Prediction of Concrete and Steel Materials Contained by Cantilever Retaining Wall by Modeling the Artificial Neural Networks

Document Type : Regular Article


1 Professor, Civil Engineering Department, Manisa Celal Bayar University, Turkey

2 Research Assistant (M.Sc.), Civil Engineering Department, Manisa Celal Bayar University, Turkey

3 M.Sc. Student, Institute of Natural and Applied Sciences, Manisa Celal Bayar University, Turkey


In this study, the Artificial Neural Network (ANN) application is implemented for predicting the required concrete volume and amount of the steel reinforcement within the inversed-T-shaped and stem-stepped reinforced concrete (RC) walls. For this aim, seven-different RC wall designs were approached differentiated within the wall heights and various internal friction angles of backfill materials. Each RC wall is proportionally designed and subjected to active lateral earth pressure defined with the Mononobe-Okabe approach foreseen by Turkish Specification for Building to be Built in Seismic Zones (TSC-2007). Following the stability analysis of the RC retaining walls, the structural and reinforced concrete analyses are performed according to the Turkish Standard on Requirements for Design and Construction in Reinforced Concrete Structures (TS500-2000). Input parameters such as concrete volumes, weights of the steel bars, soil and wall material properties are subjected to the ANN modeling. The prediction of the concrete volume and amount of the steel bars are achieved with the implementation of the ANN model trained with the Artificial Bee Colony (ABC) algorithm. As a result of this study, it is revealed that ANN models are useful for verifying the existing RC retaining wall designs or performing preliminary designs for the L-shaped and stem-stepped cantilever retaining walls.


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[1]     Lee S-C. Prediction of concrete strength using artificial neural networks. Eng Struct 2003;25:849–57. doi:10.1016/S0141-0296(03)00004-X.
[2]     Mansour MY, Dicleli M, Lee JY, Zhang J. Predicting the shear strength of reinforced concrete beams using artificial neural networks. Eng Struct 2004;26:781–99. doi:10.1016/j.engstruct.2004.01.011.
[3]     Manjunath K, Gowda K, Prasad GLE. Optimization of Cantilever Earth Retaining Wall Using Artificial Neural Network. Topology 2012;8:12–5.
[4]     Gowda BSK, Chethan VR, Rao Sri Rama TA. Optimization of counterfort retaining wall using artificial neural network. A Proc Natl Conf Contemp Civ Eng Res Pract, Manipal: 2012, p. 1–8.
[5]     Shehata HF. Retaining walls with relief shelves. Innov Infrastruct Solut 2016;1:4. doi:10.1007/s41062-016-0007-x.
[6]     Chougule AC, Patankar JP, Chougule PA. Effective Use of Shelves in Cantilever Retaining Walls. Int Res J Eng Technol 2017;4:2635–9.
[7]     Alias R, Kasa A, Taha MR. Artificial neural networks approach for predicting the stability of cantilever RC retaining walls. Int J Appl Eng Res 2015;10:26005–14.
[8]     Patil SS, Bagban AAR. Analysis and Design of Stepped Cantilever Retaining Wall. Int J Res Technol 2015;4.
[9]     GOKKUS U, ZOGLU AY. Comparison of Footing Widths of Proportionally-Sized Reinforced Concrete Retaining Walls under Extreme Loading. Int J Civ Eng 2018;5:13–9. doi:10.14445/23488352/IJCE-V5I1P103.
[10]    Turkish Specification for Buildings to be Built in Seismic Zones, Ministry of Public Works and Settlement, Government of Republic of Turkey, Ankara 2007.
[11]    EUROCODE-8 (EUROPEAN PRE-STANDARD). "Design Provisions for Earthquake Resistance of Structures- Part 5: Foundations, Retaining Structures and Geotechnical Aspects”, The Commisssion of the European Communities 1994.
[12]    TS500, TURKISH STANDARD, Requirements for Design and Construction of Reinforced Concrete Structures, Institute of Turkish Standard, Ankara. Ankara Turkey; 2000.
[13]    EUROCODE-8 EN , Seismic Design of Buildings Worked examples, Ed. B. Acun, A. Athanasopoulou, A. Pinto E. Carvalho, M. Fardis, European Commission Joint Research Centre, Lisbon 2011.