Application of Artificial Neural Networks for Assessing the Reinforcement of Reinforced-Concrete Floor Slabs

https://doi.org/10.35579/2076-6033-2019-11-04

ABSTRACT

In this article, the problem of assessment of working documentation quality in terms of trustworthiness of the calculation of area of main reinforcement of reinforced-concrete structures is reviewed. In spite of development of automated designing systems, no application solutions for fully automated check of quality of working documentation for reinforced-concrete structures as regards sufficiency and necessity of reinforcement of them have been proposed until now. Moreover, this rather routine procedure can be fully automated to exclude the subjective nature of its results. Artificial neural networks (ANN) constitute the most promising mathematical model for this purpose. There are known examples demonstrating the possibility of applying the ANN for various types of calculations and analysis of experimental data for reinforced-concrete structures. In particular, the ANN allows predicting the actual deformation parameters of reinforced-concrete structures with significantly greater accuracy than any of the current national design standards. The article presents the results of calculations of reinforcement and sag for various input parameters using the example of reinforced-concrete slab structure. Using the simplest ANN with one hidden layer over the entire training sample, the predicted values with sufficient accuracy for practical use were obtained. It has been established that ANN makes it possible to predict effectively not only values of the required reinforcement for slab structures, but also their deformation. Within the framework of BIM-technologies used currently in building design, the use of ANN to assess the quality of ready-made design documentation in terms of reinforcement will reduce considerably the cost and time of relevant examinations with significantly higher trustworthiness of their results.

Keywords: reinforced-concrete structures, reinforcement, neural network, artificial intelligence, design documentation.

For citation: Liashkevich A. Application of Artificial Neural Networks for Assessing the Reinforcement of Reinforced-Concrete Floor Slabs. Contemporary Issues of Concrete and Reinforced Concrete: Collected Research Papers. Minsk. Institute BelNIIS. Vol. 11. 2019. pp. 51–62.

Full text in English:



References:

1. Javadi A. A., Tan T. P., Zhang M. X. (2002) Neural network for constitutive modelling in finite element analysis. Computer Assisted Mechanics and Engineering Sciences. 2003. Vol. 10. pp. 523-529.
2. Kaczmarek М., Szymanska A. (2016) Application of artificial neural networks to predict the deflections of reinforced concrete beams. Studia Geotechnica et Mechanica. 2016. Vol. 38. No. 2. pp. 37-46.
3. Li L., Zheng W., Wang Y.(2019) Prediction of Moment Redistribution in Statically Indeterminate Reinforced Concrete Structures Using Artificial Neural Network and Support Vector Regression. Applied Sciences. 2019. 9(1):28.
4. Roewer-Despres F., Khan N., Stavness I. (2018) Towards finite element simulation using deep learning. Papers of the 15th International Symposium on Computer Methods in Biomechanics and Biomedical Engineering. 2018. Mode of access:http://cmbbe2018.tecnico.ulisboa.pt/pen_cmbbe2018/pdf/EB_PAPERS/CMBBE2018_paper_89.pdf. Date of access: 25.11.2019
5. Cybenko G. V. (1989) Approximation by Superpositions of a Sigmoidal function. Mathematics of Control Signals and Systems. 1989. Т. 2, № 4. pp. 303-314.
6. Lazovsky D. N., Glukhov D. O., Liashkevich O. N. Metodika rascheta staticheski neopredelimykh sterzhnevykh zhelezobetonnykh konstrukciy [Technique of calculation of statically indeterminable reinforced concrete structures]. Bulletin of the Polotsk State University. Series B, Applied sciences: Scientific theoretical journal. Novopolotsk: PSU. 2004. No.1. pp. 66-73. (rus)
7. Maximova O. M. Razvitiye i primeneniye neyrosetevykh tekhnologiy dlia zadach mekhaniki i stroitelnykh konstrukciy [Development and application of neural network technologies for the tasks of mechanics and building structures]. Bulletin of the Irkutsk State Technical University. Irkutsk: IrSTU. 2013. № 8 (79). pp. 81-89. (rus)