artificial neural networks. Environ. Geol. 57(1), 203–215 (2009)
Gurtug, Y., Sridharan, A.: Prediction of compaction characteristics of fine-grained soils. Géotechnique. 52(10), 761–763 (2002)
Hossain, A., Rahman, A., Mohiuddin, A.K.M.: Fuzzy evaluation for an intelligent air-cushion tracked vehicle performance investigation. J. Terrramech. 49(2), 73–80 (2012). https://doi.org/10.1016/j.jterra.2011.08.002
Ibrahim, E.M., El-Badawy, S.M., Ibrahim, M.H., Gabr, A., Azam, A.: Effect of geogrid reinforcement on flexible pavements. Innov. Infrastruct. Solut. 2(1), 54 (2017). https://doi.org/10.1007/s41062-017-0102-7
Lee, D., Donnell, E.T.: Analysis of nighttime driver behavior and pavement marking effects using fuzzy inference system. J. Comput. Civ. Eng. 21, 200–210 (2007). https://doi.org/10.1061/ASCE0887-3801200721:3200
Moghaddas-Nejad, F., Small, J.C.: Effect of geogrid reinforcement in model track tests on pavements. J. Transp. Eng. 122(6), 468–474 (1996)
Nagrale, P.P., Sawant, P.H., Pusadkar, S.S.: Laboratory investigation of reinforced sub grade soils. Indian Geotechnical Conference, GEOtrendz. 637–640 (2010)
Negi, M.S., Singh, S.K.: Experimental and numerical studies on geotextile reinforced subgrade soil. Int. J. Geotech. Eng. 1–12 (2019). https://doi.org/10.1080/19386362.2019.1684654
Palmeira, E.M., Antunes, L.G.S.: Large scale tests on geosynthetic reinforced unpaved roads subjected to surface maintenance. Geotext. Geomembr. 28(6), 547–558 (2010). https://doi.org/10.1016/j.geotexmem.2010.03.002
Perkins, S.W., Christopher, B.R., Lacina, B.A., Klompmaker, J.: Mechanistic-empirical modeling of geosynthetic-reinforced unpaved roads. Int J Geomech. 12(4), 370–380 (2012). https://doi.org/10.1061/(ASCE)GM.1943-5622.0000184
Rajesh, U., Sajja, S., Chakravarthi, V.K.: Studies on engineering performance of geogrid reinforced soft subgrade. Transp. Res. Procedia. 17, 164–173 (2016). https://doi.org/10.1016/j.trpro.2016.11.072
Rashidian, V., Naeini, S.A., Mirzakhanlari, M.: Laboratory testing and numerical modeling on bearing capacity of geotextile reinforced granular soils. Int. J. Geotech. Eng. 12, 241–251 (2016). https://doi.org/10.1080/19386362.2016.1269042
Sandra, A.K., Sarkar, A.K.: Application of fuzzy logic and clustering techniques for pavement maintenance. Transp. Infrastruct. Geotech. 2, 103–119 (2015). https://doi.org/10.1007/s40515-015-0021-z
Shukla, S.K.: An introduction to geosynthetic engineering. CRC Press, London (2016)
Singh, P., Gill, K.S.: CBR improvement of clayey soil with geo-grid reinforcement. International Journal of Emerging Technology and Advanced Engineering. 2(6), 315–318 (2012)
Singh, M., Trivedi, A., Shukla, S.K.: Strength enhancement of the subgrade soil of unpaved road with geosynthetic reinforcement layers. Transp Geotech. 19, 54–60 (2019). https://doi.org/10.1016/j.trgeo.2019.01.007
Smith, G.N.: Probability and statistics of civil engineering. Collins, London (1986)
Stephens, D.J.: Prediction of the California bearing ratio. J. S. Afr. Inst. Civ. Eng. 32(12), 523–527 (1990)
Suku, L., Prabhu, S.S., Babu, G.L.S.: Effect of geogrid-reinforcement in granular bases under repeated loading. Geotext. Geomembranes. 45(4), 377–389 (2017). https://doi.org/10.1016/j.geotexmem.2017.04.008
Taghavifar, H., Mardani, A.: Fuzzy logic system based prediction effort: a case study on the effects of tire parameters on contact area and contact pressure. Appl Soft Comput. 14, 390–396 (2014). https://doi.org/10.1016/j.asoc.2013.10.005
Taskiran, T.: Prediction of California bearing ratio (CBR) of fine grained soils by AI methods. Adv. Eng. Softw. 41(6), 886–892 (2010). https://doi.org/10.1016/j.advengsoft.2010.01.003
Tenpe, A.R., Patel, A.: Application of genetic expression programming and artificial neural network for prediction of CBR. Road Materials and Pavement Design. 1–18 (2018). https://doi.org/10.1080/14680629.2018.1544924
Topçu, I.B., Saridemir, M.: Prediction of mechanical properties of recycled aggregate concretes containing silica fume using artificial neural networks and fuzzy logic. Comput. Mater. Sci. 42(1), 74–82 (2008). https://doi.org/10.1016/j.commatsci.2007.06.011
Trivedi, A.: Estimating in situ deformation of rock masses using a hardening parameter and RQD. Int J Geomech. 13(4), 348–364 (2013)
Venkatasubramanian, C., Dhinakaran, G.: ANN model for predicting CBR from index properties of soils. Int. J. Civ. Struct. Eng. 2(2), 605–611 (2011)
Wu, H., Huang, B., Shu, X., Zhao, S.: Evaluation of geogrid reinforcement effects on unbound granular pavement base courses using loaded wheel tester. Geotext. Geomembranes. 43(5), 462–469 (2015). https://doi.org/10.1016/j.geotexmem.2015.04.014
Yildirim, B., Gunaydin, O.: Expert systems with applications estimation of California bearing ratio by using soft computing systems. Expert Syst. Appl. 38(5), 6381–6391 (2011)
Zedeh, L., Kacprzyk, J.: Fuzzy logic for the management of uncertainty. John Wiley and Sons, Inc, New York (1992)
Zehtabchi, A., Hashemi, S.A.H., Asadi, S.: Predicting the strength of polymer-modified thin-layer asphalt with fuzzy logic. Constr Build Mater. 169, 826–834 (2018). https://doi.org/10.1016/j.conbuildmat.2018.02.002
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Singh, M., Trivedi, A. & Shukla, S.K. Fuzzy-Based Model for Predicting Strength of Geogrid-Reinforced Subgrade Soil with Optimal Depth of Geogrid Reinforcement. Transp. Infrastruct. Geotech. 7, 664–683 (2020). https://doi.org/10.1007/s40515-020-00113-y
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DOI: https://doi.org/10.1007/s40515-020-00113-y