Resilience framework for seaport infrastructure under extreme wind

Abstract

The efficient transportation of goods is vital for the economic growth of communities, making developing and maintaining seaport infrastructure an essential component of the marine transportation system. Given their geographic locations, ports are consistently at risk from natural hazards, making the resilience of port infrastructure an essential goal.

Despite considerable progress in resilience research, there remains a gap in methods tailored explicitly to assessing port resilience, particularly under extreme wind events. Current approaches often do not capture the full complexity of port systems, as they tend to focus on isolated aspects, such as structural resilience.

This paper introduces the PORT Resilience Framework, addressing these gaps by evaluating resilience through a comprehensive list of indicators gathered from various legitimate sources. The indicators are then organized under four comprehensive resilience dimensions: Physical Infrastructure, ICT (i.e., Information and Communication Technology) and Equipment; Organization and Business Management; Resources and Economic Development; and Territory, Environment, and Stakeholders. This classification is summarized under the acronym "PORT."

This paper also introduces a method for aggregating resilience indicators by considering their performance before and after a specific hazard, transforming the data into a quantifiable Loss of Resilience index. The approach is applied to a case study, assessing the resilience of a real Terminal against wind action using real data sourced from the port management.

The case study analysis revealed that human resources and quay operations were the most critical factors affecting recovery, with insufficient staffing leading to prolonged recovery periods. The study further demonstrated that post-disruption activity surges, captured by different serviceability function methodologies, often created operational bottlenecks, challenging the port's overall recovery.

Keywords

Extreme events / Infrastructure performance / Natural disaster / Port infrastructure / Resilience / Wind

Relevance to resilience

This work contributes to resilience practices by offering a measurable and actionable methodology for port authorities and stakeholders to improve the resilience of seaport infrastructure under challenging conditions.

The findings from this research emphasize critical aspects of port resilience by analysing how seaports perform under extreme wind events. The study provides a structured approach to identify vulnerabilities and improve recovery processes of seaport infrastructure. The proposed framework allows for a more comprehensive evaluation of port performance during disruptions considering both physical and operational dimensions. The case study highlights the use of real-world data to guide practical decision-making, demonstrating how targeted actions can enhance recovery and preparedness.

Declaration of generative AI and AI-assisted technologies in the writing process

During the preparation of this work, the authors used ChatGPT in order to improve the grammar and readability of the text. After using this tool, the authors reviewed and edited the content as needed, and take full responsibility for the content of the publication.

CRediT authorship contribution statement

A. Balbi: Writing - original draft, Visualization, Validation, Methodology, Formal analysis, Conceptualization. O. Kammouh: Writing - review & editing, Writing - original draft, Visualization, Supervision, Methodology, Formal analysis, Conceptualization. G.P. Cimellaro: Supervision, Project administration, Methodology, Funding acquisition, Conceptualization. M.P. Repetto: Writing - review & editing, Writing - original draft, Supervision, Project administration, Methodology, Funding acquisition, Formal analysis, Data curation, Conceptualization.

Declaration of competing interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgement

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors

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