Resilience Framework for Aged Bridges Subjected to Human-Induced Hazard - Case Study in Ukraine

Lecture notes in civil engineering, Journal Year: 2024, Volume and Issue: unknown, P. 50 - 62

Published: Jan. 1, 2024

Abstract Bridge structures are key components of transport networks, enabling connections between important centres and regions countries. Their operability functionality loss due to long-term deterioration or extreme hazards could cause crucial social economic impacts. Assessment bridge resilience against these is needed predict functionality, optimal management, sustainable development, decision-making in maintenance post-conflict restoration measures. Nevertheless, no studies exist date optimize metrics for aged bridges subjected human-induced stressors, considering indirect losses disruption the network. This a capability gap that gave motivation this research paper. The study covers functionality-related damaged bridges, associated with direct terms repair cost, socio-economic inoperability logistic route. application framework assessment was illustrated an example case Ukrainian structures, which experienced extensive war-induced destruction. presents novel assets, both losses, introduces cost safety-based indexes.

Language: Английский

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Enhancing Regional Resilience Assessment: Integrating Night-Time Light Data and Interpretable Machine Learning to Unveil Nonlinear Dynamics and Threshold Effects

Published: Jan. 1, 2024

To build a more resilient and prepared coastal urban environment in the face of increased risk from climate change-related disasters, it is necessary to assess regional resilience adaptive capacity cities. In this study, we use NASA's Black Marble Collection V1 daily nighttime lights products (VNP46A) analyse spatiotemporal pattern inter-city differences based on "recovery trajectory" framework. We employ LightGBM algorithm Accumulated Local Effects (ALE) Plot explore nonlinear threshold effects identify key factors influencing resilience. Results validate light data as reliable indicator resilience, effectively distinguishing between cities regions with varying levels recovery potential. Urban built-up areas demonstrate greater than less urbanised areas, highlighting disparities capabilities. Employing interpretable machine learning methods allows for systematic identification resilience-influencing factors. For instance, environmental features like Nearest Distance Typhoon Track (ND) significantly influence especially how natural impact dynamics. DEM affects vegetation-dominated notable losses at altitudes around 0 meters 200 800 meters. Socioeconomic such road density GDP also show significant impact, particularly areas. Road Density (RD) exhibits below 2 km/km2, negatively affecting Dntl predictions both rural This research provides set techniques assessing cross-city system factor mechanisms, results can serve valuable reference support process quantification studies change disasters other crises driven by remote sensing data.

Language: Английский

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State-of-the-art review of bridges and bridge networks resilience using science mapping approach

Proceedings of the Institution of Civil Engineers - Bridge Engineering, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 19

Published: Aug. 19, 2024

In recent years, many attempts have been made to incorporate resilience frameworks into decision making, establish the best use of available resources and reduce impact hazards on bridges bridge networks. However, scholarly research in this area is still its early phases, with minimal exploration gaps future prospects. A state-of-the-art review network studies using a science mapping approach presented here. Research topic was obtained from Scopus literature database. The database then analysed VOSviewer Nvivo tools display domain-specific body knowledge. study focused most prolific researchers disaster types, assessment approaches, current trends, directions theoretical practical implications. paradigm networks changing toward usage digital technologies. context, framework id proposed by integrating building information modelling/geographic systems twin models. can aid owners accumulating iterative data, creating multi-hazard preparedness policies designing enhancing life-cycle management maintenance tasks

Language: Английский

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Key influencing factors identification of rail systems resilience: a structural equation model

Transportation Planning and Technology, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 32

Published: Dec. 22, 2024

Enhancing the resilience of railway systems is an urgent problem to be solved since they provide basic services that form backbone a country's economy, security, and well-being. Based on literature review, eleven variables were considered, Structural Equation model was estimated. Model calibration carried out through results survey. Specifically, questionnaire submitted employees Ferrovie dello Stato Italiane (the Italian state company). A total 745 valid responses received. The internal consistency reliability study assessed by determining both Cronbach's alpha Jöreskog's rho, as former assumes all are equally reliable. To evaluate convergent validity model, Average Variance Extracted (AVE) used. Future research developments will consist in possibility introducing new such 'monitoring environmental state' promptly detect manage any emergencies.

Language: Английский

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Damage characterisation using stand-off observations to enable recovery: the case of infrastructure affected by targeted attacks

Report, Journal Year: 2024, Volume and Issue: 24, P. 785 - 791

Published: Jan. 1, 2024

<p>During conflicts, bridges are prime targets due to their strategic importance in transportation and economic growth. Their destruction hampers resilience efforts, delaying recovery. Limited research exists on characterising bridge damage via stand-off observations. This paper integrates diverse data sources emerging technologies for comprehensive assessment based observations using remote sensing techniques. A case study Ukraine employs Sentinel-1 SAR images, crowd-sourced data, deep learning techniques assess at various scales, from regional, asset component scale. approach facilitates swift decision-making infrastructure development restoration planning. By providing crucial intelligence decision-makers funders, it aids prioritising recovery investments expediting post-disaster planning critical infrastructure.</p>

Language: Английский

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