Utilizing intelligent technologies in construction and demolition waste management: From a systematic review to an implementation framework

Abstract

The rapid increase in global urbanization, along with the growth of the construction industry, highlights the urgent need for effective management of construction and demolition (C&D) waste. Intelligent technologies offer a viable solution to this critical challenge. However, there remains a significant challenge in integrating these technologies into a cohesive framework. This study conducts a quantitative analysis of 214 papers from 2000 to 2023, highlighting the extensive use of artificial intelligence (AI) and building information modeling (BIM), along with geographic information systems (GIS) and big data (BD). A further qualitative analysis of 73 selected papers investigates the use of seven different intelligent technologies in the context of C&D waste management (CDWM). To overcome current limitations in knowledge, future research should concentrate on (1) the comprehensive integration of technology, (2) inclusive studies throughout all lifecycle phases of CDWM, and (3) the continued examination of new technologies, such as blockchain. Based on these insights, this study suggests a strategic framework for the effective implementation of intelligent technologies in CDWM. This framework aims to assist professionals in merging various technologies, undertaking lifecycle-wide research, and narrowing the divide between existing and new technologies. It also lays a solid foundation for future academic work to examine specific intelligent technologies, conduct comparative studies, and refine strategic decisions. Regular updates on technological developments are essential for stakeholders to consistently enhance CDWM standards.

Graphical abstract

Keywords

C&D waste management / construction and demolition waste / intelligent technologies / literature review / implementation framework

References

[1] Abioye S O, Oyedele L O, Akanbi L, Ajayi A, Delgado J M D, Bilal M, Akinade O O, Ahmed A, (2021). Artificial intelligence in the construction industry: A review of present status, opportunities and future challenges. Journal of Building Engineering, 44: 103299

[2] Ağaçsapan B, Çabuk S N, (2020). Determination of suitable waste transfer station areas for sustainable territories: Eskisehir case. Sustainable Cities and Society, 52: 101829

[3] Akanbi L A, Oyedele A O, Oyedele L O, Salami R O, (2020). Deep learning model for demolition waste prediction in a circular economy. Journal of Cleaner Production, 274: 122843

[4] Akanbi L A, Oyedele L O, Akinade O O, Ajayi A O, Delgado M D, Bilal M, Bello S A, (2018). Salvaging building materials in a circular economy: A BIM-based whole-life performance estimator. Resources, Conservation and Recycling, 129: 175–186

[5] Akinade O O, Oyedele L O, (2019). Integrating construction supply chains within a circular economy: An ANFIS-based waste analytics system (A-WAS). Journal of Cleaner Production, 229: 863–873

[6] Akinade O O, Oyedele L O, Bilal M, Ajayi S O, Owolabi H A, Alaka H A, Bello S A, (2015). Waste minimisation through deconstruction: A BIM based Deconstructability Assessment Score (BIM-DAS). Resources, Conservation and Recycling, 105: 167–176

[7] AkindeleOAjayi SOyegokeA SAlakaH AOmotayoT (2023). Application of Geographic Information System (GIS) in construction: a systematic review. Smart and Sustainable Built Environment

[8] Almendros-JimenezJ MBecerra-TerónATorres M (2021). The retrieval of social network data for points-of-Interest in openstreetMap. Human-Centric Computing and Information Sciences, 11

[9] Alshawabkeh Y, Baik A, Fallatah A, (2021). As-textured As-built BIM using sensor fusion, Zee Ain historical village as a case study. Remote Sensing, 13( 24): 5135

[10] Alwan Z, Jones P, Holgate P, (2017). Strategic sustainable development in the UK construction industry, through the framework for strategic sustainable development, using Building Information Modelling. Journal of Cleaner Production, 140: 349–358

[11] AlZaghrini N, Srour F J, Srour I, (2019). Using GIS and optimization to manage construction and demolition waste: The case of abandoned quarries in Lebanon. Waste Management, 95: 139–149

[12] AlzubiK MSalah Alaloul WAl SalaheenMHannan QureshiAMusaratM A Mohsen AlawagA (2022). Reviewing the applications of internet of things in construction projects. In: 2022 International Conference on Decision Aid Sciences and Applications, DASA 2022, 169–73. Institute of Electrical and Electronics Engineers Inc

[13] Ang W L, Mohammad A W, Johnson D, Hilal N, (2019). Forward osmosis research trends in desalination and wastewater treatment: A review of research trends over the past decade. Journal of Water Process Engineering, 31: 100886

[14] Arabshahi M, Wang D, Sun J, Rahnamayiezekavat P, Tang W, Wang Y, Wang X, (2021). Review on sensing technology adoption in the construction industry. Sensors, 21( 24): 8307

[15] Aslam M S, Huang B J, Cui L F, (2020). Review of construction and demolition waste management in China and USA. Journal of Environmental Management, 264: 110445

[16] Atta I, Bakhoum E S, Marzouk M M, (2021). Digitizing material passport for sustainable construction projects using BIM. Journal of Building Engineering, 43: 103233

[17] Bakchan A, Faust K M, Leite F, (2019). Seven-dimensional automated construction waste quantification and management framework: Integration with project and site planning. Resources, Conservation and Recycling, 146: 462–474

[18] Banerjee M, Lee J, Choo K K R, (2018). A blockchain future for internet of things security: A position paper. Digital Communications and Networks, 4( 3): 149–160

[19] Bang S, Olsson N, (2022). Artificial intelligence in construction projects: A systematic scoping review. Journal of Engineering, Project, and Production Management, 12: 224–238

[20] Bao Z K, (2023). Developing circularity of construction waste for a sustainable built environment in emerging economies: New insights from China. Developments in the Built Environment, 13: 100107

[21] Bao Z K, Lee W M, Lu W S, (2020). Implementing on-site construction waste recycling in Hong Kong: Barriers and facilitators. Science of the Total Environment, 747: 141091

[22] Bao Z K, Lu W S, (2023). Applicability of the environmental Kuznets curve to construction waste management: A panel analysis of 27 European economies. Resources, Conservation and Recycling, 188: 106667

[23] Bao Z K, Lu W S, Chi B, Yuan H P, Hao J L, (2019). Procurement innovation for a circular economy of construction and demolition waste: Lessons learnt from Suzhou, China. Waste management, 99: 12–21

[24] Bao Z K, Lu W S, Peng Z Y, Ng S T, (2023). Balancing economic development and construction waste management in emerging economies: A longitudinal case study of Shenzhen, China guided by the environmental Kuznets curve. Journal of Cleaner Production, 396: 136547

[25] Bernardo M, Gomes M C, de Brito J, (2016). Demolition waste generation for development of a regional management chain model. Waste Management, 49: 156–169

[26] Bi W, Lu W S, Zhao Z, Webster C J, (2022). Combinatorial optimization of construction waste collection and transportation: A case study of Hong Kong. Resources, Conservation and Recycling, 179: 106043

[27] Bruschi D M, Rusconi D, Zoia M, (2022). Blockchain technologies state of the art and evolution: An overview. Osservatorio del Diritto Civile e Commerciale, 11: 9–22

[28] Chandrasekaran H, Subramani S E, Partheeban P, Sridhar M, (2023). IoT- and GIS-based environmental impact assessment of construction and demolition waste dump yards. Sustainability, 15( 17): 13013

[29] Chen Q, Cheng Q H, Wang J F, Du M Y, Zhou L, Liu Y, (2021). Identification and evaluation of urban construction waste with VHR remote sensing using multi-feature analysis and a hierarchical segmentation method. Remote Sensing, 13( 1): 158

[30] Chen S Z, Xu H, Liu D K, Hu B, Wang H C, (2014). A vision of IoT: Applications, challenges, and opportunities with China perspective. IEEE Internet of Things Journal, 1( 4): 349–359

[31] Chen X, Lu W S, (2017). Identifying factors influencing demolition waste generation in Hong Kong. Journal of Cleaner Production, 141: 799–811

[32] Colomina I, Molina P, (2014). Unmanned aerial systems for photogrammetry and remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 92: 79–97

[33] da Paz D H F, Lafayette K P V, Sobral M D, (2018). GIS-based planning system for managing the flow of construction and demolition waste in Brazil. Waste Management & Research, 36( 6): 541–549

[34] Debrah C, Chan A P C, Darko A, (2022). Artificial intelligence in green building. Automation in Construction, 137: 104192

[35] Deepa N, Pham Q V, Nguyen D C, Bhattacharya S, Prabadevi B, Gadekallu T R, Maddikunta P K R, Fang F, Pathirana P N, (2022). A survey on blockchain for big data: Approaches, opportunities, and future directions. Future Generation Computer Systems, 131: 209–226

[36] DingF (2020). How to apply and implement BIM technology and knowledge in engineering projects. In: E3S Web of Conferences, edited by W. Qin. EDP Sciences

[37] Ding X, Yang Z, (2022). Knowledge mapping of platform research: a visual analysis using VOSviewer and CiteSpace. Electronic Commerce Research, 22( 3): 787–809

[38] Donthu N, Kumar S, Pattnaik D, (2020). Forty-five years of Journal of Business Research: A bibliometric analysis. Journal of Business Research, 109: 1–14

[39] Duan H B, Li J H, (2016). Construction and demolition waste management: China’s lessons. Waste Management & Research, 34( 5): 397–398

[40] Duan Y Q, Edwards J S, Dwivedi Y K, (2019). Artificial intelligence for decision making in the era of Big Data—evolution, challenges and research agenda. International Journal of Information Management, 48: 63–71

[41] Ferasso M, Beliaeva T, Kraus S, Clauss T, Ribeiro-Soriano D, (2020). Circular economy business models: The state of research and avenues ahead. Business Strategy and the Environment, 29( 8): 3006–3024

[42] Fraser C S, Woods A, Brizzi D, (2005). Hyper redundancy for accuracy enhancement in automated close range photogrammetry. Photogrammetric Record, 20( 111): 205–217

[43] GanQWangW SunJHuangQ (2021). Application of BIM in the process of building construction management. In: Sugumaran V, Xu Z, Zhou H, eds. Advances in Intelligent Systems and Computing. Springer Science and Business Media Deutschland GmbH, 319–325

[44] Gencel O, Ozel C, Koksal F, Erdogmus E, Martínez-Barrera G, Brostow W, (2012). Properties of concrete paving blocks made with waste marble. Journal of Cleaner Production, 21( 1): 62–70

[45] GhoreishiMHapponen A (2019). New promises AI brings into circular economy accelerated product design: a review on supporting literature. In: 7th International Conference on Environment Pollution and Prevention (ICEPP). Melbourne, AUSTRALIA

[46] Glinka S, (2022). Cross-sectional SWOT analysis of BIM and GIS integration. Geomatics and Environmental Engineering, 16( 3): 157–183

[47] Göswein V, Gonçalves A B, Silvestre J D, Freire F, Habert G, Kurda R, (2018). Transportation matters–Does it? GIS-based comparative environmental assessment of concrete mixes with cement, fly ash, natural and recycled aggregates. Resources, Conservation and Recycling, 137: 1–10

[48] Graham S, Depp C, Lee E E, Nebeker C, Tu X, Kim H C, Jeste D V, (2019). Artificial intelligence for mental health and mental illnesses: An overview. Current Psychiatry Reports, 21( 11): 116

[49] Guan S, Zhu Z, Wang G, (2022). A review on UAV-based remote sensing technologies for construction and civil applications. Drones, 6( 5): 117

[50] Guerra B C, Bakchan A, Leite F, Faust K M, (2019). BIM-based automated construction waste estimation algorithms: The case of concrete and drywall waste streams. Waste Management, 87: 825–832

[51] Guo J, Miatto A, Shi F, Tanikawa H, (2019). Spatially explicit material stock analysis of buildings in Eastern China metropoles. Resources, Conservation and Recycling, 146: 45–54

[52] Guo Y, Wang N, Xu Z Y, Wu K, (2020). The internet of things-based decision support system for information processing in intelligent manufacturing using data mining technology. Mechanical Systems and Signal Processing, 142: 106630

[53] Gupta S, Jha K N, Vyas G, (2022). Proposing building information modeling-based theoretical framework for construction and demolition waste management: Strategies and tools. International Journal of Construction Management, 22( 12): 2345–2355

[54] Han D C, Kalantari M, Rajabifard A, (2021). Building Information Modeling (BIM) for construction and demolition waste management in Australia: A research agenda. Sustainability, 13( 23): 12983

[55] Heigermoser D, de Soto B G, Abbott E L S, Chua D K H, (2019). BIM-based Last Planner System tool for improving construction project management. Automation in Construction, 104: 246–254

[56] Hu H, Wen Y G, Chua T S, Li X L, (2014). Toward scalable systems for big data analytics: A technology tutorial. IEEE Access: Practical Innovations, Open Solutions, 2: 652–687

[57] Hu Q D, Liu R S, Su P Y, Huang J, Peng Y, (2023). Construction and demolition waste generation prediction and spatiotemporal analysis: a case study in Sichuan, China. Environmental Science and Pollution Research International, 30( 14): 41623–41643

[58] Hu R B, Chen K, Fang W L, Zheng L Z, Xu J Y, (2022a). The technology-environment relationship revisited: Evidence from the impact of prefabrication on reducing construction waste. Journal of Cleaner Production, 341: 130883

[59] Hu X Y, Zhou Y, Vanhullebusch S, Mestdagh R, Cui Z Y, Li J B, (2022b). Smart building demolition and waste management frame with image-to-BIM. Journal of Building Engineering, 49: 104058

[60] Huang T, Kou S C, Liu D Y, Li D W, Xing F, (2022). A BIM-GIS-IoT-based system for excavated soil recycling. Buildings, 12( 4): 457

[61] Huang X Q, Fan X W W, Ying J, Chen S Y, (2019). Emerging trends and research foci in gastrointestinal microbiome. Journal of Translational Medicine, 17( 1): 67

[62] Huh J H, Park J H, (2020). Decrepit building monitoring solution for zero energy building management using PLC and android application. Sustainability, 12( 5): 1993

[63] Irizarry J, Karan E P, Jalaei F, (2013). Integrating BIM and GIS to improve the visual monitoring of construction supply chain management. Automation in Construction, 31: 241–254

[64] Jalaei F, Zoghi M, Khoshand A, (2021). Life cycle environmental impact assessment to manage and optimize construction waste using Building Information Modeling (BIM). International Journal of Construction Management, 21( 8): 784–801

[65] Jiang G C, Dong T F, Cui K X, He Y B, Quan X H, Yang L L, Fu Y, (2022a). Research status and development directions of intelligent drilling fluid technologies. Petroleum Exploration and Development, 49( 3): 660–670

[66] Jiang Y H, Huang Y L, Liu J K, Li D P, Li S Y, Nie W J, Chung I H, (2022b). Automatic volume calculation and mapping of construction and demolition debris using drones, deep learning, and GIS. Drones, 6( 10): 279

[67] Jin R Y, Yuan H P, Chen Q, (2019). Science mapping approach to assisting the review of construction and demolition waste management research published between 2009 and 2018. Resources, Conservation and Recycling, 140: 175–188

[68] Kamali M, Hewage K, (2016). Life cycle performance of modular buildings: A critical review. Renewable & Sustainable Energy Reviews, 62: 1171–1183

[69] Kang K, Besklubova S, Dai Y Q, Zhong R Y, (2022). Building demolition waste management through smart BIM: A case study in Hong Kong. Waste Management, 143: 69–83

[70] Kant K, Jolfaei A, Moessner K, (2024). IoT systems for extreme environments. IEEE Internet of Things Journal, 11( 3): 3671–3675

[71] Karimi N, Ng K T W, Richter A, (2022). Development and application of an analytical framework for mapping probable illegal dumping sites using nighttime light imagery and various remote sensing indices. Waste Management, 143: 195–205

[72] KonikovAGaryaev N (2021). Comprehensive use of IT solutions to monitor the state of construction sites. In: E3S Web of Conferences, EDP Sciences

[73] Kor M, Yitmen I, Alizadehsalehi S, (2023). An investigation for integration of deep learning and digital twins towards Construction 4.0. Smart and Sustainable Built Environment, 12( 3): 461–487

[74] Ku Y D, Yang J H, Fang H Y, Xiao W, Zhuang J T, (2021). Deep learning of grasping detection for a robot used in sorting construction and demolition waste. Journal of Material Cycles and Waste Management, 23( 1): 84–95

[75] Kuang W H, Liu J Y, Tian H Q, Shi H, Dong J W, Song C Q, Li X Y, Du G M, Hou Y L, Lu D S, Chi W F, Pan T, Zhang S W, Hamdi R, Yin Z R, Yan H M, Yan C Z, Wu S X, Li R D, Yang J C, Dou Y Y, Wu W B, Liang L Q, Xiang B, Yang S Q, (2022). Cropland redistribution to marginal lands undermines environmental sustainability. National Science Review, 9( 1): nwab091

[76] LeeM W WLu W (2019). Behaviour and big data in construction waste management: A critical review of research. In: 2nd International Conference in Sustainable Buildings & Structures (ICSBS), Suzhou 277–82

[77] Lei L, Ozturk I, Murshed M, Abrorov S, Alvarado R, Mahmood H, (2023). Environmental innovations, energy innovations, governance, and environmental sustainability: Evidence from South and Southeast Asian countries. Resources Policy, 82: 103556

[78] Li C Z D, Zhao Y Y, Xiao B, Yu B, Tam V W Y, Chen Z, Ya Y Y, (2020a). Research trend of the application of information technologies in construction and demolition waste management. Journal of Cleaner Production, 263: 121458

[79] Li G K, Liu J K, Giordano A, (2022a). Robust optimization of construction waste disposal facility location considering uncertain factors. Journal of Cleaner Production, 353: 131455

[80] Li X, Wu P, Shen G Q P, Wang X Y, Teng Y, (2017). Mapping the knowledge domains of Building Information Modeling (BIM): A bibliometric approach. Automation in Construction, 84: 195–206

[81] Li X Q, Jiang P, Chen T, Luo X P, Wen Q Y, (2020b). A survey on the security of blockchain systems. Future Generation Computer Systems, 107: 841–853

[82] Li Z L, Guo H D, Zhang L, Liang D, Zhu Q, Liu T, Zhou H, (2022b). Time-series monitoring of dust-proof nets covering urban construction waste by multispectral images in Zhengzhou, China. Remote Sensing, 14( 15): 3805

[83] Lin Z Q, Xie Q, Feng Y B, Zhang P, Yao P, (2020). Towards a robust facility location model for construction and demolition waste transfer stations under uncertain environment: The case of Chongqing. Waste Management, 105: 73–83

[84] Liu J K, Xiao Y Q, Wang D, Pang Y S, (2019). Optimization of site selection for construction and demolition waste recycling plant using genetic algorithm. Neural Computing & Applications, 31( S1): 233–245

[85] Long W Y, Bao Z K, Chen K, Ng S T, Wuni I Y, (2024). Developing an integrative framework for digital twin applications in the building construction industry: A systematic literature review. Advanced Engineering Informatics, 59: 102346

[86] Loo B P Y, Wong R W M, (2023). Towards a conceptual framework of using technology to support smart construction: The Case of Modular Integrated Construction (MiC). Buildings, 13( 2): 372

[87] Lu W, (2019). Big data analytics to identify illegal construction waste dumping: A Hong Kong study. Resources, Conservation and Recycling, 141: 264–272

[88] Lu W, Chen X, Peng Y, Shen L, (2015). Benchmarking construction waste management performance using big data. Resources, Conservation and Recycling, 105: 49–58

[89] Lu W S, Chen J J, Xue F, (2022). Using computer vision to recognize composition of construction waste mixtures: A semantic segmentation approach. Resources, Conservation and Recycling, 178: 106022

[90] Lu W S, Chen X, Ho D C W, Wang H D, (2016a). Analysis of the construction waste management performance in Hong Kong: The public and private sectors compared using big data. Journal of Cleaner Production, 112: 521–531

[91] Lu W S, Lee W M W, Xue F, Xu J Y, (2021a). Revisiting the effects of prefabrication on construction waste minimization: A quantitative study using bigger data. Resources, Conservation and Recycling, 170: 105579

[92] Lu W S, Lou J F, Webster C, Xue F, Bao Z K, Chi B, (2021b). Estimating construction waste generation in the Greater Bay Area, China using machine learning. Waste Management, 134: 78–88

[93] Lu W S, Peng Y, Chen X, Skitmore M, Zhang X L, (2016b). The S-curve for forecasting waste generation in construction projects. Waste Management, 56: 23–34

[94] Lucena L, Juca J F T, Soares J B, Portela M G, (2014). Potential uses of sewage sludge in highway construction. Journal of Materials in Civil Engineering, 26( 9): 04014051

[95] LvMZhouL WangYYang KYangC (2022). Summary of BIM technology application in a dormitory building project. In: Yang M, Lanzinha J C G, Samui P, eds. Advances in Transdisciplinary Engineering, IOS Press BV, 506–512

[96] Madi N, Srour I, (2019). Managing emergency construction and demolition waste in Syria using GIS. Resources, Conservation and Recycling, 141: 163–175

[97] Malik P K, Sharma R, Singh R, Gehlot A, Satapathy S C, Alnumay W S, Pelusi D, Ghosh U, Nayak J, (2021). Industrial internet of things and its applications in industry 4.0: State of the Art. Computer Communications, 166: 125–139

[98] Marchese D, Reynolds E, Bates M E, Morgan H, Clark S S, Linkov I, (2018). Resilience and sustainability: Similarities and differences in environmental management applications. Science of the Total Environment, 613-614: 1275–1283

[99] Marzouk M, Elmaraghy A, (2021). Design for deconstruction using integrated lean principles and BIM approach. Sustainability, 13( 14): 7856

[100] Mastrucci A, Marvuglia A, Popovici E, Leopold U, Benetto E, (2017). Geospatial characterization of building material stocks for the life cycle assessment of end-of-life scenarios at the urban scale. Resources, Conservation and Recycling, 123: 54–66

[101] MathurAGupta C P (2020). Big data challenges and issues: A review. In: Lecture Notes on Data Engineering and Communications Technologies. Springer Science and Business Media Deutschland GmbH

[102] Mesta C, Kahhat R, Santa-Cruz S, (2019). Geospatial characterization of material stock in the residential sector of a Latin-American City. Journal of Industrial Ecology, 23( 1): 280–291

[103] Miatto A, Schandl H, Forlin L, Ronzani F, Borin P, Giordano A, Tanikawa H, (2019). A spatial analysis of material stock accumulation and demolition waste potential of buildings: A case study of Padua. Resources, Conservation and Recycling, 142: 245–256

[104] Nandakumar K, Ratha N, Pankanti S, Pentland A, Herlihy M, (2020). Blockchain: From technology to marketplaces. Computer, 53( 7): 14–18

[105] Nasir M H A, Genovese A, Acquaye A A, Koh S C L, Yamoah F, (2017). Comparing linear and circular supply chains: A case study from the construction industry. International Journal of Production Economics, 183: 443–457

[106] Nikmehr B, Hosseini M R, Wang J, Chileshe N, Rameezdeen R, (2021). BIM-based tools for managing construction and demolition waste (CDW): A scoping review. Sustainability, 13( 15): 8427

[107] Oluleye B I, Chan D W M, Antwi-Afari P, (2023). Adopting artificial intelligence for enhancing the implementation of systemic circularity in the construction industry: A critical review. Sustainable Production and Consumption, 35: 509–524

[108] Pan X Y, Xie Q, Feng Y B, (2020). Designing recycling networks for construction and demolition waste based on reserve logistics research field. Journal of Cleaner Production, 260: 120841

[109] PantPTanwar R (2016). An overview of big data opportunity and challenges. In: Nayak M, Singh D, Mishra D K, eds. Communications in Computer and Information Science, Springer Verlag, 691–697

[110] Pătrăucean V, Armeni I, Nahangi M, Yeung J M, Brilakis I, Haas C, (2015). State of research in automatic as-built modelling. Advanced Engineering Informatics, 29( 2): 162–171

[111] Perboli G, Musso S, Rosano M, (2018). Blockchain in logistics and supply chain: A lean approach for designing real-world use cases. IEEE Access: Practical Innovations, Open Solutions, 6: 62018–62028

[112] Porwal A, Parsamehr M, Szostopal D, Ruparathna R, Hewage K, (2023). The integration of building information modeling (BIM) and system dynamic modeling to minimize construction waste generation from change orders. International Journal of Construction Management, 23( 1): 156–166

[113] Prăvălie R, (2016). Drylands extent and environmental issues. A global approach. Earth-Science Reviews, 161: 259–278

[114] Quiñones R, Llatas C, Montes M V, Cortés I, (2021). A multiplatform BIM-Integrated construction waste quantification model during design phase. The Case of the Structural System in a Spanish Building. Recycling, 6( 3): 62

[115] Rahimi M, Ghezavati V, (2018). Sustainable multi-period reverse logistics network design and planning under uncertainty utilizing conditional value at risk (CVaR) for recycling construction and demolition waste. Journal of Cleaner Production, 172: 1567–1581

[116] Rajpurkar P, Chen E, Banerjee O, Topol E J, (2022). AI in health and medicine. Nature Medicine, 28( 1): 31–38

[117] Ravindra K, Kaur K, Mor S, (2015). System analysis of municipal solid waste management in Chandigarh and minimization practices for cleaner emissions. Journal of Cleaner Production, 89: 251–256

[118] Ray P P, (2018). A survey on Internet of Things architectures. Journal of King Saud University—Computer and Information Sciences, 30( 3): 291–319

[119] RodrigoNOmrany HChangR DZuoJ (2023). Leveraging digital technologies for circular economy in construction industry: a way forward. Smart and Sustainable Built Environment

[120] Ruiz L A L, Ramón X R, Domingo S G, (2020). The circular economy in the construction and demolition waste sector—A review and an integrative model approach. Journal of Cleaner Production, 248: 119238

[121] SabarmathiGChinnaiyan R (2017). Investigations on big data features research challenges and applications. In: Proceedings of the 2017 International Conference on Intelligent Computing and Control Systems, 2017, 782–786

[122] Sánchez-Lozano J M, Teruel-Solano J, Soto-Elvira P L, García-Cascales M S, (2013). Geographical Information Systems (GIS) and Multi-Criteria Decision Making (MCDM) methods for the evaluation of solar farms locations: Case study in south-eastern Spain. Renewable & Sustainable Energy Reviews, 24: 544–556

[123] SarrabMAlnaeli S M (2018). Critical Aspects Pertaining Security of IoT Application Level Software Systems. In: 2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference, 2018, 960–964

[124] Schandl H, Hatfield-Dodds S, Wiedmann T, Geschke A, Cai Y Y, West J, Newth D, Baynes T, Lenzen M, Owen A, (2016). Decoupling global environmental pressure and economic growth: Scenarios for energy use, materials use and carbon emissions. Journal of Cleaner Production, 132: 45–56

[125] SelvamSManisha AVidhyaJVenkatramananS (2019). Fundamentals of GIS. In: Selvam S, Manisha A, Vidhya J, Venkatramanan S. GIS and Geostatistical Techniques for Groundwater Science, Elsevier

[126] Seror N, Portnov B A, (2018). Identifying areas under potential risk of illegal construction and demolition waste dumping using GIS tools. Waste Management, 75: 22–29

[127] Setaki F, van Timmeren A, (2022). Disruptive technologies for a circular building industry. Building and Environment, 223: 109394

[128] Shen Z F, Wu H Y, Chen Z S, Hu J T, Pan J X, Kong J Q, Lin T X, (2022). The global research of artificial intelligence on prostate cancer: A 22-Year bibliometric analysis. Frontiers in Oncology, 12: 843735

[129] Shi Q W, Ren H, Ma X R, Xiao Y Q, (2019). Site selection of construction waste recycling plant. Journal of Cleaner Production, 227: 532–542

[130] Shvets Y, Hanák T, (2023). Use of the internet of things in the construction industry and facility management: Usage examples overview. In: Procedia Computer Science, 219: 1670–1677

[131] Smol M, Kulczycka J, Henclik A, Gorazda K, Wzorek Z, (2015). The possible use of sewage sludge ash (SSA) in the construction industry as a way towards a circular economy. Journal of Cleaner Production, 95: 45–54

[132] Song Y L, Wang Y, Liu F, Zhang Y X, (2017). Development of a hybrid model to predict construction and demolition waste: China as a case study. Waste Management, 59: 350–361

[133] Soto-Paz J, Hernandez A, Mejía-Parada C A, Mora-Ruiz V, Hernández W, Luna-Guevara F, Casallas-Ojeda M, Parra-Orobio B A, (2023). A hybrid decision tool for site selection of construction and demolition waste (CDW) facilities in developing countries. Environmental Processes, 10( 2): 35

[134] Su S, Wang Q, Han L X, Hong J Q, Liu Z W, (2020). BIM-DLCA: An integrated dynamic environmental impact assessment model for buildings. Building and Environment, 183: 107218

[135] Trang N H, (2020). Limitations of Big Data partitions technology. Journal of Applied Data Sciences, 1: 11–19

[136] Umar U A, Shafiq N, Isa M H, (2018). Investigation of construction wastes generated in the Malaysian residential sector. Waste Management & Research, 36( 12): 1157–1165

[137] Uyarra E, Gee S, (2013). Transforming urban waste into sustainable material and energy usage: The case of Greater Manchester (UK). Journal of Cleaner Production, 50: 101–110

[138] VinckeSBassier MVergauwenM (2019). Image recording challenges for photogrammetric construction site monitoring. In: Remondino F, Fassi F, Cardaci A, eds. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Copernicus GmbH, 747–753

[139] VolkMStaegemann DPohlMTurowskiK (2019). Challenging big data engineering: Positioning of current and future development. In: Ramachandran M, Walters R, Wills G, eds. IoTBDS 2019—Proceedings of the 4th International Conference on Internet of Things, Big Data and Security, SciTePress, 351–358

[140] Wamba S F, Akter S, Edwards A, Chopin G, Gnanzou D, (2015). How “big data” can make big impact: Findings from a systematic review and a longitudinal case study. International Journal of Production Economics, 165: 234–246

[141] Wang H Q, Yi W, Liu Y N, (2022a). Optimal route design for construction waste transportation systems: mathematical models and solution algorithms. Mathematics, 10( 22): 4340

[142] Wang H Y, Chen D J, Duan H B, Yin F F, Niu Y N, (2019a). Characterizing urban building metabolism with a 4D-GIS model: A case study in China. Journal of Cleaner Production, 228: 1446–1454

[143] Wang J J, Wei J J, Liu Z S, Huang C, Du X L, (2022b). Life cycle assessment of building demolition waste based on building information modeling. Resources, Conservation and Recycling, 178: 106095

[144] Wang J Y, Wu H Y, Duan H B, Zillante G, Zuo J, Yuan H P, (2018). Combining life cycle assessment and building information modelling to account for carbon emission of building demolition waste: A case study. Journal of Cleaner Production, 172: 3154–3166

[145] Wang T K, Wu Z Q, Luo C Y, (2021). Multi-participant construction waste demolition and transportation decision-making system. Resources, Conservation and Recycling, 170: 105575

[146] Wang Z L, Li H, Yang X T, (2020). Vision-based robotic system for on-site construction and demolition waste sorting and recycling. Journal of Building Engineering, 32: 13

[147] Wang Z L, Li H, Zhang X L, (2019b). Construction waste recycling robot for nails and screws: Computer vision technology and neural network approach. Automation in Construction, 97: 220–228

[148] Wei X X, Ye M, Yuan L, Bi W, Lu W S, (2022). Analyzing the freight characteristics and carbon emission of construction waste hauling trucks: Big data analytics of Hong Kong. International Journal of Environmental Research and Public Health, 19( 4): 2318

[149] Won J, Cheng J C P, (2017). Identifying potential opportunities of building information modeling for construction and demolition waste management and minimization. Automation in Construction, 79: 3–18

[150] Won J, Cheng J C P, Lee G, (2016). Quantification of construction waste prevented by BIM-based design validation: Case studies in Republic of Korea. Waste Management, 49: 170–180

[151] Wu H Y, Wang J Y, Duan H B, Ouyang L, Huang W K, Zuo J, (2016). An innovative approach to managing demolition waste via GIS (geographic information system): A case study in Shenzhen city, China. Journal of Cleaner Production, 112: 494–503

[152] Wu L, Lu W S, Peng Z Y, Webster C, (2023). A blockchain non-fungible token-enabled ‘passport’ for construction waste material cross-jurisdictional trading. Automation in Construction, 149: 104783

[153] Wu Z Z, Yu A T W, Poon C S, (2020). Promoting effective construction and demolition waste management towards sustainable development: A case study of Hong Kong. Sustainable Development, 28( 6): 1713–1724

[154] Wu Z Z, Yu A T W, Shen L Y, (2017). Investigating the determinants of contractor’s construction and demolition waste management behavior in China’s mainland. Waste Management, 60: 290–300

[155] Wu Z Z, Yu A T W, Shen L Y, Liu G W, (2014). Quantifying construction and demolition waste: An analytical review. Waste Management, 34( 9): 1683–1692

[156] Xiao F, Lu T G, Ai Q, Wang X L, Chen X Y, Fang S D, Wu Q W, (2020). Design and Implementation of a data-driven approach to visualizing power quality. IEEE Transactions on Smart Grid, 11( 5): 4366–4379

[157] Xiao W, Yang J H, Fang H Y, Zhuang J T, Ku Y D, (2019). A robust classification algorithm for separation of construction waste using NIR hyperspectral system. Waste Management, 90: 1–9

[158] XingDTao J (2016). Design and application of green building based on BIM. In: Xie Y, Bian F. Communications in Computer and Information Science, Springer Verlag, 901–907

[159] Xu J, Lu W, Ye M, Xue F, Zhang X, Lee B F P, (2020). Is the private sector more efficient? Big data analytics of construction waste management sectoral efficiency. Resources, Conservation and Recycling, 155: 104674

[160] Xu J P, Shi Y, Xie Y C, Zhao S W, (2019a). A BIM-based construction and demolition waste information management system for greenhouse gas quantification and reduction. Journal of Cleaner Production, 229: 308–324

[161] Xu M, Chen X T, Kou G, (2019b). A systematic review of blockchain. Financial Innovation, 5( 1): 27

[162] Lu Y, (2019). The blockchain: State-of-the-art and research challenges. Journal of Industrial Information Integration, 15: 80–90

[163] Yang L C, Ao Y B, Ke J T, Lu Y, Liang Y, (2021). To walk or not to walk? Examining non-linear effects of streetscape greenery on walking propensity of older adults. Journal of Transport Geography, 94: 103099

[164] Yang L C, Yu B J, Liang Y, Lu Y, Li W X, (2023). Time-varying and non-linear associations between metro ridership and the built environment. Tunnelling and Underground Space Technology, 132: 104931

[165] Yang Q, Du Q, Razzaq A, Shang Y F, (2022). How volatility in green financing, clean energy, and green economic practices derive sustainable performance through ESG indicators? A sectoral study of G7 countries. Resources Policy, 75: 102526

[166] Yang R, Wakefield R, Lyu S N, Jayasuriya S, Han F L, Yi X, Yang X C, Amarasinghe G, Chen S P, (2020). Public and private blockchain in construction business process and information integration. Automation in Construction, 118: 103276

[167] YaoPFengY B XieQZhangY ZhangP (2022). Optimizing site selection for construction demolition waste treatment plants considering demand and supply uncertainty: A case study in Chongqing, China. Engineering Optimization: 20

[168] You Z, Wu C, Zheng L, Feng L, (2020). An informatization scheme for construction and demolition waste supervision and management in China. Sustainability, 12( 4): 1672

[169] Yu K H, Beam A L, Kohane I S, (2018). Artificial intelligence in healthcare. Nature Biomedical Engineering, 2( 10): 719–731

[170] Yu Y F, Yazan D M, Junjan V, Iacob M E, (2022). Circular economy in the construction industry: A review of decision support tools based on Information & Communication Technologies. Journal of Cleaner Production, 349: 131335

[171] Zavadskas E K, Bausys R, Lazauskas M, (2015). Sustainable assessment of alternative sites for the construction of a waste incineration plant by applying WASPAS method with Single-Valued Neutrosophic set. Sustainability, 7( 12): 15923–15936

[172] Zhan Y, Zhang Y R, Nie Z L, Luo Z W, Qiu S, Wang J, Zhang A A, Ai C F, Tang X, Tan C Y, (2023). Intelligent paving and compaction technologies for asphalt pavement. Automation in Construction, 156: 105081

[173] Zhang B, Wu Y F, Zhao B Y, Chanussot J, Hong D F, Yao J, Gao L R, (2022). Progress and challenges in intelligent remote sensing satellite systems. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15: 1814–1822

[174] Zhang L Z, Atkins A S, (2015). A decision support application in tracking construction waste using rule-based reasoning and RFID technology. International Journal of Computational Intelligence Systems, 8: 128–137

[175] Zhang X Q, Ahmed R R, (2022). A queuing system for inert construction waste management on a reverse logistics network. Automation in Construction, 137: 104221

[176] Zhao R, Shi Z W, Zou Z X, (2022). High-resolution remote sensing image captioning based on structured attention. IEEE Transactions on Geoscience and Remote Sensing, 60: 1

[177] Zhao X B, (2017). A scientometric review of global BIM research: Analysis and visualization. Automation in Construction, 80: 37–47

[178] Zheng X, Lu Y J, Li Y K, Le Y, Xiao J Z, (2019). Quantifying and visualizing value exchanges in building information modeling (BIM) projects. Automation in Construction, 99: 91–108

[179] Zheng Z B, Xie S A, Dai H N, Chen X P, Wang H M, (2018). Blockchain challenges and opportunities: A survey. International Journal of Web and Grid Services, 14( 4): 352–375

[180] Zhong R Y, Newman S T, Huang G Q, Lan S L, (2016). Big Data for supply chain management in the service and manufacturing sectors: Challenges, opportunities, and future perspectives. Computers & Industrial Engineering, 101: 572–591

[181] Zhou L, Luo T, Du M Y, Chen Q, Liu Y, Zhu Y N, He C C, Wang S Y, Yang K, (2021). Machine learning comparison and parameter setting methods for the detection of dump sites for construction and demolition waste using the Google Earth Engine. Remote Sensing, 13( 4): 787

[182] Zhu J W, Liu W S, (2020). A tale of two databases: the use of Web of Science and Scopus in academic papers. Scientometrics, 123( 1): 321–335

[183] Zhu J X, Wang X Y, Wang P, Wu Z Y, Kim M J, (2019). Integration of BIM and GIS: Geometry from IFC to shapefile using open-source technology. Automation in Construction, 102: 105–119

[184] ZhuXWangD (2019). Research on blockchain application for E-commerce, finance and energy. In: IOP Conference Series: Earth and Environmental Science. Institute of Physics Publishing

RIGHTS & PERMISSIONS

The Author(s). This article is published with open access at link.springer.com and journal.hep.com.cn