Title:
Neural Network, Machine Learning, and Evolutionary Approaches for Concrete Material Characterization
Author(s):
Mohammad H. Rafiei, Waleed H. Khushefati, Ramazan Demirboga, and Hojjat Adeli
Publication:
Materials Journal
Volume:
113
Issue:
6
Appears on pages(s):
781-789
Keywords:
backpropagation neural networks; concrete properties; fuzzy logic; genetic algorithm; regression; self-organization feature map; support vector machine
DOI:
10.14359/51689360
Date:
11/1/2016
Abstract:
Costly and time-consuming destructive methods of sampling, curing, and testing under hydraulic jacks are often used to determine concrete properties. Computational intelligence techniques provide the ability to estimate concrete properties quickly at almost no cost. This paper presents a state-of-the-art review of statistical, pattern recognition/machine learning, evolutionary algorithms, and hybrid approaches for estimation of concrete properties such as strength, adhesion, flow, slump, and serviceability using previously collected data. Advantages and disadvantages of the methods are delineated.
Related References:
1. Gribniak, V.; Mang, H. A.; Kupliauskas, R.; and Kaklauskas, G., “Stochastic Tension-Stiffening Approach for the Solution of Serviceability Problems in Reinforced Concrete: Constitutive Modeling,” Computer-Aided Civil and Infrastructure Engineering, V. 30, No. 9, 2015, pp. 684-702. doi: 10.1111/mice.12133
2. Butcher, J. B.; Day, C. R.; Austin, J. C.; Haycock, P. W.; Verstraeten, D.; and Schrauwen, B., “Defect Detection in Reinforced Concrete Using Random Neural Architectures,” Computer-Aided Civil and Infrastructure Engineering, V. 29, No. 3, 2014, pp. 191-207. doi: 10.1111/mice.12039
3. Boscato, G.; Russo, S.; Ceravolo, R.; and Fragonara, L. Z., “Global Sensitivity-Based Model Updating for Heritage Structures,” Computer-Aided Civil and Infrastructure Engineering, V. 30, No. 8, 2015, pp. 620-635. doi: 10.1111/mice.12138
4. Oh, T., and Popovics, J. S., “Practical Visualization of Local Vibration Data Collected over Large Concrete Elements,” Computer-Aided Civil and Infrastructure Engineering, V. 30, No. 1, 2015, pp. 68-81. doi: 10.1111/mice.12065
5. Silva, A.; Neves, R.; and de Brito, J., “Statistical Modelling of Carbonation in Reinforced Concrete,” Cement and Concrete Composites, V. 50, 2014, pp. 73-81. doi: 10.1016/j.cemconcomp.2013.12.001
6. Møen, E.; Høiseth, K. V.; Leira, B.; and Høyland, K. V., “Experimental Study of Concrete Abrasion Due to Ice Friction—Part II: Statistical Representation of Abrasion Rates and Simple, Linear Models for Estimation,” Cold Regions Science and Technology, V. 110, 2015, pp. 202-214. doi: 10.1016/j.coldregions.2014.10.007
7. Yoon, S.; Macphee, D. E.; and Imbabi, M. S., “Estimation of the Thermal Properties of Hardened Cement Paste on the Basis of Guarded Heat Flow Meter Measurements,” Thermochimica Acta, V. 588, 2014, pp. 1-10. doi: 10.1016/j.tca.2014.04.015
8. Sajedi, S., and Huang, Q., “Probabilistic Prediction Model for Average Bond Strength at Steel-Concrete Interface Considering Corrosion Effect,” Engineering Structures, V. 99, 2015, pp. 120-131. doi: 10.1016/j.engstruct.2015.04.036
9. Young, K., and Adeli, A., “Fundamental Period of Irregular Moment-Resisting Steel Frame Structures,” Structural Design of Tall and Special Buildings, V. 23, No. 15, 2014, pp. 1141-1157. doi: 10.1002/tal.1112
10. Young, K., and Adeli, A., “Fundamental Period of Irregular Concentrically Braced Steel Frame Structures,” Structural Design of Tall and Special Buildings, V. 23, No. 16, 2014, pp. 1211-1224. doi: 10.1002/tal.1136
11. Adeli, H., and Wu, M., “Regularization Neural Network for Construction Cost Estimation,” Journal of Construction Engineering and Management, ASCE, V. 124, No. 1, 1998, pp. 18-24. doi: 10.1061/(ASCE)0733-9364(1998)124:1(18)
12. Adeli, H., and Yeh, C., “Perceptron Learning in Engineering Design,” Microcomputers in Civil Engineering, V. 4, No. 4, 1989, pp. 247-256. doi: 10.1111/j.1467-8667.1989.tb00026.x
13. Cabessa, J., and Siegelmann, H. T., “The Super-Turing Computational Power of Evolving Recurrent Neural Networks,” International Journal of Neural Systems, V. 24, No. 8, 2014, 22 pp.
14. Adeli, H., and Hung, S. L., “A Concurrent Adaptive Conjugate-Gradient Learning Algorithm On Mimd Shared-Memory Machines,” International Journal of Supercomputer Applications and High Performance Computing, V. 7, No. 2, 1993, pp. 155-166. doi: 10.1177/109434209300700206
15. Hung, S. L., and Adeli, H., “Object-Oriented Back Propagation and Its Application to Structural Design,” Neurocomputing, V. 6, No. 1, 1994, pp. 45-55. doi: 10.1016/0925-2312(94)90033-7
16. Siddique, N., and Adeli, H., Computational Intelligence Logic, Neural Networks and Evolutionary Computing, Wiley, West Sussex, UK, 2013, 532 pp.
17. Adeli, H., and Hung, S. L., “An Adaptive Conjugate Gradient Learning Algorithm for Effective Training of Neural Networks,” Applied Mathematics and Computation, V. 62, No. 1, 1994, pp. 81-102. doi: 10.1016/0096-3003(94)90134-1
18. Eldin, N. N., and Senouci, A. B., “Measurement and Prediction of the Strength of Rubberized Concrete,” Cement and Concrete Composites, V. 16, No. 4, 1994, pp. 287-298. doi: 10.1016/0958-9465(94)90041-8
19. Wang, N., and Adeli, H., “Sustainable Building Design,” Journal of Civil Engineering and Management, V. 20, No. 1, 2014, pp. 1-10. doi: 10.3846/13923730.2013.871330
20. Lai, S., and Serra, M., “Concrete Strength Prediction by Means of Neural Network,” Construction and Building Materials, V. 11, No. 2, 1997, pp. 93-98. doi: 10.1016/S0950-0618(97)00007-X
21. Kaseko, M.; Lo, Z.; and Ritchie, S., “Comparison of Traditional and Neural Classifiers for Pavement-Crack Detection,” Journal of Transportation Engineering, ASCE, V. 120, No. 4, 1994, pp. 552-569. doi: 10.1061/(ASCE)0733-947X(1994)120:4(552)
22. Yeh, I., “Modeling of Strength of High-Performance Concrete Using Artificial Neural Networks,” Cement and Concrete Research, V. 28, No. 12, 1998, pp. 1797-1808. doi: 10.1016/S0008-8846(98)00165-3
23. Lee, S., “Prediction of Concrete Strength Using Artificial Neural Networks,” Engineering Structures, V. 25, No. 7, 2003, pp. 849-857. doi: 10.1016/S0141-0296(03)00004-X
24. Öztaş, A.; Pala, M.; Özbay, E.; Kanca, E.; Çağlar, N.; and Bhatti, M. A., “Predicting the Compressive Strength and Slump of High Strength Concrete using Neural Network,” Construction and Building Materials, V. 20, No. 9, 2006, pp. 769-775. doi: 10.1016/j.conbuildmat.2005.01.054
25. Yeh, I., “Modeling Slump Flow of Concrete using Second-Order Regressions and Artificial Neural Networks,” Cement and Concrete Composites, V. 29, No. 6, 2007, pp. 474-480. doi: 10.1016/j.cemconcomp.2007.02.001
26. Atici, U., “Prediction of the Strength of Mineral Admixture Concrete using Multivariable Regression Analysis and an Artificial Neural Network,” Expert Systems with Applications, V. 38, No. 8, 2011, pp. 9609-9618. doi: 10.1016/j.eswa.2011.01.156
27. Song, H., and Kwon, S., “Evaluation of Chloride Penetration in High Performance Concrete Using Neural Network Algorithm and Micro Pore Structure,” Cement and Concrete Research, V. 39, No. 9, 2009, pp. 814-824. doi: 10.1016/j.cemconres.2009.05.013
28. Venkiteela, G.; Gregori, A.; Sun, Z.; and Shah, S. P., “Artificial Neural Network Modeling of Early-Age Dynamic Young’s Modulus of Normal Concrete,” ACI Materials Journal, V. 107, No. 3, May-June 2010, pp. 282-290.
29. Boukhatem, B.; Ghrici, M.; Kenai, S.; and Tagnit-Hamou, A., “Prediction of Efficiency Factor of Ground-Granulated Blast-Furnace Slag of Concrete Using Artificial Neural Network,” ACI Materials Journal, V. 108, No. 1, Jan.-Feb. 2011, pp. 55-63.
30. Nehdi, M. L., and Soliman, A. M., “Artificial Intelligence Model for Early-Age Autogenous Shrinkage of Concrete,” ACI Materials Journal, V. 109, No. 3, May-June 2012, pp. 353-361.
31. Özbay, E., and Lachemi, M., “Relative Compressive Strength of Concretes under Elevated Temperatures,” ACI Materials Journal, V. 109, No. 2, Mar.-Apr. 2012, pp. 165-175.
32. Ghodrati Amiri, G.; Abdolahi Rad, A.; and Khanmohamadi Hazaveh, N., “Wavelet Based Method for Generating Non-Stationary Artificial Pulse-Like Near-Fault Ground Motions,” Computer-Aided Civil and Infrastructure Engineering, V. 29, No. 10, 2014, pp. 758-770. doi: 10.1111/mice.12110
33. Su, W. C.; Huang, C. S.; Chen, C. H.; Liu, C. Y.; Huang, H. C.; and Le, Q. T., “Identifying the Modal Parameters of a Structure from Ambient Vibration Data via the Stationary Wavelet Packet,” Computer-Aided Civil and Infrastructure Engineering, V. 29, No. 10, 2014, pp. 738-757. doi: 10.1111/mice.12115
34. Amini, F., and Samani, M. Z., “A Wavelet-Based Adaptive Pole Assignment Method for Structural Control,” Computer-Aided Civil and Infrastructure Engineering, V. 29, No. 6, 2014, pp. 464-477. doi: 10.1111/mice.12072
35. Hsu, W. Y., “Assembling a Multi-feature EEG Classifier for Left-Right Motor Data Using Wavelet-Based Fuzzy Approximate Entropy for Improved Accuracy,” International Journal of Neural Systems, V. 25, No. 8, 2015, 13 pp.
36. Vahabi, Z.; Amirfattahi, R.; Ghassemi, F.; and Shayegh, F., “Online Epileptic Seizure Prediction Using Wavelet-Based Bi-Phase Correlation of Electrical Signal Tomography,” International Journal of Neural Systems, V. 25, No. 6, 2015, 22 pp.
37. Perez, G.; Conci, A.; Moreno, A. B.; and Hernandez-Tamames, J. A., “Rician Noise Attenuation in the Wavelet Packet Transformed Domain for Brain MRI,” Integrated Computer-Aided Engineering, V. 21, No. 2, 2014, pp. 163-175.
38. Erdal, H. I.; Karakurt, O.; and Namli, E., “High Performance Concrete Compressive Strength Forecasting Using Ensemble Models Based on Discrete Wavelet Transform,” Engineering Applications of Artificial Intelligence, V. 26, No. 4, 2013, pp. 1246-1254. doi: 10.1016/j.engappai.2012.10.014
39. Bal, L., and Buyle-Bodin, F., “Artificial Neural Network for Predicting Creep of Concrete,” Neural Computing & Applications, V. 25, No. 6, 2014, pp. 1359-1367. doi: 10.1007/s00521-014-1623-z
40. Ghafari, E.; Bandarabadi, M.; Costa, H.; and Júlio, E., “Prediction of Fresh and Hardened State Properties of UHPC: Comparative Study of Statistical Mixture Design and an Artificial Neural Network Model,” Journal of Materials in Civil Engineering, ASCE, V. 27, No. 11, 2015, p. 04015017. doi: 10.1061/(ASCE)MT.1943-5533.0001270
41. Eriksson, L.; Johansson, E.; and Wikström, C., “Mixture Design—Design Generation, PLS Analysis, and Model Usage,” Chemometrics and Intelligent Laboratory Systems, V. 43, No. 1-2, 1998, pp. 1-24. doi: 10.1016/S0169-7439(98)00126-9
42. Kohonen, T., Self-Organization and Associative Memory, Springer-Verlag, Berlin, Germany, 2012, pp. 119-157.
43. Lopez-Rubio, E.; Palomo, E. J.; and Dominguez, E., “Bregman Divergences for Growing Hierarchical Self-Organizing Networks,” International Journal of Neural Systems, V. 24, No. 4, 2014, 18 pp.
44. Sadowski, Ł.; Nikoo, M.; and Nikoo, M., “Principal Component Analysis Combined with a Self Organization Feature Map to Determine the Pull-Off Adhesion between Concrete Layers,” Construction and Building Materials, V. 78, 2015, pp. 386-396. doi: 10.1016/j.conbuildmat.2015.01.034
45. Hung, S. L., and Adeli, H., “Parallel Backpropagation Learning Algorithms on Cray Y-MP8/864 Supercomputer,” Neurocomputing, V. 5, No. 6, 1993, pp. 287-302. doi: 10.1016/0925-2312(93)90042-2
46. Adeli, H., and Park, H. S., Neurocomputing for Design Automation, CRC Press, Boca Raton, FL, 1998, 240 pp.
47. Rafiei, M., and Adeli, H., “A Novel Machine Learning Model for Estimation of Sale Prices of Real Estate Units,” Journal of Construction Engineering and Management, ASCE, V. 142, No. 2, 2016, p. 04015066 doi: 10.1061/(ASCE)CO.1943-7862.0001047
48. Hinton, G. E., and Salakhutdinov, R. R., “Reducing the Dimensionality of Data with Neural Networks,” Science, V. 313, No. 5786, 2006, pp. 504-507. doi: 10.1126/science.1127647
49. Cortes, C., and Vapnik, V., “Support-Vector Networks,” Machine Learning, V. 20, No. 3, 1995, pp. 273-297. doi: 10.1007/BF00994018
50. Yuvaraj, P.; Ramachandra Murthy, A.; Iyer, N. R.; Sekar, S. K.; and Samui, P., “Support Vector Regression Based Models to Predict Fracture Characteristics of High Strength and Ultra High Strength Concrete Beams,” Engineering Fracture Mechanics, V. 98, 2013, pp. 29-43. doi: 10.1016/j.engfracmech.2012.11.014
51. Chou, J. S., and Pham, A. D., “Smart Artificial Firefly Colony Algorithm-Based Support Vector Regression for Enhanced Forecasting in Civil Engineering,” Computer-Aided Civil and Infrastructure Engineering, V. 30, No. 9, 2015, pp. 715-732. doi: 10.1111/mice.12121
52. Juncai, X.; Qingwen, R.; and Zhenzhong, S., “Prediction of the Strength of Concrete Radiation Shielding Based on LS-SVM,” Annals of Nuclear Energy, V. 85, 2015, pp. 296-300. doi: 10.1016/j.anucene.2015.05.030
53. Jia, L.; Wang, Y.; and Fan, L., “Multiobjective Bilevel Optimization for Production-Distribution Planning Problems Using Hybrid Genetic Algorithm,” Integrated Computer-Aided Engineering, V. 21, No. 1, 2014, pp. 77-90.
54. Reyes, O.; Morell, C.; and Ventura, S., “Evolutionary Feature Weighting to Improve the Performance of Multi-Label Lazy Algorithms,” Integrated Computer-Aided Engineering, V. 21, No. 4, 2014, pp. 339-354.
55. Atashpaz-Gargari, E., and Lucas, C., “Imperialist Competitive Algorithm: An Algorithm for Optimization Inspired by Imperialistic Competition,” Proceedings of IEEE Congress on Evolutionary Computation 7, Singapore, Sept. 25-28, 2007, pp. 4661-4667.
56. Gandomi, A.; Babanajad, S.; Alavi, A.; and Farnam, Y., “Novel Approach to Strength Modeling of Concrete under Triaxial Compression,” Journal of Materials in Civil Engineering, ASCE, V. 24, No. 9, 2012, pp. 1132-1143. doi: 10.1061/(ASCE)MT.1943-5533.0000494
57. Sadowski, L., and Nikoo, M., “Corrosion Current Density Prediction Reinforced Concrete Imperialist Competitive Algorithm,” Neural Computing and Applications, V. 25, No. 7-8, 2014, pp. 1627-1638. doi: 10.1007/s00521-014-1645-6
58. Chai, C., and Wong, Y. D., “Fuzzy Cellular Automata Models for Signalized Intersections,” Computer-Aided Civil and Infrastructure Engineering, V. 30, No. 12, 2014.
59. Jahani, E.; Muhanna, R. L.; Shayanfar, M. A.; and Barkhordari, M. A., “Reliability Assessment with Fuzzy Random Variables Using Interval Monte Carlo Simulation,” Computer-Aided Civil and Infrastructure Engineering, V. 29, No. 3, 2014, pp. 208-220. doi: 10.1111/mice.12028
60. Ponz-Tienda, J. L.; Pellicer, E.; Benlloch-Marco, J.; and Andrés-Romano, C., “The Fuzzy Project Scheduling Problem with Minimal Generalized Precedence Relations,” Computer-Aided Civil and Infrastructure Engineering, V. 30, No. 11, 2015, pp. 872-891. doi: 10.1111/mice.12166
61. Takagi, T., and Sugeno, M., “Fuzzy Identification of Systems and Its Applications to Modeling and Control,” IEEE Transactions on Systems, Man, and Cybernetics, V. SMC-15, No. 1, 1985, pp. 116-132. doi: 10.1109/TSMC.1985.6313399
62. Zhang, Y., and Ge, H., “Freeway Travel Time Prediction Using Takagi-Sugeno-Kang Fuzzy Neural Network,” Computer-Aided Civil and Infrastructure Engineering, V. 28, No. 8, 2013, pp. 594-603. doi: 10.1111/mice.12014
63. Forero Mendoza, L.; Vellasco, M.; and Figueiredo, K., “Intelligent Multiagent Coordination Based on Reinforcement Hierarchical Neuro-Fuzzy Models,” International Journal of Neural Systems, V. 24, No. 8, 2014, 20 pp.
64. Sarıdemir, M.; Topçu, İ. B.; Özcan, F.; and Severcan, M. H., “Prediction of Long-Term Effects of GGBFS on Compressive Strength of Concrete by Artificial Neural Networks and Fuzzy Logic,” Construction and Building Materials, V. 23, No. 3, 2009, pp. 1279-1286. doi: 10.1016/j.conbuildmat.2008.07.021
65. Tayfur, G.; Erdem, T.; and Kırca, Ö., “Strength Prediction of High-Strength Concrete by Fuzzy Logic and Artificial Neural Networks,” Journal of Materials in Civil Engineering, ASCE, V. 26, No. 11, 2014, p. 04014079. doi: 10.1061/(ASCE)MT.1943-5533.0000985
66. Cheng, M.; Chou, J.; Roy, A. F. V.; and Wu, Y., “High-Performance Concrete Compressive Strength Prediction Using Time-Weighted Evolutionary Fuzzy Support Vector Machines Inference Model,” Automation in Construction, V. 28, 2012, pp. 106-115. doi: 10.1016/j.autcon.2012.07.004
67. Nikoo, M.; Zarfam, P.; and Sayahpour, H., “Determination of Compressive Strength of Concrete Using Self Organization Feature Map (SOFM),” Engineering with Computers, V. 31, No. 1, 2015, pp. 113-121. doi: 10.1007/s00366-013-0334-x
68. Nazari, A., and Sanjayan, J. G., “Modelling of Compressive Strength of Geopolymer Paste, Mortar and Concrete by Optimized Support Vector Machine,” Ceramics International, V. 41, No. 9, Part B, 2015, pp. 12,164-12,177.
69. Luna, J. M.; Romero, J. R.; Romero, C.; and Ventura, S., “Reducing Gaps in Quantitative Association Rules: A Genetic Programming Free-Parameter Algorithm,” Integrated Computer-Aided Engineering, V. 21, No. 4, 2014, pp. 321-337.
70. Zeng, Z.; Xu, J.; Wu, S.; and Shen, M., “Antithetic Method-based Particle Swarm Optimization for a Queuing Network Problem with Fuzzy Data in Concrete Transportation Systems,” Computer-Aided Civil and Infrastructure Engineering, V. 29, No. 10, 2014, pp. 771-800. doi: 10.1111/mice.12111
71. Boulkaibeit, I.; Mthembu, L.; De Lima Neto, F.; and Marwala, T., “Finite Element Model Updating Using Fish School Search and Volitive Particle Swarm Optimization,” Integrated Computer-Aided Engineering, V. 22, No. 4, 2015, pp. 361-376. doi: 10.3233/ICA-150495
72. Shabbir, F., and Omenzetter, P., “Particle Swarm Optimization with Sequential Niche Technique for Dynamic Finite Element Model Updating,” Computer-Aided Civil and Infrastructure Engineering, V. 30, No. 5, 2015, pp. 359-375. doi: 10.1111/mice.12100
73. Forcael, E.; González, V.; Orozco, F.; Vargas, S.; Pantoja, A.; and Moscoso, P., “Ant Colony Optimization Model for Tsunamis Evacuation Routes,” Computer-Aided Civil and Infrastructure Engineering, V. 29, No. 10, 2014, pp. 723-737. doi: 10.1111/mice.12113
74. Adeli, H., and Hung, S. L., Machine Learning—Neural Networks, Genetic Algorithms, and Fuzzy Systems, John Wiley and Sons, Inc., New York, 1995, 211 pp.