Analysis of the construction waste management performance in Hong Kong: the public and private sectors compared using big data

Lu, Weisheng; Chen, Xi; Ho, Daniel Chi Wing; Wang, Hongdi

doi:10.1016/j.jclepro.2015.06.106

Cited by 119 publications

(49 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The aim is to comprehend 103 better and interpret the complicated structures and interconnec-104 tion buried inside the Big BIM Data for design exploration and 105 optimisation. [107], [108], [167], [168]. Currently, BIM is prevalent in the design world, with very 57 limited utilisation across the construction and FM stages of 58 the building.…”

mentioning

confidence: 99%

“…Visual Analytics: Analytical problems are of two kinds: 103 (1) the problems that have clearly defined and logical solutions; 104 and (2) the problems that have approximate heuristic solutions 105 (and no logic-based straightforward solution applies). The 106 former category is handled through automated approaches, 107 whereas the later ones are tackled through visualisation 108. Human knowledge, creativity, and intuition are pivotal for 109 effective visualisation.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Big Data in the construction industry: A review of present status, opportunities, and future trends

Bilal

Oyedele

Qadir

et al. 2016

Advanced Engineering Informatics

527

307

View full text Add to dashboard Cite

The ability to process large amounts of data and 1 to extract useful insights from data has revolutionised society. 2 This phenomenon-dubbed as Big Data-has applications for a 3 wide assortment of industries, including the construction industry. 4 The construction industry already deals with large volumes of 5 heterogeneous data; which is expected to increase exponentially 6 as technologies such as sensor networks and the Internet of 7 Things are commoditized. In this paper, we present a detailed 8 survey of the literature, investigating the application of Big Data 9 techniques in the construction industry. We reviewed related 10 works published in the databases of American Association of 11 Civil Engineers (ASCE), Institute of Electrical and Electronics 12 Engineers (IEEE), Association of Computing Machinery (ACM), 13 and Elsevier Science Direct Digital Library. While the application 14 of data analytics in the construction industry is not new, the 15 adoption of Big Data technologies in this industry remains at a 16 nascent stage and lags the broad uptake of these technologies in 17 other fields. To the best of our knowledge, there is currently no 18 comprehensive survey of Big Data techniques in the context of 19 the construction industry. This paper fills the void and presents a 20 wide-ranging interdisciplinary review of literature of fields such 21 as statistics, data mining and warehousing, machine learning, and 22 Big Data Analytics in the context of the construction industry. 23 We discuss the current state of adoption of Big Data in the 24 construction industry and discuss the future potential of such 25 technologies across the multiple domain-specific sub-areas of the 26 construction industry. We also propose open issues and directions 27 for future work along with potential pitfalls associated with Big 28 Data adoption in the industry. 29 I. INTRODUCTION 30 The world is currently inundated with data, with fast advanc-31 ing technology leading to its steady increase. Today, companies 32 deal with petabytes (10 15 bytes) of data. Google processes 33 above 24 petabytes of data per day [1], while Facebook gets 34 more than 10 million photos per hour [1]. The glut of data 35 increased in 2012 is approximately 2.5 quintillion (10 18) bytes 36 per day [2]. This data growth brings significant opportunities 37 to scientists for identifying useful insights and knowledge. 38 Arguably, the accessibility of data can improve the status 39 quo in various fields by strengthening existing statistical and 40 algorithmic methods [3], or by even making them redundant 41 [4]. 42 The construction industry is not an exception to the per-43 vasive digital revolution. The industry is dealing with sig-44 nificant data arising from diverse disciplines throughout the 45 life cycle of a facility. Building Information Modelling (BIM) 46 is envisioned to capture multi-dimensional CAD information 47 systematically for supporting multidisciplinary collaboration 48 among the stakeholders [5]. BIM data is typically 3D ge-49 ometric enc...

show abstract

mentioning

confidence: 99%

mentioning

confidence: 99%

Big Data in the construction industry: A review of present status, opportunities, and future trends

Bilal

Oyedele

Qadir

et al. 2016

Advanced Engineering Informatics

527

307

View full text Add to dashboard Cite

show abstract

“…Future research should focus on the following two aspects: Firstly, integrate the big data of buildings with geographic information systems (GIS) to take advantage of the huge building information and the physical simulation space so that the model could be more consistent with the real world (Feo and Gisi, 2014;Lu et al, 2016). Moreover, further research may inlcude landfill site selection and transportation routes optimization (Coelho and de Brito, 2013).…”

Section: Discussionmentioning

confidence: 99%

An agent based environmental impact assessment of building demolition waste management: Conventional versus green management

Ding

Wang

Zou

2016

Journal of Cleaner Production

123

View full text Add to dashboard Cite

Waste materials generated from building demolition have become a great challenge to sustainable urban development due to its consumption of the limited landfill spaces, water pollution, energy consumption and harmful gas emissions. Proper management of demolition waste (DW) is a complex process and requires systematic thinking and analysis. Many methods have been proposed to study the environmental impact assessment of demolition waste management (DWM). However, it is found that the currently available studies pay little attention from the perspective of complex adaptive system (CAS) to consider the attitude and interaction of the heterogeneous stakeholders as well as the importance of green DWM which has a great influence on the effectiveness of DW management. The aim of this research is to simulate and explore how the change of attitude and the dynamic interaction among heterogeneous stakeholders can influence the environmental performance of DWM. To achieve this aim, a model for evaluating the environmental impact of DWM was developed by using an agent-based modeling (ABM) approach. The main factors considered in the model are the ratio of green deconstruction (i.e., building deconstruction) managers vs. conventional demolition (i.e. building destruction) managers, the ratio of green design managers (i.e. design for deconstruction) vs. conventional design managers, and the interaction behavior of heterogeneous stakeholders following the herd theory. In the model, the environmental impact assessment was quantified into four categories i.e. land resources, water resources, air resource and energy resources. The proposed model is demonstrated by using the data drawn from the Chinese construction industry. The results reveal that if the deconstruction method and the deconstruction oriented design are widely adopted by architects and engineers, the negative environmental impacts generated by DW can be reduced by at least 50%. Furthermore, the results provide valuable information for government departments to make decisions on how to improve environmental performance of DWM.

show abstract

“…In both categorization methods 9 researches fit into the category while 5 of them fit the category by using both methods. (Lu, Chen, Ho, & Wang, 2016) performed an analysis of the waste management in Hong Kong area comparing public and private sectore performance by using big data. Same author only alone performed research on Hong Kong illegal waste dump identification based on big data solutions (Lu, 2019).…”

Section: Resource and Waste Optimizationmentioning

confidence: 99%

“…All publications that are assigned to this category - Bilal et al (2016a), Bilal et al (2016b), Han and Wang (2017), R. H. Liu, Kuo, Yang, Chen, and J. C. Liu (2016), Lu (2019), Lu et al (2016), A. Konikov and G. Konikov (2017), Weijie, Jinna, Zhengzheng, and Na (2016), Xianglan (2017).…”

Section: Resource and Waste Optimizationmentioning

confidence: 99%

Big data in construction industry: systematic literature overview

Verdenhofs

Geipele

Tambovceva

2019

Modern Building Materials, Structures and Techniques (MBMST)

View full text Add to dashboard Cite

Technological advancement has led to tremendous increase of data. Many industries utilize big data to become more efficient or even to create new products or services. Applications of big data in construction industry has been extensively researched in Asia that can be explained with huge construction volumes in the area. This study is aimed at identifying big data applications in construction industry in time period beyond 2016. Research object is construction industry, research subject is big data applications. Research methods used in this research are systematic literature overview and meta-analysis. Novelty of the research is classification of big data applications based on systematic literature overview. Authors conclude that existing categorization (Bilal et al., 2016b) can be applied to researches about big data applications in construction industry published in 2016 and later. However, potential for new applications is identified in category of emerging trends triggered by big data and authors propose to perform cross-industry analysis to identify solutions that can be adopted to construction industry.

show abstract

Analysis of the construction waste management performance in Hong Kong: the public and private sectors compared using big data

Cited by 119 publications

References 26 publications

Big Data in the construction industry: A review of present status, opportunities, and future trends

Big Data in the construction industry: A review of present status, opportunities, and future trends

An agent based environmental impact assessment of building demolition waste management: Conventional versus green management

Big data in construction industry: systematic literature overview

Contact Info

Product

Resources

About