Disaster Management Strategies Research Articles

Development of Polar Lows in Future Climate Scenarios over the Barents Sea

Abstract Polar lows (PLs) are intense mesoscale cyclones that form over polar oceans during colder months. Characterized by high wind speeds and heavy precipitation, they profoundly impact coastal communities, shipping, and offshore activities. Amid the substantial environmental changes in polar regions due to global warming, PLs are expected to undergo noteworthy transformations. In this study, we investigate the response of PL development in the Barents Sea to climate warming based on two representative PLs. Sensitivity experiments were conducted including the PLs in the present climate and the PLs in a pseudo–global warming scenario projected by the late twenty-first century for Shared Socioeconomic Pathway (SSP) 2-4.5 and SSP 3-7.0 scenarios from phase 6 of the Coupled Model Intercomparison Project (CMIP6). In both warming climate scenarios, there is an anticipated decrease in PL intensity, in terms of the maximum surface wind speed and minimum sea level pressure. Despite the foreseen increase in latent heat release in the future climate, contributing to the enhancement of PL intensity, other primary factors such as decreased baroclinic instability, heightened atmospheric static stability, and reduced overall surface heat fluxes play pivotal roles in the overall decrease in PL intensity in the Barents Sea under warming conditions. The augmentation of surface latent heat flux, however, results in increased precipitation associated with PLs by enhancing the latent heat release. Furthermore, the regional steering flow shifts in the warming climate can influence the trajectory of PLs during their development. Significance Statement Global warming is anticipated to impact cyclone systems worldwide. Polar lows (PLs), intense mesocyclones in polar regions with potential socioeconomic and human life implications, pose uncertainties regarding intensity changes in a warming climate. In this study, we aimed to better understand how PLs over the Barents Sea will respond to the environmental changes in future climate conditions [Shared Socioeconomic Pathway (SSP) 2-4.5 and SSP 3-7.0] by the end of the twenty-first century. Our results find that the intensity of PLs is expected to decrease in the future while there is an expected increase in precipitation associated with PLs in the warming climate. These findings aim to contribute valuable insights for disaster management strategies in the face of evolving climate scenarios.

Lesson Learned from Post-Disaster Psychosocial Support for Children in Palu, Central Sulawesi—Indonesia

In the aftermath of the devastating Palu disaster in September 2018, communities, especially children, suffered deep psychological trauma. At the request of faith leaders, local NGO Wahana Visi Indonesia (WVI), in partnership with Krida Wacana Christian University (UKRIDA), conducted a psychosocial support training program for volunteers, including Qur’an recitation teachers and Sunday school teachers in Palu. These trained volunteers then initiated a psychosocial support initiative for children in the area. Years later, WVI and UKRIDA conducted a reflective study of the program, collecting data from 40 volunteers through focus group discussions and surveys. The study identified key themes, including the effectiveness of the training, reflections on program implementation, lessons learned, and recommendations for future programs. The study underscores the importance of multi-stakeholder involvement, particularly that of faith-based organizations, in providing psychosocial support in disaster-affected areas. Given the recurring threat of disasters in the region, the study advocates for transparent collaboration among various social stakeholders. To build community resilience, disaster management strategies need to be widely disseminated, and the need for continuous training to strengthen community preparedness is emphasized. In addition, the development of culturally sensitive training modules tailored to the community’s language, age, and religious background is essential. Such a comprehensive commitment will ensure the community’s ability to deal effectively with future disasters. Detailed findings are discussed further in this paper.

Soil Vulnerability Analysis Using the Microtremor Method for Mapping the Landslide Susceptibility Area in Batu City, Indonesia

Based on Regional Agency for Disaster Management (BPBD) Batu City data, Indonesia, 36 landslides have been recorded in the last three years around the Payung area, where this area connects several regions around Batu City. So, it is necessary to map areas with potential ground movement as an initial step for the local government in formulating a landslide disaster management strategy. This research aims to map landslide susceptibility areas around Payung, Batu City, identified based on the soil vulnerability index value range in the peak ground acceleration (PGA) mapping results. The data was measured using the microtremor method. There were 22 measurement points in 3 zones with different heights, and the microtremor signal was recorded for 35-40 minutes at each point. The microtremor signal from seismic recording is composed of three components (NS, EW, and Up-Down), which are used to develop the Horizontal to Vertical Spectral Ratio (HVSR) technique, where the ratio value is obtained by comparing horizontal (H) components (HNS and HEW) to the vertical component (Up-Down (V)). The research results show that landslide susceptibility areas are located at points M10, M13, and M16, with a soil vulnerability index range of 21-40.1. When a landslide occurs in that area with high vulnerability, it will impact points along Payung Street at points M3–M9. The points with high soil vulnerability index values need special treatment to reduce the impact of landslides.

Environmental governance is critical for mitigating human displacement due to weather-related disasters

The impact of climate change is increasingly evident through intensified and more frequent weather-related events, resulting in a higher likelihood of disaster-induced displacement. In response, effective adaptation and resilience-building measures implemented through environmental governance are crucial. Here, we investigate the relationship between environmental governance (measured by the Notre Dame Country Index) and weather-related disaster displacements. Using a panel dataset of 92 countries from 2010 to 2020, Granger non-causality tests reveal a strong bidirectional relationship between environmental governance and disaster displacement. The relationship is primarily driven by governance readiness in the higher-income group, while both governance and economic readiness are crucial in the middle-income group. Notably, all three readiness components appear significant in the lower-income group, emphasizing the imperative for comprehensive disaster management strategies that address economic development, governmental effectiveness, and social conditions. These findings underscore the importance of effective environmental governance for climate change adaptation and sustainable development goals.

Quantitative assessment of population risk to tropical cyclones using hybrid modeling combining GAM and XGBoost: A case study of Hainan Province

Global climate change is expected to increase the proportion of intense tropical cyclones (TCs) in the Northwest Pacific. This study focused on how factors, especially extreme events, may affect disaster losses. To address this issue, an event-based multivariate TC risk assessment model, which employs copulas, a generalized additive model, and undersampling extreme gradient boosting decision tree techniques, is developed to enhance the accuracy of disaster loss prediction. The results suggest that on Hainan Island, the rate of the affected population is positively correlated with the maximum wind speed and maximum daily rainfall while negatively correlated with the gross domestic product and elevation. The study also shows that the TC risk in the cities of Hainan Island increases as the return period expands, and each return period scenario shows a unique geospatial distribution of TC risk on Hainan Island, with higher risks in the coastal and eastern regions. These results highlight the importance of implementing effective disaster management strategies to mitigate the impact of severe TCs in the region.

Impact of Summer North Atlantic Sea Surface Temperature Tripole on Precipitation over Mid–high-latitude Eurasia

Abstract Eurasia is a sensitive and high-risk region for global climate changes, where climate anomalies significantly influence natural ecosystems, human health, and economic development. The North Atlantic tripole (NAT) sea surface temperature anomaly is crucial to interannual precipitation variations in Eurasia. Several studies have focused on the link between the NAT and climate anomalies in winter and spring. However, the mechanism by which the summer NAT impacts climate anomalies in Eurasia remains unclear. This study examines how the NAT impacts interannual variations of summer precipitation in mid-high-latitude Eurasia. Precipitation variations are associated with the atmospheric teleconnection triggered by the NAT. When the NAT is in its positive phase, the anomalous atmospheric diabatic heating over the North Atlantic excites an equivalent-barotropic Rossby wave train response that propagates eastward toward the Eurasia, resulting in atmospheric circulation anomalies over the region. The combined effects of atmospheric circulation, radiative forcing, and water vapor transport anomalies lead to decreased precipitation across Northern Europe and central Eurasia, with higher precipitation anomalies over the Northeast Asia. Finally, numerical experiments verify that the summer NAT excites atmospheric teleconnections that propagate downstream, affecting precipitation anomalies in mid-high-latitude Eurasia. This study provides a scientific basis for predicting Eurasian summer precipitation and strengthening disaster management strategies.

Comparison of Perimeter Delineation Methods for Remote Sensing Fire Spot Data in Near/Ultra-Real-Time Applications

Forest fires cause extensive damage to ecosystems, biodiversity, and human property, posing significant challenges for emergency response and resource management. The accurate and timely delineation of forest fire perimeters is crucial for mitigating these impacts. In this study, methods for delineating forest fire perimeters using near-real-time (NRT) remote sensing data are evaluated. Specifically, the performance of various algorithms—buffer, concave, convex, and combination methods—using VIIRS and MODIS datasets is assessed. It was found that increasing concave α values improves the matching percentage with reference areas but also increases the commission error (CE), indicating overestimation. The results demonstrate that combination methods generally achieve higher matching percentages, but also higher CEs. These findings highlight the trade-off between improved perimeter accuracy and the risk of overestimation. The insights gained are significant for optimizing sensor data alignment techniques, thereby enhancing rapid response, resource allocation, and evacuation planning in fire management. This research is the first to employ multiple algorithms in both individual and synergistic approaches with NRT or ultra-real-time (URT) active fire data, providing a critical foundation for future studies aimed at improving the accuracy and timeliness of forest fire perimeter assessments. Such advancements are essential for effective disaster management and mitigation strategies.

Estimation of Prediction Intervals for Performance Assessment of Building Using Machine Learning.

This study utilizes artificial neural networks (ANN) to estimate prediction intervals (PI) for seismic performance assessment of buildings subjected to long-term ground motion. To address the uncertainty quantification in structural health monitoring (SHM), the quality-driven lower upper bound estimation (QD-LUBE) has been opted for global probabilistic assessment of damage at local and global levels, unlike traditional methods. A distribution-free machine learning model has been adopted for enhanced reliability in quantifying uncertainty and ensuring robustness in post-earthquake probabilistic assessments and early warning systems. The distribution-free machine learning model is capable of quantifying uncertainty with high accuracy as compared to previous methods such as the bootstrap method, etc. This research demonstrates the efficacy of the QD-LUBE method in complex seismic risk assessment scenarios, thereby contributing significant enhancement in building resilience and disaster management strategies. This study also validates the findings through fragility curve analysis, offering comprehensive insights into structural damage assessment and mitigation strategies.

Strategies for Healthcare Disaster Management in the Context of Technology Innovation: the Case of Bulgaria

In Bulgaria, integrating technology and innovation is crucial for advancing sustainable healthcare disaster management, enhancing disaster response and recovery, and minimizing long-term environmental and social impacts. The purpose of the study is to assess the impact of modern technological innovations on the effectiveness of disaster management in health care in Bulgaria with a focus on Health Information Systems (HIS), Telemedicine, Telehealth, e-Health, Electronic Health Records, Artificial Intelligence (AI), Public Communication Platforms, and Data Security and Privacy. These innovations, when integrated effectively, can significantly strengthen Bulgaria's preparedness and response capabilities, improving efficiency and reducing the environmental footprint of disaster interventions. Collaboration with technology experts, research institutions, and international organizations is essential for successful implementation. The research aims to assess how these technologies can enhance disaster management efficacy, adopting a methodical approach using Systematic Literature Reviews (SLRs). Systematic Literature Reviews (SLRs) utilize precise criteria for research selection and evaluation to reduce bias, systematically compiling and analyzing data from various studies to offer evidence-based insights. They are crucial for uncovering research gaps, indicating areas needing further exploration. Findings highlight the transformative potential of technology integration in streamlining communication, data management, and operational efficiency. The study underscores the urgent need for an integrated approach and a culture of innovation within the healthcare sector, emphasizing ongoing assessment and adaptation of technology-based strategies. While acknowledging limitations and calling for further research, the study provides valuable insights into Bulgaria's healthcare disaster management strategies and sets the stage for future investigations to refine and expand upon these strategies. It advocates for a strategic framework guiding the efficient integration of technology into disaster management, aiming for resilient and patient-centric healthcare systems.

Geospatial Analysis of Flood Susceptibility in Nigeria’s Vulnerable Coastal States: A Detailed Assessment and Mitigation Strategy Proposal

This study employs advanced geospatial analytical techniques to evaluate the vulnerability of Nigeria’s coastal states and their constituent local government areas to flood hazards, which represent a critical and escalating risk within the coastal hazard paradigm intensified by climate change phenomena. The study’s objective is to utilize geospatial data to delineate and quantify the intensity and distribution of flood susceptibility, thus establishing a foundational framework for developing comprehensive disaster management strategies in response to the challenges posed by climate variability. The research uses satellite imagery and geographic information system (GIS)-based hydrological modeling to delineate regions susceptible to flooding, synthesizing topographical and hydrological data to stratify areas into discrete flood susceptibility categories. The findings indicate that the Delta coastal State of Nigeria contains extensive medium to high-risk flood zones spanning 8304.57 km2. While the Bayelsa coastal State of Nigeria presents critical areas at high to very high flood risk, encompassing 5506.61 km2 at high risk and 1826.88 km2 at very high risk, this highlights the urgent necessity for immediate and strategic mitigation measures. This research highlights the critical importance of geospatial technology in shaping disaster management and enhancing community resilience against increasing flood frequencies. As Nigeria’s coastal regions face escalating flood susceptibility, advanced geospatial methods are vital for assessing and mitigating these climate-induced threats, contributing to climate-resilient planning and aligning with Sustainable Development Goal 13: Climate Action. The study’s geospatial approach delivers precise flood risk evaluations and guides targeted mitigation efforts, marking significant progress in managing coastal hazards in a changing climate.

Global insights from local actions: a case study on enhancing flood disaster management efficiency in China's Greater Bay Area.

This study examines the flood disaster management network within the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) from 2015 to 2021, identifying government department involvement and influence shifts. Key findings indicate a decrease in the centrality of the Public Security Office and Department of Transportation, suggesting a strategic shift toward more specialized, technology-driven disaster management. Conversely, the Science Bureau's increased engagement, from 8.43% to 12.84%, highlights a policy shift toward scientific research and technological innovation in managing flood risks. The analysis reveals underutilized communication between the Central Committee, the Poverty Alleviation Office, and the Publicity Department, highlighting opportunities for improved integration in disaster management and public communication strategies. To address these issues, the study suggests strengthening inter-departmental collaboration to leverage technological advancements in disaster management. It also recommends integrating flood disaster management with poverty alleviation initiatives to support affected populations comprehensively. Increasing the involvement of the Publicity Department is crucial for improving timely and transparent communication of flood-related data to the public. The conclusions advocate for an adaptive, strategically planned network approach to flood disaster management in the GBA, aiming to bolster responsiveness and preparedness for future flood events.

A Comprehensive Study of Biparjoy Cyclone Disaster Management in Gujarat: A Case Study

Gujarat, a coastal state in India, has a long history of cyclone occurrences and has developed comprehensive disaster management strategies over the years. The study aims to analyze the effectiveness of these strategies by examining the response to the Biparjoy Cyclone, which occurred in June, 2023. The paper explores various aspects of cyclone disaster management in Gujarat, including early warning systems, evacuation planning, coordination mechanisms, relief and recovery efforts, infrastructure resilience, and community participation. By comparing the response to the Biparjoy Cyclone with previous cyclone events in Gujarat, valuable insights and lessons are derived to enhance future disaster management strategies. Preliminary findings indicate both successes and challenges in Gujarat's cyclone disaster management. The state's early warning systems effectively alerted communities and facilitated timely evacuations, resulting in reduced casualties. However, certain areas, such as coordination among different agencies and post-disaster recovery, require further improvement. The research paper concludes by synthesizing the lessons learned from the Biparjoy Cyclone and making recommendations for enhancing cyclone disaster management in Gujarat. The findings add to the current volume of expertise on disaster management and equip policymakers, practitioners, and researchers working in the sector with practical insights. Finally, the study hopes to contribute to the creation of more robust and effective cyclone disaster management systems in Gujarat and other cyclone-prone regions of India.

Machine learning and hydrodynamic proxies for enhanced rapid tsunami vulnerability assessment

Coastal communities in various regions of the world are exposed to risk from tsunami inundation, requiring reliable modeling tools for implementing effective disaster preparedness and management strategies. This study advocates for comprehensive multi-variable models and emphasizes the limitations of traditional univariate fragility functions by leveraging a large, detailed dataset of ex-post damage surveys for the 2011 Great East Japan tsunami, hydrodynamic modeling of the event, and advanced machine learning techniques. It investigates the complex interplay of factors influencing building vulnerability to tsunami, with a specific focus on the hydrodynamic effects associated to tsunami propagation on land. Novel synthetic variables representing shielding and debris impact mechanisms prove to be suitable proxies for water velocity, offering a practical solution for rapid damage assessments, especially in post-event scenarios or large-scale analyses. Machine learning then emerges as a promising approach to tackle the complexities of vulnerability assessment, while providing valuable and interpretable insights.

Strategic Deployment of Ducklink Wireless Devices for Disaster Mitigation and Management in Nueva ECIJA University of Science and Technology Sumacab Campus

A significant challenge during disasters is the breakdown of traditional communication systems, impeding rescue operations and information dissemination. To tackle this issue, researchers conducted a study on strategically deploying DuckLink wireless devices within a small community, particularly at NEUST Sumacab Campus. Utilizing tools like Google Maps, Google Earth, and WiFi Heat Map, optimal locations for device placement were identified. Analyzing signal range and performance characteristics, the study endeavored to enhance disaster management capabilities on campus. Findings revealed DuckLink devices operate with omnidirectional polarity, ensuring reliable communication, with a clear correlation between distance and message transmission time emphasizing strategic device placement for efficient campus-wide connectivity. Recommending the installation of 112 devices across 79 buildings, with key locations designated for Papaduck receivers and Mamaduck transmitters, this research offers crucial insights into disaster management strategies, optimizing wireless communication infrastructure in educational environments, thus contributing to safer and more resilient communities.

IoT Based Early Flood Alerting System

The frequency and intensity of floods have increased recently in many parts of the world, increasing the need for cutting-edge technology solutions to mitigate the effects of these natural disasters. It is crucial to keep aneye on the water flow and get early emergency alerts regarding the water level based on the riverbed in order toprevent such disasters. The goal of this project is to create asystem that employs cutting-edge sensors and a Wi-Fi module to detect the water level. The suggested system has a number of sensors that can track important variables, including temperature, humidity, and water level. If the level crosses a certain threshold, The system will send out early warnings to everyone, alerting them to the likelihood of floods. To process and store data, we have linked the Arduino UNO to each sensor. The system can notify a wider audience by sending email alerts, ensuring that individuals in flood-prone areas receive timely warnings. Additionally, the use ofa Wi-Fi module enables real-time data transmission and remote monitoring of water levels, allowing authorities to take preemptive measures and minimize the impact of potential floods. By integrating advanced sensors with communication technology, this project aims to enhance early warning systems and contribute to more effective disaster management strategies in vulnerable regions. Ultimately, the implementation of such innovative solutions can significantly improve community resilience and reduce the adverse consequences of flooding events.

FireXplainNet: Optimizing Convolution Block Architecture for Enhanced Wildfire Detection and Interpretability

The early detection of wildfires is a crucial challenge in environmental monitoring, pivotal for effective disaster management and ecological conservation. Traditional detection methods often fail to detect fires accurately and in a timely manner, resulting in significant adverse consequences. This paper presents FireXplainNet, a Convolutional Neural Network (CNN) base model, designed specifically to address these limitations through enhanced efficiency and precision in wildfire detection. We optimized data input via specialized preprocessing techniques, significantly improving detection accuracy on both the Wildfire Image and FLAME datasets. A distinctive feature of our approach is the integration of Local Interpretable Model-agnostic Explanations (LIME), which facilitates a deeper understanding of and trust in the model’s predictive capabilities. Additionally, we have delved into optimizing pretrained models through transfer learning, enriching our analysis and offering insights into the comparative effectiveness of FireXplainNet. The model achieved an accuracy of 87.32% on the FLAME dataset and 98.70% on the Wildfire Image dataset, with inference times of 0.221 and 0.168 milliseconds, respectively. These performance metrics are critical for the application of real-time fire detection systems, underscoring the potential of FireXplainNet in environmental monitoring and disaster management strategies.

Developing a framework for integrating blockchain technology into earthquake risk mitigation and disaster management strategies of smart cities

PurposeEarthquakes pose a significant challenge to human safety and the durability of infrastructure, highlighting the urgent need for innovative disaster management strategies. This study addresses the gap in current earthquake disaster management approaches, which are often related to issues of transparency, centralization and sluggish response times. By exploring the integration of blockchain technology into seismic hazard management, the purpose of the research is to overcome these limitations by offering a novel framework for integrating blockchain technology into earthquake risk mitigation and disaster management strategies of smart cities.Design/methodology/approachThis study develops an innovative approach to address these issues by introducing a blockchain-based seismic monitoring and automated decision support system for earthquake disaster management in smart cities. This research aims to capitalize on the benefits of blockchain technology, specifically its real-time data accessibility, decentralization and automation capabilities, to enhance earthquake disaster management. The methodology employed integrates seismic monitoring data into a blockchain framework, ensuring accurate, reliable and comprehensive information. Additionally, smart contracts are utilized to handle decision-making and enable rapid responses during earthquake disasters, offering an effective alternative to traditional approaches.FindingsThe study results highlight the system’s potential to foster reliability, decentralization and efficiency in earthquake disaster management, promoting enhanced collaboration among stakeholders and facilitating swift actions to minimize human and capital loss. This research lays the foundation for further exploration of blockchain technology’s practical applications in other disaster management contexts and its potential to transform traditional practices.Originality/valueCurrent methodologies, while contributing to the reduction of earthquake-related impacts, are often hindered by limitations such as lack of transparency, centralization and slow response times. In contrast, the adoption of blockchain technology can address these challenges and offer benefits over various aspects, including decentralized control, improved security, real-time data accessibility and enhanced inter-organizational collaboration.

Snow Disaster Risk Assessment Based on Long-Term Remote Sensing Data: A Case Study of the Qinghai–Tibet Plateau Region in Xizang

The risk analysis and assessment of snow disasters are essential foundational tasks in natural disaster management and profoundly impact the scientific and precise formulation of disaster prevention, preparedness, and mitigation strategies. Employing the theory and methodology of snow disaster assessment, this research focuses on historical and potential snow disasters in the Qinghai–Tibetan Plateau (QTP) Region. Utilizing a long-time-series snow depth remote sensing dataset, we extracted six assessment indicators for historical snow disaster risk factors and potential snow disaster risk factors. We determined the weights of these six assessment indicators using the entropy weight method. Subsequently, we established a snow disaster assessment model to evaluate the grade distribution of snow disasters in the study area. This method can effectively solve the problem of the sparse data distribution of meteorological stations and reflect degrees of snow disaster risk on a large spatial scale. The findings reveal that areas with a relatively high snow disaster risk are primarily concentrated in the western part of the Ali Region, the central part of Chamdo, and near the border in Southern Xizang. Additionally, regions with a high frequency of snow disasters are predominantly located at the junction of Nagchu, Chamdo, and Nyingchi in the eastern part of Xizang. These results contribute valuable insights into the risk assessment of snow disasters and facilitate the development of effective strategies for disaster management in the region.

Flood Risk Assessment for Better City Resilience; Case of The Bab El Oued Agglomeration, Algiers (Algeria)

The concept of natural risk represents a challenge to the economic and social development of a given territory, which leads to the adoption of a rational and studied approach to addressing the critical situation imposed by risks. Flooding is one of the most dangerous phenomena, capable of causing enormous damage to life, the economy, and the environment. The locality of Bab el Oued experienced a painful episode in its history in terms of flood damage during the year 2001. The risk in this locality is classified as very high, which implies that it is necessary to continuously reassess preventive measures and disaster management strategies if flooding occurs. This should be based on both natural and human factors, making risk a devastating reality and a constraint on sustainable development. Through this article, attempts have been made to find intelligent solutions for flood disaster management in the highly threatened neighbourhood of Bab El Oued within the framework of current legislation and regulations, following a likely scenario.

A logical retrofit strategy optimization framework for resiliency bridge infrastructure management considering life-cycle cost

Bridges play an important role in providing essential services to communities as one of the most critical components of transportation infrastructure. In this regard, selecting reliable, robust, and efficient indicators is necessary to prepare a disaster management strategy. This study presents a multi-objective optimization framework for decision-makers to find the most optimal retrofit strategies that satisfy a given threshold of functionality/Resilience (R) while minimizing a structure's Life-Cycle Cost (LCC). Accordingly, various retrofit strategies include different materials (steel, Carbon Fiber Reinforced Polymer (CFRP), and Glass Fiber Reinforced Polymer (GFRP)), thicknesses, arrangements, and timing of retrofitting actions. In each scenario, the fragility curves are derived through nonlinear time-history Incremental Dynamic Analysis (IDA) to evaluate the LCC and resilience. In the subsequent step, the LCC analysis is conducted, considering the proposed formulation of multiple occurrences of seismic events, which incorporates the effects of complete/incomplete repair actions of damage conditions induced by previous seismic events. This study employs an elitist Non-dominated Sorting Genetic Algorithm II (NSGA-II) to identify the optimal set of solutions. The various aspects of the optimal retrofit strategies are thoroughly investigated and discussed for a bridge as a case study infrastructure. Results show that the considered objectives lead to reasonable and sense-making retrofit strategies.