Estimating the Optimal Mixture Design of Concrete Pavements Using a Numerical Method and Meta-heuristic Algorithms

Ali Akbar Shirzadi Javid1, Hamed Naseri1, Mohammad Ali Etebari Ghasbeh1
1School of Civil Engineering, Iran University of Science and Technology, Narmak, Tehran, Iran

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Abbass HA, Sarker R, Newton C (2001) PDE: a Pareto-frontier differential evolution approach for multi-objective optimization problems. In: Proceedings of the 2001 congress on evolutionary computation, 2001, vol 2. IEEE. https://doi.org/10.1109/cec.2001.934295

Afshar A, Kazemi H (2012) Multi objective calibration of large scaled water quality model using a hybrid particle swarm optimization and neural network algorithm. KSCE J Civ Eng 16(6):913–918. https://doi.org/10.1007/s12205-012-1438-3

Ahmed F, Deb K (2013) Multi-objective optimal path planning using elitist non-dominated sorting genetic algorithms. Soft Comput 17(7):1283–1299. https://doi.org/10.1007/s00500-012-0964-8

Al-Dujaili A, Suresh S (2018) Multi-objective simultaneous optimistic optimization. Inf Sci 424:159–174. https://doi.org/10.1016/j.ins.2017.09.066

Al-Shamiri AK et al (2019) Modeling the compressive strength of high-strength concrete: An extreme learning approach. Constr Build Mater 208:204–219

Amlashi AT et al (2019) Soft computing based formulations for slump, compressive strength, and elastic modulus of bentonite plastic concrete. J Clean Prod 230:1197–1216

Bhambu P, Kumar S, Sharma K (2018) Self balanced particle swarm optimization. Int J Syst Assur Eng Manag. https://doi.org/10.1007/s13198-017-0642-4

Biegler LT, Zavala VM (2009) Large-scale nonlinear programming using IPOPT: an integrating framework for enterprise-wide dynamic optimization. Comput Chem Eng 33(3):575–582. https://doi.org/10.1016/j.compchemeng.2008.08.006

Biswas DK, Panja SC, Guha S (2014) Multi objective optimization method by PSO. Procedia Mater Sci 6:1815–1822. https://doi.org/10.1016/j.mspro.2014.07.212

Cheng M-Y, Tran D-H (2016) An efficient hybrid differential evolution based serial method for multimode resource-constrained project scheduling. KSCE J Civ Eng 20(1):90–100. https://doi.org/10.1007/s12205-015-0414-0

Choi JW et al (2017) Application of genetic algorithm for hemodialysis schedule optimization. Comput Methods Prog Biomed 145:35–43. https://doi.org/10.1016/j.cmpb.2017.04.003

Deng F et al (2018) Compressive strength prediction of recycled concrete based on deep learning. Constr Build Mater 175:562–569. https://doi.org/10.1016/j.conbuildmat.2018.04.169

El-Bakry AS et al (1996) On the formulation and theory of the Newton interior-point method for nonlinear programming. J Optim Theory Appl 89(3):507–541

Gharaibeh N, Darter M (2001) Benefits and costs of jointed plain concrete pavement design features. Transp Res Rec 1778:1–8. https://doi.org/10.3141/1778-01

Ghoddousi P et al (2013) Multi-mode resource-constrained discrete time–cost-resource optimization in project scheduling using non-dominated sorting genetic algorithm. Autom Constr 30:216–227. https://doi.org/10.1016/j.autcon.2012.11.014

Ghoddousi P, Shirzadi Javid AA, Sobhani J (2015) Arab J Sci Eng 40:2239. https://doi.org/10.1007/s13369-015-1731-9

Hegazy T (1999) Optimization of resource allocation and leveling using genetic algorithms. J Constr Eng Manag 125(3):167–175. https://doi.org/10.1061/(ASCE)0733-9364(1999)125:3(167)

Hu H et al (2015) An adaptive hybrid PSO multi-objective optimization algorithm for constrained optimization problems. Int J Pattern Recognit Artif Intell 29(06):1559009. https://doi.org/10.1142/s0218001415590090

Kalhor E et al (2011) Stochastic time–cost optimization using non-dominated archiving ant colony approach. Autom Constr 20(8):1193–1203. https://doi.org/10.1016/j.autcon.2011.05.003

Kaveh A, Bakhshpoori T (2019) Metaheuristics: outlines, MATLAB codes and examples. In: Metaheuristics: outlines, MATLAB codes and examples. https://doi.org/10.1007/978-3-030-04067-3

Kaveh A, Bakhshpoori T, Hamze-Ziabari SM (2018a) M5’ and mars based prediction models for properties of selfcompacting concrete containing fly ash. Periodica Polytechnica Civ Eng 62(2):281–294. https://doi.org/10.3311/PPci.10799

Kaveh A, Hamze-Ziabari SM, Bakhshpoori T (2018b) Estimating drying shrinkage of concrete using a multivariate adaptive regression splines approach. Int J Optim Civ Eng 8(2):181–194

Kavvadias KC, Maroulis ZB (2010) Multi-objective optimization of a trigeneration plant. Energy Policy 38(2):945–954. https://doi.org/10.1016/j.enpol.2009.10.046

Kennedy R, Eberhart J (1995) Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks IV, vol 1000. https://doi.org/10.1109/icnn.1995.488968

Kyprianou A, Worden K, Panet M (2001) Identification of hysteretic systems using the differential evolution algorithm. J Sound Vib 248(2):289–314. https://doi.org/10.1006/jsvi.2001.3798

Li Y-L et al (2015) Differential evolution with an evolution path: a DEEP evolutionary algorithm. IEEE Trans Cybern 45(9):1798–1810. https://doi.org/10.1109/tcyb.2014.2360752

Li X, Ma S, Jiehua H (2017) Multi-search differential evolution algorithm. Appl Intell 47(1):231–256. https://doi.org/10.1007/s10489-016-0885-9

Liu J et al (2017) Ecosystem particle swarm optimization. Soft Comput 21(7):1667–1691. https://doi.org/10.1007/s00500-016-2111-4

Madurwar M, Sakhare V, Ralegaonkar R (2015) Multi objective optimization of mix proportion for a sustainable construction material. Procedia Eng 118:276–283. https://doi.org/10.1016/j.proeng.2015.08.427

Mehta PK, Monteiro PJM (2006) Chapter 1. Introduction. Part I-Microstructure and properties of hardened concrete. In: Concrete: microstructure, properties and materials, 3rd edn, McGraw-Hill, New York, pp 3-20

Mirjalili SZ et al (2018) Grasshopper optimization algorithm for multi-objective optimization problems. Appl Intell 48(4):805–820. https://doi.org/10.1007/s10489-017-1019-8

Mirzahosseini M et al (2019) New machine learning prediction models for compressive strength of concrete modified with glass cullet. Eng Comput 36(3):876–898. https://doi.org/10.1108/ec-08-2018-0348

Naseri H (2019) Cost optimization of no-slump concrete using genetic algorithm and particle swarm optimization. Int J Innov Manag Technol. https://doi.org/10.18178/ijimt.2019.10.1.832

Naseri H, Ghasbeh MAE (2018) Time-cost trade off to compensate delay of project using genetic algorithm and linear programming. Int J Innov Manag Technol 9:6. https://doi.org/10.18178/ijimt.2018.9.6.826

Neeraja D et al (2017) Weight optimization of plane truss using genetic algorithm. In: IOP conference series: materials science and engineering, vol 263, No. 3. IOP Publishing. https://doi.org/10.1088/1757-899x/263/3/032015

Noguchi T, Maruyama I, Kanematsu M (2003) Performance based design system for concrete mixture with multi-optimizing genetic algorithm. In: Proceedings of the 11th international congress on the chemistry of cement “Cements Contribution to the Development in the 21st Century”, Durban

Obasanjo E, Tzallas-Regas G, Rustem B (2010) An interior-point algorithm for nonlinear minimax problems. J Optim Theory Appl 144(2):291–318. https://doi.org/10.1007/s10957-009-9599-z

Öztaş A et al (2006) Predicting the compressive strength and slump of high strength concrete using neural network. Constr Build Mater 20(9):769–775. https://doi.org/10.1016/j.conbuildmat.2005.01.054

Park CH et al (2004) Simultaneous optimization of composite structures considering mechanical performance and manufacturing cost. Compos Struct 65(1):117–127. https://doi.org/10.1016/j.compstruct.2003.10.010

Poli R, Kennedy J, Blackwell T (2007) Particle swarm optimization. Swarm Intell 1(1):33–57. https://doi.org/10.1007/s11721-007-0002-0

Qi C et al (2018) A strength prediction model using artificial intelligence for recycling waste tailings as cemented paste backfill. J Clean Prod 183:566–578. https://doi.org/10.1016/j.jclepro.2018.02.154

Qin AK, Huang VL, Suganthan PN (2009) Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans Evol Comput 13(2):398–417. https://doi.org/10.1109/tevc.2008.927706

Qin S et al (2018) Model updating in complex bridge structures using kriging model ensemble with genetic algorithm. KSCE J Civ Eng 10:1–12. https://doi.org/10.1007/s12205-017-1107-7

Schenk O, Wächter A, Hagemann M (2007) Matching-based preprocessing algorithms to the solution of saddle-point problems in large-scale nonconvex interior-point optimization. Comput Optim Appl 36(2-3):321–341. https://doi.org/10.1007/s10589-006-9003-y

Shi Y, Eberhart R (1998) A modified particle swarm optimizer. In: IEEE World congress on computational intelligence. The 1998 IEEE international conference on evolutionary computation proceedings, 1998. IEEE, 1998. https://doi.org/10.1109/icec.1998.699146

Smith T, Maillard PL (2007) Sustainable benefits of concrete pavement. 42e Congres annuel de l’AQTR-Defi: Transport Durable

Sobhani J et al (2010) Prediction of the compressive strength of no-slump concrete: a comparative study of regression, neural network and ANFIS models. Constr Build Mater 24(5):709–718. https://doi.org/10.1016/j.conbuildmat.2009.10.037

Sonmez R, Bettemir ÖH (2012) A hybrid genetic algorithm for the discrete time–cost trade-off problem. Expert Syst Appl 39(13):11428–11434. https://doi.org/10.1016/j.eswa.2012.04.019

Storn R, Price K (1997) Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–359. https://doi.org/10.1023/a:1008202821328

Suo X-S, Xiong-Qing Yu, Li H-S (2017) Subset simulation for multi-objective optimization. Appl Math Model 44:425–445. https://doi.org/10.1016/j.apm.2017.02.005

Trummer I, Koch C (2017) Multi-objective parametric query optimization. VLDB J 26(1):107–124. https://doi.org/10.14778/2735508.2735512

Tsai H-C (2017) Unified particle swarm delivers high efficiency to particle swarm optimization. Appl Soft Comput 55:371–383. https://doi.org/10.1016/j.asoc.2017.02.008

Vesting F, Bensow RE (2018) Particle swarm optimization: an alternative in marine propeller optimization? Eng Optim 50(1):70–88. https://doi.org/10.1080/0305215x.2017.1302438

Wang Y, Cai Z, Zhang Q (2011) Differential evolution with composite trial vector generation strategies and control parameters. IEEE Trans Evol Comput 15(1):55–66. https://doi.org/10.1109/tevc.2010.2087271

Wang L, Liu X, Zhang Z (2017) An efficient interior-point algorithm with new non-monotone line search filter method for nonlinear constrained programming. Eng Optim 49(2):290–310. https://doi.org/10.1080/0305215X.2016.1176828

Yang Z, Tang K, Yao X (2008) Self-adaptive differential evolution with neighborhood search. Evol Comput. CEC 2008 (IEEE World Congress on Computational Intelligence). IEEE Congress on. IEEE, 2008. https://doi.org/10.1109/cec.2008.4630935

Yu W-J et al (2014) Differential evolution with two-level parameter adaptation. IEEE Trans Cybern 44(7):1080–1099. https://doi.org/10.1109/tcyb.2013.2279211

Zain MFM, Abd SM (2009) Multiple regression model for compressive strength prediction of high performance concrete. J Appl Sci 9(1):155–160. https://doi.org/10.3923/jas.2009.155.160