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Integrating construction supply chains within a circular economy: An ANFIS-based waste analytics system (A-WAS)

机译:在循环经济中整合施工供应链:基于ANFIS的废物分析系统(A-WAS)

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The circular economy agenda makes it paramount for construction supply chains to reduce material waste. Although a collaborative platform called Building Information Modelling (BIM) offers a means of supply chains integration, it has not been efficiently upscaled for delivering waste efficient building designs. This study, therefore, develops a BIM-based computational tool for building waste analytics and reporting in the construction supply chains. A Construction Waste (ON) prediction model using Adaptive Neuro-Fuzzy Inference System (ANFIS) was developed and integrated into Autodesk Revit BIM platform. The model development process reveals that "Gross Floor Area" and "Construction type" are the two key predictors for CW. The results of the study show that the tool offers useful insights into CW minimisation opportunities. The study makes a huge contribution to CW management practices by developing a computational approach to CW measurement. The contribution of the study is fundamental because achieving accurate waste prediction is crucial to waste prevention through adequate design principles and BIM. (C) 2019 The Authors. Published by Elsevier Ltd.
机译:循环经济议程使施工供应链至关重要以减少物质废物。虽然一个称为建筑信息建模(BIM)的协作平台提供了一种供应链集成的手段,但它没有有效地升级为提供废物高效建筑设计。因此,本研究开发了一种基于BIM的计算工具,用于在施工供应链中建立废物分析和报告。使用Adaptive Neuro-Fuzzy推理系统(ANFIS)的建筑垃圾(ON)预测模型进行了开发并集成到Autodesk Revit BIM平台中。模型开发过程揭示了“总建筑面积”和“建筑型”是CW的两个关键预测因子。研究结果表明,该工具对CW最小化机会提供了有用的见解。该研究通过开发CW测量的计算方法对CW管理实践进行了巨大贡献。该研究的贡献是基本的,因为通过适当的设计原则和BIM实现准确的废物预测是对防止预防的关键。 (c)2019年作者。 elsevier有限公司出版

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