Prediction of resilient modulus for subgrade soils based on ANN approach

Abstract

The resilient modulus (M_R) of subgrade soils is usually used to characterize the stiffness of subgrade and is a crucial parameter in pavement design. In order to determine the resilient modulus of compacted subgrade soils quickly and accurately, an optimized artificial neural network (ANN) approach based on the multi-population genetic algorithm (MPGA) was proposed in this study. The MPGA overcomes the problems of the traditional ANN such as low efficiency, local optimum and over-fitting. The developed optimized ANN method consists of ten input variables, twenty-one hidden neurons, and one output variable. The physical properties (liquid limit, plastic limit, plasticity index, 0.075 mm passing percentage, maximum dry density, optimum moisture content), state variables (degree of compaction, moisture content) and stress variables (confining pressure, deviatoric stress) of subgrade soils were selected as input variables. The M_R was directly used as the output variable. Then, adopting a large amount of experimental data from existing literature, the developed optimized ANN method was compared with the existing representative estimation methods. The results show that the developed optimized ANN method has the advantages of fast speed, strong generalization ability and good accuracy in M_R estimation.

Keywords

resilient modulus / subgrade soils / artificial neural network / multi-population genetic algorithm / prediction method

References

[1] ZhangJ-h, PengJ-h, ZhengJ-l, DaiL-l, YaoY-sheng. Prediction of resilient modulus of compacted cohesive soils in South China [J]. International Journal of Geomechanics, 2019, 19(7): 04019068

[2] ZhaoY, LuZ, YaoH-l, GuF, DuanY-hui. Experimental study of dynamic resilient modulus of subgrade soils under coupling of freeze-thaw cycles and dynamic load [J]. Journal of Central South University, 2020, 2772043-2053

[3] YanK-z, XuH-b, ShenG-hui. Novel approach to resilient modulus using routine subgrade soil properties [J]. International Journal of Geomechanics, 2014, 146: 04014025

[4] AASHTOStandard method of test for determining the resilient modulus of soils and aggregate materials [S], 2003, Washington, DC, AASHTO

[5] AASHTOGuide for mechanistic-empirical design of new and rehabilitated pavement structures [S], 2004, Washington, DC, AASHTO

[6] ZhangJ-h, PengJ-h, ZhengJ-long. Progress and prospect of the prediction model of the resilient modulus of subgrade soils [J]. China Journal of Highway and Transport, 2020, 33(1): 1-13(in Chinese)

[7] SeedH B, ChanC K, LeeC E. Resilience characteristics of subgrade soils and their relation to fatigue failures in asphalt pavements [C]. International Conference on the Structural Design of Asphalt Pavements, 1962, Ann Arbor, University of Michigan, 77-113

[8] SeedH B, MitryF G, MonismithC L, ChanC KPrediction of flexible pavement deflections from laboratory repeated-load tests [R], 1967, Washington, DC, Transportation Research Board

[9] UzanJCharacterization of granular material [M], 1985, Washington, DC, Transportation Research Record, 5259

[10] WitczakM W, UzanJThe universal airport pavement design system, Report I of IV: Granular material characterization [R], 1988, College Park, MD, University of Maryland

[11] NCHRPHarmonized test methods for laboratory determination of resilient modulus for flexible pavement design [S], 2003, Washington, DC, NCHRP

[12] AzamA M, CameronD A, RahmanM M. Model for prediction of resilient modulus incorporating matric suction for recycled unbound granular materials [J]. Canadian Geotechnical Journal, 2013, 50(11): 1143-1158

[13] QianJ-s, LiJ-y, ZhouD, LingJ-ming. Prediction model of resilient modulus for unsaturated clay soils considering the effect of matric suction [J]. Rock and Soil Mechanics, 2018, 39(1): 123-128(in Chinese)

[14] ZhangJ-h, PengJ-h, ZhengJ-l, YaoY-sheng. Characterisation of stress and moisture-dependent resilient behaviour for compacted clays in South China [J]. Road Materials and Pavement Design, 2020, 21(1): 262-275

[15] ZHANG Jun-hui, PENG Jun-hui, ZENG Ling, LI Jue, LI Feng. Rapid estimation of resilient modulus of subgrade soils using performance-related soil properties [J]. International Journal of Pavement Engineering, 2019. DOI: https://doi.org/10.1080/10298436.2019.1643022.

[16] YaoY-s, ZhengJ-l, ZhangJ-h, PengJ-h, LiJue. Model for predicting resilient modulus of unsaturated subgrade soils in South China [J]. KSCE Journal of Civil Engineering, 2018, 226: 2089-2098

[17] SahaS, GuF, LuoX, LyttonR L. Use of an artificial neural network approach for the prediction of resilient modulus for unbound granular material [J]. Transportation Research Record: Journal of the Transportation Research Board, 2018, 2672(52): 23-33

[18] KimS H, YangJ-d, JeongJ H. Prediction of subgrade resilient modulus using artificial neural network [J]. KSCE Journal of Civil Engineering, 2014, 18(5): 1372-1379

[19] NazzalM D, TatariO. Evaluating the use of neural networks and genetic algorithms for prediction of subgrade resilient modulus [J]. International Journal of Pavement Engineering, 2013, 144: 364-373

[20] WangX-k, HuaL, WangX-x, MeiX-s, ZhuQ-h, DaiY-tong. Soft measurement model of ring’s dimensions for vertical hot ring rolling process using neural networks optimized by genetic algorithm [J]. Journal of Central South University, 2017, 24(1): 17-29

[21] SadrossadatE, GhorbaniB, ZohourianB, KaboutariM, Rahimzadeh OskooeiP. Predictive modelling of the MR of subgrade cohesive soils incorporating CPT-related parameters through a soft-computing approach [J]. Road Materials and Pavement Design, 2020, 21(3): 701-719

[22] SadrossadatE, HeidaripanahA, OsouliS. Prediction of the resilient modulus of flexible pavement subgrade soils using adaptive neuro-fuzzy inference systems [J]. Construction and Building Materials, 2016, 123: 235-247

[23] GopalakrishnanK. Neural network-swarm intelligence hybrid nonlinear optimization algorithm for pavement moduli back-calculation [J]. Journal of Transportation Engineering, 2010, 136(6): 528-536

[24] MccullochW S, PittsW. A logical calculus of the ideas immanent in nervous activity [J]. Bulletin of Mathematical Biology, 1990, 52(1): 99-115 2

[25] NielsenMNeural networks and deep learning [M], 2015, San Francisco, CA, USA, Determination Press

[26] RumelhartD E, HintonG E, WilliamsR J. Learning representations by back-propagating errors [J]. Nature, 1986, 323(6088): 533-536

[27] PengJ-h, ZhangJ-h, LiJ, YaoY-s, ZhangA-shun. Modeling humidity and stress-dependent subgrade soils in flexible pavements [J]. Computers and Geotechnics, 2020, 120: 103413

[28] ZhangJ-h, LiF, ZengL, PengJ-h, LiJue. Numerical simulation of the moisture migration of unsaturated clay embankments in Southern China considering stress state [J]. Bulletin of Engineering Geology and the Environment, 2021, 80(1): 11-24

[29] ZidanA F, AboukhadraA A, GaberY. Enhancement of resilient modulus of cohesive soil using an enzymatic preparation [J]. Journal of Central South University, 2019, 26(9): 2596-2608

[30] ZHANG Jun-hui, PENG Jun-hui, ZHANG An-shun, LI Jue. Prediction of permanent deformation for subgrade soils under traffic loading in Southern China [J]. International Journal of Pavement Engineering, 2020: 1–10. DOI: https://doi.org/10.1080/10298436.2020.1765244.

[31] YangS-r, HuangW-h, TaiY-tsung. Variation of resilient modulus with soil suction for compacted subgrade soils [J]. Journal of the Transportation Research Board, 2005, 1913(1): 99-106

[32] ZHANG Shi-ping, PAK R Y S, ZHANG Jun-hui. Vertical time-harmonic coupling vibration of an impermeable, rigid, circular plate resting on a finite, poroelastic soil layer [J]. Acta Geotechnica, 2020. DOI: https://doi.org/10.1007/s11440-020-01067-8.

[33] ShiF, WangX-c, YuL, LiYangMATLAB neural network analysis of 30 cases [M], 2010, Beijing, Beijing University of Aeronautics and Astronautics Press(in Chinese)

[34] ZiaeeS A, SadrossadatE, AlaviA H, Mohammadzadeh ShadmehriD. Explicit formulation of bearing capacity of shallow foundations on rock masses using artificial neural networks: Application and supplementary studies [J]. Environmental Earth Sciences, 2015, 73(7): 3417-3431

[35] AlaviA H, GandomiA H. Prediction of principal ground-motion parameters using a hybrid method coupling artificial neural networks and simulated annealing [J]. Computers & Structures, 2011, 89(23): 2176-2194 24

[36] VadoodM, JohariM S, RahaiA R. Relationship between fatigue life of asphalt concrete and polypropylene/polyester fibers using artificial neural network and genetic algorithm [J]. Journal of Central South University, 2015, 22(5): 1937-1946

[37] AghajaniH F, YengejehM G, KarimzadehA, Soltani-JighehH. A new procedure for determining dry density of mixed soil containing oversize gravel [J]. Journal of Central South University, 2018, 25(12): 2841-2856

[38] LiangY-j, RenC, WangH-y, HuangY-b, ZhengZ-tian. Research on soil moisture inversion method based on GA-BP neural network model [J]. International Journal of Remote Sensing, 2019, 40(5): 2087-2103 6

[39] GUPTA S, RANAIVOSON A, EDIL T, BENSON C, SAWANGSURIYA A. Pavement design using unsaturated soil technology [R]. Minnesota Dept. of Transportation, St. Paul, MN, 2007.

[40] LiangR Y, Rabab’ahS, KhasawnehM. Predicting moisture-dependent resilient modulus of cohesive soils using soil suction concept [J]. Journal of Transportation Engineering, 2008, 134(1): 34-40

[41] LanWeiUnsaturated characteristics and resilient modulus prediction model of subgrade soil [D], 2009, Shanghai, Tongji University(in Chinese)

[42] QiuX, QianJ-s, ZhangS-zhou. Research on prediction model of dynamic resilient modulus of subgrade soil based on matric suction [J]. Hydrogeology and Engineering Geology, 2011, 38(3): 49-90(in Chinese)

[43] NgC W W, ZhouC, YuanQ, XuJ. Resilient modulus of unsaturated subgrade soil: Experimental and theoretical investigations [J]. Canadian Geotechnical Journal, 2013, 50(2): 223-232

[44] HanZ, VanapalliS K. Relationship between resilient modulus and suction for compacted subgrade soils [J]. Engineering Geology, 2016, 21185-97

[45] HanZ, VanapalliS K, ZouW-lie. Integrated approaches for predicting soil-water characteristic curve and resilient modulus of compacted fine-grained subgrade soils [J]. Canadian Geotechnical Journal, 2017, 54(5): 646-663

[46] LiuW-z, ZengY-j, YaoY-s, ZhangJ-hui. Experimental study and prediction model of dynamic resilient modulus of compacted subgrade soils subjected to moisture variation [J]. Chinese Journal of Geotechnical Engineering, 2019, 41(1): 175-183(in Chinese)

[47] HedayatA A, Ahmadi AfzadiE, KalantaripourH, MorshediE, IranpourA. A new predictive model for the minimum strength requirement of steel moment frames using artificial neural network [J]. Soil Dynamics and Earthquake Engineering, 2019, 116: 69-81

[48] TchabanT, TaylorM J, GriffinJ P. Establishing impacts of the inputs in a feedforward neural network [J]. Neural Computing & Applications, 1998, 7(4): 309-317

[49] GevreyM, DimopoulosI, LekS. Two-way interaction of input variables in the sensitivity analysis of neural network models [J]. Ecological Modelling, 2006, 195(1): 43-50 2

[50] LeongH Y, OngD E L, SanjayanJ G, NazariA, KuehS M. Effects of significant variables on compressive strength of soil-fly ash geopolymer: Variable analytical approach based on neural networks and genetic programming [J]. Journal of Materials in Civil Engineering, 2018, 30704018129