An integrated infrastructure asset management model to improve the resilience of high-density cities

Abstract

Recently, there has been a growing focus on resilience to address the unprecedented challenges facing infrastructure systems due to ageing assets, frequent natural disasters, and unpredictable man-made threats. However, it is rather difficult for the stakeholders to understand and implement the resilience concept and principles in infrastructure asset management paradigm, as resilient infrastructure management not only requires sector-specific asset management knowledge, traditional risk and disaster management techniques, but it also demands quality data, holistic information integration and competent data analytical capabilities to identify infrastructure vulnerabilities, evaluate and predict infrastructure adaptabilities to different hazards, as well as to make damage restoration and resilience improvement strategies and plans. As infrastructure asset management (IAM) and resilience management (RM) are two disparate paradigms emphasizing different aspects of infrastructure systems, streamlining and synergizing the analytical processes of IAM and RM serve as prerequisite to develop an integrated resilience IAM model. Besides, fragmented approaches are generally adopted to address different dimensions of infrastructure system resilience. For example, topological-based methods abstract infrastructure systems as connected networks with limited concentration on functional properties. Whilst flowed-based approaches consider the capacities of infrastructure nodes and links, they neglect the intrinsic heterogeneities in operating regimes of distinctive infrastructure systems. Furthermore, current models seldom assess community resilience holistically from both technical and social aspects. Such resilience assessment results would be myopic in prioritizing mitigation and recovery strategies. Practically, it is daunting for stakeholders with different expertise to collaboratively assessing interdependent infrastructure system resilience as it is a multidisciplinary task which needs to consolidate decision-making toolkits from a spectrum of knowledge domains. A flexible and agile platform capable of consolidating domain-specific toolkits for integrative resilience decision-making is indispensable in industry practices. It is therefore stringent to provide infrastructure operators and community managers with an effective model to operationalize the resilience theories and principles in their daily management practices.

In response, the aim of the research is to develop an integrated infrastructure asset management model to improve the resilience of high-density cities. It includes the theoretical investigation of IAM and RM paradigm to pinpoint their potential interactions; physics-based resilience assessment approach proposition in order to capture domain-specific infrastructure functioning properties; community resilience assessment method development from a socio-technical standpoint; and lastly a pragmatic and effective platform that flexibly assimilates cutting-edge decision-making toolkits in each infrastructure domain to help stakeholders to operationalize the resilience-related concepts and frameworks.

The research made original contributions to resilient infrastructure asset management in both theoretical, methodological and practical dimensions. Theoretically, this research proposed the resilient IAM framework (RIAM) that consolidate both IAM and RM paradigm. Methodologically, this research formulated physics-based methods to assessing resilience of interdependent infrastructure systems. Furthermore, a federated pre-event community resilience assessment approach was developed that integrates topological-based, physics-based and also multi-criteria decision analysis methods to holistically evaluate community resilience in both technical and social dimensions. Practically, this research proposed an integrated BIM, GIS and domain-specific computational engine (DCE) platform to facilitate infrastructure operators and manager to investigate infrastructure vulnerabilities.