Towards Resilient Civil Infrastructure Asset Management: An Information Elicitation and Analytical Framework

Abstract

It is rather difficult for the stakeholders to understand and implement the resilience concept and principles in the infrastructure asset management paradigm, as it demands quality data, holistic information integration and competent data analytics 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. To meet the stakeholder’s urgent needs, this paper proposes an information elicitation and analytical framework for resilient infrastructure asset management. The framework is devised by leveraging the best practices and processes of integrated infrastructure asset management and resilience management in the literature, synergizing the common elements and critical concepts of the two paradigms, ingesting the state-of-the-art interconnected infrastructure systems resilience analytical approaches, and eliciting expert judgments to iteratively improve the derived framework. To facilitate the stakeholders in implementing the framework, two use case studies are given in this paper, depicting the detailed workflow for information integration and resilience analytics in infrastructure asset management. The derived framework is expected to provide an operational basis to the quantitative resilience management of civil infrastructure assets, which could also be used to enhance community resilience.

Infrastructure resilience curves: Performance measures and summary metrics

“…minimum performance [5], [103], residual capacity [195], residual functionality [3], [196]- [200], residual performance [170], [201], resistance index [202], robustness [61], [72], [73], [167], [186], [194] and static resilience [76]. Related forms included performance relative to a critical threshold [193], [195] and the average performance during the period of disruption [193].…”

“…Λ-metric [55], [56], amplitude [173], [203], consequence [4], depth of failure propagation [79], drop of functionality [3], initial loss [72], [73], lost functionality [197], maximum impact level [75], maximum incurred performance loss [5], peak severity [177], performance attenuation [204], and risk [194]. Residual performance and depth of impact were associated with system attributes of absorptive capacity [5], [100], survivability [16], [205], vulnerability [206], and, most often, robustness [35], [68], [158], [159], [172], [196], [200], [201], [207].…”

“…Cumulative impact was proposed, in Bruneau et al's foundation work, as loss of resilience [65]. It has been widely adopted since, with terminology including: area [55], [56], conditional vulnerability [200], functional service loss [188], impact area [75], [193], loss indicator [173], loss of resilience [82], [189],…”

On Applying Machine Learning and Simulative Approaches to Railway Asset Management: The Earthworks and Track Circuits Case Studies

“…In many sectors, a preventive approach to maintenance has become key for achieving the sustainability targets, such as in Industry 4.0 [8], in the asset management of civil infrastructure [9] and linear assets [10], such as roads and bridges [11].…”

LOSS OF INFORMATION DURING DESIGN & CONSTRUCTION FOR HIGHWAYS ASSET MANAGEMENT: A GEOBIM PERSPECTIVE

“…From a data perspective, these two interlinked challenges are underpinned by the implementation of Asset Information Management Systems (AIMS), with the common underlying element of any proposition being efficient and effective utilisation of data (Yang et al, 2019). Location-enabled data is of fundamental importance to Infrastructure Asset Management, not only to address issues relating to condition assessment, maintenance scheduling, health and safety, and strategic decision making (Garramone et al, 2020) but also to enable democratization of information via the power of 3D Visualisation .…”