File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

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

TitleHandling missing data for construction waste management: machine learning based on aggregated waste generation behaviors
Authors
Issue Date2021
PublisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/resconrec
Citation
Resources, Conservation and Recycling, 2021, v. 175, p. article no. 105809 How to Cite?
AbstractIn the era of big data, data is increasingly driving the construction waste management (CWM) for minimizing the impacts on the environment and recycling construction materials. However, missing data, led by various information barriers, often undermines the decision-making and hinders effective CWM. This paper applies aggregated behavior-based Machine Learning (ML) methods to handling the project-level ‘Missing Not At Random’ (MNAR) data by using aggregated waste generation behaviors as a case study. First, we define a set of 821 waste generation behavioral features based on waste big data, then screen the indicative and decisive behaviors using automatic feature selection. Then, the most predictive ML method, trained via data of 2,451 construction projects in 2011–2016 in Hong Kong, is selected for handling the MNAR data. The experiments showed that the prediction of project missing data was satisfactory (validation F1 = 0.87, test F1 = 0.80). The contribution of this paper is to pinpoint the potential of waste big data in portraying project behaviors for more value-added applications, at the same time, to present a handling method for MNAR data that is automatic, fast, and low-cost from the CWM practitioner's perspective.
Persistent Identifierhttp://hdl.handle.net/10722/302092
ISSN
2020 Impact Factor: 10.204
2020 SCImago Journal Rankings: 2.468

 

DC FieldValueLanguage
dc.contributor.authorYang, Z-
dc.contributor.authorXue, F-
dc.contributor.authorLu, W-
dc.date.accessioned2021-08-21T03:31:27Z-
dc.date.available2021-08-21T03:31:27Z-
dc.date.issued2021-
dc.identifier.citationResources, Conservation and Recycling, 2021, v. 175, p. article no. 105809-
dc.identifier.issn0921-3449-
dc.identifier.urihttp://hdl.handle.net/10722/302092-
dc.description.abstractIn the era of big data, data is increasingly driving the construction waste management (CWM) for minimizing the impacts on the environment and recycling construction materials. However, missing data, led by various information barriers, often undermines the decision-making and hinders effective CWM. This paper applies aggregated behavior-based Machine Learning (ML) methods to handling the project-level ‘Missing Not At Random’ (MNAR) data by using aggregated waste generation behaviors as a case study. First, we define a set of 821 waste generation behavioral features based on waste big data, then screen the indicative and decisive behaviors using automatic feature selection. Then, the most predictive ML method, trained via data of 2,451 construction projects in 2011–2016 in Hong Kong, is selected for handling the MNAR data. The experiments showed that the prediction of project missing data was satisfactory (validation F1 = 0.87, test F1 = 0.80). The contribution of this paper is to pinpoint the potential of waste big data in portraying project behaviors for more value-added applications, at the same time, to present a handling method for MNAR data that is automatic, fast, and low-cost from the CWM practitioner's perspective.-
dc.languageeng-
dc.publisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/resconrec-
dc.relation.ispartofResources, Conservation and Recycling-
dc.titleHandling missing data for construction waste management: machine learning based on aggregated waste generation behaviors-
dc.typeArticle-
dc.identifier.emailYang, Z: zhongze@hku.hk-
dc.identifier.emailXue, F: xuef@hku.hk-
dc.identifier.emailLu, W: wilsonlu@hku.hk-
dc.identifier.authorityXue, F=rp02189-
dc.identifier.authorityLu, W=rp01362-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.resconrec.2021.105809-
dc.identifier.hkuros324475-
dc.identifier.volume175-
dc.identifier.spagearticle no. 105809-
dc.identifier.epagearticle no. 105809-
dc.publisher.placeNetherlands-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats