On the afternoon of December 13th, the research group held the 13th session of “Li Nan Research Group’s Academic Lecture Series” online. The activity was presided over by Dr. Li Nan, and Dr. Yang Yifan from Nanjing University of Aeronautics and Astronautics was invited to give a keynote report on the “Intelligent resilience assessment method for interdependent infrastructure systems integrating physical modeling and data drive approaches ”. The report was attended online by more than 20 teachers and students from universities at home and abroad such as Tsinghua University, Shanghai Jiaotong University, and Loughborough University.
Fig. 1 Challenges faced by resilience research
Dr. Yang Yifan pointed out that the academic community had conducted extensive and in-depth research on the concept of resilience. However, in converting the concept, theory and principles of resilience into operational engineering practice, there are challenges such as fragmentation of the concept and framework of resilience, inconsistent indicators of resilience, data and technical obstacles incurred by massive heterogeneous data. These challenges hinder the study of problems such as identification of systematic spatial and temporal evolution characteristics of infrastructure systems, and dependency modeling in multi-category infrastructure clusters.
Facing the above challenges, many scholars in the field of resilience research have proposed their own solutions. Dr. Yang summed it up as the following three points: from conceptual debate to operational paradigm, resilience assessment from a single facility to large-scale infrastructure systems, and interdependency considerations.
Fig. 2 Status of resilience research
In response to the resilience challenges, Dr. Yifan Yang has constructed a study on the intelligent assessment of infrastructure resilience integrating physical modeling and data drive approaches. Dr. Yang Yifan first explained the first part of the research: the theoretical basis. The connotation and characteristics of the infrastructure operation and maintenance management and system resilience analysis framework were explored and summarized. The similarities and differences between resilience and other related concepts were compared, and the related infrastructure resilience analysis frameworks under the influence of various disasters and integrating management factors were established successively.
Fig. 3 Resilience analysis framework of associated infrastructure integrating management factors
The second part of the study was unfolded around the modeling method. Dr. Yang first introduced the concepts of physical modeling and data drive approaches and summarized their scope of application. Integrating data drive and physical modeling has many advantages: improving modeling efficiency, saving time and cost, improving the accuracy and reliability of modeling; and supplementing the lack of infrastructure operation and maintenance data and unclear physical laws of operations. Later, Dr. Yang took the urban waterlogging disaster in Hong Kong as an example to illustrate the application of integrated data drive and physical modeling in the identification of infrastructure system interdependencies and their effects under specific disaster conditions, and assessed the resilience of interdependent infrastructures from a socio-technical perspective.
Fig. 4 Flow chart based on data drive and physical modeling
Dr. Yang Yifan introduced the third part of the study, application support, namely the intelligent decision-making support system on the resilience of interdependent infrastructure systems. This system, assisted by semantic technology, integrates various applications such as building information model, geographic information system and professional simulation engine. It can build the ontology, instantiate the ontology, and finally realize the intelligent assessment and decision-making of the interdependent infrastructure systems. Using highly accurate building information, GIS system data and professional simulation engine, the system can also realize the functions such as vulnerable points identification of associated infrastructure systems and post-disaster functional assessment.
Fig. 5 Intelligent resilience assessment framework of associated infrastructure assisted by semantic technology
At the end of the reporting was a discussion session. Teachers and students had an in-depth discussion with Dr. Yang Yifan on the issues such as applicability of the knowledge base in the professional field, how to consider the influence of the time dimension in the disaster simulation, and how to physically verify the correlation properties identified by the data drive. After sharing his research results, Dr. Yang Yifan also discussed with the students about his understanding in the academic process, and recalled how he had changed his research direction and gradually adjusted his mindset in the process from doctoral study to joining the workforce. At the end of the meeting, Dr. Yang Yifan encouraged the students to communicate more with scholars from other disciplines in the post-graduate study, and strive to cultivate their own multi-disciplinary background.
