Evaluation of Rutting Potential of Polymer Modified Asphalt Binder Using Multiple Stress Creep and Recovery Method

Authors

  • Ahad Ali Department of Civil Engineering, University of Engineering and Technology, Lahore, Pakistan
  • Zia ur Rehman Associate Professor, University of Engineering and Technology, Lahore
  • Umer Farooq Department of Transportation Engineering and Management, University of Engineering and Technology, Lahore
  • Muhammad Waseem Mirza Pavement Manager, Parsons, Doha, Qatar

Abstract

Asphalt is a viscoelastic material as its properties depend on the temperature, loading and aging conditions. High temperature causes flexibility in asphalt concrete as a result the mix is more susceptible to the rutting and the low temperature causes the stiff asphalt and the thermal cracking problem is more significant in this case. This research is aimed at evaluating the rutting susceptibility of neat and polymer modified asphalt by Performance Grading Plus test Multiple Stress Creep and Recovery test (MSCR). MSCR is effective for neat and polymer modified asphalts and also it is blind to the modification type. In this research asphalt neat samples of KRL (40-50, 60-70, 80-100) and ARL (60-70, 80-100) were used. ARL (60-70) was modified with Elvaloy® RET (Reactive Elastomeric Terpolymer) and KRL (60-70) was modified with Elvaloy® AC (Acrylate Terpolymer). Polymer percentages used for ARL (60-70) were 0% (neat), 1.35%, 1.70% and 2.0% whereas for KRL (60-70) was 0% (neat), 2.50%, 3.50% and 4.5%. Dynamic Shear Rheometer (DSR) was used to carry out MSCR test at stress levels of 100Pa and 3200Pa and at temperatures of 580C, 640C, 700C and 760C. Performance of asphalt was evaluated by analyzing the non-recoverable creep compliance (Jnr), Peak strain and Percent recovery. Modified temperature grade of asphalt were determined by comparing the actual Jnr value to the Jnr=9.46 as it corresponds to the G*/Sin δ =1000Pa. Results showed that neat sample of KRL compared to neat samples of ARL were found good to prevent rutting as they showed less peak strain, more percent recovery and less Jnr value. Polymer modification improved the properties of asphalt as it showed decreasing trend of peak strain, increasing trend of the percent recovery and also decreasing trend of Jnr values. Further high temperature grade bumping happened for polymer modified asphalts.

References

Vinciarelli, A.: A survey on off-line cursive word recognition. Pattern recognition 35(7), 1433–1446 (2002)

Plamondon, R., Srihari, S.N.: Online and off-line handwriting recognition: a comprehensive survey. Pattern Analysis and Machine Intelligence, IEEE Transactions on 22(1), 63–84 (2000)

Frinken, V., Fischer, A., Manmatha, R., Bunke, H.: A novel word spotting method based on recurrent neural networks. Pattern Analysis and Machine Intelligence, IEEE Transactions on 34(2), 211–224 (2012)

Liu, Y., Xu, M., Cai, L.: Improved keyword spotting system by optimizing posterior confidence measure vector using feed-forward neural network. In: Neural Networks (IJCNN), 2014 International Joint Conference On, pp. 2036–2041 (2014).

Tarafdar, A., Pal, U., Roy, P.P., Ragot, N., Ramel, J.-Y.: A two-stage approach for word spotting in graphical documents. In: 12th International Conference On Document Analysis and Recognition (ICDAR), 2013 pp. 319–323 (2013).

Impedovo, S., Mangini, F.M., Pirlo, G., Barbuzzi, D., Impedovo, D.: Voronoi tessellation for effective and efficient handwritten digit classification. In: Document

Analysis and Recognition (ICDAR), 2013 12th International Conference On, pp. 435–439 (2013).

Li, J., Fan, Z.-G., Wu, Y., Le, N.: Document image retrieval with local feature sequences. In: Document Analysis and Recognition, 2009. ICDAR’09. 10th International Conference On, pp. 346–350 (2009).

Andreev, A., Kirov, N.: Word image matching based on hausdorff distances. In: Proc. 10th International Conference on Document Analysis and Recognition, pp. 396–400 (2009).

Rothfeder, J.L., Feng, S., Rath, T.M.: Using corner feature correspondences to rank word images by similarity. In: Computer Vision and Pattern Recognition Workshop, 2003. CVPRW’03. Conference On, vol. 3, pp. 30–30 (2003).

Adamek, T., O’Connor, N.E., Smeaton, A.F.: Word matching using single closed contours for indexing handwritten historical documents. International Journal of Document Analysis and Recognition (IJDAR) 9(2-4), 153–165 (2007)

Marinai, S., Faini, S., Marino, E., Soda, G.: Efficient word retrieval by means of som clustering and pca. In: Document Analysis Systems VII, pp. 336–347. Springer, (2006)

Gatos, B., Pratikakis, I.: Segmentation-free word spotting in historical printed documents. In: Document Analysis and Recognition, 2009. ICDAR’09. 10th International Conference On, pp. 271–275 (2009).

Rath, T.M., Manmatha, R.: Word spotting for historical documents. International Journal of Document Analysis and Recognition (IJDAR) 9(2-4), 139–152 (2007)

Zagoris, K., Papamarkos, N., Chamzas, C.: Web document image retrieval system based on word spotting. In: Image Processing, 2006 IEEE International Conference On, pp. 477–480 (2006).

Rusi˜nol, M., Llad´os, J.: Word and symbol spotting using spatial organization of local descriptors. In: Document Analysis Systems, 2008. DAS’08. The Eighth IAPR International Workshop On, pp. 489–496 (2008).

Bai, S., Li, L., Tan, C.L.: Keyword spotting in document images through word shape coding. In: Document Analysis and Recognition, 2009. ICDAR’09. 10th International Conference On, pp. 331–335 (2009).

Bertolami, R., Gutmann, C., Bunke, H., Spitz, A.L.: Shape code based lexicon reduction for offline handwritten word recognition. In: Document Analysis Systems, 2008. DAS’08. The Eighth IAPR International Workshop On, pp. 158–163 (2008).

Kluzner, V., Tzadok, A., Shimony, Y., Walach, E., Antonacopoulos, A.: Word-based adaptive ocr for historical books. In: Document Analysis and Recognition, 2009. ICDAR’09. 10th International Conference On, pp. 501–505 (2009).

Abidi, A., Siddiqi, I., Khurshid, K.: Towards searchable digital urdu libraries-a word spotting based retrieval approach. In: Document Analysis and Recognition (ICDAR), 2011 International Conference On, pp. 1344–1348 (2011).

Khurshid, K., Faure, C., Vincent, N.: Word spotting in historical printed documents using shape and sequence comparisons. Pattern Recognition 45(7), 2598–2609 (2012)

Siddiqi, I., Vincent, N.: A set of chain code based features for writer recognition. In: In Proc. of 10th International Conference on Document Analysis and Recognition, pp. 981– 985 (2009).

Khurshid, K., Faure, C., Vincent, N.: Feature-based word spotting in ancient printed documents. In: PRIS, pp. 193–198 (2008)

Lu, Y., Shridhar, M.: Character segmentation in handwritten words—an overview. Pattern recognition 29(1), 77–96 (1996)

Terasawa, K., Imura, H., Tanaka, Y.: Automatic evaluation framework for word spotting. In: Document Analysis and Recognition, 2009. ICDAR’09. 10th International Conference On, pp. 276–280 (2009).

Vamvakas, G., Gatos, B., Stamatopoulos, N., Perantonis, S.J.: A complete optical character recognition methodology for historical documents. In: Document Analysis Systems, 2008. DAS’08. The Eighth IAPR International Workshop On, pp. 525–532 (2008).

Moghaddam, R.F., Cheriet, M.: Application of multi-level classifiers and clustering for automatic word spotting in historical document images. In: Document Analysis and Recognition, 2009. ICDAR’09. 10th International Conference On, pp. 511–515 (2009).

Leydier, Y., LeBourgeois, F., Emptoz, H.: Textual indexation of ancient documents. In: Proceedings of the 2005 ACM Symposium on Document Engineering, pp. 111–117 (2005).

Frinken, V., Fischer, A., Bunke, H., Manmatha, R.: Adapting blstm neural network based keyword spotting trained on modern data to historical documents. In: Frontiers in Handwriting Recognition (ICFHR), 2010 International Conference On, pp. 352–357 (2010).

Khurshid, K., Faure, C., Vincent, N.: A novel approach for word spotting using merge-split edit distance. In: Computer Analysis of Images and Patterns, pp. 213–220 (2009).

Fischer, A., Keller, A., Frinken, V., Bunke, H.: Hmm-based word spotting in handwritten documents using subword models. In: Pattern Recognition (icpr), 2010 20th International Conference On, pp. 3416–3419 (2010).

Siddiqi, I., Vincent, N.: Text independent writer recognition using redundant writing patterns with contour-based orientation and curvature features. Pattern Recognition 43(11), 3853– 3865 (2010)

Nakano, H.Y.Y.: Cursive handwritten word recognition using multiple segmentation determined by contour analysis. IEICE Transactions on Information and Systems E79- D(5), 464–470 (1996)

Kimura, F., Kayahara, N., Miyake, Y., Shridhar, M.: Machine and human recognition of segmented characters from handwritten words. In: In Proc. of the 4th International Conference on Document Analysis and Recognition, pp. 866–869 (1997)

Blumenstein, M., Verma, B., Basli, H.: A novel feature extraction technique for the recognition of segmented handwritten characters. In: In Proc. of the Seventh International Conference on Document Analysis and Recognition, pp. 137–141 (2003)

Blumenstein, M., Liu, X.Y., Verma, B.: An investigation of the modified direction feature for cursive character recognition. Pattern Recognition 40(2), 376–388 (2007)

M.E.Dehkordi, N.Sherkat, T.Allen: Handwriting style classification. International Journal of Document Analysis and Recognition 6, 55–74 (2003)

Siddiqi, I., Djeddi, C., Raza, A., Souici-meslati, L.: Automatic analysis of handwriting for gender classification. Pattern Analysis and Applications (2014)

Wall, K., Danielsson, P.-E.: A fast sequential method for polygonal approximation of digitized curves. Computer Vision, Graphics, and Image Processing 28(3), 220–227 (1984)

Bensefia, A., Paquet, T., Heutte, L.: A writer identification and verification system. Pattern Recognition Letters 26(13), 2080–2092 (2005)

Marti, U.-V., Bunke, H.: The iam-database: an english sentence database for offline handwriting recognition. International Journal on Document Analysis and Recognition 5(1), 39–46 (2002)

AbbyyFinereader, Online: http://www.abbyy.com/finereader/

Wshah, Safwan, Gaurav Kumar, and VenuGovindaraju. "Script independent word spotting in offline handwritten documents based on hidden markov models." Frontiers in Handwriting Recognition (ICFHR), (2012).

Frinken, Volkmar, et al. "A novel word spotting method based on recurrent neural networks." IEEE Transactions on Pattern Analysis and Machine Intelligence, 34.2 (2012): 211-224.

Rodríguez-Serrano, José A., and FlorentPerronnin. "A model-based sequence similarity with application to handwritten word spotting." IEEE Transactions on Pattern Analysis and Machine Intelligence 34.11 (2012): 2108-2120.

Fischer, Andreas; Frinken, Volkmar; Bunke, Horst; Suen, Ching Y. "Improving hmm-based keyword spotting with character language models." 12th International Conference on Document Analysis and Recognition. (ICDAR), 2013.

Kumar, G.; Govindaraju, V., "A Bayesian Approach to Script Independent Multilingual Keyword Spotting," 14th International Conference on Frontiers in Handwriting Recognition (ICFHR), 2014, vol., no., pp.357, 362, 1-4 Sept. 2014

Ranjan, V.; Harit, G.; Jawahar, C.V., "Document Retrieval with Unlimited Vocabulary," IEEE Winter Conference on Applications of Computer Vision (WACV), 2015, pp.741-748, 5-9 Jan. 2015

J. Almazan, A. Gordo, A. Fornes, and E. Valveny, “Word Spotting and Recognition with Embedded Attributes." IEEE Transactions on Pattern Analysis and Machine Intelligence. vol.36, no.12, pp.2552,2566, Dec. 1 2014

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Published

2018-03-07

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Civil Engineering,Structures, Construction, Geo technology, Water, Transportation