Damage characterisation using stand-off observations to enable recovery: the case of infrastructure affected by targeted attacks DOI
Stergios Α. Mitoulis, Nadiia Kopiika, Jelena Ninić

et al.

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

Sustainability and resilience-driven prioritisation for restoring critical infrastructure after major disasters and conflict DOI Creative Commons
Nadiia Kopiika, Roberta Di Bari, Sotirios Argyroudis

et al.

Transportation Research Part D Transport and Environment, Journal Year: 2025, Volume and Issue: unknown, P. 104592 - 104592

Published: Jan. 1, 2025

Language: Английский

Citations

4
Key factors shaping post-disaster building damage assessment: insights from the Gaza Strip as a conflict zone DOI Creative Commons

Sahar Salah El Ghoul,

Bassam A. Tayeh, Ahmad Baghdadi

et al.

Journal of Asian Architecture and Building Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 21

Published: April 1, 2025

Language: Английский

Citations

0
Multi-source image feature extraction and segmentation techniques for karst collapse monitoring DOI Creative Commons
Wenbo Lin, Xiaozhen Li, Tingting Li

et al.

Frontiers in Earth Science, Journal Year: 2025, Volume and Issue: 13

Published: April 15, 2025

Introduction Karst collapse monitoring is a complex task due to data sparsity, underground dynamics, and the demand for real-time risk assessment. Traditional approaches often fall short in delivering timely accurate evaluations of risks. Methods To address these challenges, we propose Integrated Collapse Prediction Network (IKCPNet), novel framework that combines multi-source imaging, geophysical modeling, machine learning techniques. IKCPNet processes seismic hydrological patterns, environmental factors using an advanced encoding mechanism physics-informed module capture subsurface changes. A dynamic assessment strategy incorporated enable feedback probabilistic mapping. Results Experimental on OpenSARShip dataset demonstrate achieves accuracy 94.34 ± 0.02 IoU 90.23 ±0.02, outperforming previous best model by 1.22 0.89 points, respectively. Discussion These results highlight effectiveness improving prediction mitigation, showcasing its potential enhancing geological hazard through integration.

Language: Английский

Citations

0
Reliability-based analysis and residual life forecasting for corrosion-affected RC structures DOI Creative Commons
Nadiia Kopiika, Yaroslav Blikharskyy, Jacek Selejdak

et al.

Structures, Journal Year: 2025, Volume and Issue: 76, P. 108965 - 108965

Published: April 19, 2025

Language: Английский

Citations

0
Reinforcement effects of bonding Fe-SMA in steel bridge diaphragms based on machine learning DOI
Yue Shu, Qiang Xu, Xu Jiang

et al.

Structures, Journal Year: 2025, Volume and Issue: 76, P. 108984 - 108984

Published: April 25, 2025

Language: Английский

Citations

0
Damage characterisation using stand-off observations to enable recovery: the case of infrastructure affected by targeted attacks DOI
Stergios Α. Mitoulis, Nadiia Kopiika, Jelena Ninić

et al.

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

Citations

0