Neural network prediction of concrete degradation by sulphuric acid attack

Structure and Infrastructure Engineering

Volumn 3, Issue 1, 2007, Pages 17-27

  Hewayde, E ^a   Nehdi, M ^a   Allouche, E ^a   Nakhla, G ^a  

^a UNIVERSITY OF WESTERN ONTARIO   (Canada)

Author keywords

Analysis; Artificial neural network; Compressive strength; Concrete; Sulphuric acid

Indexed keywords

ARTIFICIAL INTELLIGENCE; BACKPROPAGATION; COMPRESSIVE STRENGTH; CONCRETE CONSTRUCTION; CONCRETE MIXERS; CONCRETES; DEGRADATION; ELECTRIC FAULT LOCATION; ICE; LIGHTNING; STRENGTH OF MATERIALS; SULFATE MINERALS; SULFURIC ACID; VEGETATION; WASTEWATER TREATMENT; WATER TREATMENT;

ARTIFICIAL NEURAL NETWORK (ANN) MODELS; ARTIFICIAL NEURAL NETWORK (ANNS); COMPRESSIVE STRENGTH OF CONCRETE; CONCRETE DEGRADATION; CONCRETE MIXTURES; INPUT VARIABLES; MICROBIOLOGICALLY INDUCED CORROSION (MIC); NETWORK PREDICTION; SULPHURIC ACID (SA); WASTEWATER COLLECTION; WEIGHT LOSSES;

NEURAL NETWORKS;

EID: 48249084182     PISSN: 15732479     EISSN: 17448980     Source Type: Journal    
DOI: 10.1080/15732470500253065     Document Type: Article

References (28)

1. ACPA, Sulfide and Corrosion Prediction and Control. American Concrete Pipe Association, 1984, pp. 23-32.
2. ASCE, Manual of Practice on Sulfide in Wastewater Collection and Treatment System. ASCE, Report No. 36, 1994, pp. 142-148.
3. Attiogbe, K.E. and Rizkallah, H.S., Response of concrete to sulfuric acid attack. ACI Mater. J., 1988, 85, 481-488.
4. Bai, J., Wild, J. and Sabir, B., Using neural networks to predict workability of concrete incorporating metakaolin and fly ash. Adv. Eng. Software, 2003, 34, 663-669.
5. Buenfeld, N., Hassanein, N. and Jones, A., An artificial neural network for predicting carbonation depth in concrete structure. Manual and Reports on Engineering Practice, pp. 61-67, 1998 (ASCE: Sacramento, CA).
6. Daczko, J.A., Johnson, D.A. and Amey, S.L., Decreasing concrete sewer pipe degradation using admixtures. Mater. Perform. J., 1997, January, 51-56.
7. Dias, W. and Pooliyadda, S., Neural networks for predicting properties of concretes with admixtures. Construct. Build. Mater. J., 2001, 15, 371-379.
8. El-Chabib, H. and Nehdi, M., Neural network modelling of properties of cement-based materials demystified. Adv. Cement Res. 2005, 17, 91-102.
9. EPA, Hydrogen Sulfide Corrosion in Wastewater Collection and Treatment Systems. EPA, Report to Congress, EPA/09-91-010, 1991, pp. 36-42.
10. Fattuhi, N.I. and Hughes, B.P., Ordinary Portland Cement mixes with selected admixtures subjected to sulfuric acid attack. ACI Mater. J., 1988a, 85, 512-518.
11. Fattuhi, N.I. and Hughes, B.P., The performance of cement paste and concrete subjected to sulfuric acid attack. Cem. Concrete Res., 1988b, 18, 545-555.
12. Ghaboussi, J., Garret, J. and Wu, X., Knowledge-based modeling of material behavior with neural networks. ASCE J. Eng. Mech., 1991, 117, 132-153.
13. Haykin, S., Neural Networks: A Comparative Foundation, 2nd edn, 1999 (Prentice-Hall: Upper Saddle River, NJ).
14. Hewayde, E., Allouche, E. and Nakhla, G., Degradation of Wastewater Concrete Pipes by Hydrogen Sulfide. Geotcchnical Research Center (GRC), The University of Western Ontario, 2003, pp. 3-22.
15. Hewayde, E., Allouche, E. and Nakhla, G., The use of Type 50E cement with selected admixtures to improve concrete resistance to sulfuric acid attack. Cem. Concrete Res., 2004a, submitted for publication.
16. Hewaydw, E., Allouche, E. and Nakhla, G., Comparative study of the response of concrete with selected admixtures to severe sulfuric acid attack. Adv. Cem. Res., 2004b, submitted for publication.
17. Hooton, R., Influence of silica fume replacement of cement on physical properties and resistance to sulfate attack. ACI Mater. J., 1993, 90, 143-151.
18. Kaemmpfer, W. and Berndt, M., Estimation of service life of concrete pipe in sewer network, in 8th Conference of Durability of Building Materials and Components, Vol. 1, 1999, pp. 36-45.
19. Lee, S., Prediction of concrete strength using artificial neural networks. Eng. Struct., 2003, 25, 849-857.
20. Monteny, N., DeBelie, E., Vinke, V. and Taerwe, L., Chemical and microbiological tests to simulate sulfuric acid corrosion of polymer-modified concrete. Cem. Concrete Res., 2001, 31, 1359-1365.
21. Nehdi, M., Djebbar, Y. and Khan, A., Neural network model for performed-foamed cellular concrete. ACI Mater. J., 2001a, 98, 402-409.
22. Nehdi, M., El Chabib, H. and El-Naggar, M., Predicting the performance of self-compacting concrete mixtures using artificial neural networks. ACI Mater. J., 2001b, 98, 394-401.
23. Neuralware, Neural Works Professional II/Plus, Carnegie, PA, USA, 2001.
24. Sabastia, M., Olmo, I. and Irabien, A., Neural network prediction of unconfined compressive strength of coal fly ash-cement mixtures. Cem. Concrete Res., 2003, 33, 1137-1146.
25. Sand, W., Dumas, T. and Cabiron, J.L., Test for biogenic sulfuric acid corrosion in a simulation chamber confirms the on-site performance of calcium aluminate based concrete in sewer application, in ASCE Material Engineering Conference, 1994, pp. 14-16.
26. Thistlethwayte, D.K., The Control of Sulfides in Sewerage Systems, pp. 10-21, 1972 (Ann Arbor Science: Ann Arbor, MI).
27. Torri, K. and Kawamura, M., Effects of fly ash and silica fume on the resistance of mortar to sulfuric acid and sulfate attack. Cem. Concrete Res., 1994, 24, 361-370.
28. Yeh, I., Modeling of strength of high-performance concrete using artificial neural networks. Cem. Concrete Res., 1998, 28, 1797-1808.