Open Access
Issue |
MATEC Web Conf.
Volume 392, 2024
International Conference on Multidisciplinary Research and Sustainable Development (ICMED 2024)
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Article Number | 01003 | |
Number of page(s) | 21 | |
DOI | https://doi.org/10.1051/matecconf/202439201003 | |
Published online | 18 March 2024 |
- Ince, R., 2004. Prediction of fracture parameters of concrete by Artificial Neural Networks. Eng. Fract. Mech. 71, 2143–2159. https://doi.org/10.1016/j. engfracmech.2003.12.004. [CrossRef] [Google Scholar]
- Parichatprecha, R., Nimityongskul, P., 2009. Analysis of durability of highperformance concrete using artificial neural networks. Constr. Build. Mater. 23, 910–917. [CrossRef] [Google Scholar]
- Topçu, I.B., Saridemir, M., 2008. Prediction of mechanical properties of recycled aggregate concretes containing silica fume using artificial neural networks and fuzzy logic. Comput. Mater. Sci. 42, 74–82. [CrossRef] [Google Scholar]
- Mohamed, M.T., 2009. Artificial neural network for prediction and control of blasting vibrations in Assiut (Egypt) limestone quarry. Int. J. Rock Mech. Min. Sci. 46, 426–431. [CrossRef] [Google Scholar]
- Nur, W., Wan, F., Ismail, M.A., Lee, H., Seddik, M., Kumar, J., Warid, M., Ismail, M., 2020. Mixture optimization of high-strength blended concrete using central composite design. Constr. Build. Mater. 243-59 [Google Scholar]
- IS: 12269-1987. Specification for 53 grade ordinary Portland cement. Bureau of Indian Standards, New Delhi, India. [Google Scholar]
- IS: 383-1970. Specification for coarse and fine aggregates from natural sources for concrete. Bureau of Indian Standards, New Delhi, India. [Google Scholar]
- IS: 456-2000. Plain and reinforced concrete code for practice. Bureau of Indian Standards, New Delhi, India. [Google Scholar]
- IS: 10262-2009. Concrete mix proportions guide line. Bureau of Indian standards, New Delhi. India. [Google Scholar]
- IS: 516-1991. Methods of tests for strength of concrete. New Delhi (India): Bureau of Indian Standards. [Google Scholar]
- IS: 5816-1999. Splitting tensile strength of concrete method of test. New Delhi (India): Bureau of Indian Standards. [Google Scholar]
- Saridemir M. Predicting the compressive strength of mortars containing metakaolin by artificial neural networks and fuzzy logic. Adv. Eng. Software 2009;40(9):920–7. [CrossRef] [Google Scholar]
- Gunoglu K, Demir N, Akkurt I, Demirci Z.N. , ANN modeling of the bremsstrahlung photon flux in tantalum target, Neural Comput. Appl. 23 (6) (2013) 1591–1595, https://doi.org/10.1007/s00521-012-1111-2. [CrossRef] [Google Scholar]
- Mohamed, M.T., 2009. Artificial neural network for prediction and control of blasting vibrations in Assiut (Egypt) limestone quarry. Int. J. Rock Mech. Min. Sci. 46, 426–431. [CrossRef] [Google Scholar]
- Rashid, T.A., Ahmad, H.A., 2016. Lecturer performance system using neural network with Particle Swarm Optimization. Comput. Appl. Eng. Educ. 24, 629–638. [CrossRef] [Google Scholar]
- Kewalramani, M.A., Gupta, R., 2006. Concrete compressive strength prediction using ultrasonic pulse velocity through artificial neural networks. Autom. Constr. 15, 374–379. [CrossRef] [Google Scholar]
- Nayak, C.B., 2020. Experimental and numerical investigation on compressive and flexural behavior of structural steel tubular beams strengthened with AFRP composites. J. King Saud Univ. – Eng. Sci. 33, 88–94. [Google Scholar]
- Vandamme, J.-P., Meskens, N., Superby, J.-F., 2007. Predicting academic performance by data mining methods. Educ. Econ. 15, 405–419. [CrossRef] [Google Scholar]
- Alshihri, M.M., Azmy, A.M., El-Bisy, M.S., 2009. Neural networks for predicting compressive strength of structural light weight concrete. Constr. Build. Mater. 23, 2214–2219. [CrossRef] [Google Scholar]
- Gallo, C., 2015. Artificial Neural Networks Tutorial. igi-global. 179–189. https://doi.org/10.4018/978-1-4666-5888-2.ch626 [Google Scholar]
- Alemu, H.Z., Wu, W., Zhao, J., 2018. Feedforward neural networks with a hidden layer regularization method. Symmetry (Basel). 10, 525. [CrossRef] [Google Scholar]
- Fadja, A.N., Lamma, E., Riguzzi, F., 2018. Vision inspection with neural networks. CEUR Workshop Proc. 2272. [Google Scholar]
- Öztas, A., Pala, M., Özbay, E., Kanca, E., Çaǧlar, N., Bhatti, M.A., 2006. Predicting the compressive strength and slump of high strength concrete using neural network. Constr. Build. Mater. 20, 769–775. [CrossRef] [Google Scholar]
- Omar, C., Al-Hemiri, A., 2008. Prediction of Extraction Efficiency in Rdc Column Using Artificial Neural Network. J. Eng. 14, 2607–2621. [CrossRef] [Google Scholar]
- Hajmeer, M.N., Basheer, I.A., 2003. A hybrid Bayesian – Neural network approach for probabilistic modeling of bacterial growth/no-growth interface. Int. J. Food Microbiol. 82, 233–243. [CrossRef] [Google Scholar]
- Metin Gürü, Süleyman Tekeli, Emin Akin, Manufacturing of polymer matrix composite material using marble dust and fly ash, Key Engineering Materials, Trans Tech Publications, 2007. [Google Scholar]
- Samaneh Sahebian et al., The effect of nano-sized calcium carbonate on thermodynamic parameters of HDPE, J. Mater. Process. Technol. 209 (3) (2009) 1310–1317. [CrossRef] [Google Scholar]
- Saad A. Najim, Nizar Jawad Hadi, Dhay Jawad Mohamed, Study the effect of CaCO3 nanoparticles on the mechanical properties of virgin and waste polypropylene Trans Tech Publications, Adv. Mater. Res. 1016 (2014). [Google Scholar]
- Nahla Naji Hilal, Mohammed Freeh Sahab, Taghreed Khaleefa Mohammed Ali, “Fresh and hardened properties of lightweight self-compacting concrete containing walnut shells as coarse aggregate”, Journal of King Saud University – Engineering Sciences 33 (2021) 364–372. [CrossRef] [Google Scholar]
- Nematzadeh M, Shahmansouri AA, Fakoor M. Post-fire compressive strength of recycled PET aggregate concrete reinforced with steel fibers: Optimization and Prediction via RSM and GEP. Constr Build Mater 2020; 252:119057 [CrossRef] [Google Scholar]
- Tenza-Abril AJ, Villacampa Y, Solak AM, Baeza-Brotons F.Prediction and sensitivity analysis of compressive strength in segregated lightweight concrete based on artificial neural network using ultrasonic pulse velocity. Constr Build Mater [Internet]. 2018; 189:1173–83. Available from: https://doi.org/10.1016/j.conbuildmat.2018.09.096 [CrossRef] [Google Scholar]
- Gregor Trtnik, Franci Kavcic, Goran Turk, Prediction of concrete strength using ultrasonic pulse velocity and artificial neural networks, Ultrasonics. 49 (1) (2009) 53–60. [CrossRef] [Google Scholar]
- Shahmansouri AA, Bengar HA, Jahani E. Predicting compressive strength and electrical resistivity of eco-friendly concrete containing natural zeolite via GEP algorithm. Constr Build Mater 2019; 229:116883. [CrossRef] [Google Scholar]
- Asteris PG, Mokos VG. Concrete compressive strength using artificial neural networks. Neural Comput Appl [Internet]. 2020;32(15):11807–26. Available from: https://doi.org/10.1007/s00521-019-04663-2 [CrossRef] [Google Scholar]
- Duan ZH, Kou SC, Poon CS. Prediction of compressive strength of recycled aggregate concrete using artificial neural networks. Constr Build Mater [Internet]. 2013;40:1200–6. Available from: http://dx.doi.org/10.1016/j.conbuildmat.2012.04.063 [CrossRef] [Google Scholar]
- Fu Z, Mo J, Chen L, Chen W. Using genetic algorithm-back propagation neural network prediction and finite-element model simulation to optimize the process of multiple-step incremental air-bending forming of sheet metal. Mater Des 2010;31:267–77. [CrossRef] [Google Scholar]
- Khan AU, Bandopadhyaya T, Sharma S. Genetic algorithm-based backpropagation neural network performs better than backpropagation neural network in stock rates prediction. J Comput Sci Network Secur 2008;8:162–6. [Google Scholar]
- Sudarsana RH, Subba RP, Vaishali GG. Development of genetic algorithm-based hybrid network model for predicting the ultimate flexural strength of ferrocement elements. Int J Eng Sci 2012. [Google Scholar]
- Baykasoglu A, Oztas A, Ozbay E. Prediction and multi-objective optimization of highstrength concrete parameters via soft computing approaches. Expert Syst Appl 2009;36(3):6145–56. [CrossRef] [Google Scholar]
- Saridemir, I.B.Topçu, F. Ozcan, ¨ M.H. Severcan, Prediction of long-term effects of GGBFS on compressive strength of concrete by artificial neural networks and fuzzy logic, Constr. Build. Mater. 23 (3) (2009) 1279–1286, https://doi.org/10.1016/j.conbuildmat.2008.07.021. [CrossRef] [Google Scholar]
- Topcu IB, Saridemir M. Prediction of compressive strength of concrete containing fly ash using artificial neural networks and fuzzy logic. Comput Mater Sci 2008;41(3):305–11. [CrossRef] [Google Scholar]
- Safarzadegan Gilan S, MashhadiAli A, Ramezanianpour AA. Evolutionary fuzzy function with support vector regression for the prediction of concrete compressive strength. In: Proceedings of Fifth UKSim European Symposium on Computer Modeling and, Simulation; 2011. p. 263–8. [Google Scholar]
- Neshat M, Adeli A, Sepidnam G, Sargolzaei M. Predication of concrete mix design using adaptive neural fuzzy inference systems and fuzzy inference systems. Int J Adv Manuf Technol 2012;63:373–90. [CrossRef] [Google Scholar]
- Lampinen J, Vehtari A. Bayesian approach for neural networks – review and case studies. Neural Networks 2001;14(3):7–24. [Google Scholar]
- CEB-FIP, Model Code for Concrete Structures, (1990). [Google Scholar]
- ACI 363R, State-of-the-art Report on High-strength Concrete, American Concrete Institute Detroit, 1992. [Google Scholar]
- Wei Jiang, Youjun Xie, Wenxu Li, Jianxian Wu and Guangcheng Longa, Prediction of the splitting tensile strength of the bonding interface by combining the support vector machine with the particle swarm optimization algorithm, Engineering Structures, Vol. 230(2021), 116-136. [Google Scholar]
- Ali Alsalman, Rahman Kareem, Canh N. Dang, José R. Martí-Vargas and W. Micah Hale, Prediction of modulus of elasticity of UHPC using maximum likelihood estimation method, Structures, Vol (35), 2022, pp. 1308-1320. [CrossRef] [Google Scholar]
- Medine Ispir, Ali Osman Ates and Alper Ilki, Low strength concrete: Stress-strain curve, modulus of elasticity and tensile strength, Structures, Vol (38), 2022, pp. 1615-1632 [CrossRef] [Google Scholar]
- Semih Gonen and Serdar Soyoz, Investigations on the elasticity modulus of stone masonry, Structures, Vol (30), 2021, pp. 378-389. [CrossRef] [Google Scholar]
- Guru Jawahar J, Sashidhar C, Ramana Reddy I.V, Annie Peter J, Effect of coarse aggregate blending on short-term mechanical properties of self-compacting concrete, Mater. Des. 43 (2013) 185–194 [Google Scholar]
- Ramesh Babu T. S, Neeraja D, An experimental study on effect of natural admixture on mechanical properties of Class C fly ash blended concrete, Asian J. Civil Eng. (BHRC) 17 (7) (2016) 737–752. [Google Scholar]
- American Association of Highway and Transportation Officials, AASHTO LRFD Bridge Design Specifications, American Association of Highway and Transportation Officials, Washington, D.C, 2006. [Google Scholar]
- Jianyu Xu, Qing Liu, Hongda Guo, Miaomiao Wang, Zongjin Li and Guoxing Sun, Low melting point alloy modified cement paste with enhanced flexural strength, lower hydration temperature, and improved electrical properties, Composites Part B: Engineering, Vol 232, 2022, pp. 109-28. [Google Scholar]
- Xianyue Gua, Hongbo Tan, Xingyang He, Junjie Zhang, Xiufeng Deng, Zhengqi Zheng, Maogao Li and Jin Yang, Improvement in flexural strength of Portland cement by lamellar structured montmorillonite, Construction and Building Materials, (Vol) 329, 2022, pp. 127-45. [Google Scholar]
- Leslie Howard Sperling, Introduction to Physical Polymer Science, Wiley, New York, 2006. [Google Scholar]
- H. Ş. Arel, “Recyclability of waste marble in concrete production,” Journal of Cleaner Production, vol. 131, pp. 179–188, 2016. [CrossRef] [Google Scholar]
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