A Machine Learning-Enhanced Method for Quantifying and Recycling Construction and Demolition Waste in India

2023 IEEE International Conference on Integrated Circuits and Communication Systems (ICICACS) Pub Date : 2023-02-24 DOI:10.1109/ICICACS57338.2023.10099602

Ramnarayan, Pragya Malla

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Abstract

Construction and Demolition Waste in construction industries generate 10 to 12 million tons of waste per year. Not even 50% of the waste generated by major construction materials such as cement, bricks, wires, stones, wood, plastic, and steel pipes is recycled. And 70% of Indian construction industry is not aware of recycling technologies. The solid waste extracted from municipal waste is used in industries and the resulting sludge is eventually dumped on urban land. A large amount of sludge is emitted as a result of coal and ash used in the use of nuclear reactors, iron and metal industries, which produce large amounts of waste. In this paper, a machine learning-enhanced method for quantifying and recycling construction and demolition waste. Various industrial wastes such as wastes from non-ferrous industries, sugar manufacturing, paper manufacturing, and fertilizer industries also generate solid waste. The local administration is not responsible for the management of solid waste from industries. The respective factories themselves manage them and take the necessary prior permission.

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基于机器学习的印度建筑和拆除垃圾量化和回收方法

建筑行业的建筑和拆除废物每年产生1000万至1200万吨废物。水泥、砖块、电线、石头、木材、塑料和钢管等主要建筑材料产生的废物甚至不到50%被回收利用。70%的印度建筑行业不了解回收技术。从城市垃圾中提取的固体废物用于工业，产生的污泥最终倾倒在城市土地上。由于核反应堆、钢铁和金属工业中使用的煤和灰会产生大量的废物，因此会排放大量的污泥。本文提出了一种基于机器学习的建筑和拆除垃圾量化和回收方法。有色金属、制糖、造纸、化肥等工业废弃物也会产生固体废弃物。地方政府不负责工业固体废物的管理。各工厂自行管理，并取得必要的事先许可。

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来源期刊

2023 IEEE International Conference on Integrated Circuits and Communication Systems (ICICACS)

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