Predicting Generation of Different Demolition Waste Types Using Simple Artificial Neural Networks
Author
Abstract
Suggested Citation
Download full text from publisher
References listed on IDEAS
- Shi, Jianguang & Xu, Yuezhou, 2006. "Estimation and forecasting of concrete debris amount in China," Resources, Conservation & Recycling, Elsevier, vol. 49(2), pages 147-158.
- Andersen, Frits Møller & Larsen, Helge & Skovgaard, Mette & Moll, Stephan & Isoard, Stéphane, 2007. "A European model for waste and material flows," Resources, Conservation & Recycling, Elsevier, vol. 49(4), pages 421-435.
- Gi-Wook Cha & Se-Hyu Choi & Won-Hwa Hong & Choon-Wook Park, 2022. "Development of Machine Learning Model for Prediction of Demolition Waste Generation Rate of Buildings in Redevelopment Areas," IJERPH, MDPI, vol. 20(1), pages 1-17, December.
- Stan Lipovetsky & Michael Conklin, 2001. "Analysis of regression in game theory approach," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 17(4), pages 319-330, October.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Gi-Wook Cha & Hyeun Jun Moon & Young-Min Kim & Won-Hwa Hong & Jung-Ha Hwang & Won-Jun Park & Young-Chan Kim, 2020. "Development of a Prediction Model for Demolition Waste Generation Using a Random Forest Algorithm Based on Small DataSets," IJERPH, MDPI, vol. 17(19), pages 1-15, September.
- Pera, Rebecca & Viglia, Giampaolo & Furlan, Roberto, 2016. "Who Am I? How Compelling Self-storytelling Builds Digital Personal Reputation," Journal of Interactive Marketing, Elsevier, vol. 35(C), pages 44-55.
- García-Torres, Samy & Kahhat, Ramzy & Santa-Cruz, Sandra, 2017. "Methodology to characterize and quantify debris generation in residential buildings after seismic events," Resources, Conservation & Recycling, Elsevier, vol. 117(PB), pages 151-159.
- Stan Lipovetsky, 2021. "Predictor Analysis in Group Decision Making," Stats, MDPI, vol. 4(1), pages 1-14, February.
- Hugh Chen & Scott M. Lundberg & Su-In Lee, 2022. "Explaining a series of models by propagating Shapley values," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
- Emrah Arbak, 2017. "Identifying the provisioning policies of Belgian banks," Working Paper Research 326, National Bank of Belgium.
- Anna M. Grabiec & Jeonghyun Kim & Andrzej Ubysz & Pilar Bilbao, 2021. "Some Remarks towards a Better Understanding of the Use of Concrete Recycled Aggregate: A Review," Sustainability, MDPI, vol. 13(23), pages 1-19, December.
- Viglia, Giampaolo & Abrate, Graziano, 2017. "When distinction does not pay off - Investigating the determinants of European agritourism prices," Journal of Business Research, Elsevier, vol. 80(C), pages 45-52.
- Xingwei Hu, 2020.
"A theory of dichotomous valuation with applications to variable selection,"
Econometric Reviews, Taylor & Francis Journals, vol. 39(10), pages 1075-1099, November.
- Hu, Xingwei, 2017. "A Theory of Dichotomous Valuation with Applications to Variable Selection," MPRA Paper 80457, University Library of Munich, Germany.
- Dmitry Sharapov & Paul Kattuman & Diego Rodriguez & F. Javier Velazquez, 2021. "Using the SHAPLEY value approach to variance decomposition in strategy research: Diversification, internationalization, and corporate group effects on affiliate profitability," Strategic Management Journal, Wiley Blackwell, vol. 42(3), pages 608-623, March.
- Xingwei Hu, 2018. "A Theory of Dichotomous Valuation with Applications to Variable Selection," Papers 1808.00131, arXiv.org, revised Mar 2020.
- Filotto, Umberto & Caratelli, Massimo & Fornezza, Fabrizio, 2021. "Shaping the digital transformation of the retail banking industry. Empirical evidence from Italy," European Management Journal, Elsevier, vol. 39(3), pages 366-375.
- Agovino, Massimiliano & Cerciello, Massimiliano & Javed, Aamir & Rapposelli, Agnese, 2023. "Environmental legislation and waste management efficiency in Italian regions in view of circular economy goals," Utilities Policy, Elsevier, vol. 85(C).
- Elena Pokryshevskaya & Evgeny Antipov, 2013. "Importance-performance analysis for internet stores: a system based on publicly available panel data," HSE Working papers WP BRP 08/MAN/2013, National Research University Higher School of Economics.
- Pelin Ayranci & Phung Lai & Nhathai Phan & Han Hu & Alexander Kolinowski & David Newman & Deijing Dou, 2022. "OnML: an ontology-based approach for interpretable machine learning," Journal of Combinatorial Optimization, Springer, vol. 44(1), pages 770-793, August.
- Hongmei Liu & Rong Guo & Junjie Tian & Honghao Sun & Yi Wang & Haiyan Li & Lu Yao, 2022. "Quantifying the Carbon Reduction Potential of Recycling Construction Waste Based on Life Cycle Assessment: A Case of Jiangsu Province," IJERPH, MDPI, vol. 19(19), pages 1-16, October.
- Eranga M. Wimalasiri & Ebrahim Jahanshiri & Tengku Adhwa Syaherah Tengku Mohd Suhairi & Hasika Udayangani & Ranjith B. Mapa & Asha S. Karunaratne & Lal P. Vidhanarachchi & Sayed N. Azam-Ali, 2020. "Basic Soil Data Requirements for Process-Based Crop Models as a Basis for Crop Diversification," Sustainability, MDPI, vol. 12(18), pages 1-20, September.
- Andersen, Frits Møller & Larsen, Helge V., 2012. "FRIDA: A model for the generation and handling of solid waste in Denmark," Resources, Conservation & Recycling, Elsevier, vol. 65(C), pages 47-56.
- Zhao, W. & Leeftink, R.B. & Rotter, V.S., 2010. "Evaluation of the economic feasibility for the recycling of construction and demolition waste in China—The case of Chongqing," Resources, Conservation & Recycling, Elsevier, vol. 54(6), pages 377-389.
- Khoa Tran & Hai-Canh Vu & Lam Pham & Nassim Boudaoud & Ho-Si-Hung Nguyen, 2024. "Robust-MBDL: A Robust Multi-Branch Deep-Learning-Based Model for Remaining Useful Life Prediction of Rotating Machines," Mathematics, MDPI, vol. 12(10), pages 1-25, May.
More about this item
Keywords
waste management; demolition waste generation; machine learning; artificial neural network; SHAP analysis;All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:23:p:16245-:d:1286445. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.