پیوندهای مفید
آمار
| تعداد نشریات | 21 |
| تعداد شمارهها | 679 |
| تعداد مقالات | 9,900 |
| تعداد مشاهده مقاله | 70,088,081 |
| تعداد دریافت فایل اصل مقاله | 49,392,757 |
Predicting Resilient Modulus of Clayey Subgrade Soils by Means of Cone Penetration Test Results and Back-Propagation Artificial Neural Network | ||
| Journal of Rehabilitation in Civil Engineering | ||
| مقاله 9، دوره 10، شماره 4 - شماره پیاپی 28، بهمن 2022، صفحه 146-162 اصل مقاله (1.28 M) | ||
| نوع مقاله: Regular Paper | ||
| شناسه دیجیتال (DOI): 10.22075/jrce.2022.25013.1568 | ||
| نویسندگان | ||
| Ali Reza Ghanizadeh* 1؛ Arash Ziaee1؛ Seyed Mohammad Hossein Khatami2؛ Pouyan Fakharian3 | ||
| 1Department of Civil Engineering, Sirjan University of Technology, Sirjan, Iran | ||
| 2Department of Civil Engineering, Technical and Vocational University (TVU), Tehran, Iran | ||
| 3Faculty of Civil Engineering, Semnan University, Semnan, Iran | ||
| تاریخ دریافت: 15 آبان 1400، تاریخ بازنگری: 27 آذر 1400، تاریخ پذیرش: 24 بهمن 1400 | ||
| چکیده | ||
| Resilient modulus (Mr) of subgrade soils is considered as one of the most important factors for designing flexible pavements using empirical methods as well as mechanistic-empirical methods. The resilient modulus is commonly measured by a dynamic triaxial loading test, which is complex and expensive. In this research, back-propagation artificial neural network method has been employed to model the resilient modulus of clayey subgrade soils based on the results of the cone penetration test. The prediction of the resilient modulus of clayey subgrade soil can be possible through the developed neural network based on the parameters of the cone tip resistance (qc), sleeve friction (fs), moisture content (w), and dry density (γd). The results of the present study show that the coefficients of determination (R2) for training and testing sets are 0.9837 and 0.9757, respectively. According to the sensitivity analysis results, the moisture content is the least important parameter to predict the resilient modulus of clayey subgrade soils, while the importance of other parameters is almost the same. In this study, the effect of different parameters on the resilient modulus of clayey subgrade soil was evaluated using parametric analysis and it was found that with increasing the cone tip resistance (qc), the sleeve friction (fs) and the dry density (γd) and also with decreasing the moisture content (w) of soils, the resilient modulus of clayey subgrade soils increases. | ||
| کلیدواژهها | ||
| Resilient modulus؛ Clayey soils؛ Cone penetration test؛ Back-propagation algorithm؛ Artificial neural network | ||
| مراجع | ||
|
[1] Zaman M, Solanki P, Ebrahimi A, White L. Neural network modeling of resilient modulus using routine subgrade soil properties. Int J Geomech 2010;10:1–12. https://doi.org/10.1061/(ASCE)1532-3641(2010)10:1(1).
[2] AASHTO AA of SH and TO (AASHTO). Guide for Design of Pavement Structures. vol. 1. Washington, D.C: American Association of State Highway and Transportation Officials,Washington, D.C; 1993.
[3] ARA. Guide for Mechanistic–Empirical Design of New and Rehabilitated Pavement Structures, Final Report for Project 1-37A. Washington, DC: National Cooperative Research Program; 2004.
[4] Gudishala R. Development of resilient modulus prediction models for base and subgrade pavement layers from in situ devices test results. M.SLouisiana State University and Agricultural and Mechanical College, 2004.
[5] Hicks RG, Monismith CL. Factors influencing the resilient response of granular materials. Highw Res Rec 1971;345:15–31.
[6] Kim W, Labuz JF. Resilient Modulus And Strength Of Base Course With Recycled Bituminous Material. vol. 05. 2007.
[7] Kim SH, Yang J, Jeong JH. Prediction of subgrade resilient modulus using artificial neural network. KSCE J Civ Eng 2014;18:1372–9. https://doi.org/10.1007/s12205-014-0316-6.
[8] NCHRP. (NCHRP), National Cooperative Highway Research Program. Guid. Mech. Des. New Rehabil. Pavement Struct. Part 2, Des. Inputs, 2004.
[9] Drumm E, Boateng P, Johnson P. Estimation of subgrade resilient modulus from standard tests. J Geotech Eng 1990;116:774--789. https://doi.org/https://doi.org/10.1061/(ASCE)0733-9410(1990)116:5(774).
[10] Kim D-G. Engineering properties affecting the resilient modulus of fine-grained soils as subgrade. M.S Thesis, The Ohio State University, 1999.
[11] George K. Prediction of resilient modulus from soil index properties. Washington, D.C: No. FHWA/MS-DOT-RD-04-172,University of Mississippi,; 2004.
[12] Andrei D, Witczak MW, Schwartz CW, Uzan J. Harmonized resilient modulus test method for unbound pavement materials. Transp Res Rec 2004;1874:29–37. https://doi.org/10.3141/1874-04.
[13] Kim, Kim JR. Resilient behavior of compacted subgrade soils under the repeated triaxial test. Constr Build Mater 2007;21:1470–9.
[14] Mazari M, Navarro E, Abdallah I, Nazarian S. Comparison of numerical and experimental responses of pavement systems using various resilient modulus models. Soils Found 2014;54:36–44.
[15] AASHTO. Guide for Design of Pavement Structures. vol. 1. Washington: American Association of State Highway and Transportation Officials,Washington, D.C; 1993.
[16] Park, Kweon G, Lee SR. Prediction of resilient modulus of granular subgrade soils and subbase materials using artificial neural network. Road Mater Pavement Des 2009;10:647–65. https://doi.org/https://doi.org/10.1080/14680629.2009.9690218.
[17] Heidarabadizadeh N, Ghanizadeh AR, Behnood A. Prediction of the resilient modulus of non-cohesive subgrade soils and unbound subbase materials using a hybrid support vector machine method and colliding bodies optimization algorithm. Constr Build Mater 2021;275:122140. https://doi.org/10.1016/j.conbuildmat.2020.122140.
[18] Ghanizadeh, Rahrovan M. Application of artifitial neural network to predict the resilient modulus of stabilized base subjected to wet dry cycles. Comput Mater Civ Eng 2016;1:37–47.
[19] Ghanizadeh AR, Heidarabadizadeh N, Heravi F. Gaussian Process Regression (GPR) for Auto-Estimation of Resilient Modulus of Stabilized Base Materials. J Soft Comput Civ Eng 2021;5:80–94.
[20] Heukelom W, Klomp A. DYNAMIC TESTING AS A MEANS OF CONTROLLING PAVEMENTS DURING AND AFTER CONSTRUCTION 1962;203:495–510.
[21] Duncan JM, Buchignani AL. An Engineering Manual for Settlement Studies. University of California, Berkeley CA.; 1976.
[22] Mohammad LN, Titi HH, Herath A. Evaluation of resilient modulus of subgrade soil by cone penetration test. Transp Res Rec 1999;1:236–45. https://doi.org/10.3141/1652-30.
[23] Mohammad LN, Titi HH, Herath A; Effect of moisture content and dry unit weight on the resilient modulus of subgrade soils predicted by cone penetration test. Washington, DC: Publication FHWA-LA-00-355. U.S. Department of Transportation, FHWA.; 2002.
[24] Dehler W, Labuz J. Cone Penetration Testing in Pavement Design. 2007.
[25] Mohammad LN, Herath A, Abu-Farsakh MY, Gaspard K, Gudishala R. Prediction of Resilient Modulus of Cohesive Subgrade Soils from Dynamic Cone Penetrometer Test Parameters. J Mater Civ Eng 2007;19:986–92. https://doi.org/10.1061/(asce)0899-1561(2007)19:11(986).
[26] Puppala AJ, Acar YB, Tumay MT. Cone Penetration in Very Weakly Cemented Sand. J Geotech Eng 1995;121:589–600. https://doi.org/https://doi.org/10.1061/(ASCE)0733-9410(1995)121:8(589).
[27] Lunne T, Robertson PK, Powell JJM. Cone Penetration Testing in Geotechnical Practice. London: Blackie Academic and Professional,CRC Press; 1997.
[28] Mayne PW. Cone penetration testing: a synthesis of highway practice. Transportation Research Board; 2007.
[29] Liu S, Cai G, Puppala AJ, Tu Q. Prediction of embankment settlements over marine clay using piezocone penetration tests. Bull Eng Geol Environ 2011;70:401–9. https://doi.org/10.1007/s10064-010-0329-4.
[30] Cai G, Liu S, Puppala AJ, Tong L. Assessment of the coefficient of lateral earth pressure at rest (K o) from in situ seismic tests. Geotech Test J 2011;34. https://doi.org/10.1520/GTJ102520.
[31] Cai G, Liu S, Puppala AJ. Reliability assessment of CPTU-based pile capacity predictions in soft clay deposits. Eng Geol 2012;141–142:84–91. https://doi.org/10.1016/j.enggeo.2012.05.006.
[32] Cai G, Puppala AJ, Liu S. Characterization on the correlation between shear wave velocity and piezocone tip resistance of Jiangsu clays. Eng Geol 2014;171:96–103. https://doi.org/10.1016/j.enggeo.2013.12.012.
[33] Hassan A Bin. The effects of material parameters on Dynamic Cone Penetrometer results for fine-grained soils and granular materials. Oklahoma: Oklahoma State University Stillwater; 1996.
[34] George KP, Uddin W. Subgrade characterization for highway pavement design. Final Report. Jackson: Mississippi Department of Transportation.: 2000.
[35] Herath A, Mohammad L, Gaspard K, Gudishala R, Abu-Farsakh M. The use of dynamic cone penetrometer to predict resilient modulus of subgrade soils. Adv. pavement Eng., Austin, Texas, United States: Geotechnical Special Publication ASCE, Reston; 2005, p. 1–16. https://doi.org/https://doi.org/10.1061/40776(155)2.
[36] Liu S, Zou H, Cai G, Bheemasetti TV, Puppala AJ, Lin J. Multivariate correlation among resilient modulus and cone penetration test parameters of cohesive subgrade soils. Eng Geol 2016;209:128–42. https://doi.org/10.1016/j.enggeo.2016.05.018.
[37] Zou H, Liu S, Cai G, Puppala AJ, Bheemasetti TV. Multivariate correlation analysis of seismic piezocone penetration (SCPTU) parameters and design properties of Jiangsu quaternary cohesive soils. Eng Geol 2017;228:11–38. https://doi.org/https://doi.org/10.1016/j.enggeo.2017.07.005.
[38] Zhang Y, Li T, Wang Y. Theoretical elastic solutions for foundations improved by geosynthetic-encased columns. Geosynth Int 2011;18:12–20. https://doi.org/https://doi.org/10.1680/gein.2011.18.1.12.
[39] Zhang W. MARS Use in Prediction of Collapse Potential for Compacted Soils. MARS Appl. Geotech. Eng. Syst., Singapore: Springer Singapore; 2020, p. 27–46. https://doi.org/10.1007/978-981-13-7422-7_4.
[40] Sadrossadat E, Ghorbani B, Zohourian B, Kaboutari M, Rahimzadeh Oskooei P. Predictive modelling of the MR of subgrade cohesive soils incorporating CPT-related parameters through a soft-computing approach. Road Mater Pavement Des 2020;21:701–19. https://doi.org/https://doi.org/10.1080/14680629.2018.1527241.
[41] Ghorbani, Behnam Arulrajah A, Narsilio G, Horpibulsuk S, Bo MW. Hybrid Formulation of Resilient Modulus for Cohesive Subgrade Soils Utilizing CPT Test Parameters. J Mater Civ Eng 2020;32:06020011. https://doi.org/https://doi.org/10.1061/(ASCE)MT.1943-5533.0003329.
[42] Ghanizadeh AR, Delaram A. Development of Predictting Model for Clay Subgrade Soil Resilient Modulus based on the Results of Cone Penetration Test using Evolutionary Polynomial Regression Method. Civ Infrastruct Res 2021;7:1–22.
[43] Rezazadeh Eidgahee D, Haddad A, Naderpour H. Evaluation of shear strength parameters of granulated waste rubber using artificial neural networks and group method of data handling. Sci Iran 2019;26:3233–44. https://doi.org/10.24200/sci.2018.5663.1408.
[44] Rezazadeh Eidgahee D, Rafiean AH, Haddad A. A Novel Formulation for the Compressive Strength of IBP-Based Geopolymer Stabilized Clayey Soils Using ANN and GMDH-NN Approaches. Iran J Sci Technol - Trans Civ Eng 2020;44:219–29. https://doi.org/10.1007/s40996-019-00263-1.
[45] Naderpour H, Nagai K, Fakharian P, Haji M. Innovative models for prediction of compressive strength of FRP-confined circular reinforced concrete columns using soft computing methods. Compos Struct 2019;215:69–84. https://doi.org/10.1016/j.compstruct.2019.02.048.
[46] Naderpour H, Rafiean AH, Fakharian P. Compressive strength prediction of environmentally friendly concrete using artificial neural networks. J Build Eng 2018;16. https://doi.org/10.1016/j.jobe.2018.01.007.
[47] Naderpour H, Sharei M, Fakharian P, Heravi MA. Shear Strength Prediction of Reinforced Concrete Shear Wall Using ANN, GMDH-NN and GEP. J Soft Comput Civ Eng 2022;6:66–87. https://doi.org/10.22115/scce.2022.283486.1308.
[48] Khademi A, Behfarnia K, Kalman Šipoš T, Miličević I. The Use of Machine Learning Models in Estimating the Compressive Strength of Recycled Brick Aggregate Concrete. Comput Eng Phys Model 2021;4:1–25. https://doi.org/10.22115/cepm.2021.297016.1181.
[49] Farhangi V, Jahangir H, Eidgahee DR, Karimipour A, Javan SAN, Hasani H, et al. Behaviour Investigation of SMA-Equipped Bar Hysteretic Dampers Using Machine Learning Techniques. Appl Sci 2021;11:10057. https://doi.org/10.3390/app112110057.
[50] Rezazadeh Eidgahee D, Fasihi F, Naderpour H. Optimized Artificial Neural Network for Analyzing Soil-Waste Rubber Shred Mixtures. Sharif J Civ Eng 2015;31.2:105–11.
[51] Rumelhart DE, Hinton GE, Williams RJ. Learning representations by back-propagating errors. Nature 1986;323:533–6. https://doi.org/10.1038/323533a0.
[52] Siddique N, Adeli H. Computational intelligence: synergies of fuzzy logic, neural networks and evolutionary computing. John Wiley & Sons; 2013.
[53] Naderpour H, Rezazadeh Eidgahee D, Fakharian P, Rafiean AH, Kalantari SM. A new proposed approach for moment capacity estimation of ferrocement members using Group Method of Data Handling. Eng Sci Technol an Int J 2020;23:382–91. https://doi.org/10.1016/j.jestch.2019.05.013.
[54] Jahangir H, Rezazadeh Eidgahee D. A new and robust hybrid artificial bee colony algorithm – ANN model for FRP-concrete bond strength evaluation. Compos Struct 2021;257:113160. https://doi.org/10.1016/J.COMPSTRUCT.2020.113160.
[55] ASTM D5778. International Standard Test Method for Electronic Friction Cone and Piezocone Penetration Testing of Soils. Annual Book of ASTM Standards. ASTM International, West Conshohocken, PA.; 2012.
[56] ISSMFE I. Report of the ISSMFE Technical Committee on Penetration Testing of Soils -TC 16 with Reference Test Procedures. 1989.
[57] AASHTO T 307. Determining the Resilient Modulus of Soils and Aggregate Materials. 2003. | ||
|
آمار تعداد مشاهده مقاله: 1,265 تعداد دریافت فایل اصل مقاله: 1,089 |
||