| Title | Data-driven prediction of construction and demolition waste generation using limited datasets in developing countries: an optimized extreme gradient boosting approach |
|---|---|
| ID_Doc | 9346 |
| Authors | Maged, A; Elshaboury, N; Akanbi, L |
| Title | Data-driven prediction of construction and demolition waste generation using limited datasets in developing countries: an optimized extreme gradient boosting approach |
| Year | 2024 |
| Published | |
| Abstract | An effective waste management plan must include reliable building waste generation data. This study seeks to build a trustworthy approach for predicting Construction and Demolition Waste (CDW) generation utilizing limited data. Different optimization algorithms are applied to eXtreme Gradient Boosting (XGBOOST) and compared to standard machine learning models including artificial neural network, support vector regression, and decision tree. The versatility and generalizability of the proposed approach are showcased by utilizing two distinct datasets in the Greater Bay Area of China and Tanta City of Egypt. The developed model reported excellent results, with testing R2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${R}<^>{2}$$\end{document} values equal to 99.9. The research findings may be applied to the strategic development of waste management facilities, monitoring the urban metabolism, creating a circular economy using limited data. The study's outcomes, as aligned with these 3R principles, lay the foundation for proactive waste management planning, resource optimization, and material valorization in the construction sector. This can potentially foster a more sustainable and circular approach to waste management, promoting a shift towards a more resource-efficient and environmentally conscious construction industry. |
Similar Articles
| ID | Score | Article |
|---|---|---|
12950
|
Lu, WS; Lou, JF; Webster, C; Xue, F; Bao, ZK; Chi, B Estimating construction waste generation in the Greater Bay Area, China using machine learning(2021) | |
| 14701
|
Elshaboury, N; Almetwaly, WM Modeling construction and demolition waste quantities in Tanta City, Egypt: a synergistic approach of remote sensing, geographic information system, and hybrid fuzzy neural networks(2023)Environmental Science And Pollution Research, 30, 48 | |
| 44576
|
Kaya, K; Ak, E; Yaslan, Y; Oktug, SF Waste-to-Energy Framework: An intelligent energy recycling management(2021) | |
| 6480
|
Soultanidis, V; Voudrias, EA Modelling of demolition waste generation: Application to Greek residential buildings(2023)Waste Management & Research, 41, 9 | |
| 9083
|
Wang, DC; Tan, QH; Wang, YR; Liu, GY; Lu, Z; Zhu, CQ; Sun, BC Carbonation depth prediction and parameter influential analysis of recycled concrete buildings(2024) | |
| 27125
|
Xiong, SY; Escamilla, EZ; Habert, G Uncovering The Circular Potential: Estimating Material Flows For Building Systems Components Reuse In The Swiss Built Environment(2024) | |
| 38003
|
Lipianina-Honcharenko, K; Komar, M; Osolinskyi, O; Shymanskyi, V; Havryliuk, M; Semaniuk, V Intelligent Waste-Volume Management Method in the Smart City Concept(2024)Smart Cities, 7, 1 |