بهینه ‏سازی طرح مخلوط بتن با مقاومت بالا با استفاده از الگوریتم ژنتیک فراابتکاری

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه عمران، واحد اهواز، دانشگاه آزاد اسلامی، اهواز، ایران

2 گروه عمران، دانشگاه لرستان، خرم آباد، ایران

چکیده

علاوه بر خواص مکانیکی و دوام بتن، هزینه تولید آن نیز می‏تواند بر انتخاب میزان مصالح تاثیرگذار باشد. از طرف دیگر تغییر کمیت مصالح در طرح مخلوط بتن، بر خواص بتن از جمله مقاومت فشاری، هم‌‌چنین قیمت تمام شده مصالح آن موثر است و این دو عامل از عوامل بسیار مهم برای تصمیم‌‌گیری در مصرف میزان مصالح بر اساس قیمت خرید برای تولیدکنندگان در صنعت بتن می‌‌باشند. بتن با مقاومت بالا در رده بتن‏هایی است که علاوه بر داشتن مقاومت بالا، دارای نسبت آب به مواد سیمانی پایین و تنوع مصالح بیشتری است، لذا انتخاب مقدار دقیق مصالح برای رسیدن به یک رده مقاومتی خاص در این نوع بتن، به نحوی که کمترین قیمت تمام‏شده را دربر داشته باشد، مشکل است. در تحقیق حاضر با استفاده از الگوریتم ژنتیک فراابتکاری داده‏های یک تحقیق مرجع، از نظر مقاومت فشاری و قیمت مصالح ساخت بهینه‌‌سازی شدند تا علاوه بر دستیابی به بالاترین مقاومت ممکن با کمترین هزینه، برای رسیدن به مقاومت‌‌های هدف نیز بتوان هزینه کمینه را به‌‌دست آورد. نتایج بررسی‌‌ها نشان داد که پاسخ بهینه الگوریتم ژنتیک فراابتکاری، در مقایسه با نقطه بهینه تحقیق مرجع دارای مقاومتی به میزان %10/2 بالاتر و با هزینه %8/2 پایین‏تر بود. همچنین با توجه به اینکه الگوریتم ژنتیک فراابتکاری توانایی انتخاب طرح مخلوط برای رده‏های مقاومتی مختلف را دارد، بررسی طرح مخلوط های مختلف نشان داد که، الگوریتم نسبت به تغییر مقدار مصالح طرح مخلوط با هوشمندی عمل نموده و تغییرات را به نحوی اعمال نموده است که در فرآیند کسب مقاومت‏های مورد نظر، تغییرات میزان مصالح مصرفی در طرح مخلوط، با اعمال کمترین هزینه ممکن، پیشنهاد شوند. 

کلیدواژه‌ها


عنوان مقاله [English]

Optimization High-strength concrete mixing design using meta-heuristic genetic algorithm

نویسندگان [English]

  • Mohsen Masihi 1
  • Seyed Fathollah Sajedi 1
  • Ahmad Dalvand 2
1 PhD student, Department of Civil Eignieerng, Ahvaz Branch, Islamic Azad University, Ahvaz, Iran
2 Associate professor, Department of Civil Engineering, Lorestan University, Khoramabad, Iran
چکیده [English]

In addition to the mechanical properties and durability of concrete, its production cost can also affect the choice of materials. On the other hand, changing the quantity of materials in the concrete mixing design affects the properties of concrete, including compressive strength and cost of materials, and these two factors are very important factors in deciding to use more or less materials in terms of purchase price for manufacturers in the concrete industry. High-strength concrete (HSC) is in the category of concretes that in addition to having high strength, has a low ratio of water to binder and a greater variety of materials, so in this type of concrete, choosing the exact amount of material to achieve a certain strength class, to have the lowest cost is difficult. In the present study, using a meta-heuristic genetic algorithm, the data of a reference study related to a class of HSC were optimized in terms of compressive strength and fabrication price in addition to achieving the highest possible strength at the lowest cost, the target strengths can also be minimized. The results showed that the meta-heuristic genetic algorithm acted intelligently to change the amount of materials in the mixing design and made the changes in such a way that in the process of obtaining the desired strengths, changes in the amount of materials used in the mixing design, with the least costs may be offered.

کلیدواژه‌ها [English]

  • High strength concrete mix design
  • Meta-heuristic genetic algorithm
  • Strength
  • Slump
  • Price
[1] S.H. Ahmad, S.P. Shah, High Performance Concrete and Application, Mcgraw-Hill (TX), (1994).
[2] FIP/CEB, High strength concrete, State of the art report, Bulletin d’information, 197 (1990).
[3] S. Mindes, J. F. Young, Concrete Prentice Hall Inc. Englwood Cliffs, New Jersey USA, 530 (1981).
[4] T. V. N Narques, R. C. Carvalho, A. Christoforo, F. J. R. Mascarenhas, F.N. Arroyo, F. C. Bomfim Junior, H. Fd. Santos, Use of Real Coded Genetic Algorithm as a Pre-Dimensioning Tool for Prestressed Concrete Beams, Buildings,13(2023), https://doi.org/10.3390/buildings13030819.
[5] S. Han, L. Xiao, An improved adaptive genetic algorithm. SHS Web of Conferences 140, (2022), https://doi.org/10.1051/shsconf/202214001044
[7] P. Kondapally, A. Chepuri, V.P. Elluri, B. S. K. Reddy, Optimization of concrete mix design using genetic algorithms, Earth and Environmental Science, (2022) Doi:10.1088/1755-1315/1086/1/012061
[8] R. K. TipuV. R. PanchalK. S. Pandya, Multi-objective optimized high-strength concrete mix design using a hybrid machine learning and metaheuristic algorithm, Asian Journal of Civil Engineering volume 24 (2023) 849–867.
[9]K.R. Wu, B. Chen, W. Yao, D. Zhang, Effect of coarse aggregate type on mechanical properties of high-performance concrete, Cement and Concrete Research, 31(10) (2001) 1421–1425.
[10] K.S. AL-Jabri, A.H. AL-Saidy, R. Taha, A.J. AL-Kemyany, Effect of using Wastewater on the Properties of High Strength Concrete, Procedia Engineering, 14 (2011) 370–376.
[11] A. Shamsai, K. Rahmani, S. Peroti, L. Rahemi, the Effect of Water-Cement Ratio in Compressive and Abrasion Strength of the Nano Silica Concretes, World Applied Sciences Journal, 17(4)(2012) 540-545.
[12] A. Behnood, H. Ziari, Effects of silica fume addition and water to cement ratio on the properties of high-strength concrete after exposure to high temperatures, Cement & Concrete Composites. 30 (2008) 106–112.
[13] M. Mazloom, A.A. Ramezanianpour, J.J. Brooks, Effect of silica fume on mechanical properties of high-strength concrete, Cement & Concrete Composites, 26(4) (2004) 347–357.
[14] M.A. Safn, Compresive Sterength of Portland cement pastesand mortar containing CU-ZN nano-frite, International Journal of Nano Dimention,3(2) (2012) 91-100.
[15] P.N. Balaguru, Nanotechnology and concrete, Opportunities and challenges proceeding of the international conference- application of technology in concrete design. Scotland. UK, (2005) 113-122
[16] M.J. Shanang, High strength concrete containing natural Pozzolan and Silica Fume, Cement & Concrete Composites, 22(6) (2000) 399-406.
[17] M. Amin, K. Abu el-hassan, Effect of using different types of nano materials on mechanical properties of high strength concrete, Construction and Building Material, 80(1) (2015) 116-124.
[18] R. Kishore, V. Bhikshma, P.J. Prakash, Study on Strength Characteristics of High Strength Rice Husk Ash Concrete, The Twelfth East Asia-Pacific Conference on Structural Engineering and Construction. 14 (2011) 2666–2672.
[19] R. Rathan Raja, E.B. Perumal Pillaib, A.R. Santhakumarc, Evaluation and mix design for ternary blended high strength concrete, Chemical, Civil and Mechanical Engineering Tracks of 3rd Nirma University International Conference. (NUiCONE 2012). 51 (2013) 65 – 74.
[20] V. Cernya, M. Kocianovaa, R. Drochytkaa, Possibilities of lightweight high strength concrete production from sintered fly ash aggregate, 18th International Conference on Rehabilitation and Reconstruction of Buildings. (CRRB2016). 195 (2017) 9 – 16.
[21] J.M. Khatib, P.S. Mangat, In-uence of superplasticizer and curing on porosity and pore structure of cement paste. Cement & Concrete Composites. 21 (1999) 431-437.
[22] B. Łazniewska-Piekarczyk, J. Szwabowski, Influence of the Type of Anti-Foaming Admixture and Superplasticizer on the Properties of Self-Compacting Mortar and Concrete, Journal of Civil Engineering And Management, 18(3) (2012) 408–415.
[23] S. Alsadey, Influence of Superplasticizer on Strength of Concrete, International Journal of Research in Engineering and Technology (IJRET), 1(3) (2012).
[24] A.M. Mansor, R. P. Borg, A. M. M. Hamed, M. M. Gadeem, M. M. Saeed, The effects of water-cement ratio and chemical admixtures on the workability of concrete, Materials Science and Engineering. 442 (2018) 012017 doi:10.1088/1757-899X/442/1/012017
[25] T. Ozturan, C. Cecen, Effect of coarse aggregate type on mechanical properties of concretes with different strengths, Cement and Concrete Research, 27(2) (1997) 165–170.
[26] P.C. Aitcin, P.K. Mehta, Effect of coarse aggregate characteristics on mechanical properties of high strength concrete. ACI Material journal, American Concrete Institute Ditroit. 87(2) (1990) 03–107.
[27] H. Beshr, A.A. Almusallam, M. Maslehuddin, Effect of coarse aggregate quality on the mechanical properties of high strength concrete, Construction and Building Materials. 17(2) (2003) 97–103.
[28] A. Kılıc, C.D. Atis, A. Teymen, O. Karahan, F. O¨zcan, C. Bilim, M. O¨zdemir, The influence of aggregate type on the strength and abrasion resistance of high strength concrete, Cement & Concrete Composites,30(4) (2008) 290–296.
[29] M. Masihi, N. Shahsavaripour, A.K. Abbasi, Optimization of the Multi-Mode Resource Constrained Time –Cost Trade off Project Scheduling Problem by using Modified Genetic Algorithm. MD thesis, Ahwaz Islamic Azad University. Chapter2. (2012). (In Persian).
[30] F. Rosenblatt, 1957. The Perceptron a perceiving and recognizing automaton, Cornell Aeronautical Laboratory. Report 85-460-1.
[31] J.H. Holland, Adaptation in Natural and Artificial Systems (1975).
[32] M. Dorigo, Optimization Learning and natural algorithms. PhD Thesis, Dip Electronica information, Politecnico di Milan. Italy. (1992).
[33] J. Kennedy, R. Eberhart, A New Optimizer Using Particle Swarm Theory, In the Sixth International Symposium on Micro-Machine and Human Sciences. (1992) 43-39.
[34] E. Atashpaz-Gargari, C. Lucas, Imperialist Competitive Algorithm: An algorithm for optimization inspired by imperialistic competition, IEEE Congress on Evolutionary Computation. (2007) 4661-4667.
[35] S. Kirkpatrick, C.D. Gelatt, M.P. Vecchi, Optimization by Simulated Annealing, American Association for the Advancement of Science, 220(4598) (1983) 671-680.
[36] K. Von Frisch, the Dance Language and Orientation of Bees, Harvard University Press Cambridge. MA US: Harvard University Press. (1967).
[37] H. Shah-Hosseini, Problem solving by intelligent water drops, Proceedings of the IEEE Congress on Evolutionary Computation. (2009) 3226-3231.
[38] S.Hr. Aghay Kaboli, J. Selvaraj, N.A. Rahim, Rain-fall optimization algorithm: a population-based algorithm for solving constrained optimization problems, Journal of Computational Science, 19 (2017)31-42.
[39] F. Sajedi F, H. Abdul-Razak, Relationship between 7- and 28-days CS for HSC by use of ANN and regression methods, Asian Journal of Civil Engineering (AJCE), Iran. 11(2) (2010) 207-218.
[40] A. Guerra, P.D. Kiousis, Design optimization of reinforced concrete structures, Computers and Concrete, 3(5) (2006) 313-334.
[41] P. Chopra, R. Kumar Sharma, M. Kumar, Prediction of Compressive Strength of Concrete Using Artificial Neural Network and Genetic Programming. Hindawi Publishing Corporation, (2016), Article ID 7648467, 10 pages.
[42] M. Masihi, N. Shahsavari Pour, H. Daneshvar, M. Veissii, Optimization of the Multi-Mode Resource –Constrained Time –Cost Trade off Project Scheduling Problem by using Modified Genetic Algorithm, Indian Journal of Natural Sciences. 5(30) (2015) 6798-6811.
[43] A. Hghighi A. Ahmadi-Najl, Simultaneous Optimization of Operating Rules and Rule Curves for Multi reservoir Systems Using a Self-Adaptive Simulation-GA Model, Journal of Water Resources Planning and Management, 142(10) (2016) 1943-5452.
[44] M.J. Simon, 2003. Concrete Mixture Optimization Using Statistical Methods, Final Report. FHWA-RD-03-060, Grant No. DTFH61-97-Y-30033.