Artificial Intelligence and Risk Management for Sustainable Infrastructure

A special issue of Infrastructures (ISSN 2412-3811).

Deadline for manuscript submissions: closed (31 August 2024) | Viewed by 890

Special Issue Editors


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Associate Professor, Department of Civil and Environmental Engineering, College of Engineering and Computing, Florida International University, 10555 West Flagler Street, EC 3660, Miami, FL 33174, USA
Interests: civil infrastructure design, maintenance, and rehabilitation with a focus on pavements and bridges; development of innovative concepts, models, methods, and tools that integrate sustainability principles with civil engineering practices; statistical analysis, multi-decision criteria, simulation processes, optimization techniques, and risk management methods for sustainable and resilient infrastructure
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Civil and Environmental Engineering, Florida International University, 11200 SW 8th St, Miami, FL, USA
Interests: sustainable environmental engineering; water 5.0; water resource recovery facility; water and wastewater treatment engineering; desalination; desinfection; advanced oxidation processes
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue of Infrastructures focuses on the intersection of artificial intelligence (AI) and risk management to develop and preserve sustainable and resilient infrastructure systems. With the increasing complexity and vulnerability of infrastructure systems worldwide, there is a growing need to integrate advanced AI technologies for risk assessment, mitigation, and overall sustainability. Planners, engineers, and managers delve into the potential of AI to improve the resilience of infrastructure against natural disasters, climate change impacts, and other uncertainties. Articles in this Special Issue will seek to cover concepts and application of AI in infrastructure risk management, including predictive modeling, decision support systems, and real-time monitoring. Furthermore, the Special Issue also seeks to address the ethical and societal implications of deploying AI in infrastructure management, ensuring that sustainable development goals are upheld while leveraging technological advancements. By highlighting innovative methodologies, case studies, and best practices, the journal aims to foster interdisciplinary dialogue and contribute to the advancement of AI-driven approaches to building and maintaining resilient and sustainable infrastructure systems.

Dr. Carlos M. Chang
Dr. Walter Z. Tang
Guest Editors

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Keywords

  • artificial intelligence (AI)
  • risk management
  • sustainable infrastructure
  • resilience

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Published Papers (1 paper)

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Research

28 pages, 1596 KiB  
Article
A Climate Adaptation Asset Risk Management Approach for Resilient Roadway Infrastructure
by Carlos M. Chang and Abid Hossain
Infrastructures 2024, 9(12), 226; https://doi.org/10.3390/infrastructures9120226 - 9 Dec 2024
Viewed by 464
Abstract
As climate change intensifies, roadway infrastructure is increasingly at risk from extreme weather events including floods, hurricanes, and wildfires. This paper presents a system-of-systems performance-based asset risk management approach, designed to integrate various elements for effective investment prioritization and infrastructure resilience. Central to [...] Read more.
As climate change intensifies, roadway infrastructure is increasingly at risk from extreme weather events including floods, hurricanes, and wildfires. This paper presents a system-of-systems performance-based asset risk management approach, designed to integrate various elements for effective investment prioritization and infrastructure resilience. Central to this approach are an Asset Inventory Database and a Risk Registry Database, supported by a Common Reference Location System (GIS). These components are the foundation for analytical modules to assess vulnerability and resilience based on exposure, sensitivity, and adaptive capacity. The approach includes an actionable framework to support a proactive data-driven performance-based management process for prioritizing investments. The project prioritization process consists of four steps: identifying risk factors, integrating climate data, conducting advanced risk assessments, and project prioritization. The goal is to prioritize resource allocation and develop climate-adaptive risk mitigation management strategies. Key performance indicators (KPIs) are recommended for setting goals, monitoring the outcomes of these strategies, and measuring their benefits. A Climate Impact Vulnerability Score (CIVS) is proposed to assess the susceptibility of infrastructure assets to environmental conditions. The approach also leverages artificial intelligence (AI) tools to analyze roadway infrastructure vulnerabilities and climate risk exposure. A case study applied to bridges using k-means clustering and multi-criteria decision analysis (MCDA) demonstrates the potential of advanced analytical methods in improving decision-making. This research concludes that the approach will contribute to enhancing resource allocation, supporting strategic decisions, aligning goals with budgets prioritizing investments, and strengthening the resilience and sustainability of roadway infrastructure. Full article
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