Green Ground: Construction and Demolition Waste Prediction Using a Deep Learning Algorithm
The waste management and recycling industry in Saudi Arabia is facing ongoing challenges in reducing the negative impact resulting from the recycling process. Different types of waste lack an efficient and accurate method for classification, especially in cases that require the rapid processing of m...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-06-01
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| Series: | Technologies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7080/13/6/247 |
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| Summary: | The waste management and recycling industry in Saudi Arabia is facing ongoing challenges in reducing the negative impact resulting from the recycling process. Different types of waste lack an efficient and accurate method for classification, especially in cases that require the rapid processing of materials. A deep learning prediction model based on a convolutional neural network algorithm was developed to classify and predict the types of construction and demolition waste (CDW). The CDW image dataset used contained 9273 images, including concrete, asphalt, ceramics, and autoclaved aerated concrete. The model obtained an overall accuracy of 97.12%. The Green Ground image prediction model is extremely useful in the construction and demolition industry for automating sorting processes. The model improves recycling rates by ensuring that materials are sorted correctly, thus reducing waste sent to landfills, by accurately identifying different types of materials in CDW images. As part of Saudi Arabia’s 2030 sustainability objectives, these steps contribute to achieving a greener future, complying with environmental regulations, and promoting sustainability. |
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| ISSN: | 2227-7080 |