References
- [1] RAMESH, G.: Geopolymer Concrete: A Review. Indian Journal of Structure Engineering, Vol.1, Iss. 2, 2021, pp. 5-8.10.35940/ijse.A1302.111221
- [2] VERMA, M. – DEV, N.: Effect of liquid to binder ratio and curing temperature on the engineering properties of the geopolymer concrete. Silicon, Vol.14, Iss. 4, 2022, pp. 1743-1757.10.1007/s12633-021-00985-w
- [3] DAVIDOVITS, J.: Geopolymers: inorganic polymeric new materials. Journal of Thermal Analysis and calorimetry, Vol.37, Iss. 8, 1991, pp. 1633-1656.10.1007/BF01912193
- [4] JAWAHAR, J. G. – LAVANYA, D. – SASHIDHAR, C.: Performance of fly ash and ggbs based geopolymer concrete in acid environment. Int. J. Res. Sci. Innovation, Vol.3, Iss. 8, 2016, pp. 101-104.
- [5] WORKMAN, G. L. - MOORE, P. O.: Nondestructive Testing Handbook, Third Edition: Volume 10, Overview. American Society of Nondestructive Testing, 2012, 594 p.
- [6] SHARIATI, M. – RAMLI-SULONG, N. H. – ARABNEJAD, M. M. – SHAFIGH, P. – SINAEI, H.: Assessing the strength of reinforced concrete structures through Ultrasonic Pulse Velocity and Schmidt Rebound Hammer tests. Scientific research and essays, Vol.6, Iss. 1, 2011, pp. 213-220.
- [7] JERGA, J. – KRAJČI, Ľ.: Damage in Concrete and its Detection by Use of Stress-Volumetric Strain Diagram. Civil and Environmental Engineering, Vol.10, Iss. 1, 2014, pp. 16-25.10.2478/cee-2014-0003
- [8] HELAL, J. – SOFI, M. – MENDIS, P.: Non-destructive testing of concrete: A review of methods. Electronic Journal of Structural Engineering, Vol.14, Iss. 1, 2015, pp. 97-105.10.56748/ejse.141931
- [9] PATIL, S. V. – CHABBI, S. – CHABBI, S. – PUDAKALKATTI, N. – PATIL, P.: Experimental Analysis of Non-Destructive Testing (NDT) on Ground Granulated Blast-Furnace Slag (GGBS) based Geopolymer Concrete. Int. J. Adv. Sci. Eng. Inf. Technol, Vol. 5, Iss. 4, 2019, pp. 1137-1145.10.29294/IJASE.5.4.2019.1137-1145
- [10] RAVALI, B. – KRISHNA, K. – KANTH, D. R. – CHARI, K. J. – PRABHU, S. V. – RAMESH, R.: Empirical Investigation on Compressive Strength of Geopolymer and Conventional Concretes by Nondestructive Method. Advances in Materials Science and Engineering, Vol. 2021, Iss. 1, 2021, pp. 1-10, doi: 10.1155/2021/9575964.
- [11] MARAS, M. M.: Characterization of performable geopolymer mortars for use as repair material. Structural Concrete, Vol. 22, Iss. 5, 2021, pp. 3173-3188.10.1002/suco.202100355
- [12] BREYSSE, D.: Nondestructive evaluation of concrete strength: An historical review and a new perspective by combining NDT methods. Construction and Building Materials, Vol. 33, Iss. 1, 2012, pp. 139-163.10.1016/j.conbuildmat.2011.12.103
- [13] BREYSSE, D. – KLYSZ, G. – DÉROBERT, X. – SIRIEIX, C. – LATASTE, J. F.: How to combine several non-destructive techniques for a better assessment of concrete structures. Cement and Concrete Research, Vol. 37, Iss. 6, 2008, pp. 783-93.10.1016/j.cemconres.2008.01.016
- [14] CAMARGO, M. V. – FERRARI, V. J.: Compressive strength and elasticity module of concrete using non-destructive techniques (NDT). Matéria, Vol. 26, Iss. 3, 2021. pp 11-26.
- [15] SREENIVASULU, C. – JAWAHAR, J. G. – SASHIDHAR, C.: Predicting compressive strength of geopolymer concrete using NDT techniques. Asian Journal of Civil Engineering, Vol. 19, Iss. 4, 2018, pp. 513-525.10.1007/s42107-018-0036-1
- [16] NOORI, A. S. – OWEED, K. M. – RAOUF, R. M. – ABDULREHMAN, M. A.: The relation between destructive and non-destructive tests of geopolymer concrete. Materials Today: Proceedings, Vol. 42, Iss. 1, 2021, pp. 2125-2133.10.1016/j.matpr.2020.12.296
- [17] KHAN, S. – RAHMANI, H. – SHAH, S. A. – BENNAMOUN, M.: A guide to convolutional neural networks for computer vision. Synthesis lectures on computer vision, Vol. 8, Iss. 1, 2018, pp. 1-207.10.1007/978-3-031-01821-3
- [18] ALMASAEID, H. H. – SALMAN, D. G.: Application of Artificial Neural Network to Predict the Properties of Permeable Concrete. Civil engineering and architecture, Vol. 10, Iss. 6, 2022, pp. 2292-2305.10.13189/cea.2022.100605
- [19] DEMIR, A.: Prediction of hybrid fibre-added concrete strength using artificial neural networks. Computers and Concrete, Vol. 15, Iss. 4, 2015, pp. 503-514.10.12989/cac.2015.15.4.503
- [20] ALMASAEID, H. H. – SULEIMAN, A. – ALAWNEH, R.: Assessment of high-temperature damaged concrete using non-destructive tests and artificial neural network modelling. Case Studies in Construction Materials, Vol. 16, 2022, 16 p., doi: 10.1016/J.CSCM.2022.E01080.
- [21] KOMLOS, K. – POPOVICS, S. – NÜRNBERGEROVÁ, T. – BABAL, B. – POPOVICS, J. S.: Ultrasonic pulse velocity test of concrete properties as specified in various standards. Cement and Concrete Composites, Vol. 18, Iss. 5, 1996, pp. 357-364.10.1016/0958-9465(96)00026-1
- [22] MALHOTRA, V. M. – CARINO, N. J.: Handbook on nondestructive testing of concrete. CRC press, 2003, 384 p.10.1201/9781420040050
- [23] NAJJAR, Y. M. – BASHEER, I.A. – MCREYNOLDS, R.: Neural modeling of Kansas soil swelling. Transportation Research Record, Vol. 1526, Iss. 1, 1996, pp. 14-19.10.1177/0361198196152600103
- [24] ALLOUZI, R. A. – ALMASAEID, H. H. – SALMAN, D. G. – ABENDEH, R. M. – RABAYAH, H. S.: Prediction of bond-slip behavior of circular/squared concrete-filled steel tubes. Buildings, Vol. 12, Iss. 4, 2022, pp. 456-477, doi: 10.3390/buildings12040456.
- [25] OLDEN, J. D. – JACKSON, D. A.: Illuminating the “black box”: a randomization approach for understanding variable contributions in artificial neural networks. Ecological modelling, Vol. 154, Iss. 1-2, 2002, pp. 135-150.10.1016/S0304-3800(02)00064-9