Resilience Assessment and Enhancement Strategies for Urban Transportation Infrastructure to Cope with Extreme Rainfalls

Sustainability, Journal Year: 2024, Volume and Issue: 16(11), P. 4780 - 4780

Published: June 4, 2024

As climate change intensifies, urban transportation infrastructure faces unprecedented challenges from extreme weather events, such as floods. This study investigates the resilience and vulnerability of under rainfall conditions in Changchun City. Utilizing Multi-Criteria Decision-Making Analysis (MCDM) Geographic Information System (GIS) techniques, we comprehensively assess physical, functional, service vulnerabilities network. Our analysis reveals that only 3.57% area is classified highly resilient, demonstrating effective flood management capabilities. In contrast, a significant 61.73% exhibits very low resilience, highlighting substantial could impact operations. Based on our findings, propose specific strategies to enhance including optimizing drainage systems, upgrading standards, implementing green initiatives, integrating disaster risk factors into planning. These insights provide valuable references for global cities facing similar climatic challenges.

Language: Английский

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Application of clustering algorithms for dimensionality reduction in infrastructure resilience prediction models

Structure and Infrastructure Engineering, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 13

Published: June 20, 2024

Recent studies increasingly adopt simulation-based machine learning (ML) models to analyse critical infrastructure system resilience. For realistic applications, these ML consider the component-level characteristics that influence network response during emergencies. However, such an approach could result in a large number of features and cause suffer from 'curse dimensionality'. A clustering-based method is presented simultaneously minimises problem high-dimensionality improves prediction accuracy developed for resilience analysis large-scale interdependent networks. The methodology has three parts: (a) generation simulation dataset, (b) component clustering, (c) dimensionality reduction development models. First, model simulates network-wide consequences various disruptive events. are extracted simulated data. Next, clustering algorithms used derive cluster-level by grouping based on their topological functional characteristics. Finally, develop predict impacts events using features. applicability demonstrated power-water-transport testbed. proposed can be decision-support tools post-disaster recovery

Language: Английский

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Learn to decompose multiobjective optimization models for large‐scale networks

International Transactions in Operational Research, Journal Year: 2022, Volume and Issue: 31(2), P. 949 - 978

Published: June 24, 2022

Abstract Infrastructures can be modeled as large‐scale networks consisting of nodes and arcs, making network optimization a popular modeling option for arising problems. In specific, providing timely restoration plans interdependent infrastructures facing disruptions has been challenge decision makers. this study, we focus on geospatial (co‐location) functional interdependencies to capture the impact cascading failures infrastructure systems. The dynamics real are more complicated captured by one objective function. Therefore, define three functions in pillars sustainability: (a) economic, (b) social, (c) environmental. To solve multiobjective model, develop learn‐to‐decompose framework, evolutionary algorithm based decomposition module Gaussian process regression (GPR) periodically learn from obtained Pareto front guide search direction. We also included heuristic address two significant challenges restoring infrastructures: island scenario co‐location interdependencies. applied proposed framework benchmark problems water transportation City Tampa, FL. carried out sensitivity analyses monitor performance GPR different kernel functions. provided insights makers finding trade‐off between fortification (proactive) (reactive) costs. result demonstrates is feasible applicable networks.

Language: Английский

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Cluster Analysis and Predictive Modeling of Urban Water Distribution System Leaks with Socioeconomic and Engineering Factors

Water Resources Management, Journal Year: 2023, Volume and Issue: 38(1), P. 385 - 400

Published: Dec. 2, 2023

Language: Английский

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Measurement and prediction of subway resilience under rainfall events: An environment perspective

Transportation Research Part D Transport and Environment, Journal Year: 2024, Volume and Issue: unknown, P. 104479 - 104479

Published: Oct. 1, 2024

Language: Английский

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