FIGURE 6 - available via license: CC BY-NC-ND
Content may be subject to copyright.
Pattern of overloaded or underloaded of waste haulers in five consecutive days (Sample size=20,841 track loads).
Source publication
Context in source publication
Context 1
... analyzing waste haulers' transportation behavior, one day's data was ran- domly selected and plotted (Figure 4). When the analyses were gradually extended to more days, it is noticed that the patterns are largely stable without new insights added (see Figure 6), i.e. it has reached a point of data saturation. With really big data the computational power, energy and time savings can be very high. ...
Similar publications
Cardiovascular diseases (CVDs) have turned out to be one of the life-threatening diseases in recent times. The key to effectively managing this is to analyze a huge amount of datasets and effectively mine it to predict and further prevent heart-related diseases. The primary objective of this chapter is to understand and survey various information m...
Se aborda el problema de correlacionar modelos teóricos con espectros electromagnéticos observados en estrellas masivas en el contexto del gran volumen de información astronómica disponible en la actualidad. Se analizan las características de los códigos de atmósferas estelares y el proceso de modelado y ajuste del espectro observado de una estrell...
Citations
... For the emerging technologies, future research can expand the application of geographic information systems (GIS) to identify CDW illegal dumping areas, BIM to estimate waste quantities, ensure waste management cooperation among stakeholders, and analyze waste throughout the building life cycle, big data to study the CDW practice, and prefabricated construction to reduce the generated waste [124][125][126][127]. ...
Construction and demolition waste treatment has become an increasingly pressing economic, social, and environmental concern across the world. This study employs a science mapping approach to provide a thorough and systematic examination of the literature on waste management research. This study identifies the most significant journals, authors, publications, keywords, and active countries using bibliometric and scientometric analysis. The search retrieved 895 publications from the Scopus database between 2001 and 2021. The findings reveal that the annual number of publications has risen from less than 15 in 2006 to more than 100 in 2020 and 2021. The results
declare that the papers originated in 80 countries and were published in 213 journals. Review, urbanization, resource recovery, waste recycling, and environmental assessment are the top five keywords. Estimation and quantification, comprehensive analysis and assessment, environmental impacts, performance and behavior tests, management plan, diversion practices, and emerging technologies
are the key emerging research topics. To identify research gaps and propose a framework for future research studies, an in-depth qualitative analysis is performed. This study serves as a multi disciplinary reference for researchers and practitioners to relate current study areas to future trends by presenting a broad picture of the latest research in this field.
... There are different methods for collecting waste data from construction projects, such as direct observation, questionnaire survey, sorting and weighing the waste materials on-site, and indirect observation via employees (Lu et al., 2018). Hu et al. (2021) proposed a five-step on-site measurement strategy for construction waste generation. ...
... Similarly, Bilal et al. (2016) proposed a conceptual big data architecture for construction waste analytics, and they claimed that big data technologies are necessary for performing waste analytics since performing these analytics requires large and complex datasets which cannot be handled using traditional technologies. However, because of the costly and disruptive structure of these methods, they are not widely and effectively used in construction projects (Lu et al., 2018). On the other hand, one of the most effective methods for capturing knowledge that emerged in construction projects is live knowledge capture (Eken et al., 2020;Okudan et al., 2021;Tan et al., 2007). ...
Although the construction industry is critical for sustainability goals due to the high amount of waste it generates, its waste management performance is still not at the desired level. One of the main reasons for this can be attributed to the lack of enough knowledge of construction companies on the sources/reasons of waste and amount of waste. The major aim of this study was to develop a knowledge-based tool for capturing, storing, and disseminating waste-related knowledge for prefabricated steel construction projects. The developed tool can be used for waste estimation, monitoring, and minimization of all materials used during different phases of prefabricated steel structure projects based on the data captured from current and previous projects. One advantage of the tool is the integration of all processes of waste management, which have been usually considered independently in previous studies. Moreover, being a knowledge-based tool, it can increase awareness and learning ability of companies about waste and its management. Finally, although the process model of the tool is generic and can operate in alternative domains, the tool is applicable for only prefabricated steel projects. It is believed that the process model and the tool presented in this paper can be further customized for different project types and considering different company needs.
... An interesting field worth mentioning is the utilization of BD to support smart cities [134]. Lu et al. [135] presented the possibility of using BD in construction waste management and even used BD analytics to identify illegal construction waste dumping [136]. Moreover, it was concluded that an accurate analysis of BD makes it possible to discover new phenomena characteristic of the project, which can help reduce risks in project management [137]. ...
This paper presents a systematic review of Construction 4.0 in the context of the building information modeling (BIM) 4.0 premise. It comprises a review of the industry in the pre-fourth industrial revolution (4IR) age, the current and anticipated development of the 4IR, Construction 4.0's origin and applications, and the synergy of its main drivers, i.e., the synergy of BIM with the internet of things (IoT) and big data (BD). The main aim of the paper is to determine the Construction 4.0 drivers and to what extent are they initialized by the 4IR, their development and their syn-ergy with BIM, and the direction of BIM's implementation in the construction phase. It was found that the main drivers of Construction 4.0, which originated from the 4IR, are BIM, IoT, and BD, but with specific implementations. The results of the analysis of BIM with IoT and/or BD revealed that the integrative approaches combining the aforementioned drivers show signs of project enhancement by providing significant benefits, such as improved real-time monitoring, data exchange and analysis, construction planning, and modeling. Furthermore, it was revealed that the main drivers are mostly applied in the project's preconstruction phase, which is continuously developing and becoming more automated. The state-of-the-art review presented in this paper suggests that BIM is in transition, adopting Construction 4.0 to become BIM 4.0.
... One aspect that this paper explores is ICT. Research in the industry showed that data captured through ICT can be used to address crucial issues associated with cost, planning, risk management, safety, progress monitoring, and quality control [4]. This collected data is the major source of Big Data, which, compared to small data sets or sampling, can help to draw a fuller, more trustworthy picture in all areas of construction [4]. ...
... Research in the industry showed that data captured through ICT can be used to address crucial issues associated with cost, planning, risk management, safety, progress monitoring, and quality control [4]. This collected data is the major source of Big Data, which, compared to small data sets or sampling, can help to draw a fuller, more trustworthy picture in all areas of construction [4]. ...
... The last stage of a project is demolition. Big Data gathered from construction waste management indicators, especially in the demolition of a project, can assist in managing the disposal of the deconstructed materials and reduce the waste generation of the contractor [4]. Examples of performance indicators include waste generation rates, costs associated with waste collection, storage, transportation and recycling, and revenues and savings from selling waste [35]. ...
As the world witnesses the fourth industrial revolution, construction remains a newcomer that lags in its adoption of technological innovations. The information-intensive nature of the construction industry, coupled with its multidisciplinary nature, has pushed construction to enter the era of Big Data and has provided fertile ground for research on Big Data. Numerous efforts have been undertaken to investigate the use of Big Data and its impact on construction. This paper builds on the existing body of knowledge and explores the implementation of Big Data throughout the different phases of a construction project. By synthesizing the extant research corpus, this empirical study provides insights into the current state-of-adoption of Big Data throughout the lifecycle of a construction project, its sources, capabilities, and benefits. The findings of this paper are combined into a Big Data Framework that summarizes the "what, how, where, and why" of Big Data implementation in the construction industry.
... Obtaining secondary data from a central source is not always feasible, so researchers often have to collect firsthand data themselves. They can only do so from a relatively small sample owing to the difficulties of conducting surveys of large-scale building projects over a long period (Lu et al., 2018a). Difficulties also lie in the transient nature of building projects (Demian and Walters, 2014). ...
Understanding waste generation flow is vital to any evidence-based effort by policy-makers or practitioners to successfully manage construction project waste. Previous research has found that accumulative waste generation in construction projects follows an S-curve, but improving our understanding of waste generation requires its investigation at a higher level of granularity. Such efforts, however, are often constrained by lack of quality "bigger" data, i.e. data that is bigger than normal small data. This research aims to provide an anatomy of waste generation flow in building projects by making use of a large set of data on waste generation in 19 demolition, 59 foundation, and 54 new building projects undertaken in Hong Kong between 2011 and 2019. We know that waste is generated in far from a steady stream as it is always impacted by contingent factors. However, we do find that peaks of waste generation in foundation projects appear when project duration is at 50~85%, and in new building projects at 40~70% of total project time. Our research provides useful information for waste managers in developing their waste management plans, arranging waste hauling logistics, and benchmarking waste management performance.
... Hence, value is advocated as the fifth 'V' of big data. Lu et al. (2018) critiqued that too often big data analytics is mistakenly associated with 'pattern detection algorithms', 'unsupervised machine learning', 'deep learning', 'artificial intelligence', and other fascinating data mining methods. Instead, they echoed with Leek (2014) that applied statistics should not be left out when harnessing the value of big data. ...
The construction industry accounts for approximately one-third of the total waste generation globally. With the United Nations projecting a population increase of 2 billion within the next three decades, there is a heightened demand for building stock, generating unprecedented volumes of construction and demolition waste (CDW). To combat this, circular economy strategies are purported to help alleviate the prevailing situation. But a number of challenges are jeopardizing their implementation in the construction industry and preventing from achieving the UN Sustainable Development Goals, net zero carbon and zero avoidable waste targets. This paper systematically analyzes 54 research articles, published in the past decade within major peer-reviewed English-language scholarly publications in the form of a systematic research review. In doing so, it aims to identify and classify the challenges that prevent improved CDW management by assimilating previous research results in support of a circular economy. The classification and analysis using a PESTLE model offers insights into gaps and differences between categories, as well as regions and countries. This initial step could contribute to a better understanding of these barriers, along with associated solutions, which could result in a significant reduction on the impact of construction activities, therefore facilitating the development of an effective circular economy in the sector.
The increase in construction projects with rising construction demolition waste (CDW) challenges is alarming, and it poses a significant threat to sustainable urban development. The challenge of CDW management (CDWM) is noteworthy with varied consequences on social, economic, environmental, and physical development perspectives. Although there has been research from different perspectives on CDWM for over 40 years, there is a limited scientometric review research in these areas to date. This study, therefore, conducts a global scientometric analysis of CDWM articles to understand its sustainable development research approach for further studies. Article titles, keywords, and abstract search methods were used to extract related articles from the year 2000 to 2021. A total of 4374 articles retrieved from the Web of Science Core Collection were analysed using CiteSpace software for scientometric analysis. The result revealed active research on CDWM from influential researchers, research institutes, institutions, reputable journals, and countries. The policy recommendations and frameworks adopted in the past have focused more on reducing, recycling and reuse (3R), and they adopt these strategies and others as single strategies for improving CDWM. These strategies are limited in integrating sustainable development strategies such as the circular economy and bio-dynamic. This study concludes that there is a need for a comprehensive research approach that incorporates the economic, social, and physical implications of CDWM to maximise the value of CDW for sustainable development. It presents a comprehensive scientometric analysis of the CDWM, adds to the existing knowledge of CDWM sustainability approach, and provides insights for future research direction relevant for the academics, professionals, and government agencies in CDWM projects.
In the new digital age, Data is the collection of the observation and facts in terms of events, thus data is continuously growing, getting denser and more varied by the minute across multiple channels. Nowadays, consumers generate mass amounts of data on a daily basis. Hence, Big Data (BD) emerged and is evolving rapidly, the various types of data being processed are huge, and ensuring that this data is being used efficiently is becoming increasingly more difficult. BD has been differentiated into several characteristics (the V’s) and many researchers have been developing more characteristics for new purposes over the past years. Therefore, it is shown from observation that there is a clear gap between researchers about the current status of the BD characteristics. Even after the introduction of newer characteristics, many papers are still proposing the use of 3 or 5 V’s, while some researchers are far more progressed and has reached up to 10V’s. This paper will provide an overview of the main characteristics that have been added over time and investigate the recent growth of Big Data Analytics (BDA) characteristics in each industry sector which will provide some detailed and general scope for most researchers to consider and learn from.
The study will appraise the need to improve current waste management practices in the area of construction waste reduction. An integrative review of purposively sampled literature on CW construction waste reduction practice is used to arrive at propositions for an improved practice to achieve more sustainable outcomes. The study will establish the need for a paradigm shift in waste management researches in the area of construction waste reduction to target a higher degree of such philosophy at building design development stage, and not just quantity reduction. Results show that existing waste reduction practices are sub-optimally exploited, which appears as consistent waste generation. There is an opportunity for quality results when the industry aims at best the practice that will affect the process, quality, and performance during the building design process. This study will inform the construction professionals to aim at achieving more than just waste reduction at building design development.
