Neural network prediction of concrete degradation by sulphuric acid attack

References

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