Estimation of rebar weight in residential buildings of  concrete intermediate moment frame using Artificial Neural Network

Effati, Meysam; Ebrahimi, Hamed; Faraji, Mojtaba

doi:10.22065/jsce.2021.279806.2425

Estimation of rebar weight in residential buildings of concrete intermediate moment frame using Artificial Neural Network

Document Type : Original Article

Authors

Meysam Effati ¹

Hamed Ebrahimi ²

Mojtaba Faraji ²

¹ Faculty of engineering, Guilan University, Rasht, Iran

² PHD Student in Structural Engineering, Faculty of engineering, Guilan University, Rasht, Iran

10.22065/jsce.2021.279806.2425

Abstract

Estimating the cost of building construction, especially in the early stages of studies, is a topic of interest and importance for employers and investors. The high inflation of the country's economy in recent years and the sharp fluctuations in the price of construction materials have doubled the importance of estimating the cost of building construction, even in small urban projects. However, until the design of the building is completed and the final plans are not prepared in full detail, it is not possible to measure the project and estimate the construction costs with acceptable accuracy. On the other hand, the proposed approximate methods for initial estimates are associated with many differences from reality that are not consistent with the existential philosophy of project economic estimation. Therefore, in this study, using training capability of Artificial Intelligence and Artificial Neural Network algorithms, the weight of rebar used in concrete buildings of concrete moment frame, without complete design and preparation of executive plans is calculated and the results are compared with real values. The proposed artificial neural network model is a multilayer feeder type feeder with post-diffusion learning algorithm and is based on the parameters of number of openings in longitudinal and transverse direction, floor height, number of floors, number of columns per floor, floor area and seismic base shear. The weight of rebar used in the studied buildings has been estimated. The results indicate that the proposed neural network model can estimate the weight of rebar used in regular buildings with 95% accuracy and in irregular buildings with 80% accuracy.

Keywords

Soft computing"

Artificial Neural Network"

Structural frame weight"

concrete flexural frame"

structural weight Estimation"

Subjects

Structural Analysis and Design (Concrete)

Kaveh,A. (2007) Application of artificial neural networks for predicting the displacements of domes under wind loading. International journal of industrial engineering and production management (IJIE) Volume 18 , Number 2; Page(s) 45 To 53.
Mortezaei,A. (2012). Modeling and estimation of plastic hinge length of RC columns using artificial neural networks. Journal of modeling in engineering, Volume 10, Issue 29, Pages 1-17.
Effati,M. (2017). Prediction of the Concrete Slump with Artificial Neural Network Model and the Multivariable Linear Regression Method. Concrete Research Quarterly Journal, Volume 10, Issue 19, Pages 125-136
Hashemi, S.Sh. (2019). Investigation of the effects of number and height of the floors on the weight of the structures with moment-resisting steel frames using artificial neural. Sharif journal of civil engineering, Volume 35-2, Issue 1, Pages 81-89.
Akbari, M. (2018). Shear strength estimation of the concrete beams reinforced with FRP; comparison of artificial neural network and equations of regulations. Journal of Structural and Construction Engineering, Volume 4, Issue 4, Pages 79-9.
Naderpour, H. (2018). Predicting the Torsional Strength of Reinforced Concrete Beams Strengthened with FRP Sheets in terms of Artificial Neural Networks, Volume 5, Issue 1, Pages 20-35.
Labibzadeh, M. (2019). Determining of Load Bearing Capacity of Rectangular Concrete Columns Reinforced with Longitudinal Spirals Using FEM and ANN Methods. Volume 6, Issue 2, Pages 157-176.
Sujith Mangalathu. (2018).Artificial neural network based multi-dimensional fragility development of skewed concrete bridge classes. Engineering Structures volume 162, Pages 166-176.
Shahmansouri, AA. (2021). Data-Driven Models for Predicting the Shear Strength of Rectangular and Circular Reinforced Concrete Columns. Journal of Structural Engineering, Volume 147(1).
Moradi, MJ. (2019). Developing a Library of Shear Walls Database and the Neural Network Based Predictive Meta-Model. Applied Sciences ; doi:10.3390/app9122562.
De-Cheng Feng. (2021). Data-Driven Approach to Predict the Plastic Hinge Length of Reinforced Concrete Columns and Its Application. Journal of Structural Engineering, Volume 147.2: 04020332.
Berenjian, J.(2019). Predicting the compressive strength of self-compacting concrete using Elman artificial neural network with two different sets of input parameters. Journal of Structural and Construction Engineering, Volume 5, Issue 4, Pages 162-178.
Hubick, K. T. (1992). Artificial neural networks in Australia. Canberra: Department of Industry, Technology and Commerce, 1-132.
Suzuki, K. (2011). Artificial Neural Networks: Methodological Advances and Biomedical Applications. Rijeka: InTech, 1-362.
Masters, T. (1993). Practical neural network recipes in C++. San Diego, California: Academic Press publication, 1-493.

[16]Marquardt, C. (2009). Lifetime calculation under variable amplitude loading with the application of artificial neural networks. International Journal of Fatigue, Volume 27(8), Pages 920-927

Masters, T. (1993). Practical neural network recipes in C++. San Diego, California: Academic Press publication, 1-493.
Sarıdemir, M. (2009). Prediction of long-term effects of GGBFS on compressive strength of concrete by artificial neural networks and fuzzy logic. Construction and Building Materials, Volume 23, Pages 1279–1286.
Smith, M. (1993). Neural networks for statistical modelling. New York: Van Nostrand Reinhold, 1-235.
Iranian National Building Code, Part 6. (2013). Loading on structures.Tehran: Ministry of Road and Urban Development.
Iranian National Building Code, Part 9. (2013). Design and Construction of RC Buildings.Tehran: Ministry of Road and Urban Development.
Road, Housing and Urban Development Research Center. (2013). Iranian code of practice for seismic-resistant design of buildings. Iranian Building Code, Standard No. 2800, 4th Edn.
CSI, “ETABS 9.7.4 Extended 3D Analysis of Building Systems,” Computers and Structures Inc., Berkeley, California.
Beer, M. Spanos, P.D. (2009). “A neural network approach for simulating stationary stochastic processes”. Structural Engineering and Mechanics, Vol. 32, No. 1, pp. 71-94.
MathWorks, MATLAB. (2018). The language of technical computing.Version 7.11.0. (R2018b).