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Data Mining Techniques and Its Application in Civil Engineering—A Review

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Advances in Interdisciplinary Research in Engineering and Business Management

Part of the book series: Asset Analytics ((ASAN))

Abstract

Data Mining (DM) is the extrication of inevitable, formerly unknown, and probably useful data from statistics. In the current scenario, data mining studies had been accomplished in many engineering disciplines. DM is the new dollar. It is the advance method of analyzing records from different parameters and abridgment into functional data. It allows users to investigate records from numerous parameters and categorizes it to summarize the relationships recognized. Technically, DM is the method of finding correlations or styles among multi-fields in big relational databases. Data Mining is doubtlessly beneficial record from records. It is the interpretation of big data in the required formats. It is the process through which different patterns are discovered from large data. New information is generated by the assessment of the pre-existing databases. This paper represents the significance and application of data mining tools and its techniques in different fields of civil engineering.

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References

  1. Fayyad, U. M., Piatetsky-Shapiro, G., Smyth, P., & Uthurusamy, R. (1996). Advances in knowledge discovery and data mining. Cambridge, MA: AAAI Press/The MIT Press.

    Google Scholar 

  2. Han, J. &, Kamber, M. (2001). Data mining: Concepts and techniques. Higher Education Press.

    Google Scholar 

  3. Hall, M. J., Minns, A. W., & Ashrafuzzaman, A. K. M. (2002). The application of data mining techniques for the regionalisation of hydrological variables. Hydrology and Earth System Sciences,6, 685–694.

    Google Scholar 

  4. Chau, K. W., & Cao, Y. (2002). The Application of data warehouse and decision support system in construction management. Automation in Construction,12, 213–224.

    Article  Google Scholar 

  5. Zhang, J. P., & Wang, H.-J. (2002). Towards 4D management for construction planning and resource utilization. In The 9 th International Conference on Computing in Civil and Building Engineering, Taiwan (pp. 1281–1286).

    Google Scholar 

  6. Hyperion Software Corp. (1999). The role of OLAP server in a data warehousing solution.

    Google Scholar 

  7. Chakrabarti, S. (2002), Mining the web: Statistical analysis of hypertex and semi-structured data. Morgan Kaufmann.

    Google Scholar 

  8. . Hong-Yan, L. I, Bu-Ying, C., & Li, D. (2013). Application of data warehouse and data mining in coal information management (Vol. 31, no. 8, pp. 31–32).

    Google Scholar 

  9. Forbes,L. H., & Ahmed, S. M. (2003). Construction integration and innovation through lean methods and E-business applications, construction research 2003, Copyright ASCE 2004.

    Google Scholar 

  10. Inmon, W. H. (2000). Building the data warehouse (2nd ed.). China Machine Press.

    Google Scholar 

  11. Bilal M., & Oyedele, O. L. (2016). Big Data Architecture for Construction Waste analytics (CWA): A conceptual framework. Journal of Building Engineering, 144–156.

    Google Scholar 

  12. Dasu, T., & Johnson, T. (2003). Exploratory data mining and data cleaning. Wiley.

    Google Scholar 

  13. Hore, A. (2006). Use of IT in managing information and data on construction projects—A perspective for the IRISH construction industry, information technology in construction project management.

    Google Scholar 

  14. Zhou, Y., & Ding, L. Y. (2006) International symposium on “Advancement of Construction Management and Real Estate” The CRIOCM 2006.

    Google Scholar 

  15. Jin, C. (2017). Real-time damage detection for civil structures using Big Data.

    Google Scholar 

  16. Adrians, P., & Zantinge, D. (1996). Data mining. England: Addison-Wesley Longman.

    Google Scholar 

  17. Cabena, P. (1997). Discovering data mining: From concept to implementation. NJ: Prentice Hall.

    Google Scholar 

  18. Han, J. (2001). Data mining: Concepts and techniques. San Francisco: Morgan Kaufmann Publishers.

    Google Scholar 

  19. Hand, D. J., Mannila, H., & Smyth, P. (2001). Principles of data mining. Massachusetts: MIT press.

    Google Scholar 

  20. Han, J., & Kamber, M. (2006). Data mining: Concepts and techniques (2nd ed.). Morgan Kaufmann.

    Google Scholar 

  21. Attoh-Okine, N. O. (1997). Rough set application to data-mining principles in pavement management database. Journal of Computing in Civil Engineering, American Society of Civil Engineers, 11(4), 231–237.

    Google Scholar 

  22. Soibelman, L., & Hyunjoo, K. (2002). Data preparation process for construction knowledge generation through knowledge discovery in databases. Journal of Computing in Civil Engineering, ASCE,16(1), 39–47.

    Article  Google Scholar 

  23. Jae-Gil Lee, M. K. (2015), Geospatial Big Data: Challenges and opportunities. Big Data Research, 74–81.

    Google Scholar 

  24. Ahmad, I., & Ahmed, S. M. (2001). Integration in the construction industry: Information technology as the driving force. In R. L. K. Tiong (Ed.), Proceedings of the 3rd International Conference on Construction Project Management. Nan yang Technical University Press.

    Google Scholar 

  25. Hu, D. (2005). Research on the systematic framework of computer integrated construction. Huazhong University of Science and Technology.

    Google Scholar 

  26. Han, J., & Kamber, M. (2001). Data mining: Concepts and techniques. Higher Education Press.

    Google Scholar 

  27. Rezania, M., Javadi, A., Giustolisi, O. (2008). An evolutionary-based data mining technique for assessment of civil engineering systems.

    Google Scholar 

  28. Shanti, M. A., & Saravanan, K. (2017). Knowledge data map—A framework for the field of data mining and knowledge discovery. International Journal of Computer Engineering & Technology,8(5), 67–77.

    Google Scholar 

  29. Barai, S. V., & Reich. (2001). Data mining of experimental data: Neural networks approach. In Proceedings of 2nd International Conference on Theoretical, Applied Computational and Experimental Mechanics ICTACEM (CD-ROM).

    Google Scholar 

  30. Rabee M. Reffat, John S. Gero, Wei Peng (2004). Using data mining on building maintenance during the building life cycle.

    Google Scholar 

  31. Kohavi, R. (2001). Data mining and visualization. In Sixth Annual Symposium on Frontiers of Engineering(p.p. 30--40). National Academy Press, D. C.

    Google Scholar 

  32. Amado, V. (2000). Expanding the use of pavement management data. In Transportation Scholars Conference, University of Missouri.

    Google Scholar 

  33. Tan, P., Steinbach, M., & Kumar, V. (2005). Introduction to data mining. Addison Wesley.

    Google Scholar 

  34. Dzeroski, S. (2003). Environmental applications of data mining. Lecture Notes of Knowledge Technologies, University of Trento.

    Google Scholar 

  35. Stojic, A., Stojic, S. S., Reljin, I., Cabarkapa, M., Sostaric, A., Perisic, M., & Mijic, Z. (2016). Comprehensive analysis of PM10 in Belgrade urban area on the basis of long-term measurements. Environmental Science and Pollution Research, 23, 10722–10732. https://doi.org/10.1007/s11356-016-6266-4.

  36. Gaal, M., Moriondo, M., & Bindi, M. (2012). Modelling the impact of climate change on the Hungarian wine regions using random forest. Applied Ecology and Environmental Research,10, 121–140. https://doi.org/10.15666/aeer/1002_121140.

    Article  Google Scholar 

  37. Crimmins, S. M., Dobrowski, S. Z., & Mynsberge, A. R. (2013). Evaluating ensemble forecasts of plant species distributions under climate change. Ecological Modelling,266, 126–130. https://doi.org/10.1016/j.ecolmodel.07.006.

    Article  Google Scholar 

  38. Lei, K. S., Wan, F. (2012). Applying ensemble learning techniques to ANFIS for air pollution index prediction in Macau. In International Symposium on Neural Networks (ISNN’12), 11–14 July 2012 (pp. 509–516). Berlin, Heidelberg: Springer.

    Google Scholar 

  39. Budka, M., Gabrys, B., & Ravagnan, E. (2010). Robust predictive modelling of water pollution using biomarker data. Water Research,44, 3294–3308. https://doi.org/10.1016/j.watres.2010.03.006.

    Article  Google Scholar 

  40. Singh, K. P., Gupta, S., & Rai, P. (2013). Identifying pollution sources and predicting urban air quality using ensemble learning methods. Atmospheric Environment,80, 426–437. https://doi.org/10.1016/j.atmosenv.2013.08.023.

    Article  Google Scholar 

  41. Nelson, T. A., Coops, N. C., Wulder, M. A., Perez, L., Fitterer, J., Powers, R., & Fontana, F. (2014). Predicting climate change impacts to the Canadian Boreal forest. Diversity,6, 133–157. https://doi.org/10.3390/d6010133.

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Civil Engineering, Amity School of Engineering & Technology, Amity University, Noida, Uttar Pradesh, India

    Priyanka Singh

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  1. Priyanka Singh
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Correspondence to Priyanka Singh .

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Editors and Affiliations

  1. Amity Center for Interdisciplinary Research, Amity University, Noida, Uttar Pradesh, India

    P. K. Kapur

  2. Group VC Amity International Business School, Amity University, Noida, Uttar Pradesh, India

    Gurinder Singh

  3. Department of Operational Research, University of Delhi, New Delhi, Delhi, India

    Saurabh Panwar

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Singh, P. (2021). Data Mining Techniques and Its Application in Civil Engineering—A Review. In: Kapur, P.K., Singh, G., Panwar, S. (eds) Advances in Interdisciplinary Research in Engineering and Business Management. Asset Analytics. Springer, Singapore. https://doi.org/10.1007/978-981-16-0037-1_15

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