Yunhao Li
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Yunhao Li has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Arnab Majumdar
Imperial College London
1 shared publication - Lintong Li
Department of Civil and Environmental Engineering, Faculty of Engineering, Imperial College London, London SW7 2AZ, UK
1 shared publication - Mingyang Huang
Institute of Artificial Intelligence, University of Science and Technology Beijing, Beijing, People’s Republic of China
1 shared publication - Mireille Elhajj
Astra-Terra Limited, London HA0 1HD, UK
1 shared publication - Washington Yotto Ochieng
Imperial College London
1 shared publication - William Graham Proud
Centre for Active Resilience and Security (CARS), Department of Physics, Imperial College London, London SW7 4AZ, UK
1 shared publication
Latest Publications
- Open Access
- 28 May 2026
- Published by MDPI AG in Applied Sciences
- Vol. 16 (11) , 5406
A Review of Airport Security and Resilience Analysis: Integration of Risk Modelling Frameworks
Airports, as Critical National Infrastructure (CNI), operate as tightly coupled socio-technical systems exposed to multifaceted threats, including cyber, physical, social, environmental, and Chemical, Biological and Radiological (CBR) threats. This study presents a structured review of the synthesis of conceptual frameworks, airport structural configurations, sensor networks, and multi-domain threat landscapes, as well as airport security and resilience analysis, while comparatively examining risk assessment approaches. The review shows that existing approaches are effective for threat identification and prioritisation but remain predominantly static, with limitations in scalability, data dependency, and real-time applicability. To address these limitations, Threat-Vulnerability-Risk Assessment (TVRA) is adopted as a structured, reusable approach to support metric allocation, redundancy design, and emergency capability development. It further serves as a bridge between traditional risk assessment and resilience-oriented system design by enabling the transformation of static risk scores into scenario-based inputs, thereby supporting stress-testing and lifecycle-based resilience planning across the prepare, act, and recover phases. However, its inherently static structure limits its ability to capture temporal dynamics and cascading interdependencies, highlighting the need to integrate it with dynamic modelling approaches.
- 28 May 2026
- Published by MDPI AG in Applied Sciences
- Vol. 16 (11) , 5406
Open AccessA Review of Airport Security and Resilience Analysis: Integration of Risk Modelling Frameworks
Airports, as Critical National Infrastructure (CNI), operate as tightly coupled socio-technical systems exposed to multifaceted threats, including cyber, physical, social, environmental, and Chemical, Biological and Radiological (CBR) threats. This study presents a structured review of the synthesis of conceptual frameworks, airport structural configurations, sensor networks, and multi-domain threat landscapes, as well as airport security and resilience analysis, while comparatively examining risk assessment approaches. The review shows that existing approaches are effective for threat identification and prioritisation but remain predominantly static, with limitations in scalability, data dependency, and real-time applicability. To address these limitations, Threat-Vulnerability-Risk Assessment (TVRA) is adopted as a structured, reusable approach to support metric allocation, redundancy design, and emergency capability development. It further serves as a bridge between traditional risk assessment and resilience-oriented system design by enabling the transformation of static risk scores into scenario-based inputs, thereby supporting stress-testing and lifecycle-based resilience planning across the prepare, act, and recover phases. However, its inherently static structure limits its ability to capture temporal dynamics and cascading interdependencies, highlighting the need to integrate it with dynamic modelling approaches.