An Application of Optimization With Multiple Rebar Length For Reducing Construction Rebar Waste Rate

Öz

In construction projects, highly rebar waste occurs in the unplanned construction rebar cutting made on the construction site. Therefore, in this study, an optimization method is offered to minimize the reinforced concrete rebar waste before the construction phase of the building. Using this method, it is aimed to further reduce the amount of waste if the rebar production lengths are produced at different lengths. The optimization method used in the study was made with CutLogic1D software which based on genetic algorithm and combined with a fuzzy logic system. Optimization was performed with rebar quantities calculated on 3 different projects on a small, medium and large scale. Also, in addition to the standard 12-meter production length, the most optimum rebar waste rates were determined in the direction of the analysis made with alternative different production lengths and sets of these lengths. It is observed that these waste rates are greatly reduced by the analysis. Especially in small, medium and large scale constructions, trim loss in S3 and S4 alternative production length sets reach minimum values with approximately 1%. According to the proposed optimization methods and production alternatives in this study, Turkey's yields from rebar will be approximately 120597.585 tons, taking into account the optimum rebar waste rates. This will contribute to reducing construction costs as well as contribute to the country's economy.

Anahtar Kelimeler

• Rebar waste
• Optimization
• Genetic algorithm
• Fuzzy logic system
• Construction management

İnşaat demiri atık oranının azaltılması için inşaat demiri standart uzunluklarının çeşitlendirilmesi ve optimizasyon uygulaması

Öz

İnşaat projelerinde şantiyede yapılan plansız inşaat demiri kesimlerinde oldukça fazla demir atıkları meydana gelmektedir. Bu yüzden çalışmada yapının inşaat aşaması başlamadan önce betonarme inşaat demiri atığının minimize edilmesini sağlamak için bir optimizasyon yöntemi sunulmaktadır. Bu yöntem kullanılarak inşaat demiri üretim boylarının farklı uzunluklarda üretilmesi durumunda atık miktarının daha da azaltılması amaçlanmaktadır. Çalışmada kullanılan optimizasyon yöntemi genetik algoritma ve bulanık mantık sisteminin birlikte çalışmasına dayanan CutLogic1D yazılımı ile yapıldı. Küçük, orta ve büyük ölçekli 3 farklı proje üzerinden hesaplanan inşaat demiri miktarları ile optimizasyon işlemi yapıldı. Aynı zamanda standart 12 metre üretim uzunluğunun yanı sıra alternatif farklı üretim uzunlukları ve bu uzunluklardan oluşan setler ile yapılan analizler doğrultusunda en optimum inşaat demiri atık oranları belirlendi. Yapılan analizler ile bu atık oranlarının büyük ölçüde azaltıldığı görülmektedir. Özellikle küçük, orta ve büyük ölçekli inşaatlarda S3 ve S4 alternatif üretim uzunluk setlerinde kesme kayıpları yaklaşık %1 ile minimum değerlere ulaşmaktadır. Bu çalışmada önerilen optimizasyon yöntemi ve üretim alternatiflerine göre optimum inşaat demiri atık oranlarına göre Türkiye’nin inşaat demirinden sağladığı kazanç 120597,585 ton seviyelerinde olacaktır. Bu da inşaat maliyetlerinin azaltılmasına katkı sağlayacağı gibi, ülke ekonomisine de katkı sağlayacaktır.

Anahtar Kelimeler

• İnşaat demir atığı
• Optimizasyon
• Genetik algoritma
• Bulanık mantık sistemi
• inşaat yönetimi

Kaynakça

1. 1. Arbib C., Marinelli F., Ventura P., One-dimensional cutting stock with a limited number of open stacks: Bounds and solutions from a new integer linear programming model, Int. Trans. Oper. Res., 23 (1–2), 47–63, 2016.
2. 2. Benjaoran V., Sooksil N., Metham M., Effect of demand variations on steel bars cutting loss, Int. J. Constr. Manag., 19 (2), 137–148, 2019.
3. 3. Braga N., Alves C., Macedo R., Carvalho J.V. de, A model-based heuristic for the combined cutting stock and scheduling problem, Int. Conf. Comput. Sci. Its Appl. Springer, Cham, 490–505, 2015.
4. 4. Chen Z., Li H., Wong C.T.C., An application of bar-code system for reducing construction wastes, Autom. Constr., 11 (5), 521–533, 2002.
5. 5. Chu B., Kim D., Hong D., Park J., Chung J.T., Chung J.H., Kim T.H., GA-based fuzzy controller design for tunnel ventilation systems, Autom. Constr., 17 (2), 130–136, 2008.
6. 6. Cordon O., Jose Del Jesus M., Herrera F., Genetic Learning of Fuzzy Rule-Based Classification Systems Cooperating with Fuzzy Reasoning Methods, Int. J. Intell. Syst., 13 1025–1053, 1998.
7. 7. Fishbein B., Building for the future: Strategies to reduce construction and demolition waste in municipal projects, INFORM, Inc., 1998.
8. 8. Flores-Quiroz A., Pinto J.M., Zhang Q., A column generation approach to multiscale capacity planning for power-intensive process networks, Optim. Eng., 20 (4), 1001–1027, 2019.

9. 9. Gavilan R.M., An analysis of construction solid wastes, PhD Thesis, North Carolina State University, USA, 1992.
10. 10. Gilmore P.C., Gomory R.E., A Linear Programming Approach to the Cutting-Stock Problem, Oper. Res., 9 (6), 849–859, 1961.
11. 11. Goulimis C., Optimal solutions for the cutting stock problem, Eur. J. Oper. Res., 44 (2), 197–208, 1990.
12. 12. Gradišar M., Resinovič G., Kljajić M., Hybrid approach for optimization of one-dimensional cutting, Eur. J. Oper. Res., 119 (3), 719–728, 1999.
13. 13. Haessler R.W., Controlling Cutting Pattern Changes in One-Dimensional Trim Problems, Oper. Res., 23 (3), 483–493, 1975.
14. 14. Khalifa Y., Salem O., Shahin A., Cutting stock waste reduction using genetic algorithms, GECCO 2006 - Genet. Evol. Comput. Conf., Seattle, Washington, USA, 1675–1680, 2006.
15. 15. Kim S.K., Hong W.K., Joo J.K., Algorithms for Reducing the Waste Rate of Reinforcement Bars, J. Asian Archit. Build. Eng., 3 (1), 17–23, 2004.
16. 16. Laribi M.A., Mlika A., Romdhane L., Zeghloul S., A combined genetic algorithm-fuzzy logic method (GA-FL) in mechanisms synthesis, Mech. Mach. Theory, 39 (7), 717–735, 2004.
17. 17. Lemos F.K., Cherri A.C., de Araujo S.A., The cutting stock problem with multiple manufacturing modes applied to a construction industry, Int. J. Prod. Res., 1–19, 2020.
18. 18. Li Y., Yang Y., Zhou L., Zhu R., Observations on Using Problem-Specific Genetic Algorithm for Multiprocessor Real-Time Task Scheduling, Int. J. Innov. Comput. Inf. Control, 5 (9), 2531–2540, 2009.
19. 19. Lu H.C., Huang Y.H., An efficient genetic algorithm with a corner space algorithm for a cutting stock problem in the TFT-LCD industry, Eur. J. Oper. Res., 246 (1), 51–65, 2015.
20. 20. Melega G.M., de Araujo S.A., Jans R., Classification and literature review of integrated lot-sizing and cutting stock problems, Eur. J. Oper. Res., 271 (1), 1–19, 2018.
21. 21. Mercader-Moyano P., Ramírez-de-Arellano-Agudo A., Selective classification and quantification model of C&D waste from material resources consumed in residential building construction., Waste Manag. Res., 31 (5), 458–474, 2013.
22. 22. Mills T., Showalter E., Jarman D., A cost-effective waste management plan, Cost Eng., 41 (3), 35–43, 1999.
23. 23. Osman M.S., Abo-Sinna M.A., Mousa A.A., A combined genetic algorithm-fuzzy logic controller (GA-FLC) in nonlinear programming, Appl. Math. Comput., 170 (2), 821–840, 2005.
24. 24. Poldi K.C., de Araujo S.A., Mathematical models and a heuristic method for the multiperiod one-dimensional cutting stock problem, Ann. Oper. Res., 238 (1–2), 497–520, 2016.
25. 25. Porwal A., Hewage K.N., Building information modeling-based analysis to minimize waste rate of structural reinforcement, J. Constr. Eng. Manag., 138 (8), 943–954, 2012.
26. 26. Salem O., Shahin A., Khalifa Y., Minimizing cutting wastes of reinforcement steel bars using genetic algorithms and integer programming models, J. Constr. Eng. Manag., 133 (12), 982–992, 2007.
27. 27. Shahin A.A., Salem O.M., Using genetic algorithms in solving the one-dimensional cutting stock problem in the construction industry, Can. J. Civ. Eng., 31 (2), 321–332, 2004.
28. 28. Tahmasebi P., Hezarkhani A., A hybrid neural networks-fuzzy logic-genetic algorithm for grade estimation, Comput. Geosci., 42 18–27, 2012.
29. 29. Ustun S.V., Demirtas M., Optimal tuning of PI coefficients by using fuzzy-genetic for V/f controlled induction motor, Expert Syst. Appl., 34 (4), 2714–2720, 2008.
30. 30. Zadeh L.A., Fuzzy Sets, Inf. Control, 8 (3), 338–353, 1965.
31. 31. Zheng C., Lu M., Optimized Reinforcement Detailing Design for Sustainable Construction: Slab Case Study, Procedia Eng., 145 1478–1485, 2016.
32. 32. Zheng C., Yi C., Lu M., Integrated optimization of rebar detailing design and installation planning for waste reduction and productivity improvement, Autom. Constr., 101 (June 2018), 32–47, 2019.