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11 results on '"Xiong, Guanyu"'

1. Cross-Domain Comparative Analysis of Digital Twins and Universalised Solutions

Author

Xiong, Guanyu, Gao, Yan, and Li, Haijiang

Subjects
Electrical Engineering and Systems Science - Systems and Control
Abstract

Digitalisation is one of the main drivers of most economic sectors nowadays and the digital twin, as a reification of digitalisation for complex systems has attracted much attention from both academics and industry. There have been studies focusing on digital twins in a specific sector while there are few exercising insightful comparisons of digital twins from different domains. Considering the digital twinning is a cross-domain transformation, it is beneficial to establish the principles of universality and variation that can explain similarities and differences in any digital twins. This paper first delivers a comparative analysis of digital twins in five domains through a six-dimensional characterisation framework. Then, by departing from the correlations among the domain-specific DT development, a cross-domain Digital Twin Platform-as-a-Service (DT-PaaS) is proposed to universalise the common process, tools and applications, meanwhile being inclusive of variations of every digital twin instance. As a centralised data, modeling and service platform, it is expected to break the barriers between domains by enabling the cross-domain digital twin data sharing, interoperability and development synergy and tackle some complex global challenges such as climate challenge, net zero, pandemics, etc.

Published
2024

4. Bridge Digital Twin for Practical Bridge Operation and Maintenance by Integrating GIS and BIM.

Author

Gao, Yan, Xiong, Guanyu, Hu, Ziyu, Chai, Chengzhang, and Li, Haijiang

Abstract

As an emerging technology, digital twin (DT) is increasingly valued in bridge management for its potential to optimize asset operation and maintenance (O&M). However, traditional bridge management systems (BMS) and existing DT applications typically rely on standalone building information modeling (BIM) or geographic information system (GIS) platforms, with limited integration between BIM and GIS or consideration for their underlying graph structures. This study addresses these limitations by developing an integrated DT system that combines WebGIS, WebBIM, and graph algorithms within a three-layer architecture. The system design includes a common data environment (CDE) to address cross-platform compatibility, enabling real-time monitoring, drone-enabled inspection, maintenance planning, traffic diversion, and logistics optimization. Additionally, it features an adaptive data structure incorporating JSON-based bridge defect information modeling and triple-based roadmap graphs to streamline data management and decision-making. This comprehensive approach demonstrates the potential of DTs to enhance bridge O&M efficiency, safety, and decision-making. Future research will focus on further improving cross-platform interoperability to expand DT applications in infrastructure management. [ABSTRACT FROM AUTHOR]

Published
2024
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5. An Efficient and Resilient Digital-twin Communication Framework for Smart Bridge Structural Survey and Maintenance

Author

Gao, Yan, Li, Haijiang, and Xiong, Guanyu

Abstract

A bridge digital twin (DT) is expected to be updated in near real time during inspection and monitoring but is usually subject to massive heterogeneous data and communication constraints. This work proposes an efficient framework for a bridge DT with decreased communication complexity to achieve updates synchronously and provide feedback to the physical bridge in time. The integrated edge computing and non-cellular long-distance wireless communication enable DT resilience when cloud servers become unresponsive due to the loss of internet connection. This framework is validated by different scenarios for DTs in support of bridge inspection and monitoring. It is demonstrated that the framework can enable dynamic interaction between on-site inspection and online bridge DT during the survey as well as knowledge transfer among different sectors in time. It can also support local decision-making on a single bridge as well as regional dynamic coordination for multiple bridges without cloud-server involvement.

Published
2022
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6. Proceedings of the 29th EG-ICE International Workshop on Intelligent Computing in Engineering

Author

Teizer, Jochen, Schultz, Carl, Jacobsen, Emil L., Johansen, Karsten Winther, Hartmann, Timo, Guyo, Eyosias, Gu, Ming, Song, Yan, Liu, Yu-Shen, Zhang, Hehua, Gao, Ge, Liu, Han, Song, Xiaoyu, Borrmann, André, Sacks, Rafael, Martinez, Jhonattan, Vilgertshofer, Simon, Yeung, Timson, Schlenger, Jonas, Abualdenien, Jimmy, Esser, Sebastian, Zhang, Heng, de Wilde, Pieter, Tian, Wei, Chen, Lu, Wei, Shunning, Li, Shengtao, Rim, Donghyun, Chi, Seokho, Hwang, Jaehyun, Moon, Seonghyeon, Lee, Gitaek, Beetz, Jakob, Werbrouck, Jeroen, Schulz, Oliver, Oraskari, Jyrki, Piesk, Jens, Tomar, Rahul, Gebhardt, Gregor, Valenzuela, Gabriel, Abbasloo, Amin, Geyer, Philipp, Lang, Werner, Margesin, Manuel, Staudt, Johannes, Saluz, Ueli, Chen, Xia, Gibson Jr, George Edward, Boring, Ronald Laurids, Yilmaz, Alper, Tang, Pingbo, Xing, Jinding, Behzadan, Amir, Cheng, Chih-Shen, Nath, Nipun, Fang, Weili, Hu, Ruibo, Liu, Yuhan, Chen, Ke, Dragos, Kosmas, Worm, Mathias, Smarsly, Kay, Kashani, Hamed, Mirzaei, Javad, Amiri, Hossein, Xiong, Guanyu, Li, Haijiang, Gao, Yan, Domer, Bernd, Schatz, Yohann, Blankenbach, Joerg, Martens, Jan, Florez-Perez, Laura, Song, Zhiyuan, Kirchner, Jakob, Reisinger, Julia, Hensel, Michael, Füssl, Josef, Kovacic, Iva, Senk, Valentin, Rasoulzadeh, Shervin, Song, Honghong, Zhu, Xiaofeng, Gavan, Valentin, Kavvada, Olga, Berges, Mario, Akinci, Burcu, Vindel, Elvin, Sharoni, Li Or, Soltani, Mohammad Mostafa, Motamedi, Ali, Shahinmoghadam, Mehrzad, Gentry, Russell, Bermek, Mehmet, Liu, Kaijian, Huang, Kaiyu, Khan, Rehan, Doree, Andre, Miller, Seirgei, Makarov, Denis, Vahdatikhaki, Faridaddin, Koch, Christian, Lai, Jason, Schickert, Martin, Artus, Mathias, Suter, Georg, Ziaee, Amir Abbas, Peng, Cheng, Ke, Ziyi, Huang, Hun, Hall, Daniel M., Bucher, David F., Hammad, Amin, Ghelmani, Ali, Luo, Hanbin, Yaun, Jiameng, Chen, Weiya, Bolourian, Neshat, Wei, Yi, Kim, Daeho, Saffari, Seyedeh Fatemeh, Tohidifar, Ali, Gade, Peter, Chronopoulos, Christos, Harichandran, Aparna, Dawood, Nashwan, Rahimian, Farzad Pour, Elghaish, Faris, Matarneh, Sandra, Wang, Wei, Zhu, Shiyang, Ungureanu, Lucian, Chacón, Roland, Ibrahim, Ahmed, Jensen, Frederik, Esterle, Lukas, Blut, Timothy, Fan, G., Liu, Pengkun, Teizer, Jochen, Schultz, Carl, Jacobsen, Emil L., Johansen, Karsten Winther, Hartmann, Timo, Guyo, Eyosias, Gu, Ming, Song, Yan, Liu, Yu-Shen, Zhang, Hehua, Gao, Ge, Liu, Han, Song, Xiaoyu, Borrmann, André, Sacks, Rafael, Martinez, Jhonattan, Vilgertshofer, Simon, Yeung, Timson, Schlenger, Jonas, Abualdenien, Jimmy, Esser, Sebastian, Zhang, Heng, de Wilde, Pieter, Tian, Wei, Chen, Lu, Wei, Shunning, Li, Shengtao, Rim, Donghyun, Chi, Seokho, Hwang, Jaehyun, Moon, Seonghyeon, Lee, Gitaek, Beetz, Jakob, Werbrouck, Jeroen, Schulz, Oliver, Oraskari, Jyrki, Piesk, Jens, Tomar, Rahul, Gebhardt, Gregor, Valenzuela, Gabriel, Abbasloo, Amin, Geyer, Philipp, Lang, Werner, Margesin, Manuel, Staudt, Johannes, Saluz, Ueli, Chen, Xia, Gibson Jr, George Edward, Boring, Ronald Laurids, Yilmaz, Alper, Tang, Pingbo, Xing, Jinding, Behzadan, Amir, Cheng, Chih-Shen, Nath, Nipun, Fang, Weili, Hu, Ruibo, Liu, Yuhan, Chen, Ke, Dragos, Kosmas, Worm, Mathias, Smarsly, Kay, Kashani, Hamed, Mirzaei, Javad, Amiri, Hossein, Xiong, Guanyu, Li, Haijiang, Gao, Yan, Domer, Bernd, Schatz, Yohann, Blankenbach, Joerg, Martens, Jan, Florez-Perez, Laura, Song, Zhiyuan, Kirchner, Jakob, Reisinger, Julia, Hensel, Michael, Füssl, Josef, Kovacic, Iva, Senk, Valentin, Rasoulzadeh, Shervin, Song, Honghong, Zhu, Xiaofeng, Gavan, Valentin, Kavvada, Olga, Berges, Mario, Akinci, Burcu, Vindel, Elvin, Sharoni, Li Or, Soltani, Mohammad Mostafa, Motamedi, Ali, Shahinmoghadam, Mehrzad, Gentry, Russell, Bermek, Mehmet, Liu, Kaijian, Huang, Kaiyu, Khan, Rehan, Doree, Andre, Miller, Seirgei, Makarov, Denis, Vahdatikhaki, Faridaddin, Koch, Christian, Lai, Jason, Schickert, Martin, Artus, Mathias, Suter, Georg, Ziaee, Amir Abbas, Peng, Cheng, Ke, Ziyi, Huang, Hun, Hall, Daniel M., Bucher, David F., Hammad, Amin, Ghelmani, Ali, Luo, Hanbin, Yaun, Jiameng, Chen, Weiya, Bolourian, Neshat, Wei, Yi, Kim, Daeho, Saffari, Seyedeh Fatemeh, Tohidifar, Ali, Gade, Peter, Chronopoulos, Christos, Harichandran, Aparna, Dawood, Nashwan, Rahimian, Farzad Pour, Elghaish, Faris, Matarneh, Sandra, Wang, Wei, Zhu, Shiyang, Ungureanu, Lucian, Chacón, Roland, Ibrahim, Ahmed, Jensen, Frederik, Esterle, Lukas, Blut, Timothy, Fan, G., and Liu, Pengkun

Abstract

This publication is the Proceedings of the 29th EG-ICE International Workshop on Intelligent Computing in Engineering from July 6-8, 2022. The EG-ICE International Workshop on Intelligent Computing in Engineering brings together international experts working on the interface between advanced computing and modern engineering challenges. Many engineering tasks require open-world resolution of challenges such as supporting multi-actor collaboration, coping with approximate models, providing effective engineer-computer interaction, search in multi-dimensional solution spaces, accommodating uncertainty, including specialist domain knowledge, performing sensor-data interpretation and dealing with incomplete knowledge. While results from computer science provide much initial support for resolution, adaptation is unavoidable and most importantly, feedback from addressing engineering challenges drives fundamental computer-science research. Competence and knowledge transfer goes both ways. &nbsp

Published
2022

7. Automated qualitative rule extraction based on bidirectional long shortterm memory model

Author

Zhu, Xiaofeng, Li, Haijiang, Xiong, Guanyu, and Song, Honghong

Abstract

Digital transformation in the construction industry demands smart compliance checking against relevant standards to ensure high-quality project delivery. Due to the diverse characteristics, the qualitative rule extraction for standards remains labour intensive. Therefore, an efficient and automated rule extraction method is pivotal. The artificial neural network has been widely used for textual feature extraction in recent years. In this paper, the authors construct an automated rule extractor based on a bidirectional Long short-term memory (LSTM) neural network model, which can automate the extraction of qualitative rules in textual standards and achieves an accuracy of 96.5% in actual tests. The automated rule extractor can greatly improve the efficiency of converting unstructured textual rules to structured data. This approach can establish the basis for knowledge mining of qualitative standards as well as the development of large-scale compliance checking systems.

10. An efficient and resilient digital-twin communication framework for smart bridge structural survey and maintenance

Author

Gao, Yan, Li, Haijiang, Xiong, Guanyu, Gao, Yan, Li, Haijiang, and Xiong, Guanyu

Abstract

A bridge digital twin (DT) is expected to be updated in near real time during inspection and monitoring but is usually subject to massive heterogeneous data and communication constraints. This work proposes an efficient framework for a bridge DT with decreased communication complexity to achieve updates synchronously and provide feedback to the physical bridge in time. The integrated edge computing and non-cellular long-distance wireless communication enable DT resilience when cloud servers become unresponsive due to the loss of internet connection. This framework is validated by different scenarios for DTs in support of bridge inspection and monitoring. It is demonstrated that the framework can enable dynamic interaction between on-site inspection and online bridge DT during the survey as well as knowledge transfer among different sectors in time. It can also support local decision-making on a single bridge as well as regional dynamic coordination for multiple bridges without cloud-server involvement.

11. Automated qualitative rule extraction based on bidirectional long shortterm memory model

Author

Zhu, Xiaofeng, Li, Haijiang, Xiong, Guanyu, Song, Honghong, Zhu, Xiaofeng, Li, Haijiang, Xiong, Guanyu, and Song, Honghong

Abstract

Digital transformation in the construction industry demands smart compliance checking against relevant standards to ensure high-quality project delivery. Due to the diverse characteristics, the qualitative rule extraction for standards remains labour intensive. Therefore, an efficient and automated rule extraction method is pivotal. The artificial neural network has been widely used for textual feature extraction in recent years. In this paper, the authors construct an automated rule extractor based on a bidirectional Long short-term memory (LSTM) neural network model, which can automate the extraction of qualitative rules in textual standards and achieves an accuracy of 96.5% in actual tests. The automated rule extractor can greatly improve the efficiency of converting unstructured textual rules to structured data. This approach can establish the basis for knowledge mining of qualitative standards as well as the development of large-scale compliance checking systems.

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