Enhancing quality inspection efficiency and reliability of unscreened recycled coarse aggregates (RCA) streams using innovative mobile sensor-based technology

IF 6.2 2区 工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY
Cheng Chang , Francesco Di Maio , Rajeev Bheemireddy , Perry Posthoorn , Abraham T. Gebremariam , Peter Rem
{"title":"Enhancing quality inspection efficiency and reliability of unscreened recycled coarse aggregates (RCA) streams using innovative mobile sensor-based technology","authors":"Cheng Chang ,&nbsp;Francesco Di Maio ,&nbsp;Rajeev Bheemireddy ,&nbsp;Perry Posthoorn ,&nbsp;Abraham T. Gebremariam ,&nbsp;Peter Rem","doi":"10.1016/j.dibe.2025.100611","DOIUrl":null,"url":null,"abstract":"<div><div>Recycled coarse aggregates (RCA) from End-of-Life (EoL) concrete face resistance due to inconsistent quality. To address this, a mobile, containerized sensor-based inspection system is developed, capable of processing over 100 tons of RCA per hour. Using advanced 3D scanning and laser-induced breakdown spectroscopy (LIBS), the system ensures reliable real-time analysis of particle size distribution (PSD) (Root Mean Square Error: &lt;5.5%) and contaminant detection (Accuracy: 0.94). Incremental learning techniques dynamically update chi-square distribution parameters as new spectral data becomes available, refining models continuously without full retraining and sustaining high classification performance. Monitoring data are recorded on radio frequency identification (RFID) tags, enhancing traceability. This innovation improves efficiency compared to traditional methods, supporting sustainable practices in the construction industry. Its applications also extend to related fields such as mining, waste management, and resource recovery, contributing to the circular economy, reducing reliance on natural aggregates, and promoting environmentally friendly infrastructure development.</div></div>","PeriodicalId":34137,"journal":{"name":"Developments in the Built Environment","volume":"21 ","pages":"Article 100611"},"PeriodicalIF":6.2000,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Developments in the Built Environment","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2666165925000110","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CONSTRUCTION & BUILDING TECHNOLOGY","Score":null,"Total":0}
引用次数: 0

Abstract

Recycled coarse aggregates (RCA) from End-of-Life (EoL) concrete face resistance due to inconsistent quality. To address this, a mobile, containerized sensor-based inspection system is developed, capable of processing over 100 tons of RCA per hour. Using advanced 3D scanning and laser-induced breakdown spectroscopy (LIBS), the system ensures reliable real-time analysis of particle size distribution (PSD) (Root Mean Square Error: <5.5%) and contaminant detection (Accuracy: 0.94). Incremental learning techniques dynamically update chi-square distribution parameters as new spectral data becomes available, refining models continuously without full retraining and sustaining high classification performance. Monitoring data are recorded on radio frequency identification (RFID) tags, enhancing traceability. This innovation improves efficiency compared to traditional methods, supporting sustainable practices in the construction industry. Its applications also extend to related fields such as mining, waste management, and resource recovery, contributing to the circular economy, reducing reliance on natural aggregates, and promoting environmentally friendly infrastructure development.
使用创新的基于移动传感器的技术,提高未筛选的再生粗骨料(RCA)流的质量检测效率和可靠性
报废混凝土(EoL)中的再生粗骨料(RCA)由于质量不一致而面临阻力。为了解决这个问题,开发了一种基于传感器的移动集装箱检测系统,每小时能够处理超过100吨的RCA。采用先进的3D扫描和激光诱导击穿光谱(LIBS),该系统可确保可靠的粒径分布(PSD)实时分析(均方根误差:5.5%)和污染物检测(精度:0.94)。增量学习技术在新的光谱数据可用时动态更新卡方分布参数,在不完全重新训练的情况下不断改进模型并保持高分类性能。监测数据记录在射频识别(RFID)标签上,增强了可追溯性。与传统方法相比,这种创新提高了效率,支持建筑行业的可持续实践。它的应用还扩展到采矿、废物管理和资源回收等相关领域,有助于循环经济,减少对天然骨料的依赖,促进环境友好型基础设施的发展。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
CiteScore
7.40
自引率
1.20%
发文量
31
审稿时长
22 days
期刊介绍: Developments in the Built Environment (DIBE) is a recently established peer-reviewed gold open access journal, ensuring that all accepted articles are permanently and freely accessible. Focused on civil engineering and the built environment, DIBE publishes original papers and short communications. Encompassing topics such as construction materials and building sustainability, the journal adopts a holistic approach with the aim of benefiting the community.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信