Cost Estimation Models for the Reinforced Concrete Retaining Walls

Yıl 2020, , 9 - 26, 23.03.2020

Uğur Dağdeviren , Burak Kaymak

https://doi.org/10.35193/bseufbd.646668

Cited By: 2

Öz

The reinforced concrete retaining walls (RCRWs) are constructed many
civil engineering projects such as highway, railway, building etc. Many
different design constraints must be considered in the design of RCRWs. In the
traditional approach, the design variables are controlled many times by
trial-error process to provide the optimum design, thus the optimization techniques
must be used to save on time for project managers. The other important subject in civil engineering
projects is to estimate correctly the cost of the project before the
construction for the tender process. In the first stage of the study, 125 optimization
problems for the RCRW, which are sitting on strong soil layer, are analyzed for
different combinations of wall heights, surcharge loads and internal friction
angles of the backfill soil by use of the modified artificial bee colony (ABC)
algorithm, and minimum costs are determined. Then, the multiple regression and
artificial neural network models are presented for minimum cost estimation of
the wall. The cost estimations obtained from the proposed models are in great
agreement with the calculated values by the modified ABC algorithm. The error
values between predicted and calculated minimum costs are almost zero. The
results show that the proposed models can be successfully used for minimum cost
estimation of the RCRWs sitting on strong soil layer.

Anahtar Kelimeler

Artificial neural network, Cost estimation, Modified artificial bee colony algorithm, Regression model, Retaining wall

Betonarme İstinat Duvarları için Maliyet Tahmin Modelleri

Öz

Betonarme istinat duvarları, karayolu, demiryolu,
bina vb. birçok inşaat mühendisliği projesinde inşa edilmektedir. Betonarme
istinat duvarlarının tasarımında birçok farklı tasarım kısıtlaması göz önünde
bulundurulmalıdır. Geleneksel yaklaşımda, tasarım değişkenleri optimum tasarımı
sağlamak için deneme yanılma işlemi ile birçok kez kontrol edilir, bu nedenle
proje yöneticileri zamandan tasarruf etmek için optimizasyon teknikleri
kullanmak durumundadır. İnşaat mühendisliği projelerindeki diğer bir önemli
konu, ihale süreci için inşaat öncesi proje maliyetinin doğru olarak tahmin
edilmesidir. Çalışmanın ilk aşamasında, duvar yükseklikleri, sürşarj yükleri ve
dolgu zemininin içsel sürtünme açılarının farklı kombinasyonlarında, sağlam
zemin tabakasına oturan betonarme istinat duvarı için 125 optimizasyon problemi
modifiye yapay arı koloni algoritması kullanılarak analiz edilmiş ve minimum
maliyetler belirlenmiştir. Daha sonra, duvarın minimum maliyet tahmini için
çoklu regresyon ve yapay sinir ağı modelleri sunulmuştur. Önerilen modellerden
elde edilen maliyet tahminleri, modifiye yapay arı koloni algoritması
tarafından hesaplanan değerlerle büyük ölçüde uyumludur. Tahmin edilen ve
hesaplanan minimum maliyetler arasındaki hata değerleri neredeyse sıfırdır.
Sonuçlar, önerilen modellerin, sağlam zemin tabakasına oturan betonarme istinat
duvarlarının minimum maliyet tahmini için başarıyla kullanılabileceğini
göstermektedir.

Anahtar Kelimeler

Maliyet tahmini, Modifiye yapay arı koloni algoritması, Regresyon modeli, İstinat duvarı, Yapay sinir ağı

Kaynakça

• [1] Karaboga, D. (2005). An idea based on honey bee swarm form numerical optimization. Technical Report TR06. Erciyes University, Turkey.
• [2] Karaboga, D. & Basturk, B. (2008). On the performance of artificial bee colony (ABC) algorithm. Applied Soft Computing, 8(1), 687-697.
• [3] Bolaji, A., Khander, A., Al-Betar, M. & Awadallah, M. (2013). Artificial bee colony algorithm, its variants and applications: a survey. Journal of Theoretical and Applied Information Technology, 47(2), 434-459.
• [4] Karaboga, D. & Akay, B. (2011). A modified artificial bee colony (ABC) algorithm for constrained optimization problems. Applied Soft Computing, 11(3), 3021-3031.
• [5] Akay, B. & Karaboga, D. (2012). A modified artificial bee colony algorithm for real-parameter optimization. Information Sciences, 192, 120-142.
• [6] Mansouri, I., Soori, S., Amraie, H., Hu, J. & Shahbazi, S. (2018). Performance based design optimum of CBFs using bee colony algorithm. Steel and Composite Structures, 27(5), 613-622.
• [7] Panda, T. & Swamy, A. (2018). An improved artificial bee colony algorithm for pavement resurfacing problem. International Journal of Pavement Research and Technology, 11(5), 509-516.
• [8] Ding, Z., Yao, R., Li, J. & Lu, Z. (2018). Structural damage identification based on modified artificial bee colony algorithm using modal data. Inverse Problems in Science and Engineering, 26(3), 422-442.
• [9] Sun, L., Koopialipoor, M., Armaghani, D., Tarinejad, R. & Tahir, M. (2019). Applying a meta-heuristic algorithm to predict and optimize compressive strength of concrete samples. Engineering with Computers, 1-13. DOI: https://doi.org/10.1007/s00366-019-00875-1.
• [10] Sharma, T., Rajpurohit, J., Sharma, V. & Prakash, D. (2019). Soft Computing: Theories and Applications. Advances in Intelligent Systems and Computing (V.742), Springer, Singapore.
• [11] Ceranic , B., Fryer , C. & Baines , R. (2001). An application of simulation annealing to the optimum design of reinforced concrete retaining structures. Computers & Structures, 79(17), 1569-1581.
• [12] Yepes, V., Gonzalez-Vidosa, F., Alcala , J. & Villalba, P. (2008). A parametric study of optimum earth-retaining walls by simulated annealing. Engineering Structures, 30(3), 821–830.
• [13] Ghazavi, M. & Bonab, S. (2011). Optimization of reinforced concrete retaining walls using ant colony method. ISGSR 2011, Vogt, Schuppener, 297-305.
• [14] Ghazavi, M., & Salavati, V. (2011). Sensitivity analysis and design of reinforced concrete cantilever retaining walls using bacterial foraging optimization algorithm. ISGSR 2011, Vogt, Schuppener, 307-314.
• [15] Camp, C. & Akin, A. (2012). Design of retaining walls using big bang–big crunch optimization. Journal of Structural Engineering, 138(3), 438-448.
• [16] Pei, Y. & Xia, Y. (2012). Design of reinforced cantilever retaining walls using heuristic optimization algortihms. Procedia Earth and Planetary Science, 5, 32-36.
• [17] Kaveh, A. & Khayatazad, M. (2014). Optimal design of cantilever retaining walls using ray optimization method. Iranian Journal of Science and Technology Transactions of Civil Engineering, 38(C1+), 261-274.
• [18] Mergos, P. & Mantoglou, F. (2019). Optimum design of reinforced concrete retaining walls with the flower pollination algorithm. Structural and Multidisciplinary Optimization, 1-11. DOI: https://doi.org/10.1007/s00158-019-02380-x.
• [19] Günaydın, H. & Doğan, S. (2004). A neural network approach for early cost estimation of structural systems of buildings. International Journal of Project Management, 22(7), 595-602.
• [20] Wang, X.-Z., Duan, X.-C. & Liu, J.-Y. (2010). Application of neural network in the cost estimation of highway engineering. Journal of Computers, 5(11), 1762-1766.
• [21] Arafa , M. & Alqedra, M. (2011). Early stage cost estimation of buildings construction projects using artificial neural networks. Journal of Artificial Intelligence, 4(1), 63-75.
• [22] Chandanshive, V. & Kambekar, A. (2014). Prediction of early stage construction cost of building projects using artificial neural network. International Journal of Scientific & Engineering Research, 5(7), 453-463.
• [23] Gencer, H. (2017). Estimated cost calculation guidelines, problems in practice and a solution recommendation. Uluslararası Katılımlı 7. İnşaat Yönetimi Kongresi 2017, Samsun, 387-398.
• [24] Juszczyk, M., Lesniak, A. & Zima, K. (2018). ANN based approach for estimation of construction costs of sports fields. Complexity, Article ID 7952434, 1-11.
• [25] Gandomi, A., Kashani, A., Roke, D. & Mousavi, M. (2017). Optimization of retaining wall design using evolutionary algorithms. Structural and Multidisciplinary Optimization, 55(3), 809-825.
• [26] Dağdeviren, U. & Kaymak, B. (2018). Investigation of parameters affecting optimum cost design of reinforced concrete retaining. Journal of the Faculty of Engineering and Architecture of Gazi University, 33(1), 239-253.
• [27] Mohammad, F. & Ahmed, H. (2018). Optimum design of reinforced concrete cantilever retaining walls according Eurocode 2 (EC2). Athens Journal of Technology and Engineering, 5(3), 277-296.
• [28] Öztürk, H., & Türkeli, E. (2019). Tabanında anahtar kesiti bulunan betonarme istinat duvarlarının jaya algoritmasıyla optimum tasarımı. Politeknik Dergisi, 22(2), 283-291.
• [29] ACI 318/2005. (2005). Building Code Requirements for Structural Concrete and Commentary. American Concrete Institute.
• [30] Saribas, A., & Erbatur, F. (1996). Optimization and Sensitivity of Retaining Structures. Journal of Geotechnical Engineering, 122(8), 649-656.
• [31] Kutner, M.H., Nachtsheim, C.J., Neter, J. & Li, W., (2005). Applied Linear Statistical Models, 5th Ed., McGraw-Hill/Irwin, p.1415.