{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T14:16:10Z","timestamp":1783606570344,"version":"3.55.0"},"reference-count":42,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2022,2,20]],"date-time":"2022-02-20T00:00:00Z","timestamp":1645315200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Natural Science Foundation of China, grant number 52178095","award":["grant number 52178095"],"award-info":[{"award-number":["grant number 52178095"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Civil infrastructure O&amp;M requires intelligent monitoring techniques and control methods to ensure safety. Unfortunately, tedious modeling efforts and the rigorous computing requirements of large-scale civil infrastructure have hindered the development of structural research. This study proposes a method for impact response prediction of prestressed steel structures driven by digital twins (DTs) and machine learning (ML). The high-fidelity DTs of a prestressed steel structure were constructed from the perspective of both a physical entity and virtual entity. A prediction of the impact response of prestressed steel structure\u2019s key parts was established based on ML, and a structure response prediction of the parts driven by data was realized. To validate the effectiveness of the proposed prediction method, the authors carried out a case study in an experiment of a prestressed steel structure. This study provides a reference for fusion applications with DTs and ML in impact response prediction and analysis of prestressed steel structures.<\/jats:p>","DOI":"10.3390\/s22041647","type":"journal-article","created":{"date-parts":[[2022,2,21]],"date-time":"2022-02-21T08:34:47Z","timestamp":1645432487000},"page":"1647","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Digital Twins-Based Impact Response Prediction of Prestressed Steel Structure"],"prefix":"10.3390","volume":"22","author":[{"given":"Zhansheng","family":"Liu","sequence":"first","affiliation":[{"name":"Faculty of Architecture, Civil and Transportation Engineering, Beijing University of Technology, Beijing 100124, China"},{"name":"The Key Laboratory of Urban Security and Disaster Engineering of the Ministry of Education, Beijing University of Technology, Beijing 100124, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chao","family":"Yuan","sequence":"additional","affiliation":[{"name":"Faculty of Architecture, Civil and Transportation Engineering, Beijing University of Technology, Beijing 100124, China"},{"name":"The Key Laboratory of Urban Security and Disaster Engineering of the Ministry of Education, Beijing University of Technology, Beijing 100124, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhe","family":"Sun","sequence":"additional","affiliation":[{"name":"Faculty of Architecture, Civil and Transportation Engineering, Beijing University of Technology, Beijing 100124, China"},{"name":"The Key Laboratory of Urban Security and Disaster Engineering of the Ministry of Education, Beijing University of Technology, Beijing 100124, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cunfa","family":"Cao","sequence":"additional","affiliation":[{"name":"Faculty of Architecture, Civil and Transportation Engineering, Beijing University of Technology, Beijing 100124, China"},{"name":"The Key Laboratory of Urban Security and Disaster Engineering of the Ministry of Education, Beijing University of Technology, Beijing 100124, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"F4016004","DOI":"10.1061\/(ASCE)ST.1943-541X.0001543","article-title":"Maintenance and Operation of Infrastructure Systems: Review","volume":"142","author":"Frangopol","year":"2016","journal-title":"J. Struct. Eng."},{"key":"ref_2","first-page":"96","article-title":"Refined simplified finite element model of cable and its sensitivity analysis","volume":"48","author":"Yu","year":"2015","journal-title":"J. Tianjin Univ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.autcon.2014.09.004","article-title":"Automated daily pattern filtering of measured building performance data","volume":"49","author":"Miller","year":"2015","journal-title":"Autom. Constr."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Cui, Y., Li, Q., and Dong, Z. (2019). Structural 3D Reconstruction of Indoor Space for 5G Signal Simulation with Mobile Laser Scanning Point Clouds. Remote Sens., 11.","DOI":"10.3390\/rs11192262"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"6638897","DOI":"10.1155\/2021\/6638897","article-title":"Digital-Twin-Based Evaluation of Nearly Zero-Energy Building for Existing Buildings Based on Scan-to-BIM","volume":"2021","author":"Zhao","year":"2021","journal-title":"Adv. Civ. Eng."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Kim, D., Kwon, S., Cho, C.S., Garc\u00eda de Soto, B., and Moon, D. (2020). Automatic Space Analysis Using Laser Scanning and a 3D Grid: Applications to Industrial Plant Facilities. Sustainability, 12.","DOI":"10.3390\/su12219087"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"758","DOI":"10.1016\/j.engstruct.2018.07.012","article-title":"Nonlinear dynamic response of steel materials and plain plate systems to impact loads: Review and validation","volume":"173","author":"Mortazavi","year":"2018","journal-title":"Eng. Struct."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1007\/s11837-001-0003-1","article-title":"Why did the World Trade Center collapse? Science, engineering, and speculation","volume":"53","author":"Eagar","year":"2001","journal-title":"JOM"},{"key":"ref_9","first-page":"136","article-title":"Dynamic response analysis of CFRP strengthened debris flow flexible cable net system","volume":"47","author":"Wang","year":"2021","journal-title":"J. Lanzhou Univ. Technol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"148539","DOI":"10.1016\/j.scitotenv.2021.148539","article-title":"Digitalization to achieve sustainable development goals: Steps towards a Smart Green Planet","volume":"794","author":"Mondejar","year":"2021","journal-title":"Sci. Total Environ."},{"key":"ref_11","first-page":"1","article-title":"Digital twin and its potential application exploration","volume":"24","author":"Tao","year":"2018","journal-title":"Comput. Integr. Manuf. Syst."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3563","DOI":"10.1007\/s00170-017-0233-1","article-title":"Digital twin-driven product design, manufacturing and service with big data","volume":"94","author":"Tao","year":"2018","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Sepasgozar, S. (2021). Differentiating digital twin from digital shadow: Elucidating a paradigm shift to expedite a smart, sustainable built environment. Buildings, 11.","DOI":"10.3390\/buildings11040151"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1319","DOI":"10.1080\/15732479.2019.1620789","article-title":"Development of a bridge maintenance system for prestressed concrete bridges using 3D digital twin model","volume":"15","author":"Shim","year":"2019","journal-title":"Struct. Infrastruct. Eng."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.strusafe.2018.12.004","article-title":"Pattern recognition approach to assess the residual structural capacity of damaged tall buildings","volume":"78","author":"Zhang","year":"2019","journal-title":"Struct. Safety"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"101816","DOI":"10.1016\/j.jobe.2020.101816","article-title":"Machine learning applications for building structural design and performance assessment: State-of-the-art review","volume":"33","author":"Sun","year":"2021","journal-title":"J. Build. Eng."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"711","DOI":"10.1111\/mice.12641","article-title":"A decentralized unsupervised structural condition diagnosis approach using deep auto-encoders","volume":"36","author":"Jiang","year":"2021","journal-title":"Comput. Civ. Infrastruct. Eng."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1930","DOI":"10.1016\/j.istruc.2020.07.063","article-title":"Predicting capacity model and seismic fragility estimation for RC bridge based on artificial neural network","volume":"27","author":"Huang","year":"2020","journal-title":"Structures"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"114656","DOI":"10.1016\/j.compstruct.2021.114656","article-title":"Damage detection on rectangular laminated composite plates using wavelet based convolutional neural network technique","volume":"278","author":"Saadatmorad","year":"2021","journal-title":"Compos. Struct."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"103517","DOI":"10.1016\/j.autcon.2020.103517","article-title":"Roles of artificial intelligence in construction engineering and management: A critical review and future trends","volume":"122","author":"Pan","year":"2020","journal-title":"Autom. Constr."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1111\/j.1467-8667.2006.00431.x","article-title":"Structural health monitoring via measured Ritz vectors utilizing artificial neural networks","volume":"21","author":"Lam","year":"2006","journal-title":"Comput.-Aided Civ. Infrastruct. Eng."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"107100","DOI":"10.1016\/j.asoc.2021.107100","article-title":"Optimization strategies of neural networks for impact damage classification of RC panels in a small dataset","volume":"102","author":"Doan","year":"2021","journal-title":"Appl. Soft Comput."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.strusafe.2004.03.004","article-title":"Structural reliability analysis for implicit performance functions using artificial neural network","volume":"27","author":"Deng","year":"2005","journal-title":"Struct. Saf."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.strusafe.2014.09.002","article-title":"Review and application of Artificial Neural Networks models in reliability analysis of steel structures","volume":"52","author":"Chojaczyk","year":"2015","journal-title":"Struct. Saf."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1193","DOI":"10.1016\/j.promfg.2018.07.155","article-title":"Modeling of Cloud-Based Digital Twins for Smart Manufacturing with MT Connect","volume":"26","author":"Hu","year":"2018","journal-title":"Procedia Manuf."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Kahlen, F.J., Flumerfelt, S., and Alves, A. (2017). Digital Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in Complex Systems. Transdisciplinary Perspectives on Complex Systems, Springer.","DOI":"10.1007\/978-3-319-38756-7"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"106282","DOI":"10.1016\/j.compstruc.2020.106282","article-title":"Development of the simulation model for Digital Twin applications in historical masonry buildings: The integration between numerical and experimental reality","volume":"238","author":"Angjeliu","year":"2020","journal-title":"Comput. Struct."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Liu, Z., Zhang, A., and Wang, W. (2020). A framework for an indoor safety management system based on digital twin. Sensors, 20.","DOI":"10.3390\/s20205771"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"104125","DOI":"10.1016\/j.tust.2021.104125","article-title":"A digital twin-based decision analysis framework for O&M of tunnels","volume":"116","author":"Yu","year":"2021","journal-title":"Tunn. Undergr. Space Technol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"112461","DOI":"10.1016\/j.engstruct.2021.112461","article-title":"Digital Twin-driven framework for fatigue life prediction of steel bridges using a probabilistic multiscale model: Application to segmental orthotropic steel deck specimen","volume":"241","author":"Jiang","year":"2021","journal-title":"Eng. Struct."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Liu, Z., Shi, G., Zhang, A., and Huang, C. (2020). Intelligent tensioning method for prestressed cables based on digital twins and artificial intelligence. Sensors, 20.","DOI":"10.3390\/s20247006"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"6640198","DOI":"10.1155\/2021\/6640198","article-title":"Intelligent Prediction Method for O&M Safety of Prestressed Steel Structure Based on Digital Twin Technology","volume":"2021","author":"Liu","year":"2021","journal-title":"Adv. Civ. Eng."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Wang, H., Xu, L., and Wang, X. (2019). Outage probability performance prediction for mobile cooperative communication networks based on artificial neural network. Sensors, 19.","DOI":"10.3390\/s19214789"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"959","DOI":"10.1002\/rnc.727","article-title":"An introduction to the use of neural networks in control systems","volume":"12","author":"Hagan","year":"2002","journal-title":"Int. J. Robust Nonlinear Control. IFAC-Affil. J."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"124951","DOI":"10.1016\/j.conbuildmat.2021.124951","article-title":"Compressive strength of rubberized concrete: Regression and GA-BPNN approaches using ultrasonic pulse velocity","volume":"307","author":"Zhang","year":"2021","journal-title":"Constr. Build. Mater."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1016\/j.scs.2018.05.050","article-title":"Predicting electricity consumption in a building using an optimized back-propagation and Levenberg\u2013Marquardt back-propagation neural network: Case study of a shopping mall in China","volume":"42","author":"Ye","year":"2018","journal-title":"Sustain. Cities Soc."},{"key":"ref_37","first-page":"946","article-title":"Sensitive teston relaxation of cable and reliability assessment of spoke cable-truss structure","volume":"47","author":"Liu","year":"2019","journal-title":"J. Tongji Univ."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Zhang, Z.H., Na Yan, D., Ju, J.T., and Han, Y. (2012). Prediction of the Flow Stress of a High Alloyed Austenitic Stainless Steel Using Artificial Neural Network. Materials Science Forum, Trans Tech Publications Ltd.","DOI":"10.4028\/www.scientific.net\/MSF.724.351"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1007\/978-1-4899-3099-6_2","article-title":"Statistical aspects of neural networks","volume":"Volume 50","author":"Ripley","year":"1993","journal-title":"Networks and Chaos\u2014Statistical and Probabilistic Aspects"},{"key":"ref_40","unstructured":"Paola, J. (1994). Neural Network Classification of Multispectral Imagery. [Master\u2019s Thesis, The University of Arizona]."},{"key":"ref_41","unstructured":"Wang, C. (1994). A Theory of Generalization in Learning Machines with Neural Network Applications, University of Pennsylvania."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Masters, T. (1993). Practical Neural Network Recipes in C++, Academic Press Professional, Inc.","DOI":"10.1016\/B978-0-08-051433-8.50017-3"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/4\/1647\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:23:22Z","timestamp":1760135002000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/4\/1647"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,20]]},"references-count":42,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2022,2]]}},"alternative-id":["s22041647"],"URL":"https:\/\/doi.org\/10.3390\/s22041647","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,2,20]]}}}