Browsing by Author "Robert D"

Now showing 1 - 4 of 4
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    A unified framework for evaluating the resilience of critical infrastructure: Delphi survey approach
    (Elsevier B.V., 2024-06-26) Rathnayaka B; Robert D; Adikariwattage V; Siriwardana C; Meegahapola L; Setunge S; Amaratunga D
    This study advocates establishing an indicator system for Critical Infrastructure (CI) resilience assessment to ensure consistency and comparability in future endeavors. Resilience has emerged as a fundamental framework for effectively managing the performance of CIs in response to the challenges posed by disaster events. However, it is evident that a lack of uniformity exists in the choice and standardization of resilience assessment across the identified frameworks. This paper proposes key attributes for facilitating resilience assessment of CIs using an in-depth literature survey for identification and two rounds of Delphi survey in the Sri Lankan context for their verification. The literature survey has analyzed the resilience assessment attributes under four types of capacities: planning (anticipative), absorptive, restorative, and adaptive. Twenty-seven resilience attributes (Planning: 6; Absorptive: 12; Restorative: 6; Adaptive: 3) under different capacities were identified, including sub-indicators for evaluating each resilience attribute. Outcomes of the Delphi survey were analyzed through descriptive statistics. The proposed attributes received high levels of agreement from the experts, indicating their suitability and applicability for assessing the resilience of the CIs. The mean ratings of the attributes varied from 4.0 to 5.0, with the majority exceeding 4.5 out of 5. The evaluation of these attributes will be useful for assessing the resilience capacity of the CIs and thereby to model the overall resilience of the CIs. The results of this study will provide a solid basis for formulating hypotheses in future research aimed at assessing CI resilience.
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    Identifying and prioritizing climate change adaptation measures in the context of electricity, transportation and water infrastructure: A case study
    (Elsevier B.V., 2023-11-17) Rathnayaka B; Robert D; Siriwardana C; Adikariwattage VV; Pasindu HR; Setunge S; Amaratunga D
    Climate Change Adaptation (CCA) has become a vital measure within every nation due to the significant impacts posed by climate change on Critical Infrastructures (CIs) and human lives. Despite scholars' identification of possible impacts on CIs, a lack of consideration for CCA measures to mitigate these impacts can be observed. This study aims to identify and prioritize CCA measures in the assets and infrastructure of critical sectors; electricity, transportation, and water supply considering Sri Lanka as a case study. The present study employed an Analytical Hierarchical Process (AHP) to prioritize CCA measures of these three infrastructure sectors as a system considering their interconnected and systematic nature. The prioritization process was informed by 42 open-ended expert interviews, and these interviews were also instrumental in validating the criteria used to evaluate the CCA measures. The study identified and discussed several CCA measures for different stages of the infrastructure life cycle, including planning, design and construction, and maintenance and retrofitting. The CCA measures were prioritized based on eight criteria obtained from a detailed review analysis. The results revealed that an asset management system at the planning stage is the most significant CCA measure for CIs. Furthermore, the study emphasizes that proper planning of evacuation routes, consideration of operational loads imposed by climate change, and nature-based solutions are significant CCA measures that need to be incorporated during infrastructure development. The outcome from this study provides insights for built environment professionals to adapt infrastructures to climate change. Additionally, the results of the study can be integrated into the rules and regulations of the developing countries to enhance climate resilience within the built environment.
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    Management of Body Condition Score between Pregnancy Scanning and Lamb Marking Impacts the Survival of Triplet-Bearing Ewes and Their Lambs.
    (MDPI (Basel, Switzerland), 2023-06-22) Haslin E; Allington T; Blumer SE; Boshoff J; Clarke BE; Hancock SN; Kearney GA; Kenyon PR; Krog J; Kubeil LJ; Lockwood A; Refshauge G; Trompf JP; Thompson AN; Robert D
    This study evaluated the impacts of management of body condition score (BCS) between pregnancy scanning and lamb marking on the mortality of triplet-bearing ewes and their lambs at 19 research sites across Southern Australia. Triplet-bearing ewes of Maternal (crossbred or composite) or Merino breed were randomly allocated to treatment at pregnancy scanning at an average of 97 days from the start of joining: High or Low BCS. The BCS of individual ewes was assessed at pregnancy scanning, pre-lambing (average of 137 days from the start of joining) and marking (average of 165 days from the end of joining), and ewe and lamb mortality to marking, recorded for each mob. The average BCS at pregnancy scanning was 3.4 for Maternal ewes and 3.3 for Merino ewes. There were no breed by BCS treatment effects on the BCS of ewes at pregnancy scanning or lamb marking or on the change in BCS between pregnancy scanning and pre-lambing or between pre-lambing and marking. The change in BCS differed between the High and Low BCS treatments, between pregnancy scanning and pre-lambing (0.12 vs. -0.33; p < 0.001) and between pre-lambing and marking (-0.39 vs. 0.07; p < 0.001) but did not differ between breeds. The average BCS at marking for ewes managed at the High and Low BCS treatments was 3.1 and 3.0 for Maternals and 3.0 and 2.8 for Merinos. Survival of triplet-bearing Merino ewes (p < 0.01) and their lambs (p < 0.001) was greater when ewes were managed at the High BCS compared to the Low BCS. The BCS treatment did not impact the survival of Maternal ewes or their lambs. The survival of Merino but not Maternal lambs was higher when ewes were in greater BCS pre-lambing (p < 0.01) and when ewes gained BCS between pregnancy scanning and pre-lambing (p < 0.01). Ewe mortality was lower when ewes gained BCS between pregnancy scanning and pre-lambing (p < 0.05). Merino ewes were more likely to die than Maternal ewes for a given change in BCS between pregnancy scanning and pre-lambing (p = 0.065). Overall, our findings demonstrate that producers should manage the nutrition of triplet-bearing Merino ewes so that ewes are in greater BCS at lambing and/or to gain BCS between pregnancy scanning and lambing to improve ewe and lamb survival. Triplet-bearing Maternal ewes should be managed to gain BCS between pregnancy scanning and lambing to improve ewe survival.
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    Novel methodology for resilience assessment of critical infrastructure considering the interdependencies: A case study in water, transportation and electricity sector
    (Elsevier Ltd, 2025-03) Rathnayaka B; Robert D; Adikariwattage V; Siriwardana C; Kuligowski E; Setunge S; Amaratunga D
    Critical Infrastructures (CI) are vital for societal and economic stability, yet their resilience against disasters remains inadequately understood with the increasing interdependencies among the CIs. A better understanding of these interdependencies and the dynamic nature of CI functionalities is crucial for advancing disaster resilience assessment within engineering systems. This paper introduces a novel approach using a Dynamic Bayesian Network (DBN) to assess resilience in interdependent CI systems. The DBN method enables a probabilistic evaluation of system resilience by incorporating interdependencies and capturing the temporal dynamics of system capacities. This approach offers a more detailed perspective on resilience by modelling system functionality using expected values of different functionality states over time. Using a case study in Sri Lankan electricity, water distribution, and road infrastructure sectors and 34 experts, this study examines the complex network of CIs. It demonstrates the applicability of the proposed methodology. P-values of the Chi-Square test performed between the variation of model-predicted resilience and expert assessments are significantly less than 0.05, confirming the model's validity. Additionally, this study explores the expansion of the methodology for resilience assessment under multiple hazards, emphasizing its real-world effectiveness. The findings highlight the efficacy of the proposed methodology and its potential to assist asset managers, owners, and decision-makers in informed resilience planning and optimization strategies. This comprehensive approach fills critical gaps in existing methodologies, offering a robust framework for assessing CI resilience in a dynamic and systematic nature.