Title:
Towards a Service Life Prediction System of Concrete Structures Based on a Neural-Computing Approach
Author(s):
Bakhta Boukhatem, Arezki Tagnit-Hamou, Mohamed Chekired and Mohamed Ghrici
Publication:
Symposium Paper
Volume:
320
Issue:
Appears on pages(s):
41.1-41.12
Keywords:
carbonation, concrete structure, corrosion, durability, fly ash, neural network, service life, sulfate attack.
DOI:
10.14359/51701079
Date:
8/1/2017
Abstract:
The cost of repairing and rehabilitating damaged reinforced-concrete structures in Canada and elsewhere continues to rise. Predicting the service life and life-cycle cost of these structures can help identify the most cost-effective solution. Many companies have joined with research partners on projects to develop reliable tools to predict the service life of concrete structures. Given the problem’s complexity, most of these projects are based on different modeling approaches producing widely different values, greatly limiting their application.
Therefore, our project consisted in applying a connectionist approach, including artificial neural networks (ANNs) models and a database, to create an intelligent system. In addition, each ANN model better grasps the complex mechanisms of concrete degradation (carbonation, sulfate expansion, chloride-induced corrosion, etc.). The proposed system will yield a powerful solution for predicting the service life of concrete structures and be useful in designing new structures. It will significantly improve codes by contributing more realistic
recommendations.
Related References:
1. Papadakis, V.G., and Efstathiou, M. P., 2005, “EUCON, a Software Package for Estimation of Concrete Service Life,”1er ed. Patras, Greece, 77p.
2. Ehlen, M.A., Thomas, M.D.A., Bentz, E.C., 2009, “Life-365, Service Life Prediction Model Version 2.0,” ConcreteInternational, 31(2), 41-46.
3. Marchand, J., 2011, “STADIUM: A Methodology to Improve and Control Concrete Durability and Service Life,”MaSTEC 2011 cum HKCI Annual Seminar on Construction Materials in the Next Decade: Innovation and Sustainability,University of Hong Kong.
4. Helland, S., 2013, “Design for Service Life: Implementation of Fib Model Code 2010 Rules in the Operational code ISO16204,” Structural Concrete, 14(1), 10-18.
5. ANSYS Mechanical User's Guide, 2008-2013, Release 15.0, Published in the U.S.A, © SAS IP, Inc. 1832p.
6. Marchand, J., and Samon, E., 2009, “Predicting the Service-Life of Concrete Structures, Limitations of SimplifiedModels,” Cement and Concrete Composites, 31(8), 515-521.
7. Bentz, D. P., Guthrie, W. S., Jones, S. Z., and Martys, N. S., 2014, “Predicting Service Life of Steel-Reinforced ConcreteExposed to Chlorides: A discussion of real-world considerations for effective modeling,” Concrete International, 36(9), 55-64.
8. CUSSIGH, F., Thauvin, B., Turcry, P., Izoret, L., Clergue, C., Vuillemin, P., Divet, L., Cassagnabère, F. Carcassès, M.,Baroghel-Bouny, V., Rozière, E., Rougeau, P., Gegout, P., Linger, L., Potier, J. M., Godart, B., and Thiery, M.,Toutlemonde, F., 2014, “Projet national : Approche Performantielle de la Durabilité des Ouvrages en Béton (PERFDUB),Étude de montage,” Report, Institut pour la Recherche Appliquée et l’Expérimentation en Génie Civil (IREX), 80p.
9. Boukhatem, B., Kenai, S., Tagnit-Hamou, A., and Ghrici, M., 2011, “Application of New Information Technology onConcrete: An Overview,” Journal of Civil Engineering and Management: International Research and Achievements, 17(2),248 -258.
10. Lu, P., Chen, S., and Zheng, Y., 2012, “Artificial Intelligence in Civil Engineering,” Mathematical Problems inEngineering, 22p.
11. Verma, S. K., Bhadauria, S. S., and Akhtar, S., 2014, “Computational Methods in Service Life Modeling of ConcreteStructures,” The Indian Concrete Journal, 49-59.
12. Sobhani, J., and Ramezanianpour, A. A., 2011, “Service Life of the Reinforced Concrete Bridge Deck in CorrosiveEnvironments: A Soft Computing System,” Applied Soft Computing, 11, 3333–3346.
13. Guneyisi, E. M., Mermerdas, K., Guneyisi, E., and Gesoglu, M., 2015, “Numerical Modeling of Time to CorrosionInduced Cover Cracking in Reinforced Concrete Using Soft-Computing Based Methods,” Materials and Structures, 48,1739–1756.
14. Boukhatem, B., Kenai, S., Tagnit-Hamou, A., and Ziou, D., Ghrici, M., 2012, “Predicting Concrete Properties UsingNeural Networks (NN) with Principal Component Analysis (PCA) technique,” Computers and Concrete, 10(6), 557-57.
15. Luo, D., Niu, D., and Dong, Z. “Application of Neural Network for Concrete Carbonation Depth Prediction,” 2014, 4thInternational Conference on the Durability of Concrete Structures Purdue University, West Lafayette, IN, USA, 24–26 Jul,66-71.
16. Inthata, S., Kowtanapanich, W., and Cheerarot, R., 2013, “Prediction of Chloride Permeability of Concretes ContainingGround Pozzolans by Artificial Neural Networks,” Materials and Structures, 46(6), 1707–1721.
17. Diab, A. M., Elyamany, H. E., Elmoaty, A., Elmoaty, M. A., and Shalan, A. H., 2014, “Prediction of ConcreteCompressive Strength Due to Long Term Sulfate Attack Using Neural Network,” Alexandria Engineering Journal, 53(3),627–642.
18. Rami M. Haj-Ali, Kimberly E. Kurtis, and Akshay R. Sthapit, 2001, “Neural Network Modeling of Concrete Expansionduring Long-Term Sulfate Exposure,” ACI Materials Journal, 98(1), 36-43
19. Ghafoori, N., Najimi, M., Sobhani, J., and Aqel, M. A., 2013, “Predicting Rapid Chloride Permeability of Self-Consolidating Concrete: A Comparative Study on Statistical and Neural Network Models,” Construction and BuildingMaterials, 44, 381–390.
20. Goktepe, A.B., Inan, G., K. Ramyar, K., and Sezer, A., 2006, “Estimation of Sulfate Expansion Level of PC MortarUsing Statistical and Neural Approaches,” Construction and Building Materials, 20(7), 441–449.
21. Parichatprecha, R., and Nimityongskul, P., 2009, “Analysis of Durability of High Performance Concrete UsingArtificial Neural Networks,” Construction and Building Materials, 23(2), 910–917.
22. Firouzi, A., and Rahaib, A., 2012, “An integrated ANN-GA for Reliability Based Inspection of Concrete Bridge DecksConsidering Extent of Corrosion-Induced Cracks and Life Cycle Costs,” Scientia Iranica A, 19(4), 974–981.
23. Hodhod, O.A., and Ahmed, H.I., 2014, “Modeling the Corrosion Initiation Time of Slag Concrete Using the ArtificialNeural Network,” HBRC Journal, 10(3), 231–234.
24. Junita, M.S. and Brian, S.H. (2008), “Improved Neural Network Performance Using Principal Component Analysis onMatlab,” International Journal of the Computer, the Internet and Management, 16(2), 1-8.
25. Dreyfus, G., Martinez, J. M., Samuelides, M., Gordon, M. B., Badran, F., Thiria, S., and Hérault, L., 2002, “Réseaux deNeurones : Méthodologie et Application,” Ed. Eyrolles, 386p.
26. Beale, M. H., Hagan, M. T. and Demuth, H. B. 2014, “Neural Network TM User Guide with MATLAB R2014,” TheMath Works, Inc, Prentice Hall.
27. Cha Y-J, Choi W, Büyüköztürk O., 2017, Deep Learning-Based Crack Damage Detection Using Convolutional NeuralNetworks. Computer-Aided Civil and Infrastructure Engineering, 32(5), 361–378.