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.
Similar content being viewed by others
References
Achu, A. L., Thomas, J., Aju, C. D., Gopinath, G., Kumar, S., & Reghunath, R. (2021). Machine-learning modelling of fire susceptibility in a forest-agriculture mosaic landscape of southern India. Ecological Informatics, 64, 101348.
Ahmad, K., Khan, K., & Al-Fuqaha, A. (2020). Intelligent fusion of deep features for improved waste classification. IEEE Access, 8, 96495–96504.
Aral, R. A., Keskin, Ş., Kaya, M., & Hacıömeroğlu, M. (2018). Classification of trashnet dataset based on deep learning models. In IEEE international conference on big data. IEEE, Seattle.
Bengio, Y. (2012). Neural networks: Tricks of the trade, chapter practical recommendations for gradient-based training of deep architectures. Springer.
Bobulski, J., & Kubanek, M. (2021). Deep learning for plastic waste classification system. Applied Computational Intelligence and Soft Computing, 2021, 7.
Dhillon, A., & Verma, G. K. (2019). Convolutional neural network: A review of models, methodologies and applications to object detection. Progress in Artificial Intelligence, 9(2), 85–112.
Duan, H., & Li, J. (2016). Construction and demolition waste management: China’s lessons. Waste Management & Research, 34(5), 397–398.
Duan, H., Wang, J., & Huang, Q. (2015). Encouraging the environmentally sound management of C&D waste in China: An integrative review and research agenda. Renewable and Sustainable Energy Reviews, 43, 611–620.
Frost, S., Tor, B., Agrawal, R., & Forbes, A. G. (2019). CompostNet: An image classifier for meal waste. In IEEE Global Humanitarian Technology Conference (GHTC) (pp. 1–4).
Fulkerson, B. (1996). Pattern recognition and neural networks. Cambridge University Press.
Garcia-Garcia, A., Orts-Escolano, S., Oprea, S., Villena-Martinez, V., Martinez-Gonzalez, P., & Garcia-Rodriguez, J. (2018). A survey on deep learning techniques for image and video semantic segmentation. Applied Soft Computing, 70, 41–65.
Gisbrecht, A., Schulz, A., & Hammer, B. (2015). Parametric nonlinear dimensionality reduction using kernel t-SNE. Neurocomputing, 147, 71–82.
Hayden, M. S., & Ghosh, S. (2014). Regulation of NF-kappaB by TNF family cytokines. Seminars in Immunology, 26(3), 253–266.
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In IEEE conference on computer vision and pattern recognition (CVPR). Las Vegas, NV, USA (pp. 770–778).
Huang, B., Gao, X., Xu, X., Song, J., Geng, Y., Sarkis, J., Fishman, T., Kua, H., & Nakatani, J. (2020). A life cycle thinking framework to mitigate the environmental impact of building materials. One Earth, 3(5), 564–573.
Khosravi, K., Shahabi, H., Pham, B. T., Adamowski, J., Shirzadi, A., Pradhan, B., Dou, J., Ly, H.-B., Gróf, G., Ho, H. L., Hong, H., Chapi, K., & Prakash, I. (2019). A comparative assessment of flood susceptibility modeling using multi-criteria decision-making analysis and machine learning methods. Journal of Hydrology, 573, 311–323.
Lin, K., Zhao, Y., Kuo, J.-H., Deng, H., Cui, F., Zhang, Z., Zhang, M., Zhao, C., Gao, X., Zhou, T., & Wang, T. (2022). Toward smarter management and recovery of municipal solid waste: A critical review on deep learning approaches. Journal of Cleaner Production, 346, 130943.
Maaten, L., & Hinton, G. (2008). Visualizing data using t-SNE. Journal of Machine Learning Research, 9, 2579–2605.
Mao, W.-L., Chen, W.-C., Wang, C.-T., & Lin, Y.-H. (2021). Recycling waste classification using optimized convolutional neural network. Resources, Conservation and Recycling, 164, 105132.
Retsinas, G., Stamatopoulos, N., Louloudis, G., Sfikas, G., & Gatos, B. (2017). Nonlinear manifold embedding on keyword spotting using t-SNE. In International conference on document analysis and recognition (ICDAR) (pp. 487–492).
Samudre, A., George, L. T., Bansal, M., & Wadadekar, Y. (2022). Data-efficient classification of radio galaxies. Monthly Notices of the Royal Astronomical Society, 509(2), 2269–2280.
Sinno, J. P., & Qiang, Y. (2010). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.
Smith, L. N. (2017). Cyclical learning rates for training neural networks. In IEEE winter conference on applications of computer vision (WACV). IEEE, Santa Rosa.
Sreelakshmi, K., Vinayakumar, R., & Soman, K.P. (2019). Deep segregation of plastic (DSP): Segregation of plastic and nonplastic using deep learning. In Big data recommender systems—Volume 1: Algorithms, architectures, big data, security and trust (pp. 169–191).
Thomaz, C. E., & Giraldi, G. A. (2010). A new ranking method for principal components analysis and its application to face image analysis. Image and Vision Computing, 28(6), 902–913.
Vidyabharathi, D., Mohanraj, V., Kumar, J. S., & Suresh, Y. (2021). Achieving generalization of deep learning models in a quick way by adapting T-HTR learning rate scheduler. Personal and Ubiquitous Computing. https://doi.org/10.1007/s00779-021-01587-4
Wang, Z., Li, H., & Yang, X. (2020). Vision-based robotic system for on-site construction and demolition waste sorting and recycling. Journal of Building Engineering, 32, 101769.
Yan, B., Liang, R., Li, B., Tao, J., Chen, G., Cheng, Z., Zhu, Z., & Li, X. (2021). Fast identification and characterization of residual wastes via laser-induced breakdown spectroscopy and machine learning. Resources, Conservation and Recycling, 174, 105851.
Yanai, K., & Kawano, Y. (2015). Food image recognition using deep convolutional network with pre-training and fine-tuning. In IEEE international conference on multimedia & expo workshops (ICMEW) (pp. 1–6). IEEE, Turin, Italy.
Yang, M., & Thung, G. (2016). Classification of trash for recyclability status. CS229 projection report 2016 (pp. 940–945).
Yang, K., Yang, T., Yao, Y., & Fan, S. (2021). A transfer learning-based convolutional neural network and its novel application in ship spare-parts classification. Ocean & Coastal Management, 215, 105971.
Zhang, H., Wang, K., Tian, Y., Gou, C., & Wang, F.-Y. (2018). MFR-CNN: Incorporating multi-scale features and global information for traffic object detection. IEEE Transactions on Vehicular Technology, 67(9), 8019–8030.
Zhang, Q., Zhang, X., Mu, X., Wang, Z., Tian, R., Wang, X., & Liu, X. (2021). Recyclable waste image recognition based on deep learning. Resources, Conservation and Recycling, 171, 105636.
Acknowledgements
This work was supported by the National Key R&D Program of China (No. 2019YFC1904001), the National Natural Science Foundation of China (Nos. 52000143, 51878470), the Shanghai Municipal Science and Technology Major Project (2021SHZDZX0100) and the Fundamental Research Funds for the Central Universities, and the International Postdoctoral Exchange Fellowship Program (YJ20200280).
Author information
Authors and Affiliations
Corresponding authors
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Below is the link to the electronic supplementary material.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Lin, K., Zhao, Y., Zhou, T. et al. Applying machine learning to fine classify construction and demolition waste based on deep residual network and knowledge transfer. Environ Dev Sustain 25, 8819–8836 (2023). https://doi.org/10.1007/s10668-022-02740-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10668-022-02740-6