Using Decision Tree for Data Mining of Pavement Maintenance and Management

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In recent years advancements in the Information Technology (IT), have enabled automation of pavement measurement data. A large amount of data can be saved for a pavement management system. The study of pavement maintenance and management has include many methods, such as expert system, decision support analysis and data mining (DM) . In this study we use decision tree for data mining algorithm C5.0 has been used in this analysis. After acceptance of the decision tree, we make use of algorithms and computing for classification. This method is used to check the pavement management system database and make a comparison of all data. The result shown a correct classification of about 61% its still improved space. According to this result we discuss three analysis results included: 1.Database information is correct or not 2.Road pavement never homogenization 3.Milling process never remove human factor. Finally useful pavement information and ways can improve system integrity and correctly.

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1015-1019

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June 2013

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© 2013 Trans Tech Publications Ltd. All Rights Reserved

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[1] Guoqing Zhou, Linbing Wang: Co-location decision tree for enhancing decision-making of pavement maintenance and rehabilitation, Journal of Transportation Reserch Part C, Vol.21 (2012), p.287.

DOI: 10.1016/j.trc.2011.10.007

Google Scholar

[2] Attoh-Okine, Nii.O.: Combing use Rough set and artificial neural networks in Doweled- Pavement-performance modeling-a hybrid approach, Journal of Transportation Engineering, Vol. 128 No.3, p.270.

DOI: 10.1061/(asce)0733-947x(2002)128:3(270)

Google Scholar

[3] Prechaverakul, S.,Hadipriono, F.C.: Using a knowledgebased expert system and fuzzy logic for minor rehabilitation projects in Ohio, Transportation Research Record 1, p.19.

Google Scholar

[4] Chou, C.C., C.T. Chen, S.M. Tseng, and J.D. Lin: Model for Critical Infrastructure Interdependencies, In Fifth International Conference on Fuzzy Systems and Knowledge Discovery. Jinan, China, 2008.10.

DOI: 10.1109/fskd.2008.503

Google Scholar

[5] Yichang Tsai,Yiching Wu,Dajin Guo: Enhanced Pavement preservation Using A Lane-based,Image Tracking, GIS-enabled, and Life-cycle Activity Integrated Chunging PMS, IJPRT,V4N1, p.11, 2011.

Google Scholar

[6] Pavement Management System-TheWashington State Experience. 2008, FHWAIF-08-010.

Google Scholar

[7] Guoqing Zhou, Linbing Wang, Dong Wang and Scott Reichle: Integration of GIS and Data Mining Technology to Enhance the Pavement Management Decision Making" JOURNAL OF TRANSPORTATION ENGINEERING, pp.332-341,(2010).

DOI: 10.1061/(asce)te.1943-5436.0000092

Google Scholar

[8] Soibelman, L. and Kim, H.: Generating construction knowledge with knowledge discovery in databases, Computing in Civil and Building Engineering, Vol. 2 (2000), p.906.

DOI: 10.1061/40513(279)118

Google Scholar

[9] Agarwal, R. and R. Srikant. Fast Algorithms for Mining Association Rules. In Proc. Of the 20th Int'l Conference on Very Large Data Bases, 1994.

Google Scholar