RESEARCH PROGRESS ON MONITORING AND SIMULATION OF SPATIAL DISTRIBUTION, VOLUME AND VARIATION OF CONSTRUCTION WASTE

ZHOU Lei; LI Yalan; ZHANG Chaoqun; SONG Wen; YANG Kun; DU Mingyi; CHEN Qiang; LIU Yang

doi:10.13205/j.hjgc.202403030

Volume 42 Issue 3

Mar. 2024

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Article Navigation > ENVIRONMENTAL ENGINEERING > 2024 > 42(3): 243-253

ZHAO Xi, WU Shan-shan, LU Ke-ding. DEVELOPMENT OF AN EVALUATION SYSTEM FOR ASSESSING CONSTRUCTION LEVEL IN OPERATION OF MUNICIPAL SOLID WASTE COMPREHENSIVE TREATMENT PARK FOR ZERO-WASTE CITIES[J]. ENVIRONMENTAL ENGINEERING , 2021, 39(2): 136-140,15. doi: 10.13205/j.hjgc.202102022

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ZHOU Lei, LI Yalan, ZHANG Chaoqun, SONG Wen, YANG Kun, DU Mingyi, CHEN Qiang, LIU Yang. RESEARCH PROGRESS ON MONITORING AND SIMULATION OF SPATIAL DISTRIBUTION, VOLUME AND VARIATION OF CONSTRUCTION WASTE[J]. ENVIRONMENTAL ENGINEERING , 2024, 42(3): 243-253. doi: 10.13205/j.hjgc.202403030

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RESEARCH PROGRESS ON MONITORING AND SIMULATION OF SPATIAL DISTRIBUTION, VOLUME AND VARIATION OF CONSTRUCTION WASTE

doi: 10.13205/j.hjgc.202403030

School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 102616, China

Received Date: 2023-02-08
Available Online: 2024-05-31

Abstract

Abstract

The complex spatial and temporal characteristics of construction waste make it difficult to estimate and accurately monitor the amount of generation and change. Both the fields of mapping science and environmental science have conducted research to address these issues. The foundation and key to strengthening urban ecological protection and revealing the mechanism of urban building metabolism is the accurate recognition of multi-source remote sensing of construction waste, accurate monitoring, and simulation of the number of piles and changes. The development trend and representative research progress of spatial distribution, volume estimation, change monitoring, and simulation of construction waste at home and abroad are reviewed from the perspective of multidisciplinary intersection, to further support environmental protection and urban building metabolism in-depth study. Construction waste monitoring and its spatial distribution are divided into traditional methods and remote sensing recognition methods assisted by machine learning or deep learning. The volume estimation methods of construction waste include estimation methods based on statistical data and stereo models. Finally, the research status of dynamic monitoring and simulation of construction waste variation is summarized. Through systematic analysis and summary of the research results in this field, the existing problems and future development trends are proposed. It is of great value to improve the supervision level of solid waste, such as construction waste, protect the urban ecological environment, and promote the interdisciplinary research of urban construction metabolism.
- urban building metabolism,
- recognition,
- change detection,
- deep learning,
- material cycle

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References

[1]	NAGAPAN S, ABDUL RAHMAN I, ASMI A. Factors contributing to physical and non-physical waste generation in construction industry[J]. International Journal of Advances in Applied Sciences, 2011, 1:2252-8814.
[2]	OLIVEIRA M L S, IZQUIERDO M, QUEROL X, et al. Nanoparticles from construction wastes:a problem to health and the environment[J]. Journal of Cleaner Production, 2019, 219:236-243.
[3]	NAGAPAN S, RAHMAN I A, ASMI A, et al. Issues on construction waste:the need for sustainable waste management[C]//2012 IEEE Colloquium on Humanities, Science & Engineering Research (Chuser 2012), 2012.
[4]	YUAN R J, GUO F, QIAN Y M, et al. A system dynamic model for simulating the potential of prefabrication on construction waste reduction[J]. Environmental Science and Pollution Research, 2021, 29(9):12589-12600.
[5]	HOANG N H, ISHIGAKI T, KUBOTA R, et al. Waste generation, composition, and handling in building-related construction and demolition in Hanoi, Vietnam[J]. Waste Management, 2020, 117:32-41.
[6]	朱东风. 城市建筑垃圾处理研究[D]. 广州:华南理工大学, 2010.
[7]	梁勇, 李博, 马刚平, 等. 建筑垃圾资源化处置技术及装备综述[J]. 环境工程, 2013, 31(4):5.
[8]	黄惠玲, 韩军, 吴飞斌, 等. 建筑垃圾的颜色特征提取与分类研究[J]. 光学与光电技术, 2018, 16(1):53-57.
[9]	郑龙海, 袁祖强, 殷晨波, 等. 基于机器视觉的建筑垃圾自动分类系统研究[J]. 机械工程与自动化, 2019(6):16-18.
[10]	贾竞珏, 刘扬, 陈强, 等. 典型建筑垃圾光谱特征分析[J]. 测绘通报, 2021(增刊2):35-38.
[11]	张方利, 杜世宏, 郭舟. 应用高分辨率影像的城市固体废弃物提取[J]. 光谱学与光谱分析, 2013, 33(8):2024-2030.
[12]	SINGH A. Remote sensing and GIS applications for municipal waste management[J]. Journal of Environmental Management, 2019, 243:22-29.
[13]	LAHTELA V, HYVÄRINEN M, KÄRKI T. Composition of plastic fractions in waste streams:toward more efficient recycling and utilization[J]. Polymers, 2019, 11(1):69.
[14]	BONIFAZI G, CAPOBIANCO G, SERRANTI S. Hyperspectral imaging and hierarchical pls-da applied to asbestos recognition in construction and demolition waste[J]. Applied Sciences, 2019, 9(21):4587.
[15]	吴文伟, 刘竞. 北京市固体废弃物分布调查中遥感技术的应用[J]. 环境卫生工程, 2000,8(2):76-78.
[16]	秦海春. 基于国产高分辨率遥感影像的城镇生活垃圾监管方法研究[J]. 中国建设信息化, 2016(4):75-77.
[17]	徐隆鑫, 孙永华, 何仕俊, 等. 基于不同光谱匹配算法的无人机高光谱遥感影像建筑垃圾分类研究[J]. 首都师范大学学报:自然科学版, 2021, 42(6):50-56.
[18]	赵敬, 臧克, 宫辉力, 等. 遥感技术在北京市垃圾定位及处理中的应用[J]. 首都师范大学学报:自然科学版, 2005,26(3):109-113.
[19]	王晨, 殷守敬, 孟斌, 等. 京津冀地区非正规垃圾场地遥感监测分析[J]. 高技术通讯, 2016, 26(8):799-807.
[20]	刘欣, 王君燕. 非正规建筑垃圾场地空间布局特征研究:以北京市昌平区为例[J]. 环境工程, 2021, 39(12):193-198 , 233.
[21]	CHEN Q, CHENG Q, WANG J, et al. Identification and evaluation of urban construction waste with vhr remote sensing using multi-feature analysis and a hierarchical segmentation method[J]. Remote Sensing, 2021, 13(1):158.
[22]	ZHOU L, LUO T, DU M, et al. Machine learning comparison and parameter setting methods for the detection of dump sites for construction and demolition waste using the Google Earth Engine[J]. Remote Sensing, 2021, 13(4):787.
[23]	祝一诺, 高婷, 王术东, 等. 基于迁移学习再训练模型和高分遥感数据的建筑垃圾自动识别方法[J]. 遥感技术与应用, 2021, 36(2):314-323.
[24]	洪姝. 基于吉林一号光学A星影像的城市建筑垃圾遥感识别与变化检测研究[D]. 北京:北京建筑大学, 2021.
[25]	LI Z, GUO H, ZHANG L, et al. Time-series monitoring of dust-proof nets covering urban construction waste by multispectral images in Zhengzhou, China[J]. Remote Sensing, 2022, 14(15):3805.
[26]	ZHU X X, TUIA D, MOU L, et al. Deep learning in remote sensing:a comprehensive review and list of resources[J]. IEEE Geoscience and Remote Sensing Magazine, 2017, 5(4):8-36.
[27]	徐丰, 胡程, 李军, 等. 遥感图像处理中的深度学习专题简介[J]. 中国科学:信息科学, 2020, 50(4):619-620.
[28]	ŠEVO I, AVRAMOVIĆ A. Convolutional neural network based automatic object detection on aerial images[J]. IEEE Geoscience and Remote Sensing Letters, 2016, 13(5):740-744.
[29]	PAN X J, REN Y Q, SHENG K K, et al. Dynamic refinement network for oriented and densely packed object detection[C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020:11204-11213.
[30]	HENRY C, AZIMI S M, MERKLE N. Road segmentation in SAR satellite images with deep fully convolutional neural networks[J]. IEEE Geoscience and Remote Sensing Letters, 2018, 15(12):1867-1871.
[31]	DIAS M, MONTEIRO J, ESTIMA J, et al. Semantic segmentation of high-resolution aerial imagery with W-Net Models[C]//Progress in Artificial Intelligence, 2019:486-498.
[32]	ZHANG C, HARRISON P A, PAN X, et al. Scale sequence joint deep learning (SS-JDL) for land use and land cover classification[J]. Remote Sensing of Environment, 2020, 237:111593.
[33]	ZHANG Y D, CHEN G, VUKOMANOVIC J, et al. Recurrent shadow attention Model (RSAM) for shadow removal in high-resolution urban land-cover mapping[J]. Remote Sensing of Environment, 2020, 247:111945.
[34]	XIAO W, YANG J H, FANG H Y, et al. A robust classification algorithm for separation of construction waste using NIR hyperspectral system[J]. Waste Management, 2019, 90:1-9.
[35]	李思琦. 基于迁移学习的多源遥感影像建筑垃圾识别[D]. 北京:北京建筑大学, 2020.
[36]	刘小玉, 刘扬, 杜明义, 等. 基于DeeplabV3+的建筑垃圾堆放点识别[J]. 测绘通报, 2022(4):16-19, 43.
[37]	ZHAO X, YANG Y, DUAN F Z, et al. Identification of construction and demolition waste based on change detection and deep learning[J]. International Journal of Remote Sensing, 2022, 43(6):2012-2028.
[38]	ZHANG C Q, ZHOU L, DU M Y, et al. A cross-channel multi-scale gated fusion network for recognizing construction and demolition waste from high-resolution remote sensing images[J]. International Journal of Remote Sensing, 2022, 43(12):4541-4568.
[39]	张兴琼. 面向对象的成都市武侯区建筑垃圾遥感信息提取[D]. 成都:成都理工大学, 2020.
[40]	VILLORIA SÁEZ P, PORRAS-AMORES C, DEL RÍO MERINO M. New quantification proposal for construction waste generation in new residential constructions[J]. Journal of Cleaner Production, 2015, 102:58-65.
[41]	FATTA D, PAPADOPOULOS A, AVRAMIKOS E, et al. Generation and management of construction and demolition waste in Greece:an existing challenge[J]. Resources Conservation and Recycling, 2003, 40(1):81-91.
[42]	许元, 李聪. 城市建筑垃圾产生量的估算与预测模型[J]. 建筑砌块与砌块建筑, 2014(3):43-47.
[43]	王桂琴, 张红玉, 李国学, 等. 灰色模型在北京市建筑垃圾产生量预测中的应用[J]. 环境工程, 2009, (增刊1):508-511.
[44]	WIMALASENA B, RUWANPURA J Y, HETTIARATCHI J. Modeling construction waste generation towards sustainability[C]//Construction Research Congress 2010:Innovation for Reshaping Construction Practice, 2010:1498-1507.
[45]	李金雪, 石峰, 崔树强. 我国建筑垃圾产生量的时空特征分析[J]. 科学与管理, 2015, 35(5):50-56.
[46]	王玉国, 李灵芝, 丁垚. 建筑垃圾产量预测与时空特征研究:以南京江北新区为例[J]. 环境工程, 2020, 38(3):15-21.
[47]	YANG D, GUO J, SUN L W, et al. Urban buildings material intensity in China from 1949 to 2015[J]. Resources, Conservation and Recycling, 2020, 159.
[48]	BERGSDAL H, BOHNE R A, BRATTEBØ H. Projection of construction and demolition waste in Norway[J]. Journal of Industrial Ecology, 2007, 11(3):27-39.
[49]	吴金莲. 南京城市房屋建筑垃圾产量趋势以及资源化产业研究[D]. 南京:南京大学, 2012.
[50]	INCEKARA A H, DELEN A, SEKER D Z, et al. Investigating the utility potential of low-cost unmanned aerial vehicles in the temporal monitoring of a landfill[J]. International Journal of Geo-Information, 2019, 8(1):22.
[51]	LIU S, YU J, KE Z, et al. Aerial-ground collaborative 3D reconstruction for fast pile volume estimation with unexplored surroundings[J]. International Journal of Advanced Robotic Systems, 2020, 17(2):172988142091994.
[52]	陈永健. 地面激光雷达在矿山斜坡移动变形测量中的应用研究[J]. 世界有色金属, 2020(15):231-232.
[53]	高梓成, 王国利, 郭明. 激光雷达技术在建筑垃圾土方量估算的应用[J]. 科学技术创新, 2019(1):110-111.
[54]	SON S W, KIM D W, SUNG W G, et al. Integrating UAV and TLS approaches for environmental management:a case study of a waste stockpile area[J]. Remote Sensing, 2020, 12(10):1615.
[55]	刘亚岚, 任玉环, 魏成阶, 等. 北京1号小卫星监测非正规垃圾场的应用研究[J]. 遥感学报, 2009,13(2):320-326.
[56]	秦海春. 基于国产高分遥感影像的城镇生活垃圾监管方法研究[J]. 中国建设信息化, 2016(4):75-77.
[57]	WU Z. Superpixel-based unsupervised change detection using multi-dimensional change vector analysis and SVM-based classification[J]. 2012.
[58]	赵敏, 赵银娣. 面向对象的多特征分级CVA遥感影像变化检测[J]. 遥感学报, 2018, 22(1):119-131.
[59]	冯文卿, 张永军. 利用多尺度融合进行面向对象的遥感影像变化检测[J]. 测绘学报, 2015, 44(10):10.
[60]	VINEETHA P, SARUN S, SHEELA A M. Landslide susceptibility analysis using frequency ratio model in a tropical region, South East Asia[J]. Journal of Geography Environment and Earth Science International, 2019,22(2):1-13.
[61]	BASU T, PAL S. A GIS-based factor clustering and landslide susceptibility analysis using AHP for Gish River Basin, India[J]. Environment, Development and Sustainability, 2020, 22(5):4787-4819.
[62]	石娟. 基于无人机影像的变化检测关键技术研究[D]. 武汉:武汉大学, 2015.
[63]	李文卓. 时序无人机影像二三维综合的面向对象建筑物变化检测关键技术研究[D]. 武汉:武汉大学, 2017.
[64]	BERGSDAL H, BRATTEBØ H, BOHNE R A, et al. Dynamic material flow analysis for Norway's dwelling stock[J]. Building Research & Information, 2007, 35(5):557-570.
[65]	HU M M, PAULIUK S, WANG T, et al. Iron and steel in Chinese residential buildings:a dynamic analysis[J]. Resources, Conservation & Recycling, 2010, 54(9):591-600.
[66]	VILAYSOUK X, ISLAM K, MIATTO A, et al. Estimating the total in-use stock of Laos using dynamic material flow analysis and nighttime light[J]. Resources, Conservation and Recycling, 2021, 170:105608.
[67]	陈波, 杨建新, 石垚, 等. 城市物质流分析框架及其指标体系构建[J]. 生态学报, 2010, 30(22):6289-6296.
[68]	肖雅心, 杨建新. 北京市住宅建筑生命周期碳足迹[J]. 生态学报, 2016, 36(18):5949-5955.
[69]	MASTRUCCI A, MARVUGLIA A, POPOVICI E, et al. Geospatial characterization of building material stocks for the life cycle assessment of end-of-life scenarios at the urban scale[J]. Resources, Conservation and Recycling, 2017, 123:54-66.
[70]	唐守娟, 张力小, 郝岩, 等. 城市住宅建筑系统流量-存量动态模拟:以北京市为例[J]. 生态学报, 2019, 39(4):1240-1247.
[71]	张妍, 杨志峰. 北京城市物质代谢的能值分析与生态效率评估[J]. 环境科学学报, 2007,27(11):1892-1899.
[72]	张妍, 杨志峰. 城市物质代谢的生态效率:以深圳市为例[J]. 生态学报, 2007,27(8):3124-3131.

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