BIG DATA IN CONSTRUCTION WASTE MANAGEMENT: PROSPECTS AND CHALLENGES

---

• Available online in Detritus - Volume 04 - December 2018
• Pages 129-139

---

Abstract

‘Big data’ has been rapidly sprawling in various research disciplines such as biology, ecology, medical science, business, finance, and public governance but rarely in construction waste management (CWM). The CWM community around the world generally relies on ‘small data’ collected via active solicitation such as sampling and ethnographic methods. This small data is intrinsically limited by its inability to account for the totality of CWM and research findings generated from the small data cannot be accepted with a high level of confidence. With the growing interests in big data, it can be reasonably expected that the waste management community will augment efforts to develop big data and its analytics. However, the efforts are currently constrained by the limited knowledge to do so. This research aims to provide a synoptic overview of the prospects and challenges of big data in CWM. It adopts an inductive, qualitative case study method whereby the empirical data is collected using an ethnographic–action-meta-analysis research approach and triangulated with data from literature, ongoing debate, and other sources. The paper offers some insights on big data acquisition, storage, analytics, implementation, and challenges. Although having a focus on waste management in the construction sector, the insights generated from this study can be of value to general waste management research, which suffers from the same problems of erratic and poor quality data as CWM.

---

Keywords

• Big data
• Construction waste management
• Big data analytics
• Hong Kong

---

Editorial History

• Received: 06 Jul 2018
• Revised: 12 Nov 2018
• Accepted: 14 Nov 2018
• Available online: 22 Nov 2018

---

References

Adler, L. (2016). How smart city Barcelona brought the internet of things to life. https://datasmart.ash.harvard.edu/news/article/how-smart-city-barcelona-brought-the-internet-of-things-to-life-789 (accessed on 17 December 2017)

Agrawal, R., Grosky, W., and Fotouhi, F. (2006). Image retrieval using multimodal keywords. In Proceedings of the Eighth IEEE International Symposium on Multimedia, 817-822

Akter, S., and Wamba, S. F. (2016). Big data analytics in E-commerce: a systematic review and agenda for future research. Electronic Markets, 26(2), 173-194

Anderson, C. (2008). The end of theory: the data deluge makes the scientific method obsolete. https://www.wired.com/2008/06/pb-theory/ (accessed on 17 December 2016)

Auer, B., Christian, S., Georgi, K., Jens, L., Richard, C., and Zachary, I. (2007). Dbpedia: A nucleus for a web of open data. In The Semantic Web, Springer Berlin Heidelberg

Bilal, M., Oyedele, L. O., Akinade, O. O., Ajayi, S. O., Alaka, H. A., Owolabi, H. A. (2016a). Big data architecture for construction waste analytics (CWA): A conceptual framework. Journal of Building Engineering, 6, 144-156

Bilal, M., Oyedele, L. O., Qadir, J., Munir, K., Ajayi, S. O., Akinade, O. O., and Pasha, M. (2016b). Big Data in the construction industry: A review of present status, opportunities, and future trends. Advanced Engineering Informatics, 30(3), 500-521

Bossink, B. A. G., and Brouwers, H. J. H. (1996). Construction waste: Quantification and source evaluation. Journal of Construction Engineering and Management, 122(1), 55-60

Boyd, D., and Crawford, K. (2012). Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon. Information, Communication & Society, 15(5), 662-679

Chen, X., and Lu, W. (2017). Identifying factors influencing demolition waste generation in Hong Kong. Journal of Cleaner Production, 141, 799-811

Cheung, A. (2016). Making sense and non-sense of consent in the big data era. In Symposium on Big Data and Data Governance

Clifton, C. (2010). Encyclopædia britannica: Definition of data mining. https://www.britannica.com/EBchecked/topic/1056150/data-mining (accessed on 17 December 2017)

Ekbia, H., Mattioli, M., Kouper, I., Arave, G., Ghazinejad, A., Bowman, T., and Sugimoto, C. R. (2015). Big data, bigger dilemmas: A critical review. Journal of the Association for Information Science and Technology, 66(8), 1523-1545

Fatta, D., Papadopoulos, A., Avramikos, E., Sgourou, E., Moustakas, K., and Kourmoussis, F. (2003). Generation and management of construction and demolition waste in Greece—an existing challenge. Resources, Conservation and Recycling, 40(1), 81-91

Flanagan, R., Jewell, C, Lu, W., and Pekericli, K. (2014). Auto-ID – Bridging the physical and the digital on construction projects. Chartered Institute of Building. ISBN 1853800191

Formoso, T. C., Soibelman, M. L., Cesare, C. D., and Isatto, E. L. (2002). Material waste in building industry: Main causes and prevention. Journal of Construction Engineering and Management, 128(4), 316-325

Goodman, S. N. (1999). Toward evidence-based medical statistics: The P value fallacy. Annals of Internal Medicine, 130(12), 995-1004

Han, J., Kamber, M., and Pei, J. (2012). Data Mining: Concepts and Techniques. Elsevier

Hand, D. J. (2015). Official statistics in the new data ecosystem. In the New Techniques and Technologies in Statistics Conference

HKEPD (2014). Construction waste disposal charging scheme. https://www.epd.gov.hk/epd/misc/cdm/scheme.htm (accessed on 17 December 2016)

Katz, A., and Baum, H. (2011). A novel methodology to estimate the evolution of construction waste in construction sites. Waste Management, 31(2), 353-358

Kazaz, A., Ulubeyli, S., and Arslan, A. (2018). Quantification of fresh ready-mix concrete waste: order and truck-mixer based planning coefficients. International Journal of Construction Management, 1-12

King, J. H., and Richards, N. M. (2014). What’s up with big data ethics? https://www.forbes.com/sites/oreillymedia/2014/03/28/whats-up-with-big-data-ethics/#4e94d3703591 (accessed on 17 December 2017)

Kitchin, R. (2015). Big data and official statistics: Opportunities, challenges and risks. The Programmable City Working Paper 9

Kitchin, R., and Lauriault, T. (2015). Small data in the era of big data. GeoJournal, 80, 463-475

Kostelnick, C. (2007). The visual rhetoric of data displays: The conundrum of clarity. IEEE Transactions on Professional Communication, 50(4), 280-294

Leek, J. (2014). 10 things statistics taught us about big data analysis. Simplystats blog, May 22. https://simplystatistics.org/2014/05/22/10-things-statistics-taught-us-about-big-data-analysis/ (accessed on 17 December 2016)

Lin, M., Lucas Jr, H. C., and Shmueli, G. (2013). Research commentary-too big to fail: Large samples and the p-value problem. Information Systems Research, 24(4), 906-917

Lu, W., Chen, X., Ho, D. C. W., and Wang, H. (2016a). Analysis of the construction waste management performance in Hong Kong: the public and private sectors compared using big data. Journal of Cleaner Production, 112, 521-531

Lu, W., Chen, X., Peng, Y., and Shen, L. (2015). Benchmarking construction waste management performance using big data. Resources, Conservation and Recycling, 105, 49-58

Lu, W., Huang, G. Q., and Li, H. (2011a). Scenarios for applying RFID technology in construction project management. Automation in Construction, 20, 101-106

Lu, W., and Tam, V. W. (2013). Construction waste management policies and their effectiveness in Hong Kong: A longitudinal review. Renewable and Sustainable Energy Reviews, 23, 214-223

Lu, W., Peng, Y., Chen, X., Skitmore, M., and Zhang, X. (2016b). The s-curve for forecasting waste generation in construction projects. Waste Management, 56, 23-34

Lu, W., Webster, C., Peng, Y., Chen, X., and Zhang, X. (2017). Estimating and calibrating the amount of building-related construction and demolition waste in urban China. International Journal of Construction Management, 17(1), 1-12

Lu, W., Yuan, H., Li, J., Hao, J. J., Mi, X., and Ding, Z. (2011b). An empirical investigation of construction and demolition waste generation rates in Shenzhen city, South China. Waste Management, 31(4), 680-687

McAfee, A., Brynjolfsson, E., Davenport, T. H., Patil, D. J., and Barton, D. (2012). Big data: The management revolution. Harvard Business Review, 90(10), 61-67

McGregor, M., Washburn, H., and Palermini, D. (1993). Characterization of construction site waste. Final report presented to the METRO Solid Waste Department, Portland, Oregon

Metcalf, J., Emily F. K., and Danah, B. (2017). Perspectives on big data, ethics, and society. Council for Big Data, Ethics, and Society. https://bdes.datasociety.net/council-output/perspectives-on-big-data-ethics-and-society/ (accessed on 17 December 2017)

MIT Technology Review (2013). The big data conundrum: How to define it? https://goo.gl/nQhGWP (accessed on 17 December 2016)

Moses, L. E. (1986). Think and explain with statistics. Addison-Wesley

Niu, Y., Lu, W., Chen, K., Huang, G. Q., and Anumba, C. (2016). Smart construction objects. Journal of Computing in Civil Engineering, 30(4), 04015070

Niu, Y., Lu, W., Liu, D., Chen, K., Anumba, C., and Huang, G. Q. (2017). An SCO-enabled logistics and supply chain management system in construction. Journal of Construction Engineering and Management, 143(3), 04016103

Ohm, P. (2009). Broken promises of privacy: Responding to the surprising failure of anonymization. UCLA Law Review, 57, 1701

Padhy, R. P. (2013). Big data processing with Hadoop-Map reduce in cloud systems. International Journal of Could Computing and Services Science, 2(1), 16-27

Poon, C. S., Yu, T. W., Wong, S. W., and Cheung, E. (2004). Management of construction waste in public housing projects in Hong Kong. Construction Management & Economics, 22(7), 675-689

Poon, C. S., Yu, T. W., and Ng, L. H. (2001). A guide for managing and minimizing building and demolition waste. Hong Kong Polytechnic University, Hong Kong

Press, G. (2013). What’s the big data? https://whatsthebigdata.com (accessed on 17 December 2017)

Russom, P. (2011). Big data analytics. TDWI Best Practices Report, Fourth Quarter

Schönberger, V. M., and Cukier, K. (2013). Big data: A revolution that will transform how we live, work, and think. John Murray: London

Sen, P. K., and Singer, M. J. (1993). Large sample method in statistics. Chapman & Hall, New York, United States

Senaratne, S., and Rasagopalasingam, V. (2017). The causes and effects of work stress in construction project managers: the case in Sri Lanka. International Journal of Construction Management, 17(1), 65-75

Shelton, T. (2017). The urban geographical imagination in the age of Big Data. Big Data & Society, 4(1), 2053951716665129

Shen, Y., Li, Y., Wu, L., Liu, S., and Wen, Q. (2016). Big data overview. In IRMA (ed.) Big Data: Concepts, Methodologies, Tools, and Applications. IGI Global

Shen, L., Lu, W., Peng, Y., and Jiang, S. (2011). Critical Assessment indicators for measuring benefits of rural infrastructure investment in China. Journal of Infrastructure Systems, 17(4), 176-183

Sivarajah, U., Kamai, M. M., Irani, Z., and Weerakkody, V. (2017). Critical analysis of Big Data challenges and analytical methods. Journal of Business Research, 70(1), 263-286

Skoyles, E. R. (1976). Materials wastage – a misuse of resources. Building Research and Practice, 232-243

Soibelman, L. (2016). Big data and its Impact in the Architecture, Engineering, and Construction Industry. A keynote speech presented on the International Conference on Advancement of Construction Management and Real Estate

Taylor, L., and Schroeder, R. (2015). Is bigger better? The emergence of big data as a tool for international development policy. GeoJournal, 80(4), 503-518

Terranova, T. (2000). Free labor: Producing culture for the digital economy. Social Text, 18(2), 33-58

Thornton, S. (2015). The internet of things in Chicago: Collaborative action for smarter cities. https://datasmart.ash.harvard.edu/news/article/the-internet-of-things-in-chicago-collaborative-action-for-smarter-cities-6 (accessed on 17 December 2017)

Treloar, G. J., Gupta, H., Love, P. E. D., and Nguyen, B. (2003). An analysis of factors influencing waste minimization and use of recycled materials for the construction of residential buildings. Management of Environmental Quality, 14(1), 134-145

Wasserstein, R. L., and Lazar, N. A. (2016). The ASA’s statement on p-values: Context, process, and purpose. The American Statistician, 70(2), 129-133

Wetzels, R., Matzke, D., Lee, M. D., Rouder, J. N., Iverson, G. J., and Wagenmakers, E. J. (2011). Statistical evidence in experimental psychology: An empirical comparison using 855 t Tests. Perspectives on Psychological Science, 6(3), 291-298

World Economic Forum (2012). Big data, big impact: New possibilities for international development. WEF

Wray, N., Markovic, M., and Manderson, L. (2007). Researcher saturation: the impact of data triangulation and intensive-research practices on the researcher and qualitative research process. Qualitative Health Research, 17(10), 1392-1402

Yin, R. K. (1989). Case study research: Design and methods. Newbury Park, CA: Sage Publications

Zaslavsky, A., Perera, C., and Georgakopoulos, D. (2013). Sensing as a service and big data. https://arxiv.org/ftp/arxiv/papers/1301/1301.0159.pdf (accessed on 17 December 2017)

Zhou, K., Fu, C., and Yang, S. (2016). Big data driven smart energy management: From big data to big insights. Renewable and Sustainable Energy Reviews, 56, 215-225

Related Contents

---