Evaluation Of Compressive Strength Of Concrete Using Ndt And Artificial Intelligence Methods
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Abstract
Non-destructive testing (NDT) techniques are frequently applied in the field to evaluate the compressive strength of concrete in the construction sector. NDT techniques are comparatively inexpensive and do not harm the current structure. The ultrasonic pulse velocity (UPV) test and the rebound hammer (RH) test are two common NDT techniques. The concrete compressive strength estimates are not particularly precise when compared to the outcomes of the destructive tests, which is one of the main disadvantages of the RH and UPV tests. The researchers used artificial intelligence prediction models to examine the correlations between the input values—the outcomes of the two NDT tests—and the output values—concrete strength—in order to enhance the estimation of concrete strength. In cooperation with a material testing facility and the Professional Civil Engineer Association, in-situ NDT data from 98 samples were gathered. Both conventional statistical and artificial intelligence (AI) prediction models were developed and validated using in-situ NDT data. The analysis's findings demonstrated that, in comparison to statistical regression models, artificial intelligence prediction models yield more accurate estimations. When AI methods (ANNs, SVM, and ANFIS) are used to predict concrete compressive strength in RH and UPV tests, the study findings demonstrate a considerable improvement.
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References
Zhang, J.; Haghighat, F. Development of Artificial Neural Network based heat convection algorithm for thermal simulation of large rectangular cross-sectional area Earth-to-Air Heat Exchangers. Energy Build. 2010, 42, 435–440.
Olofsson, T.; Andersson, S.; Östin, R. A method for predicting the annual building heating demand based on limited performance data. Energy Build. 1998, 28, 101–108.
Kalogirou, S.A.; Bojic, M. Artificial neural networks for the prediction of the energy consumption of a passive solar building. Energy 2000, 25, 479–491.
Hou, Z.; Lian, Z.; Yao, Y.; Yuan, X. Cooling-load prediction by the combination of rough set theory and an artificial neural-network based on data-fusion technique. Appl. Energy 2006, 83, 1033–1046.
Ben-Nakhi, A.E.; Mahmoud, M.A. Cooling load prediction for buildings using general regression neural networks. Energy Convers. Manag. 2004, 45, 2127–2141.
Bungey, J.H.; Millard, J.H.; Grantham, M.G. Testing of Concrete in Structures; Taylor and Francis: New York, NY, USA, 2006; p. 352.
Available online: http://www.engineeringcivil.com/rebound-hammer-test.html (accessed on 30 July 2021).
Bhat, S.T.; Lovell, C.W. Use of Coal Combustion Residues and Foundry Sands in Flowable Fill; Joint Highway Research Project, Indiana Department of Transportation and Purdue University: West Lafayette, IN, USA, 1996.
ACI-229R. Controlled-Low Strength Materials (Reproved 2005); American Concrete Institute: Farmington Hills, MI, USA, 2005.
Komloš, K.; Popovics, S.; Nürnbergerová, T.; Babál, B. Ultrasonic pulse velocity test of concrete properties as specified in various standards. Cem. Concr. Compos. 1996, 18, 357–364.
Khademi, F.; Jamal, S.M. Predicting the 28 Days Compressive Strength of Concrete Using Artificial Neural Network. i-Manag. J. Civ. Eng. 2016, 6, 34–47.
Nikoo, M.; Zarfam, P.; Nikoo, M. Determining Displacement in Concrete Reinforcement Building with Using Evolutionary Artificial Neural Networks. World Appl. Sci. J. 2012, 16, 1699–1708.
Kahraman, S. Evaluation of simple methods for assessing the uniaxial compressive strength of rock. Int. J. Rock Mech. Min. Sci. 2001, 38, 981–994.
Qasrawi, H.Y. Concrete strength by combined nondestructive methods simply and reliably predicted. Cem. Concr. Res. 2000, 30, 739–746.
Nehdi, M.; Chabib, H.E.; Naggar, A. Predicting performance of self-compacting concrete mixtures using artificial neural networks. ACI Mater. J. 2001, 98, 394–401.
Trtnink, G.; Kavcic, B.; Turk, G. Predicting of concrete strength using ultrasonic pulse velocity and artificial neural networks. Ultrasonics 2009, 49, 53–60.
Malek, J.; Kaouther, M. Destructive and Non-destructive Testing of Concrete Structures. Jordan J. Civ. Eng. 2014, 8, 432–447.
Whitehurst, E.A. Soniscope tests concrete structures. J. Proc. 1951, 47, 433–444.
Samarasinghe, S. Neural Networks for Applied Sciences and Engineering; Auerbach Publications: London, UK, 2007
Tharmaratnam, K.; Tan, B. Attenuation of ultrasonic pulse in cement mortar. Cem. Concr. Res. 1990, 20, 335–345.