Handling missing data for construction waste management: machine learning based on aggregated waste generation behaviors

Yang, Zhongze; Xue, Fan; Lu, Weisheng

doi:10.1016/j.resconrec.2021.105809

Cited by 19 publications

(2 citation statements)

References 66 publications

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“…Lu et al [20] use a big dataset of construction waste in Hong Kong to obtain heuristic rules for the bulk densities of construction waste. Yang et al [21] study how to use machine learning methods to deal with missing data in construction waste management. Yuan et al [16] develop a big data probability model to estimate waste composition.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimization Models for Reducing Off-Cuts of Raw Materials in Construction Site

Wang

Wen

2022

Mathematics

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More than ten billion tons of construction waste are generated every year in the world. The large volume of construction waste not only increases costs for contractors, but also poses a threat to the environment. A significant proportion of construction waste consists of off-cuts of raw materials. Therefore, to reduce construction waste, this study builds an optimization model to reduce the volume of off-cuts of raw materials. We then develop two solution methods—a mixed-integer linear programming method and a column generation method—to solve the proposed optimization model. We conduct numerical experiments to test the efficiency and applicability of our proposed model. The mixed-integer linear programming method obtains optimal solutions and is suitable for solving small-scale instances, whereas the column generation method gives high-quality solutions within seconds and is suitable for solving large-scale instances. In the large-scale instances, the column generation method reduces waste by over 10% compared to the use of two straightforward decisions rules. Our findings will help construction projects decrease material off-cuts, reduce costs, and achieve sustainable construction.

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Section: Literature Reviewmentioning

confidence: 99%

Optimization Models for Reducing Off-Cuts of Raw Materials in Construction Site

Wang

Wen

2022

Mathematics

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“…Several building sustainability assessment systems have been developed in practice, such as LEED, BREEAM, BSAM Scheme, Green Mark, and other regional industry standards, to encourage the reduction of greenhouse emissions associated with the construction sector. Also, digital technologies such as BIM (Wong & Kuan, 2014), blockchain technology , artificial intelligence and machine learning (Yang et al, 2021), smart sensors and wearables (Nath et al, 2017), big data, internet of things, laser scanning, and drones are being deployed to help deliver better construction and infrastructure projects that could exceed owners and occupants' satisfaction with fewer carbon footprints. Although these research developments are augmenting, there are still some knowledge gaps that need to be considered and filled up-some of which have been addressed in this Research Topic.…”

Section: Introductionmentioning

confidence: 99%