Lin, K;
Zhao, Y;
Zhou, T;
Gao, X;
Zhang, C;
Huang, B;
Shi, Q;
(2023)
Applying machine learning to fine classify construction and demolition waste based on deep residual network and knowledge transfer.
Environment, Development and Sustainability
, 25
pp. 8819-8836.
10.1007/s10668-022-02740-6.
Preview |
Text
Zhang_manuscript-clean version.pdf - Accepted Version Download (2MB) | Preview |
Abstract
Few studies reported using the convolutional neural network with transfer learning to finely classify the construction and demolition waste. This study aims to develop a highly efficient method to realize the finely sorting the construction and demolition waste, which is a key step for promoting the recycling system to realize carbon neutrality in the waste management sector. C&DWNet models, ResNet structures based on knowledge transfer and cyclical learning rate, were proposed to classify ten types of construction and demolition waste. Indexes (confusion metric, accuracy, precision, recall, F1 score, sensitivity, specificity and kappa) were adopted to evaluate the performance of various C&DWNet models. Knowledge transfer can reduce the training time and improve the performance of the C&DWNet model. The average training time is increased with the increase of the layer of C&DWNet architecture from C&DWNet-18 (946.7 s) to C&DWNet-152 (1186.6 s). The accuracy of various C&DWNet models is approximately 72–74%; the best accuracy is 73.6% in C&DWNet-152. C&DWNet-18 is more suitable for the classification of construction and demolition waste in terms of training time, accuracy, precision, and F1 score. Moreover, the t-distributed stochastic neighbor embedding can distinctly separate each type of construction and demolition waste. The environmental applications and limitations of the C&DWNet module were also discussed, which could provide a reference for the intelligent management of construction and demolition waste and promote the development of the circular economy.
| Type: | Article |
|---|---|
| Title: | Applying machine learning to fine classify construction and demolition waste based on deep residual network and knowledge transfer |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/s10668-022-02740-6 |
| Publisher version: | https://doi.org/10.1007/s10668-022-02740-6 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Construction & demolition waste classification; Waste management; Machine learning; Deep residual network; Knowledge transfer |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Civil, Environ and Geomatic Eng |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10163944 |
Archive Staff Only
 |
View Item |
