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Exploring the Role of Digital Infrastructure Asset Management Tools for Resilient Linear Infrastructure Outcomes in Cities and Towns: A Systematic Literature Review

Author

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  • Savindi Caldera

    (Cities Research Institute, Griffith University, 170 Kessels Rd, Nathan, QLD 4111, Australia)

  • Sherif Mostafa

    (Cities Research Institute, Griffith University, 170 Kessels Rd, Nathan, QLD 4111, Australia)

  • Cheryl Desha

    (Cities Research Institute, Griffith University, 170 Kessels Rd, Nathan, QLD 4111, Australia)

  • Sherif Mohamed

    (Cities Research Institute, Griffith University, 170 Kessels Rd, Nathan, QLD 4111, Australia)

Abstract

Linear infrastructure such as roads, railways, bridges and tunnels enable critical functionality within and between metropolitan and regional cities and towns, facilitating the movement of goods and services, as part of vibrant, thriving economies. However, these asset types are typically challenged by costly asset management schedules and continually eroding maintenance and refurbishment budgets. These challenges are compounded by the increasing frequency and intensity of disruptive events such as fire, floods, and storm-surge that can damage or destroy property. The United Nations Sustainable Development Goal 9 (SDG-9) highlights the urgent need for enabling evidence-based decision making for infrastructure asset management (IAM). Around the world, digital engineering (DE) efforts are underway to streamline the capture, processing, and visualization of data for IAM information requirements, towards timely and evidence-based decision support that enables resilient infrastructure outcomes. However, there is still limited understanding about which IAM information can be digitized and the types of tools that can be used. This study sought to address this knowledge gap, through reviewing the extent of available and emerging linear infrastructure related DE technologies and their IAM information requirements. A systematic literature review elicited 101 relevant conceptual and empirical papers, which were subsequently evaluated with regard to the extent and characteristics of digital infrastructure asset management tools. Findings are discussed using three themes that emerged from the analysis: (1) DE tools and their IAM asset information requirements; (2) Interoperability and integration of DE tools across IAM platforms; and (3) Application of DE tools to enable resilient linear infrastructure outcomes. A ‘Digital Technology Integration Matrix’ is presented as an immediately useful summary for government and industry decision-makers, particularly in the field of disaster management preparedness and recovery. The Matrix communicates the synthesis of tools and likely end-users, to support effective data gathering and processing towards more timely and cost-effective infrastructure asset management. The authors conclude with a research roadmap for academics, including recommendations for future investigation.

Suggested Citation

  • Savindi Caldera & Sherif Mostafa & Cheryl Desha & Sherif Mohamed, 2021. "Exploring the Role of Digital Infrastructure Asset Management Tools for Resilient Linear Infrastructure Outcomes in Cities and Towns: A Systematic Literature Review," Sustainability, MDPI, vol. 13(21), pages 1-25, October.
  • Handle: RePEc:gam:jsusta:v:13:y:2021:i:21:p:11965-:d:667951
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    References listed on IDEAS

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    4. Samantha Hayes & Cheryl Desha & Matthew Burke & Mark Gibbs & Mikhail Chester, 2019. "Leveraging socio-ecological resilience theory to build climate resilience in transport infrastructure," Transport Reviews, Taylor & Francis Journals, vol. 39(5), pages 677-699, September.
    5. Love, Peter E.D. & Zhou, Jingyang & Edwards, David J. & Irani, Zahir & Sing, Chun-Pong, 2017. "Off the rails: The cost performance of infrastructure rail projects," Transportation Research Part A: Policy and Practice, Elsevier, vol. 99(C), pages 14-29.
    6. Giglio, Joseph M. & Friar, John H. & Crittenden, William F., 2018. "Integrating lifecycle asset management in the public sector," Business Horizons, Elsevier, vol. 61(4), pages 511-519.
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    Cited by:

    1. Tan Yigitcanlar, 2021. "Greening the Artificial Intelligence for a Sustainable Planet: An Editorial Commentary," Sustainability, MDPI, vol. 13(24), pages 1-9, December.
    2. Angelo Ciccone & Sabrina Di Stasio & Domenico Asprone & Antonio Salzano & Maurizio Nicolella, 2022. "Application of openBIM for the Management of Existing Railway Infrastructure: Case Study of the Cancello–Benevento Railway Line," Sustainability, MDPI, vol. 14(4), pages 1-28, February.

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