| Title | Enhancing information standards for automated construction waste quantification and classification |
|---|---|
| ID_Doc | 19231 |
| Authors | Sivashanmugam, S; Rodriguez, S; Rahimian, FP; Elghaish, F; Dawood, N |
| Title | Enhancing information standards for automated construction waste quantification and classification |
| Year | 2023 |
| Published | |
| Abstract | Accurate quantification and detailed classification of construction waste are paramount to improving their management. Over the last decades, various quantification models have been developed to measure, manage, and report construction waste generation. A detailed understanding of those models is essential to explore their applications across the life-cycle stages of a built asset. Existing reviews primarily focused on analysing the functions of quantification methodologies, but the digital and information standards to automate the quantifi-cation process are under-explored in the existing literature. A review is adopted to analyse papers published from 2012 to 2022. Out of 279 articles retrieved, 71 papers meeting the eligibility criteria were included. A critical analysis of the models indicates that unified data structure, standard information, life-cycle approach and interoperability between the BIM and waste knowledge bases are vital to automate and reinforce the quantifi-cation efficiency. Based on the review findings, a conceptual framework is developed to demonstrate the quantification workflow for building projects. The outcomes from the review will facilitate researchers to identify the prevailing gaps and enhance the waste quantification system to meet digital construction demands. |
| https://doi.org/10.1016/j.autcon.2023.104898 |
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