A BIM-aided construction waste minimisation framework

In recent times, construction industry is enduring pressure to take drastic steps to minimise waste. Waste intelligence advocates retrospective measures to manage waste after it is produced. Existing waste intelligence based waste management software are fundamentally limited and cannot facilitate stakeholders in controlling wasteful activities. Paradoxically, despite a great amount of effort, the waste being produced by the construction industry is escalating. This undesirable situation motivates a radical change from waste intelligence to waste analytics (in which waste is propose to be tackle proactively right at design through sophisticated big data technologies). This paper highlight that waste minimisation at design (a.k.a. designing-out waste) is data-driven and computationally intensive challenge. The aim of this paper is to propose a Big Data architecture for construction waste analytics. To this end, existing literature on big data technologies is reviewed to identify the critical components of the proposed Big Data based waste analytics architecture. At the crux, graph-based components are used: in particular, a graph database (Neo4J) is adopted to store highly voluminous and diverse datasets. To complement, Spark, a highly resilient graph processing system, is employed. Provision for extensions through Building Information Modelling (BIM) are also considered for synergy and greater adoption. This symbiotic integration of technologies enables a vibrant environment for design exploration and optimisation to tackle construction waste. The main contribution of this paper is that it presents, to the best of our knowledge, the first Big Data based architecture for construction waste analytics. The architecture is validated for exploratory analytics of 200,000 waste disposal records from 900 completed projects. It is revealed that existing waste management software classify the bulk of construction waste as mixed waste, which exposes poor waste data management. The findings of this paper will be of interest, more generally to researchers, who are seeking to develop big data based simulation tools in similar non-trivial applications.

Big data architecture for Construction Waste Analytics (CWA): A conceptual framework

The overall aim of this study is to investigate the potential of Building Information Modelling (BIM) for construction waste minimization. We evaluated the leading BIM design software products and concluded that none of them currently support construction waste minimization. This motivates the development of a plug-in for predicting and minimizing construction waste. After a rigorous literature review and conducting four focused group interviews (FGIs), 12 imperative BIM factors were identified that should be considered for predicting and designing out construction waste. These factors were categorized into four layers, namely the BIM core features layer, the BIM auxiliary features layer, the waste management criteria layer, and the application layer. Further, a process to carry out BIM-enabled building waste analysis (BWA) is proposed. We have also investigated the usage of big data technologies in the context of waste minimization. We highlight that big data technologies are inherently suitable for BIM due to their support of storing and processing large datasets. In particular, the use of graph-based representation, analysis, and visualization can be employed for advancing the state of the art in BIM technology for construction waste minimization.

Analysis of critical features and evaluation of BIM software: Towards a plug-in for construction waste minimization using big data

Waste has been considered a major problem in the construction industry for its financial and environmental impacts. The attention to Construction and Demolition (CD graphs and charts were prepared and finally conclusion was developed and two WM models were recommended, the first is the use of WM subcontractor and the second is to have a WM department within the company.

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Waste Management Models and Their Applications on Construction Sites

The application of web of data technologies in building materials information modelling for construction waste analytics

ABSTACT:The excavation, construction and demolition waste production in Greece is estimated at 50 million tonnes per year and it tends to increase. Hence, a Directive has been legislated recently to lay down measures to protect the environment and human health by reducing the adverse impacts of the waste production and management, using alternative methods. Additionally, this Directive, in concert with Directive 2008/98/EC of the European Parliament and the Council, aims by 2020 to increase the preparation for re-use, recycling and other material recovery methods of non-hazardous construction and demolition waste to a minimum of 70% by weight. In this paper, current literature and studies on engineers’ approach towards construction waste minimisation are reviewed. Furthermore, by means of a postal questionnaire, the origins of waste, waste minimisation design and construction practices in Greece and waste management are investigated, as well as the waste minimisation responsibilities regarding the profession of engineers. The study shows that waste generation on site is directly related to design process. Better site planning and supervision are believed to be the solution in on-site waste minimization. Legislation and financial rewards were found to be the major motivations that could influence the integration of waste minimization practices.

Survey Regarding Control and Reduction of Construction Waste

Sihem Amer-Yahia, Divyakant Agrawal, Yael Amsterdamer, Sourav S. Bhowmick, Angela Bonifati, Renata Borovica-Gajic, Jesús Camacho-Rodríguez, Barbara Catania, Panos K. Chrysanthis, Carlo Curino, Jérôme Darmont, Gillian Dobbie, Amr El Abbadi, Avrilia Floratou, Juliana Freire, Alekh Jindal, Vana Kalogeraki, Sujaya Maiyya, Alexandra Meliou, Madhulika Mohanty, Behrooz Omidvar-Tehrani, Fatma Özcan, Liat Peterfreund, Wenny Rahayu, Shazia Sadiq, Sana Sellami, Utku Sirin, Wang-Chiew Tan, Bhavani Thuraisingham, Yuanyuan Tian, Pınar Tözün, Genoveva Vargas-Solar, Neeraja Yadwadkar, Victor Zakhary, Meihui Zhang

Kosmopoulos Panos

Papers

Survey Regarding Control and Reduction of Construction Waste

Diversity, Equity and Inclusion Activities in Database Conferences: A 2022 Report