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محاسبه معکوس مدول الاستیسیته لایههای روسازی با استفاده از الگوریتم بهینهسازی فراابتکاری GWO | ||
| مهندسی زیر ساخت های حمل و نقل | ||
| مقاله 5، دوره 11، شماره 1 - شماره پیاپی 41، فروردین 1404، صفحه 83-96 اصل مقاله (2.24 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22075/jtie.2025.36872.1716 | ||
| نویسندگان | ||
| محمود ملکوتی علون آبادی* ؛ میلاد جهانگیری | ||
| استادیار، گروه مهندسی عمران، دانشگاه خلیج فارس، بوشهر، ایران. | ||
| تاریخ دریافت: 22 بهمن 1403، تاریخ بازنگری: 12 اسفند 1403، تاریخ پذیرش: 19 اسفند 1403 | ||
| چکیده | ||
| در مهندسی روسازی، تعیین خصوصیات سازهای روسازی، از جمله ضرایب ارتجاعی و ضخامت لایهها، حائز اهمیت است. این خصوصیات، عملکرد روسازی را تعیین میکنند و تأثیر مستقیم بر عمر روسازی دارند. استفاده از نرمافزارهای تجاری بهمنظور شبیهسازی محاسبات عددی برای محاسبه تغییرات سطح روسازی به دلیل پیچیدگی تلفیق آن با تکنیک بهینهسازی، هزینه محاسبات را افزایش میدهد. در این روشها، نیاز به پایگاه داده مصنوعی از پیشتولیدشده با استفاده از نرمافزار و همچنین استفاده از شبکه عصبی و الگوریتم بهینهسازی وجود دارد. بنابراین، برای تولید جمعیت اولیه، بایستی با دسته مدولهای تخمینی متفاوت، نرمافزار را اجرا کرد تا جمعیت لازم برای تحلیل معکوس فراهم شود که هزینه محاسبات را افزایش میدهد. هدف اصلی این تحقیق، تلفیق روش عددی دیفرانسیل کوادرچر به عنوان یک روش عددی دقیق و کارامد و با سرعت بالا با الگوریتم بهینهسازی فراابتکاری گرگ خاکستری (GWO)، به منظور محاسبات معکوس مقادیر مجهول مدول الاستیسیته لایههای روسازی بدون استفاده از شبکه عصبی مصنوعی و کاهش زمان محاسبات میباشد. نتایج تحلیل با پنج اجرای مستقل نشان داد که این روش قادر است با تعداد جمعیت و تکرارهای کم، به پاسخ مطلوب دست یابد. | ||
| کلیدواژهها | ||
| روسازی؛ دیفرانسیل کوادرچر؛ الگوریتم بهینه سازی GWO؛ محاسبات معکوس؛ مدول الاستیسیته | ||
| عنوان مقاله [English] | ||
| Inverse Calculation of the Modulus of Elasticity of Pavement Layers Using the GWO Metaheuristic Optimization Algorithm | ||
| نویسندگان [English] | ||
| Mahmoud Malakouti Olounabadi؛ Milad Jahangiri | ||
| Assistant Professor, Department of Civil Engineering, Persian Gulf University, Bushehr, I. R. Iran. | ||
| چکیده [English] | ||
| In pavement engineering, determining the structural properties of the pavement, including the elastic coefficients and layer thicknesses, is highly significant. These properties determine the performance of the pavement and have a direct impact on the pavement life. Using commercial software for numerical simulation engines to calculate pavement surface changes increases calculations cost due to the complexity of integrating it into the optimization engine. In these methods, there is a need for a pre-generated artificial database using the software, as well as the use of a neural network and an optimization algorithm. Therefore, to generate the analysis population, the software must be run with a set of different estimation modules to provide the necessary population for inverse analysis, which increases the need for computational costs. The main goal of the current research is to combine the quadrature differential numerical method as an accurate, efficient, and high-speed numerical method with the Gray Wolf Optimization (GWO) metaheuristic optimization algorithm in order to inversely calculate the redundant values of the elastic modulus of pavement layers without using an artificial neural network and reducing the computational time. Results of the analysis with five independent runs showed that this method is able to achieve the desired response with a small number of populations and iterations. | ||
| کلیدواژهها [English] | ||
| Pavement, Differential Quadrature, GWO optimization algorithm, Inverse calculations, Modulus of elasticity | ||
| مراجع | ||
|
Ali, N. A. and Khosla, N. P. 1987. “Determination of layer moduli using a falling weight deflectometer”. Transport. Res. Record 1117.
de Araújo, P. C., Soares, J. B., de Holanda, Á. S., Parente, E., and Evangelista, F. 2010. “Dynamic viscoelastic analysis of asphalt pavements using a finite element formulation”. Road Mater. Pavement Design, 11(2): 409-433. https://doi.org/10.1080/14680629.2010.9690282
Egorchev, M. and Tiumentsev, Y. 2018. “Homotopy continuation training method for semi-empirical continuous-time state-space neural network models”. In International Conference on Neuroinformatics, pp. 115-120.
Eslaminia, M. and Guddati, M. N. 2016. “Fourier-finite element analysis of pavements under moving vehicular loading”. Int. J. Pavement Eng., 17(7): 602–614. https://doi.org/10.1080/10298436.2015.1007237
Faris, H., Aljarah, I., Al-Betar, M. A. and Mirjalili, S. 2018. “Grey wolf optimizer: A review of recent variants and applications”. Neural Comput. Appl., 30: 413-435. https://doi.org/10.1007/s00521-017-3272-5
Farshchian Yazdi, M., Kamel, R., Seyyed Mahdavi Chabok, S. J. and Kheir Abadi, M. 2023. “Gate allocation based on a combination of gray wolf algorithm and fuzzy logic system”. Quart. J. Transport. Eng., 14(3): 2695-2711. doi: 10.22119/jte.2021.306086.2561. [In Persian]
Garbowski, T. and Pożarycki, A. 2017. “Multi-level backcalculation algorithm for robust determination of pavement layers parameters”. Inverse Probl. Sci. Eng., 25(5): 674-693. https://doi.org/10.1080/1741597 7.2016.1191073
Gopalakrishnan, K. 2009. “Neural network–swarm intelligence hybrid nonlinear optimization algorithm for pavement moduli back-calculation”. J. Transport. Eng., 136(6): 528-536. https://doi.org/10.1061/(ASCE) TE.1943-5436.000012
Gopalakrishnan, K. and Papadopoulos, H. 2011. “Reliable pavement backcalculation with confidence estimation”. Sci. Iran., 18(6): 1214-1221. https://doi.org/10.1016/j.scient.2011.11.018
Hamim, A., Yusoff, N. I. M., Ceylan, H., Rosyidi, S. A. P. and El-Shafie, A. 2018. “Comparative study on using static and dynamic finite element models to develop FWD measurement on flexible pavement structures”. Constr. Build. Mater., 176: 583-592. https://doi.org/10.1016/j.conbuildmat.2018.05.082
Heydari, A., Malakouti, M., Meraji, S. H., Malekzadeh, P. and Ramesh Khah, S. 2020. “Back-calculation of pavement layer elastic modulus using the hybrid differential quadrature method and harmony search optimization algorithm”. Sharif J. Civ. Eng., 36: 37-45. 10.24200/j30.2019.52331.2466. [In Persian]
Khaleghi, F., Keshavarz, A. and Malakouti Olounabadi, M. 2022. “Seismic analysis of rigid walls retaining a cross-aniso-tropic soil by the differential quadrature method”. Eur. J. Environ. Civ. Eng., 26(9): 3947-3960. https://doi.org/10.1080/19648189.2020.1827045
Karami, G. and Malekzadeh, P. 2002. “A new differential quadrature methodology for beam analysis and the associated differential quadrature element method”. Comput. Meth. Appl. Mech. Eng., 191(32): 3509-3526. https://doi.org/10.1016/S0045-7825(02)00289-X
Karami, G. and Malekzadeh, P. 2003. “Application of a new differential quadrature methodology for free vibration analysis of plates”. Int. J. Numer. Meth. Eng., 56(6): 847-868. https://doi.org/10.1002/nme.590
Karami, G., Malekzadeh, P. and Shahpari, S. A. 2003. “A DQEM for vibration of shear deformable nonuniform beams with general boundary conditions”. Eng. Struct., 25(9): 1169-1178. https://doi.org/10.1016/S0141-0296(03)00065-8
Kargah-Ostadi, N. and Stoffels, S. M. 2015. “Backcalculation of flexible pavement structural properties using a restart covariance matrix adaptation evolution strategy”. J. Comput. Civ. Eng., 29(2): 04014035. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000309
Li, M. and Wang, H. 2017. “Development of ANN-GA program for backcalculation of pavement moduli under FWD testing with viscoelastic and nonlinear parameters”. Int. J. Pavement Eng., 20(4): 490-498. https://doi.org/10.1080/10298436.2017.1309197
Li, C., Ashlock, J. C., Lin, S. and Vennapusa, P. K. 2018. “In situ modulus reduction characteristics of stabilized pavement foundations by multichannel analysis of surface waves and falling weight deflectometer tests”. Constr. Build. Mater., 188: 809-819. https://doi.org/10.1016/j.conbuildmat.201 8.08.163
Liu, J., Liu, F., and Wang, L. 2024. “Automated, economical, and environmentally-friendly asphalt mix design based on machine learning and multi-objective grey wolf optimization”. J. Traffic Transport. Eng., 11(3): 381-405. https://doi.org/10.1016/j.jtte.2023.10.002
Malakouti, M., Ameri, M. and Malekzadeh, P. 2014. “Incremental layerwise finite element formulation for viscoelastic response of multilayered pavements”. Int. J. Transport. Eng., 1(3): 183-198. doi:10.22119/IJTE.2014.4788. 10.22119/ijte.2014.4788
Malakouti M., Ameri M., Malekzadeh P. 2014. “Dynamic viscoelastic incremental-layerwise finite element method for multilayered structure analysis based on relaxation approach”. J. Mech., 30: 593-602. https://doi.org/10.1017/jmech.2014.42
Malakouti Olounabadi, M., Jahangiri, M., Ghazi, M. and Keshavarz, A. 2024. “A versatile deformation-oriented criterion for optimisation-based backcalculation of the pavement moduli and thickness”. Int. J. Pavement Eng., 25(1). https://doi.org/10.1080/10298436.2024.2433608
Mirjalili, S., Mirjalili, S. M. and Lewis, A. 2014. “Grey wolf optimizer”. Adv. Eng. Software, 69: 46-61. https://doi.org/10.1016/j.advengsoft.2013.12.007
Mohammadi Golafshani, E., Behnood, A. and Karimi, M. M. 2023. “Predicting the dynamic modulus of asphalt mixture using hybridized artificial neural network and grey wolf optimizer”. Int. J. Pavement Eng., 24(1). https://doi.org/10.1080/10298436.2021.2005056
Najafi, F., Malakouti Olouunabadi, M., Soroush, M. and Fazeli, A. 2020. “Investigation on influence of temperature and moving load speed on flexible pavement response with viscoelastic behavior using 3D einite element model”. Quart. J. Transport. Eng., 11(4): 971-987. doi: 10.22119/jte.2020.88168. [In Persian]
Rabbani, A., Samui, P. and Kumari, S. 2023. “A novel hybrid model of augmented grey wolf optimizer and artificial neural network for predicting shear strength of soil”. Model. Earth Syst. Environ., 9(2): 2327-2347. https://doi.org/10.1007/s40808-022-01610-4
Rameshkhah, S., Malakouti Olounabadi, M., Malekzadeh, P. and Meraji, S. H. 2018. “Quasi-static response analysis of viscoelastic flexible pavement using differential quadrature element method”. Quart. J. Transport. Eng., 10(2): 311-334. doi:20.1001.1.20086598.1397.10.2.7.2. [In Persian]
Rameshkhah, S., Malakouti Olounabadi, M., Malekzadeh, P. and Meraji, S. H., 2020. “Dynamic response analysis of viscoelastic pavement using differential quadrature element method”. Int. J. Pavement Eng., 21(11): 1321-1335. https://doi.org/10.1080/10298436.2018.1545091
Saltan, M., Terzi, S. and Küçüksille, E. U. 2011. “Backcalculation of pavement layer moduli and Poisson’s ratio using data mining”. Expert Syst. Appl., 38: 2600-2608. https://doi.org/10.1016/j.eswa.2010.08.050
Scimemi, G. F., Turetta, T. and Celauro, C. 2016. “Backcalculation of airport pavement moduli and thickness using the Lévy Ant Colony Optimization Algorithm”. Constr. Build. Mater., 119: 288-295. https://doi.org/10.1016/j.conbuildmat.2016.05.072
Sangghaleh, A., Pan, E., Green, R., Wang, R., Liu, X. and Cai, Y. 2014. “Backcalculation of pavement layer elastic modulus and thickness with measurement errors”. Int. J. Pavement Eng., 15(6): 521-531. https://doi.org/10.1080/10298436.2013.786078
Senseney, C. T., Krahenbuhl, R. A. and Mooney, M. A. 2013. “Genetic algorithm to optimize layer parameters in light weight deflectometer backcalculation”. Int. J. Geomech., 13(4): 473-476. https://doi.org/10.1061/(ASCE)GM.1943-5622.0000222
Shabbir, F. and Omenzetter, P. 2016. “Model updating using genetic algorithms with sequential niche technique”. Eng. Struct., 120: 166-182. https://doi.org/10.1016/j.engstruct.2016.04.028
Tarefder, R. A., Ahsan, S., and Ahmed, M. U. 2015. “Neural network–based thickness determination model to improve backcalculation of layer moduli without coring”. Int. J. Geomech., 15(3): 04014058. https://doi.org/10.1061/(ASCE)GM.1943-5622.0000407
Vosoughi, A. R., Malekzadeh, P., Topal, U. and Dede, T. 2018. “A hybrid DQ-TLBO technique for maximizing first frequency of laminated composite skew plates”. Steel Compos. Struct., An Int. J., 28(4): 509-516. doi:10.12989/scs.2018.28.4.509.
Yu, Q., Cai, L. and Wang, J. 2018. “Experimental and FEM research on airport cement concrete direct‐thickening double‐deck pavement slabs under aircraft single‐wheel dynamic loads”. Adv. Mater. Sci. Eng., 2018(1): 1952671. https://doi.org/10.1155/2018/1952671 | ||
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