Quality assessment for recycling aggregates from construction and demolition waste: An image-based approach for particle size estimation

Maria, Francesco Di; Bianconi, Francesco; Micale, Caterina; Baglioni, Stefano; Marionni, Moreno

doi:10.1016/j.wasman.2015.12.005

Cited by 39 publications

(16 citation statements)

References 42 publications

(55 reference statements)

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“…This equipment generates high speed rubbing on the surface while making a smooth finish like in virgin aggregates (Ding et al, 2016). Moreover, Di Maria et al (2016) proposed a novel technology to estimate the particle-size distribution of aggregates based on image analysis. Klang et al (2003) highlighted that an electrically powered hydraulic machine with steel edges could be used to remove old mortar from bricks.…”

Section: Technologies For Qa In Rlsc Of Dwmentioning

confidence: 99%

Quality assurance in reverse logistics supply chain of demolition waste: A systematic literature review

et al. 2020

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The reverse logistics supply chain (RLSC) facilitates the greening of the traditional construction industry supply chain. Despite the growing interest in this approach, limited studies focus on quality assurance (QA) in RLSC, which is crucial to resolve the issue of the low quality in reprocessed products. This study aims to explore the elements which contribute to the QA of RLSC of demolition waste (DW) by conducting a systematic literature review (SLR). A total of 91 articles available in six search engines between 2000 and 2019 were exposed to descriptive and content analysis. The descriptive analysis revealed that most of the articles were experimental studies (35%) and published during the recent period of 2013–2019 (64%). Furthermore, 60% of articles were originated from developed countries while 32% originated from developing countries. The results of the content analysis established that an information-centric integrated system of process, people, policy and technology is needed for QA in RLSC, which is supported by government, regulatory bodies, industry and universities/research institutions. The study addresses the existing gap by proposing a conceptual framework, which could be considered as the foundation in enforcing QA in the RLSC of DW. Finally, the implications for research and practice and some recommendations for further research were presented. The study facilitates in paving the path to sustainable development through building the foundation to enforce QA in RSLC of DW.

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Section: Technologies For Qa In Rlsc Of Dwmentioning

confidence: 99%

Quality assurance in reverse logistics supply chain of demolition waste: A systematic literature review

et al. 2020

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“…Many advanced computer vision and machine learning techniques have been used to estimate the size of waste particles. To characterize waste particles, Di Maria et al [7] estimated the particle size distribution based on image analysis, e.g. image descriptors, to find image signatures (feature vectors), auto-correlation, circular filters and granulometry (a set of morphological filters at different scales).…”

Section: Related Workmentioning

confidence: 99%

Particle Size Estimation in Mixed Commercial Waste Images Using Deep Learning

Kittiworapanya¹,

Pasupa²,

Auer³

2021

Preprint

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<div>This work was presented at the 10th Joint Symposium on Computational Intelligence (JSCI10), organized by the IEEE-CIS Thailand Chapter, that aims to support research students and young researchers, to create a place enabling participants to share and discuss on their research prior to publishing their works. The event was open to all researchers who want to broaden their knowledge in the field of computational intelligence.<br></div><div><br></div><div>We assessed several state-of-the-art deep learning algorithms and computer vision techniques for estimating the particle size of mixed commercial waste from images. In waste management, the first step is often coarse shredding, using the particle size to set up the shredder machine. The difficulty is separating the waste particles in an image, which can not be performed well. This work focused on estimating size by using the texture from the input image, captured at a fixed height from the camera lens to the ground. We found that EfficientNet achieved the best performance of 0.72 on F1-Score and 75.89% on accuracy.<br></div>

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“…Determination of grain size distribution in recycled aggregate by nearly two-thirds of the facilities was attributable to the use of that parameter as a criterion for classifying the end product and to the fact that particle size plays an essential part in both bound and unbound applications, but particularly in the former (Di Maria et al, 2016). In concrete, for instance, aggregate grain size distribution affects workability, porosity, permeability, strength and durability.…”

Section: Tests Conducted On Recycled Canddwmentioning

confidence: 99%

Construction and demolition waste applications and maximum daily output in Spanish recycling plants

et al. 2020

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The pursuit of construction sustainability has driven the use of partially or wholly waste-based eco-materials. New applications are being sought for recycled aggregate (RA) to further the use of this material and ensure the survival of the construction and demolition waste (C&DW) industry. RA, currently used in the construction of pavements, fills and embankments and only incipiently to manufacture structural or non-structural concrete, is a mere 8.99% of the total aggregate extracted in EU countries where RA is produced. Against this backdrop, the utility of this study lies in the overview afforded of RA typology, the application of the product in bound or unbound materials, the pursuit of new applications, structural or otherwise, and the assessment of daily maximum output by C&DW recycling facilities in Spain. The findings show that irrespective of its origin, RA most commonly adopts the form of wet mix macadam, gravel or sand used primarily in unbound applications with only dubious quality standards. Plant managers contend that RA from clean waste can feasibly be used in bound applications that require higher-performance materials. Maximum daily output varies widely, with capacity under 200 t d-1 in 30% of the plants. One of the conclusions drawn is that the current business model is in need of revision, with an emphasis on aggregate quality control (certification) and the adoption of technology for separating out impurities, pollutants and undesirable materials to improve the quality of RA.

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Quality assessment for recycling aggregates from construction and demolition waste: An image-based approach for particle size estimation

Cited by 39 publications

References 42 publications

Quality assurance in reverse logistics supply chain of demolition waste: A systematic literature review

Quality assurance in reverse logistics supply chain of demolition waste: A systematic literature review

Particle Size Estimation in Mixed Commercial Waste Images Using Deep Learning

Construction and demolition waste applications and maximum daily output in Spanish recycling plants

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