Abstract
We propose an artificial neural network (ANN) model to predict the CO2 diffusion through the concrete to determine the carbonation depth over time, analyzing the influence of some training algorithm and the network architecture in the ANN learning process. A reliable experimental test database of the non-accelerated test with 278 results of concrete carbonation depth was created from the published literature. It was used to train, test, and validate the model. Altogether, 120 networks had been trained with different characteristics, verifying its performance. In spite of the non-linearity and complexity of the concrete carbonation phenomenon, the proposed ANN model yielded accurate prediction. Results indicate the best training algorithm and the optimum number of neurons in the hidden layer that allows faster ANN training process and generates the most accurate mapping for the concrete carbonation phenomenon. The use of ANN appears as a robust tool easily applied to the study of the concrete carbonation, aiding in decision making in engineering projects focused on durability.
Similar content being viewed by others
References
Bakker RMF (1988) Initiation period in corrorion of steel in concrete. In: Schiessl P (ed) Corrosion of steel in concrete, Rilem. Chapman and Hall, London, New York, pp 22–55
Possan E, Felix EF, Thomaz WA (2016) CO2 uptake by carbonation of concrete during life cycle of building structures. J Build Pathol Rehabil 1:1–9. https://doi.org/10.1007/s41024-016-0010-9
Izumi I, Kita D, Maeda H (1986) Carbonation. Kibodang Publication, pp 35–88
Kobayashi K, Uno Y (1990) Mechanism of carbonation of concrete. Concr Library JSCE 16:139–151
Possan E, Andrade J, Dal Molin D (2018) A conceptual framework for service life prediction of reinforced concrete structures. J Build Pathol Rehabil 3:1–11. https://doi.org/10.1007/s41024-018-0031-7
Papadakis VG, Vayenas CG, Fardis MN (1991) Fundamental modeling and experimental investigation of concrete carbonation. ACI J 88(4):363–373
Ishida T, Maekawa K (2001) Modeling of pH profile in pore water based on mass transport and chemical equilibrium theory. Concr Library JSCE 37:151–166
Maekawa K, Ishida T, Kishi T (2003) Multi-scale modeling of concrete performance. J Adv Concr Technol 1:1–1126
Taffese WZ, Sistonen E, Puttonen J (2015) CaPrM: carbonation prediction model for reinforced concrete using machine learning methods. Constr Build Mater 100:70–82. https://doi.org/10.1016/j.conbuildmat.2015.09.058
Akpinar P, Uwanuakwa ID (2016) Intelligent prediction of concrete carbonation depth using neural networks. Bull Transilv Univ Brasov 9(2):99–108
Felix EF, Carrazedo R, Possan E (2017) Parametric analysis of carbonation process in reinforced concrete structures. Rev Alconpat 7(3):302–316. https://doi.org/10.21041/ra.v7i3.245
Jain A, Duin R, Mao J (2000) Statistical pattern recognition: a review. IEEE Trans Pattern Anal Mach Intell 22:4–37. https://doi.org/10.1109/34.824819
Mitra S, Hayashi Y (2006) Bioinformatic with soft computing. IEEE Trans Syst Man Cybern C Appl Rev 36:616–635. https://doi.org/10.1109/TSMCC.2006.879384
Mol AA, Martins-Filho LS, Silva JDS, Ronilson R (2007) Efficiency parameters estimation in gemstones cut design using artificial neural networks. Comput Mater Sci 38:727–736. https://doi.org/10.1016/j.commatsci.2006.05.012
Nazari A, Hajiallahyari H, Rahimi A, Khnmohammadi H, Amini M (2012) Prediction compressive strength of Portland cement-based geopolymers by artificial neural networks. Neural Comput Appl 19(6):1–9. https://doi.org/10.1179/1433075X15Y.0000000020
Chou J, Ngo N, Chong W (2017) The use of artificial intelligence combiners for modeling steel pitting risk and corrosion rate. Eng Appl Artif Intell 65:471–486. https://doi.org/10.1016/j.engappai.2016.09.008
Madandoust R, Bungey JH, Ghavidel R (2012) Prediction of the concrete compressive strength by means of core testing using GMDH-type neural network and ANFIS models. Comput Mater Sci 51:261–272. https://doi.org/10.1016/j.commatsci.2011.07.053
Betti M, Facchini L, Biagini P (2015) Damage detection on a three-storey steel frame using artificial neural networks and genetic algorithms. Meccanica 50(3):875–886. https://doi.org/10.1007/s11012-014-0085-9
Yu Y, Wang C, Gu X, Li J (2018) A novel deep learning-based method for damage identification of smart building structures. Struct Health Monit 18(1):143–163. https://doi.org/10.1177/1475921718804132
Shang Y, Benjamin WW (1996) Global optimization for neural network training. IEEE Comput 29:45–54. https://doi.org/10.1109/2.485892
Ooyen V, Nienhuism B (1993) Improving the convergence of the backpropagation algorithm. Neural Netw 6:611–612. https://doi.org/10.1016/0893-6080(92)90008-7
Otair MA, Salameh WA (2016) Comparative Study between different versions of the backpropagation and optical backpropagation. Revista online University of PETRA, pp 1–5
Silva F, Almeida L (1990) Acceleration techniques for the backpropagation algorithm. Lect Notes Comput Sci 421:110–119. https://doi.org/10.1007/3-540-52255-7_32
Freeman JA (1991) Neural networks: algorithms, applications, and programming techniques. Addison-Wesley, Boston
Ojha VK, Abraham A, Snásel V (2017) Metaheuristic design of feedforward neural networks: a review of two decades of research. Eng Appl Artif Intell 60:97–116. https://doi.org/10.1016/j.engappai.2017.01.013
Haykin S (2009) Neural networks and learning machines. Pearson, New Jersey
Cybenko G (1989) Approximation by superpositions of a sigmoidal function. Math Control Sig Syst 2(4):303–314. https://doi.org/10.1007/BF02551274
Fausett L (1994) Fundamentals of neural networks: architectures, algorithms, and applications. Prentice-Hall, Upper Saddle River
Possan E (2010) Carbonation modeling and service life prediction of concrete structures in urban environment. Porto Alegre: Engineering School, Federal University of Rio Grande do Sul. Ph.D. thesis in Engineering (in Portuguese)
Isaia GC (1999) Concrete carbonation: a review. In: Workshop about concrete reinforcements, 1999. São José dos Campos (in Portuguese)
Meira GR, Padaratz IJ, Borba Junior JC (2006) Natural concrete carbonation: results of four years of monitoring. In: National meeting of built environment technology, 2006. Porto Alegre (in Portuguese)
Vieira RM, Meira GR, Marques VM, Padilha JM (2009) Natural and accelerated concrete carbonation—Carbonatação natural e acelerada de concretos—influence of environmental and material factors. In: 51º Brazilian congress on concrete, 2009. Curitiba (in Portuguese)
Smolczyk HG (1969) The international symposium on the chemistry of cement. In: 5º International symposium on the chemistry of cement, 1969. Tokyo
Vesikari E (1988) Service life prediction of concrete structures with regard to corrosion of reinforcement. Technical Research Center of Finland, Report No. 553, Finland
Bob C, Afana E (1993) On-site assessment of concrete carbonation. In: Proceedings of the international conference failure of concrete structures, RILEM, 1993. Bratislava
Ministerio de Fomento. EHE-08 (2008) Instrucción de Hormigón Estructural, con comentarios de los miembros de la Comisión Permanente del Hormigón. Centro de Publicaciones Secretaria General Técnica Ministerio de Fomento, Madrid, España. 704 p
Souza JLAO (2011) A Levenberg–Marquardt algorithm for fitting σ–w curves from three-point bend tests for plain and fiber reinforced concretes. Rev IBRACON Estruturas Mater 4(4):663–690. https://doi.org/10.1590/S1983-41952011000400010
Kellouche Y, Boukhatem B, Ghrici M, Tagnit-Hamou A (2017) Exploring the major factors affecting fly-ash concrete carbonation using artificial neural network. Neural Comput Appl. https://doi.org/10.1007/s00521-017-3052-2
Karmakar S, Shrivastava G, Kowar MK (2014) Impact of learning rate and momentum factor in the performance of back-propagation neural network to identify internal dynamics of chaotic motion. Kuwait J Sci 41(2):151–174
Acknowledgements
The authors thank the National Council for Scientific Research and Development (CNPq-457309/20148), the Center for Advanced Studies in Dams Safety (CEASB) and the Technology Park of Itaipu Foundation (FPTI) for the research financial support.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Felix, E.F., Possan, E. & Carrazedo, R. Analysis of training parameters in the ANN learning process to mapping the concrete carbonation depth. J Build Rehabil 4, 16 (2019). https://doi.org/10.1007/s41024-019-0054-8
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s41024-019-0054-8