Öz
Aggregate occupy approximately 80 percent of the total volume of concrete mix, and aggregate physical characteristics significantly affect the properties of concrete both fresh and hardened state. Selection of improper aggregates such as flat and elongated particles may cause failure or deterioration of a concrete structure. Therefore, selection process of aggregates for a specific job is very important. There is no standard test method for evaluating the aggregate physical properties effectively. The manual standard test methods (EN 933, ASTM D 4791, ASTM C 1252, and ASTM D 3398) are laborious, time consuming and tedious measurements. Trent to tighten specifications for aggregate properties along with recent technological advances in technology, availability of high performance computers, and low cost imaging systems support usage of image analysis methods for quantitative measurement of aggregate properties such as size, shape and texture with easy, fast, real-time and without human errors. In last decades, two dimensional (2D) and three dimensional (3D) image analysis techniques have been used to measure size, shape, and texture of aggregates. In this paper, shape and size parameters (features) of four different types of aggregates are calculated by 3D image analysis technique and aggregates are classified by three different artificial neural network models with using these parameters. Best classification performance is given by a multilayer perceptron method which is 90,84 % precision.
Keywords: Aggregate, Shape, Image analysis, 3D, Classification
Anahtar Kelimeler
Kaynakça
- Kwan, A.K.H., Mora, C.F., Chan, H.C., Particle shape analysis of coarse aggregate using digital image processing, Cement and Concrete Research 29 (9), 1403-1410, (1999).
- Weixing, W., “Image analysis of particles by modified Ferret method-best fit rectangle, Powder Technology, 165, 1-10, (2006).
- Fernuld, J.M.R.,“Image analysis method for determining 3-D shape of coarse aggregate”, Cement and Concrete Research, 35, 1629-1637, (2005).
- Fernlund,J.M.R. 3-D “Image analysis size and shape method applied to the evaluation of the Los Angeles test”, Engineering Geology, 77, 57–67, (2005).
- Erdoğan, S.T., Quiroga, P.N., Fowler, D.W., Saleh, H.A., Livingston, R.A., Garboczi, E.J., Ketcham, P.M., Hagedorn, J.G., Satterfield,S.G.,“Three- dimensional shape analysis of coarse aggregates: Methodology and preliminary results on several different coarse aggregates and reference rocks”, Cement and Concrete Research 36 (9), 1619- 1627, (2006).
- Garboczi, E.J., Cheok, G.S., Stone, W.C. “Using LADAR to characterize the 3-D shape of aggregates: Preliminary results”, Cement Concrete Research, 36 (6), 1072-1075, (2006).
- Kim, H.,“Automation of Aggregate Characterization Using Laser Profiling and Digital Image Analysis”, PhD. Disertation, UT at Austin, (2002).
- Quiroga, P.N., “The Efffect of the Aggregates Characteristics on the Performance of Portland Cement Concrete”, PhD. Disertation, UT at Austin, (2003).
- Bishop,C. M.,“Neural Network for Pattern Recognition”, Oxford University Press, (1995), ISBN: 0-19-853864-2.
- Haykin, S., “Neural Networks a Comprehensive Foundation”, Macmillan Publishing Company, 1994, ISBN: 0023527617.
- Jain, A.K., Mao, J., Mohiuddin, K.M., "Artificial Neural Networks: A Tutorial," Computer, 29(3), 31-44, (1996), doi:10.1109/2.485891
- Jain, A.K., Duin, R.P.W., Mao, J., "Statistical Pattern Recognition: A Review," IEEE Transactions on Pattern Analysis and Machine Intelligence, 22, (1), 4-37, (2000), doi:10.1109/34.824819
- Kevin L. Priddy, Paul E. Keller, “Artificial Neural Networks: An Introduction”, SPIE Press, 101- 105, 2005.
- Özen, M., Yaman, I. O.and Guler, M., “Investigation of relationship between aggregate shape parameters and concrete strength using imaging techniques”, 8th International Congress on Advances in Civil Engineering, , Eastern Mediterranean University, Famagusta, North Cyprus,15-17 September (2008).
- Özen, M., “Investigation of relationship between aggregate shape parameters and concrete strength using imaging techniques”, PhD Thesis, The Graduate School of Natural And Applied Sciences of Middle East Technical University, pp. 26-65, (2007).
Öz
Kaynakça
- Kwan, A.K.H., Mora, C.F., Chan, H.C., Particle shape analysis of coarse aggregate using digital image processing, Cement and Concrete Research 29 (9), 1403-1410, (1999).
- Weixing, W., “Image analysis of particles by modified Ferret method-best fit rectangle, Powder Technology, 165, 1-10, (2006).
- Fernuld, J.M.R.,“Image analysis method for determining 3-D shape of coarse aggregate”, Cement and Concrete Research, 35, 1629-1637, (2005).
- Fernlund,J.M.R. 3-D “Image analysis size and shape method applied to the evaluation of the Los Angeles test”, Engineering Geology, 77, 57–67, (2005).
- Erdoğan, S.T., Quiroga, P.N., Fowler, D.W., Saleh, H.A., Livingston, R.A., Garboczi, E.J., Ketcham, P.M., Hagedorn, J.G., Satterfield,S.G.,“Three- dimensional shape analysis of coarse aggregates: Methodology and preliminary results on several different coarse aggregates and reference rocks”, Cement and Concrete Research 36 (9), 1619- 1627, (2006).
- Garboczi, E.J., Cheok, G.S., Stone, W.C. “Using LADAR to characterize the 3-D shape of aggregates: Preliminary results”, Cement Concrete Research, 36 (6), 1072-1075, (2006).
- Kim, H.,“Automation of Aggregate Characterization Using Laser Profiling and Digital Image Analysis”, PhD. Disertation, UT at Austin, (2002).
- Quiroga, P.N., “The Efffect of the Aggregates Characteristics on the Performance of Portland Cement Concrete”, PhD. Disertation, UT at Austin, (2003).
- Bishop,C. M.,“Neural Network for Pattern Recognition”, Oxford University Press, (1995), ISBN: 0-19-853864-2.
- Haykin, S., “Neural Networks a Comprehensive Foundation”, Macmillan Publishing Company, 1994, ISBN: 0023527617.
- Jain, A.K., Mao, J., Mohiuddin, K.M., "Artificial Neural Networks: A Tutorial," Computer, 29(3), 31-44, (1996), doi:10.1109/2.485891
- Jain, A.K., Duin, R.P.W., Mao, J., "Statistical Pattern Recognition: A Review," IEEE Transactions on Pattern Analysis and Machine Intelligence, 22, (1), 4-37, (2000), doi:10.1109/34.824819
- Kevin L. Priddy, Paul E. Keller, “Artificial Neural Networks: An Introduction”, SPIE Press, 101- 105, 2005.
- Özen, M., Yaman, I. O.and Guler, M., “Investigation of relationship between aggregate shape parameters and concrete strength using imaging techniques”, 8th International Congress on Advances in Civil Engineering, , Eastern Mediterranean University, Famagusta, North Cyprus,15-17 September (2008).
- Özen, M., “Investigation of relationship between aggregate shape parameters and concrete strength using imaging techniques”, PhD Thesis, The Graduate School of Natural And Applied Sciences of Middle East Technical University, pp. 26-65, (2007).
Ayrıntılar
| Birincil Dil | İngilizce |
|---|---|
| Bölüm | Electrical & Electronics Engineering |
| Yazarlar | |
| Yayımlanma Tarihi | 16 Aralık 2011 |
| Yayımlandığı Sayı | Yıl 2011 Cilt: 24 Sayı: 4 |