Open Access
Issue |
MATEC Web Conf.
Volume 398, 2024
2nd International Conference on Modern Technologies in Mechanical & Materials Engineering (MTME-2024)
|
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Article Number | 01033 | |
Number of page(s) | 10 | |
DOI | https://doi.org/10.1051/matecconf/202439801033 | |
Published online | 25 June 2024 |
- Ahmad, A., Khan, QU Z., Raza, A., Reliability Analysis of Strength Models for CFRP-Confined Concrete Cylinders. Composite Structures, 2020: p. 112312. [CrossRef] [Google Scholar]
- Hollaway, L.C., M. Chryssanthopoulos, and S.S. Moy, Advanced polymer composites for structural applications in construction: ACIC 2004. 2004: Woodhead Publishing. [CrossRef] [Google Scholar]
- Baili, J., et al., Experiments and predictive modeling of optimized fiber-reinforced concrete columns having FRP rebars and hoops. Mechanics of Advanced Materials and Structures, 2022: p. 1–20. [Google Scholar]
- El Ouni, M.H., et al. Parametric investigation of GFRP-RCC jute fibre-reinforced recycled aggregate concrete elements. in Structures. 2022. Elsevier. [Google Scholar]
- Mohammed Berradia, M.A., Zeeshan Ahmad, Oussama Accouche, Ali Raza, Yasser Alashker Data-driven prediction of compressive strength of FRP-confined concrete members: An application of machine learning models. Structural Engineering and Mechanics, An International Journal, 2022. 83(4): p. 515–535. [Google Scholar]
- Raza, A., et al., Structural evaluation of recycled aggregate concrete circular columns having FRP rebars and synthetic fibers. Engineering Structures, 2022. 250: p. 113392. [CrossRef] [Google Scholar]
- Raza, A., M.H. El Ouni, and J. Baili, Data-driven analysis on axial strength of GFRP-NSC columns based on practical artificial neural network tool. Composite Structures, 2022. 291: p. 115598. [CrossRef] [Google Scholar]
- Raza, A. and Q.u.Z. Khan, Efficiency of GFRP reinforcement in concrete columns having hybrid fibres: experiments and finite element analysis. Magazine of Concrete Research, 2022. 74(21): p. 1103–1119. [Google Scholar]
- De Lorenzis, L. and R. Tepfers, Comparative study of models on confinement of concrete cylinders with fiber-reinforced polymer composites. Journal of Composites for Construction, 2003. 7(3): p. 219–237. [CrossRef] [Google Scholar]
- Parvin, A. and A.S. Jamwal, Effects of wrap thickness and ply configuration on composite-confined concrete cylinders. Composite structures, 2005. 67(4): p. 437–442. [CrossRef] [Google Scholar]
- Li, G., Kidane, S., Pang, Su S., Helms, JE, Stubblefield, Michael A. , Investigation into FRP repaired RC columns. Composite structures, 2003. 62(1): p. 83–89. [CrossRef] [Google Scholar]
- Demers, M. and K.W.J.C.J.o.C.E. Neale, Confinement of reinforced concrete columns with fibre-reinforced composite sheets-an experimental study. 1999. 26(2): p. 226–241. [Google Scholar]
- Ashrafi, H.R., M. Jalal, and K.J.E.S.w.A. Garmsiri, Prediction of load–displacement curve of concrete reinforced by composite fibers (steel and polymeric) using artificial neural network. 2010. 37(12): p. 7663–7668. [Google Scholar]
- Ghanizadeh, A.R., et al., Modeling of bentonite/sepiolite plastic concrete compressive strength using artificial neural network and support vector machine. 2019. 13(1): p. 215–239. [Google Scholar]
- Reddy, T.C.S.J.F.o.S. and C. Engineering, Predicting the strength properties of slurry infiltrated fibrous concrete using artificial neural network. 2018. 12(4): p. 490–503. [Google Scholar]
- Khademi, F., et al., Multiple linear regression, artificial neural network, and fuzzy logic prediction of 28 days compressive strength of concrete. 2017. 11(1): p. 90–99. [Google Scholar]
- Samaan, M., A. Mirmiran, and M.J.J.o.s.e. Shahawy, Model of concrete confined by fiber composites. 1998. 124(9): p. 1025–1031. [Google Scholar]
- Matthys, S., et al., Axial load behavior of large-scale columns confined with fiber-reinforced polymer composites. 2005. 102(2): p. 258. [Google Scholar]
- Soltoggio, A., K.O. Stanley, and S. Risi, Born to learn: the inspiration, progress, and future of evolved plastic artificial neural networks. Neural Networks, 2018. 108: p. 48–67. [CrossRef] [Google Scholar]
- Di Franco, G. and M. Santurro, Machine learning, artificial neural networks and social research. Quality & quantity, 2021. 55(3): p. 1007–1025. [CrossRef] [Google Scholar]
- Cladera, A. and A.J.E.S. Marí, Shear design procedure for reinforced normal and high-strength concrete beams using artificial neural networks. Part I: beams without stirrups. 2004. 26(7): p. 917–926. [Google Scholar]
- Cladera, A. and A.J.E.s. Mari, Shear design procedure for reinforced normal and high-strength concrete beams using artificial neural networks. Part II: beams with stirrups. 2004. 26(7): p. 927–936. [Google Scholar]
- LeCun, Y.A., et al., Efficient backprop, in Neural networks: Tricks of the trade. 2012, Springer. p. 9–48. [Google Scholar]
- Gesoglu, M., et al., Mechanical and fracture characteristics of self-compacting concretes containing different percentage of plastic waste powder. Construction and Building Materials, 2017. 140: p. 562–569. [CrossRef] [Google Scholar]
- Krogh, A. and J. Vedelsby, Neural Network Ensembles, Cross Validation, and Active Learning. Advances in Neyral Information Processing Systems, 1995. 7: p. 21–238. [Google Scholar]
- Utans, J., Moody, J., Rehfuss, S., Siegelmannt, H., Input Variable Selection for Neural Networks: Application to Predicting the U.S. Business Cycle. IEEE Transactions on Knowledge and Data Engineering, 1995: p. 118–122. [Google Scholar]
- Castellano, G. and A.M. Fanelli, Variable Selection Using Neural-Network Models. Neurocomputing, 2000. 31(1-4): p. 1–13. [CrossRef] [Google Scholar]
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