Deep learning-based models for environmental management: Recognizing construction, renovation, and demolition waste in-the-wild

Journal of Environmental Management, Год журнала: 2024, Номер 351, С. 119908 - 119908

Опубликована: Янв. 1, 2024

The construction industry generates a substantial volume of solid waste, often destinated for landfills, causing significant environmental pollution. Waste recycling is decisive in managing waste yet challenging due to labor-intensive sorting processes and the diverse forms waste. Deep learning (DL) models have made remarkable strides automating domestic recognition sorting. However, application DL recognize derived from construction, renovation, demolition (CRD) activities remains limited context-specific studies conducted previous research. This paper aims realistically capture complexity streams CRD context. study encompasses collecting annotating images real-world, uncontrolled environments. It then evaluates performance state-of-the-art automatically recognizing in-the-wild. Several pre-trained networks are utilized perform effectual feature extraction transfer during model training. results demonstrated that models, whether integrated with larger or lightweight backbone can composition in-the-wild which useful automated outcome emphasized applicability across various industrial domains, thereby contributing resource recovery encouraging management efforts.

Язык: Английский

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Recent Developments in Technology for Sorting Plastic for Recycling: The Emergence of Artificial Intelligence and the Rise of the Robots

Recycling, Год журнала: 2024, Номер 9(4), С. 59 - 59

Опубликована: Июль 15, 2024

Plastics recycling is an important component of the circular economy. In mechanical recycling, recovery high-quality plastics for subsequent reprocessing requires plastic waste to be first sorted by type, color, and size. chemical certain types should removed as they negatively affect process. Such sortation objects at Materials Recovery Facilities (MRFs) relies increasingly on automated technology. Critical any sorting proper identification type. Spectroscopy used this end, augmented machine learning (ML) artificial intelligence (AI). Recent developments in application ML/AI are highlighted here, state art presented. Commercial equipment recyclables identified from a survey publicly available information. Automated equipment, ML/AI-based sorters, robotic sorters currently market evaluated regarding their sensors, capability sort plastics, primary application, throughput, accuracy. This information reflects rapid progress achieved plastics. However, film, dark comprising multiple polymers remains challenging. Improvements and/or new solutions forthcoming.

Язык: Английский

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Artificial intelligence-empowered collection and characterization of microplastics: A review

Journal of Hazardous Materials, Год журнала: 2024, Номер 471, С. 134405 - 134405

Опубликована: Апрель 26, 2024

Язык: Английский

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Catalytic pyrolysis of plastic waste using metal-incorporated activated carbons for monomer recovery and carbon nanotube synthesis

Journal of environmental chemical engineering, Год журнала: 2024, Номер 12(2), С. 112226 - 112226

Опубликована: Фев. 16, 2024

Язык: Английский

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Evaluation of Artificial Intelligence-Based Solid Waste Segregation Technologies through Multi-Criteria Decision-Making and complex q-rung picture fuzzy Frank aggregation operators

Engineering Applications of Artificial Intelligence, Год журнала: 2024, Номер 133, С. 108154 - 108154

Опубликована: Март 6, 2024

Язык: Английский

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Transforming waste into innovation: a review of plastic bricks as sustainable construction materials

Deleted Journal, Год журнала: 2024, Номер 1(1)

Опубликована: Июль 30, 2024

Abstract Plastic waste has become a pressing global issue, posing significant environmental challenges. As the construction industry continues to grow, there is growing need for sustainable materials that can address this problem. This review paper focuses on transformation of into innovation by exploring use plastic bricks as materials. The examines manufacturing processes, properties, benefits, challenges, case studies, and future research directions associated with bricks. It highlights potential reduce waste, carbon emissions, resource consumption. Additionally, addresses challenges related structural integrity, long-term durability, regulatory compliance, public perception. Case studies showcase successful implementations in projects, emphasizing their innovative design possibilities, cost-effectiveness, economic feasibility, notable strength. These illustrate achieve necessary strength applications, making them viable alternative traditional also discusses impact circular economy perspectives bricks, highlighting recyclability, reusability, management implications, contribution initiatives. Finally, concludes recommendations, focusing advancements techniques, enhanced performance engineering monitoring assessment. comprehensive sheds light transformative provides insights addressing

Язык: Английский

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A digital twin system for Task-Replanning and Human-Robot control of robot manipulation

Advanced Engineering Informatics, Год журнала: 2024, Номер 62, С. 102570 - 102570

Опубликована: Май 13, 2024

Язык: Английский

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Study on efficient recognition and accurate localization method of waste plastic bottles based on deep learning

Ecological Informatics, Год журнала: 2025, Номер unknown, С. 103020 - 103020

Опубликована: Янв. 1, 2025

Язык: Английский

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Integrating circular economy while adopting digital twin for enhancing logistics efficiency: a hybrid Fuzzy Delphi-FUCOM based approach

International Journal of Logistics Research and Applications, Год журнала: 2025, Номер unknown, С. 1 - 43

Опубликована: Янв. 18, 2025

Язык: Английский

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ShARP-WasteSeg: A shape-aware approach to real-time segmentation of recyclables from cluttered construction and demolition waste

Waste Management, Год журнала: 2025, Номер 195, С. 231 - 239

Опубликована: Фев. 9, 2025

Язык: Английский

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