Title:
Artificial Neural Network Utilization for Nondestructive Testing and Evaluation of Concrete Structures
Author(s):
Wael A. Zatar, M. Ammar Alzarrad, Tu T. Nguyen, and Hai D. Nguyen
Publication:
Symposium Paper
Volume:
350
Issue:
Appears on pages(s):
167-177
Keywords:
artificial neural network; concrete imaging; evaluation; ground penetrating radar; nondestructive testing
DOI:
10.14359/51734322
Date:
11/1/2021
Abstract:
In this paper, the artificial neural network (ANN) method is utilized to predict ground-penetrating radar reflection amplitudes from four different inputs, namely, temperature, ambient relative humidity, chloride level, and corrosion condition on the surface of the reinforcing bar. A total of 288 ground penetrating radar (GPR) data points were collected from a series of chloride-contaminated concrete slabs under various environmental profiles that were used to train, validate, and test the proposed ANN model. The ANN model performed well in predicting the GPR reflection signals, with the overall coefficient of determination (R2) being 0.9958. The overall mean squared error (MSE) and root mean squared error (RSME) values are 0.015 and 0.122, respectively. These values are very low, which means that the ANN model has an excellent prediction capability. The research results show that the GPR reflection amplitudes are more sensitive to the temperature changes and chloride level parameters than the ambient relative humidity and rust condition on the reinforcing bar surface. Using the ANN method to predict the GPR reflection amplitudes is relatively new for structural concrete applications. This study paves the way for further developments of neural networks in civil and structural engineering.
Related References:
Abdalla, J.A. and Hawileh, R.A., 2020, “Assessment of Effect of Strain Amplitude and Strain Ratio on Energy Dissipation Using Machine Learning,” In International Conference on Computing in Civil and Building Engineering (pp. 98-108). Springer.
Abdalla, J.A., Elsanosi, A. and Abdelwahab, A., 2007, “Modeling and simulation of shear resistance of R/C beams using artificial neural network,” Journal of the Franklin Institute, 344(5), pp.741-756.
Alzarrad, M.A., Hatamleh, M.T., 2020, “A GIS-Based Artificial Neural Network Model to Assess Building Location Potential to Harvest Solar Energy.” Proceedings of the ASCE Construction Research Congress 2020 Conference, Tempe, Arizona, pp. 599-609.
Asteris, P.G. and Mokos, V.G., 2019, “Concrete compressive strength using artificial neural networks,” Neural Computing and Applications, pp.1-20.
Dinh, K., Gucunski, N., Kim, J., Duong, T. H., 2015, “Improved GPR-based condition assessment of reinforced concrete bridge decks using artificial neural network,” HDKBR Info. Magazine, 5:2, pp. 3–13.
Dinh, K., Gucunski, N., Kim, J., Duong, T. H., 2016, “Understanding depth-amplitude effects in assessment of GPR data from concrete bridge decks,” NDT & E International, 5:83, pp. 48–58.
Hakim, S. J. S., Razak, H. A., 2013, “Structural damage detection of steel bridge girder using artificial neural networks and finite element models,” Steel & Composite Structures, 14:4, pp. 367–377.
Kindangen, J., 1996, “Artificial neural networks and naturally ventilated buildings”, Building Research and Information, 24:4, pp. 203-208.
Lee, S.C., 2003, “Prediction of concrete strength using artificial neural networks,” Engineering structures, 25(7), pp.849-857.
Liu, T., Su, Y., Huang, C., 2018, “Inversion of ground penetrating radar data based on neural networks,” Remote Sensing, 10:5, pp. 730-750.
Naser, M.Z., 2018, “Deriving temperature-dependent material models for structural steel through artificial intelligence,” Construction and Building Materials, 191, pp.56-68.
Nikbin, M., Rahimi, R. S., Allahyari, H., 2017, “A new empirical formula for prediction of fracture energy of concrete based on the artificial neural network,” Engineering Fracture Mechanics, 3:186, pp. 466–482.
Mekki, H., 2016, “Artificial neural network-based modelling and fault detection of partial shaded photovoltaic modules, Simulation Modelling Practice and Theory, 5:67, pp. 1-13.
Min, H., Zhang, W., Gu, X., 2018, “Effects of load damage on moisture transport and relative humidity response in concrete,” Construction and Building Materials, 7:169, pp. 59–68.
Rehman, S., Mohandes, M., 2008, “Artificial neural network estimation of global solar radiation using air temperature and relative humidity”, Energy Policy, 6:36, pp. 571-576.
Tam, C., Tong, T., 2003, “GA-ANN model for optimizing the locations of tower crane and supply points for high-rise public housing construction”, Construction Management and Economics, 21:3, pp. 257-266.
Topcu, I.B. and Sarıdemir, M., 2008, “Prediction of compressive strength of concrete containing fly ash using artificial neural networks and fuzzy logic,” Computational Materials Science, 41(3), pp.305-311.
Trtnik, G., Kavčič, F. and Turk, G., 2009, “Prediction of concrete strength using ultrasonic pulse velocity and artificial neural networks,” Ultrasonics, 49(1), pp.53-60.
Uysal, M. and Tanyildizi, H., 2012. Estimation of compressive strength of self compacting concrete containing polypropylene fiber and mineral additives exposed to high temperature using artificial neural network. Construction and Building Materials, 27(1), pp.404-414.
Wan, J., Wang, D., Hoi, S. C. H., 2014, “Deep learning for content-based image retrieval: a comprehensive study.” Proc. of the 22nd ACM International Conference on Multimedia, ACM,
Orlando, FL, pp. 157-166.
Yeh, I.C., 1998, “Modeling of strength of high-performance concrete using artificial neural networks,” Cement and Concrete Research, 28(12), pp.1797-1808.
Zatar, W. and Nguyen, H., “Non-Destructive Evaluation of RC Bridge Elements Using Ground-Penetrating Radar and Ultrasonic Pitch and Catch Techniques,” Bridge Engineering Institute
Conference 2019 (BEI-2019), Honolulu, Hawaii, USA, July 2019.
Zatar, W., Nguyen, H. and Nghiem, H., 2021a “Non-Destructive Evaluation of Reinforced-Concrete Slabs Rehabilitated with CFRP/GFRP,” American Concrete Institute Special Publication on
Field Applications of Non-Conventional Reinforcing and Strengthening Methods for Bridges and Structures, ISBN-13: 978-1-64195-130-2, ACI SP346, pp11-30.
Zatar, W., Nguyen, T. T., & Nguyen, H., 2021b, “Predicting GPR Signals from Concrete Structures Using Artificial Intelligence-Based Method”, Advances in Civil Engineering. vol. 2021, Article ID 6610805, 9 Pages, doi: 10.1155/2021/6610805