Abstract
The outcome of construction activities leads to the production of large amounts of solid waste, primarily known as construction and demolition (C&D) waste. The reutilizing of C&D wastes plays a vital role in the sustainable growth of the environment, economy, and public health. The existing recycling methods have limitations, such as cost, human intervention, unstable identification process for recycling, on-site sorting techniques, irregular landfill events, and a lack of an effective waste tracking system. The paper proposes an end-to-end improved convolutional neural network (EEI-CNN) based reverse logistics model for recycling C&D waste to overcome these issues. The EEI-CNN is a customized convolutional neural network that performs the classification of C&D waste aggregates. The refine the efficacy of EEI-CNN, a preprocessed image is used. The effectiveness of the proposed method is judged for an openly available C&D waste image dataset. The evaluation metrics like accuracy, precision, true positive rate, true negative rate, and F-score are estimated. The proposed method outperforms existing methods based on comparative analysis.
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Sinha, S., Srivastava, S., Sahay, B.K., Kumar, A. (2023). A Deep Learning-Based Reverse Logistics Model for Recycling Construction and Demolition Waste. In: Tiwari, M.K., Kumar, M.R., T. M., R., Mitra, R. (eds) Applications of Emerging Technologies and AI/ML Algorithms. ICDAPS 2022. Asset Analytics. Springer, Singapore. https://doi.org/10.1007/978-981-99-1019-9_28
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DOI: https://doi.org/10.1007/978-981-99-1019-9_28
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