Knowledge Agora

Title	Applying machine learning to fine classify construction and demolition waste based on deep residual network and knowledge transfer
ID_Doc	12617
Authors	Lin, KN; Zhao, YC; Zhou, TT; Gao, XF; Zhang, CB; Huang, BJ; Shi, QN
Title	Applying machine learning to fine classify construction and demolition waste based on deep residual network and knowledge transfer
Year	2023
Published	Environment Development And Sustainability, 25.0, 8
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.
PDF

Similar Articles

ID	Score	Article
15415		Lin, KS; Zhou, T; Gao, XF; Li, ZS; Duan, HB; Wu, HY; Lu, GY; Zhao, YC Deep convolutional neural networks for construction and demolition waste classification: VGGNet structures, cyclical learning rate, and knowledge transfer(2022)
10348		Lin, KS; Zhao, YC; Gao, XF; Zhang, ML; Zhao, CL; Peng, L; Zhang, Q; Zhou, T Applying a deep residual network coupling with transfer learning for recyclable waste sorting(2022)Environmental Science And Pollution Research, 29, 60
3169		Akanbi, LA; Oyedele, AO; Oyedele, LO; Salami, RO Deep learning model for Demolition Waste Prediction in a circular economy(2020)
38862		Al Duhayyim, M; Eisa, TAE; Al-Wesabi, FN; Abdelmaboud, A; Hamza, MA; Zamani, A; Rizwanullah, M; Marzouk, R Deep Reinforcement Learning Enabled Smart City Recycling Waste Object Classification(2022)Cmc-Computers Materials & Continua, 71, 3
15841		Nnamoko, N; Barrowclough, J; Procter, J Solid Waste Image Classification Using Deep Convolutional Neural Network(2022)Infrastructures, 7, 4
21734		Alonso, SLN; Forradellas, RFR; Morell, OP; Jorge-Vazquez, J Digitalization, Circular Economy and Environmental Sustainability: The Application of Artificial Intelligence in the Efficient Self-Management of Waste(2021)Sustainability, 13.0, 4
21879		Dong, ZM; Chen, JJ; Lu, WS Computer vision to recognize construction waste compositions: A novel boundary-aware transformer (BAT) model(2022)
43656		Oguz, A; Ertugrul, OF Determining the fullness of garbage containers by deep learning(2023)
40504		Malik, M; Prabha, C; Soni, P; Arya, V; Alhalabi, WA; Gupta, BB; Albeshri, AA; Almomani, A Machine Learning-Based Automatic Litter Detection and Classification Using Neural Networks in Smart Cities(2023)International Journal On Semantic Web And Information Systems, 19, 1
4157		Eladly, AM; Abed, AM; Aly, MH; Salama, WM Enhancing circular economy via detecting and recycling 2D nested sheet waste using Bayesian optimization technique based-smart digital twin(2023)