Torsional Behavior Evaluation of Reinforced Concrete Beams Using Artificial Neural Network
Source abstract
Abstract
Artificial neural networks (ANNs) are an emerging field of research and have proven to have significant potential for use in structural engineering. In previous literature, many studies successfully utilized ANNs to analyze the structures under different loading conditions and verified the accuracy of the approach. Several studies investigated the use of ANNs to analyze the shear behavior of reinforced concrete (RC) members. However, few studies have focused on the potential use of an ANN for analysis of the torsional behavior of an RC member. Torsion is a complex problem and modeling the torsional fracture mechanism using the traditional analytical approach is problematic. Recent studies show that the nonlinear behavior of RC members under torsion can be modeled using ANNs. This paper presents a comprehensive analytical and parametric study of the torsional response of RC beams using ANNs. The ANN model was trained and validated against an experimental database of 159 RC beams reported in the literature. The results were compared with the predictions of design codes. The results show that ANNs can effectively model the torsional behavior of RC beams. The parametric study presented in this paper provides greater insight into the torsional resistance mechanism of RC beams and its characteristic parameters.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Structural Health Monitoring Techniques · Structural Behavior of Reinforced Concrete · Infrastructure Maintenance and Monitoring
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Civil, Architectural Engineering and Landscape Architecture, Sungkyunkwan University, Seoul 03063, Korea
Civil Engineering Balochistan UET, Khuzdar 89100, Pakistan
The MathWorks Inc., Natick, MA 01760, USA
Department of Architectural Engineering, Inha University, Incheon 22212, Korea
References (50)
Show source-provided references
1. ACI Committee (2019). Building Code Requirements for Structural Concrete (ACI 318-19) and Commentary, American Concrete Institute.
2. EC2 (2004). Eurocode 2: Design of Concrete Structures: Part 1-1: General Rules and Rules for Buildings, British Standards Institution.
3. CSA A23 (2014). 3-14. CSA A23. 3-14: Design of Concrete Structures, Canadian Medical Association Journal.10.1158/1535-7163.TARG-15-A23 ↗
4. JSCE (2007). Standard Specifications for Concrete Structures, Japan Society of Civil Engineers.
5. Rausch, E. (1929). Design of Reinforced Concrete in Torsion (Berechnung des Eisenbetons Gegen Verdrehung). [Ph.D. Thesis, Technische Hochschule]. (In German).
6. Kritische Bemerkungen Zur Drehungselastizitat
7. Shear Flow Zone in Torsion of Reinforced Concrete10.1061/(ASCE)0733-9445(1990)116:11(3206) ↗
8. A Softened Membrane Model for Torsion in Reinforced Concrete Members10.1016/j.engstruct.2009.02.038 ↗
9. Torsion of Structural Concrete-Behavior of Reinforced Concrete Rectangular Members
10. Effect of Stirrups on Ultimate Torque of Reinforced Concrete Beams
11. Pure Tension in Rectangular Sections-A Re-Examination
12. Shear and Torsion Design of Prestressed and Non Prestressed Concrete Beams
13. Softening of Concrete in Torsional Members-Theroy and Tests
14. Torsion of High-Strength Reinforced Concrete Beams and Minimum Reinforcement Requirement
15. Torsional Behavior of Normal-and High-Strength Concrete Beams
16. Behavior of Reinforced Concrete Beams with Minimum Torsional Reinforcement10.1016/j.engstruct.2006.11.004 ↗
17. Torsion in High-Strength Concrete Hollow Beams: Strength and Ductility Analysis
18. Torsional Behavior of Ultra-High Performance Concrete Squared Beams10.1016/j.engstruct.2013.05.027 ↗
19. Experimental Study of the Torsion of Reinforced Concrete Members10.12989/sem.2006.23.6.713 ↗
20. Experimental Investigation of RC Beams with Rectangular Spiral Reinforcement in Torsion10.1016/j.engstruct.2013.05.003 ↗
21. Investigation of Torsional Behaviour of High-Strength Reinforced Concrete Sections
22. Maximum Torsional Reinforcement of Reinforced Concrete Beams Subjected to Pure Torsion10.14359/51701108 ↗
23. Prediction of Ultimate Shear Strength of Reinforced-Concrete Deep Beams Using Neural Networks10.1061/(ASCE)0733-9445(2001)127:7(818) ↗
24. Artificial Neural Networks for the Prediction of Shear Capacity of Steel Plate Strengthened RC Beams10.1016/j.conbuildmat.2004.03.002 ↗
25. Shear Design Procedure for Reinforced Normal and High-Strength Concrete Beams Using Artificial Neural Networks. Part II: Beams with Stirrups10.1016/j.engstruct.2004.02.011 ↗
26. Predicting the Shear Strength of Reinforced Concrete Beams Using Artificial Neural Networks10.1016/j.engstruct.2004.01.011 ↗
27. Simulating Size Effect on Shear Strength of RC Beams without Stirrups Using Neural Networks10.1016/j.engstruct.2004.01.009 ↗
28. Prediction of Shear Strength of Steel Fiber RC Beams Using Neural Networks10.1016/j.conbuildmat.2005.01.047 ↗
29. Modeling and Simulation of Shear Resistance of R/C Beams Using Artificial Neural Network10.1016/j.jfranklin.2005.12.005 ↗
30. Shear Capacity of Reinforced Concrete Beams Using Neural Network10.4334/IJCSM.2007.1.1.063 ↗
31. Prediction of the Ultimate Strength of Reinforced Concrete Beams FRP-Strengthened in Shear Using Neural Networks10.1016/j.compositesb.2010.03.003 ↗
32. An Approach for Estimating the Capacity of RC Beams Strengthened in Shear with FRP Reinforcements Using Artificial Neural Networks10.1016/j.conbuildmat.2011.12.008 ↗
33. Using Radial Basis Function Neural Networks to Model Torsional Strength of Reinforced Concrete Beams10.12989/cac.2006.3.5.335 ↗
34. Genetic-Programming-Based Modeling of RC Beam Torsional Strength10.1007/s12205-010-0371-6 ↗
35. Predicting of Torsional Strength of RC Beams by Using Different Artificial Neural Network Algorithms and Building Codes10.1016/j.advengsoft.2010.05.009 ↗
36. Witten, I.H., Frank, E., Hall, M.A., and Pal, C.J. (2016). Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann Publishers.
37. Kim, C., Lee, J.-Y., and Kim, M. (2019). Prediction of the Dynamic Stiffness of Resilient Materials Using Artificial Neural Network (ANN) Technique. Appl. Sci., 9.10.3390/app9061088 ↗
38. Alwattar, T.A., and Mian, A. (2019). Development of an Elastic Material Model for BCC Lattice Cell Structures Using Finite Element Analysis and Neural Networks Approaches. J. Compos. Sci., 3.10.3390/jcs3020033 ↗
39. Al-Wattar, T.A.A. (2020). Developing Equivalent Solid Model for Lattice Cell Structure Using Numerical Approaches. [Ph.D. Thesis, Wright State University].
40. Torsion in Reinforced Normal and High-Strength Concrete Beams Part 1: Experimental Test Series
41. Simple Model for Predicting Torsional Strength of Reinforced and Pre-Stressed Concrete Sections
42. Torsional Behavior of Reinforced Concrete Beams with High-Strength Steel Bars
43. Torsional Strength of RC Beams Considering Tension Stiffening Effect10.1061/(ASCE)ST.1943-541X.0000237 ↗
44. The Geometry of Canonical Variate Analysis10.1093/sysbio/30.3.268 ↗
45. Freedman, D., Pisani, R., Purves, R., and Adhikari, A. (1991). Statistics, WW Norton. [2nd ed.].
46. Autoencoder for Words10.1016/j.neucom.2013.09.055 ↗
47. Stacked Sparse Autoencoder in PolSAR Data Classification Using Local Spatial Information10.1109/LGRS.2016.2586109 ↗
48. Stacked Sparse Autoencoder Modeling Using the Synergy of Airborne LiDAR and Satellite Optical and SAR Data to Map Forest Above-Ground Biomass10.1109/JSTARS.2017.2748341 ↗
49. Deep Learning of Constrained Autoencoders for Enhanced Understanding of Data10.1109/TNNLS.2017.2747861 ↗
50. Poultney, C., Chopra, S., and Cun, Y.L. (2007). Efficient Learning of Sparse Representations with an Energy-Based Model. Advances in Neural Information Processing Systems, Neural Information Processing Systems Foundation.