Data-Driven Predictive Analysis and Sustainable Management of Concrete Waste in Pakistan

Yuan Chen and Minhas Asim ()
Additional contact information
Yuan Chen: School of Civil Engineering, Zhengzhou University, Zhengzhou 450000, China
Minhas Asim: School of Civil Engineering, Zhengzhou University, Zhengzhou 450000, China

Sustainability, 2024, vol. 16, issue 10, 1-22

Abstract: The construction sector of Pakistan is on a cross-growth trajectory, developing under the twin pressures of emerging infrastructure-based demands and sustainable practices that need to be inculcated urgently. This article focuses on the critical evaluation of sustainable waste management practices within the fast-developing construction industry of Pakistan, and clearly delineates a research gap in the current methodologies and use of data combined with the absence of a strategy for effective management of concrete waste. This research aims to utilize an algorithm based on machine learning that will provide accurate prediction in the generation of construction waste by harnessing the potential of real-time data for improved sustainability in the construction process. This research has identified fundamental factors leading systematically to the generation of concrete waste by creating an extensive dataset from construction firms all over Pakistan. This research study also identifies the potential concrete causes and proposed strategies towards the minimization of waste with a strong focus on the reuse and recycling of the same concrete material to enhance the adoption of sustainable practices. The prediction of the model indicates that the volumes of construction are to increase to 158 cubic meters by 2030 and 192 cubic meters by 2040. Further, it projects the increase in concrete construction waste volumes to 223 cubic meters by the year 2050 through historical wastage patterns.

Keywords: construction waste; sustainable management; predictive analysis; linear regression; waste management (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/16/10/4169/pdf (application/pdf)
https://www.mdpi.com/2071-1050/16/10/4169/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:16:y:2024:i:10:p:4169-:d:1395649

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().