An Integrated Building Information Modeling (BIM) and Circular Economy (CE) Model for the Management of Construction and Deconstruction Waste Based on Construction Methods
Bibliographic record
Abstract
As the climate change signs become more noticeable, the concern to prioritize sustainability within the AEC industry intensifies. This particularly pertained to issues related to the demand of resources, the excessive consumption of raw materials, and the associated generated waste. Presently, the construction industry is ranked among the industries that are accountable for the global generation of solid waste and energy consumption, leading to detrimental environmental effects. Nonetheless, over the years, construction methods, technological innovations, and building practices have made considerable progress, influenced by a growing emphasis on sustainability, especially in energy conservation and in adopting the Industrialized Production layer of Construction 4.0 (i.e., modular prefabrication, 3D concrete printing, and BIM). The concept of Circular Economy (CE) has been identified as a potential solution to achieve sustainability in building construction through the design, construction, and end-of-life deconstruction processes that enhance the management of waste based on the framework that is related to reducing, reusing, and recycling. Moreover, information and data related to geolocation complement advanced digital technologies by providing a collaborative platform that supports the application of CE as a practical approach to sustainability. Thus, this study will provide a straightforward methodology for developing a model that integrates BIM and sustainable design with Circular Economy’s concept to enhance the sustainability of construction projects to minimize their waste based on various construction methods (i.e., conventional, modular, and 3D concrete printing). The proposed model interrelates tools and data for the evaluation and planning strategies for the construction and deconstruction waste (CDW) management at the design stage, including estimating the quantities of the wasted materials, quantifying the production rates of selected equipment for the waste handling (loading and hauling) at the various stages of a project as well their associated cost. The proposed model will help users to calculate the quantity of construction and deconstruction waste (CDW) during the design of buildings based on their different construction methods at the early stage by using the concept of Design for Deconstruction (DfD); sustainable construction methods; and deconstruction process for waste management, which will lead to the suitable construction method.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".