Search Results (73)

This study presents a cost-effective, carbon-negative construction material for affordable housing, developed entirely from locally available agricultural wastes: rice husk ash, wood ash, and rice straw—materials often problematic to dispose of in many African regions. Rice husk ash provides high amorphous silica, acting as a strong pozzolanic agent. Wood ash contributes calcium oxide and alkalis to serve as a reactive binder, while rice straw functions as a lightweight organic filler, enhancing thermal insulation and indoor climate comfort. These materials undergo natural pozzolanic reactions with water, eliminating the need for Portland cement—a major global source of anthropogenic CO₂ emissions (~900 kg CO₂/ton cement). This process is inherently carbon-negative, not only avoiding emissions from cement production but also capturing atmospheric CO₂ during lime carbonation in the hardening phase. Field trials in Kenya confirmed the composite’s sufficient structural strength for low-cost housing, with added benefits including termite resistance and suitability for unskilled laborers. In a collaboration between the University of Tartu and Kenyatta University, a semi-automatic mixing and casting system was developed, enabling fast, low-labor construction of full-scale houses. This innovation aligns with Kenya’s Big Four development agenda and supports sustainable rural development, post-disaster reconstruction, and climate mitigation through scalable, eco-friendly building solutions. Full article

Housing recovery represents a critical component of disaster recovery, and accurately forecasting household relocation decisions is essential for guiding effective post-disaster reconstruction policies. This study explores the use of machine learning algorithms to improve the prediction of household relocation in the aftermath of disasters. Leveraging data from 1304 completed interviews conducted as part of the Displaced New Orleans Residents Survey (DNORS) following Hurricane Katrina, we evaluate the performance of Logistic Regression (LR), Random Forest (RF), and Weighted Support Vector Machine (WSVM) models. Results indicate that WSVM significantly outperforms LR and RF, particularly in identifying the minority class of relocated households, achieving the highest F1 score. Key predictors of relocation include homeownership, extent of housing damage, and race. By integrating variable importance rankings and partial dependence plots, the study also enhances interpretability of machine learning outputs. These findings underscore the value of advanced predictive models in disaster recovery planning, particularly in geographically vulnerable regions like New Orleans where accurate relocation forecasting can guide more effective policy interventions. Full article

Flood hazards and their disastrous consequences disrupt economic activity and threaten human lives globally. From a remote sensing perspective, since floods are often triggered by extreme climatic events, such as heavy rainstorms or tropical cyclones, the efficacy of using optical remote sensing data for disaster and damage mapping is significantly compromised. In many flood events, obtaining cloud-free images covering the affected area remains challenging. Nonetheless, considering that floods are the most frequent type of natural disaster on Earth, optical remote sensing data should be fully exploited. In this article, firstly, we will present a critical review of remote sensing data and machine learning methods for global flood-induced damage detection and mapping. We will primarily consider two types of remote sensing data: moderate-resolution multi-spectral data and high-resolution true-color or panchromatic data. Big and semantic databases available for advanced machine learning to date will be introduced. We will develop a set of best-use case scenarios for using these two data types to conduct water-body and built-up area mapping with no to moderate cloud coverage. We will cross-verify traditional machine learning and current deep learning methods and provide both benchmark databases and algorithms for the research community. Last, with this suite of data and algorithms, we will demonstrate the development of a cloud-computing-supported computing gateway, which houses the services of both our remote-sensing-based machine learning engine and a web-based user interface. Under this gateway, optical satellite data will be retrieved based on a global flood alerting system. Near-real-time pre- and post-event flood analytics are then showcased for end-user decision-making, providing insights such as the extent of severely flooded areas, an estimated number of affected buildings, and spatial trends of damage. In summary, this paper’s novel contributions include (1) a critical synthesis of operational readiness in flood mapping, (2) a multi-sensor-aware review of optical limitations, (3) the deployment of a lightweight ML pipeline for near-real-time mapping, and (4) a proposal of the GloFIM platform for field-level disaster support. Full article

This study proposes a non-contact method for assessing building inclination and damage by integrating 3D point cloud data with image recognition techniques. Conventional approaches, such as plumb bobs, require physical contact, posing safety risks and practical challenges, especially in densely built urban areas. The proposed method utilizes a 3D scanner to capture point cloud data and images, which are processed to extract building surfaces, detect inclination, and assess secondary structural components such as window frames. Experiments were conducted on prefabricated structures, detached houses, and dense residential areas to validate the method’s accuracy. Results show that the proposed approach achieved measurement accuracy comparable to or better than traditional methods, with an error reduction of approximately 19% in prefabricated structures and 21.72% in detached houses. Additionally, the method successfully identified window frame deformations, contributing to a comprehensive assessment of structural integrity. By applying gradient-based color mapping, damage severity was visualized intuitively. The findings demonstrate that this system can replace conventional measurement techniques, enabling safe, efficient, and large-scale post-disaster assessments. Future work will focus on enhancing point cloud interpolation and refining machine learning-based damage classification for broader applicability. Full article

The Mass Housing Administration of Türkiye (TOKI) operates as the primary public organization responsible for delivering extensive affordable housing throughout Türkiye while ensuring disaster resilience. The recent decades of earthquakes and environmental hazards in Türkiye have necessitated extensive post-disaster reconstruction initiatives nationwide. In response, TOKI has completed numerous disaster housing projects across the country through an integrated infrastructure framework supporting community recovery. This study presents an extensive statistical evaluation of 664 disaster housing projects constructed by TOKI across 40 provinces. Specifically, a quantitative analysis is conducted on 434 disaster housing projects for which detailed financial data are available. This research examines differences in construction costs between urban mass housing developments and rural village settlements, particularly focusing on the integration of functional structures such as schools, mosques, commercial units, and barns. Although mass housing projects require significantly larger total budgets due to their extensive scale, statistical analysis reveals no significant difference in per-unit construction costs between mass housing and village housing projects. Regression analysis indicates that incorporating barns increased per-unit construction costs, while the presence of schools and mosques significantly decreases these expenses. The findings of this research provide critical insights into the economic and functional factors influencing disaster housing reconstruction in Türkiye and offer practical recommendations for improved planning, resource management, and community reconstruction based on an evaluation of functional structures. Full article

This article examines Chile’s post-disaster data collection and management, focusing on state legibility tools for identifying housing damage and victims’ needs. Drawing on James Scott’s theory of legibility, we explore how standardized categories are used in disaster management. Through documentary analysis and key informant interviews, we assess the strengths and limitations of the forms used for allocating aid. The 2022 Viña del Mar wildland–urban interface fire serves as a case study to illustrate how classification systems determine victim status, influencing government resource allocation. We show that Chile has made significant progress in loss and needs assessment after disasters but gaps remain in data integration and intersectoral collaboration. Three main themes emerge from the analysis: actor coordination, social legitimacy, and administrative blindness. We conclude that while legibility tools facilitate state action, they also obscure the complexities of disasters. This case study provides further evidence that disparities in aid distribution hinder recovery efforts and that for many victims, disaster aid has been both insufficient and delayed. We provide recommendations to address these challenges and strengthen disaster risk management policies in Chile and other countries facing similar challenges. Full article

As damage from natural hazards has increased globally, governments and aid agencies must have practical tools for post-disaster livelihood recovery to foster self-sufficiency among affected populations. This study presents a framework of essential components for livelihood recovery, utilising a comparative case study with interviews from communities impacted by major earthquakes in Lushan, China, and Christchurch and Kaikōura, New Zealand. Communities in both countries highly value housing, employment, well-being, and external assistance for livelihood recovery after disasters. However, people in China credit housing functionality and income resources more, while New Zealand people rely more on commercial insurance for their recovery. The insights from this comparative study can help governments and non-governmental organisations strengthen livelihood recovery efforts and enhance resilience to future disasters. Full article

U.S. government agencies execute home relocation programs, known as buyouts, in flood-prone areas to reduce hazard exposure. By converting the buyout properties into open space, these governmental agencies assume ownership and management responsibilities. As with all landscapes, the post-buyout landscape reflects power dynamics and institutional forces that shape how the land is managed, perceived, and used. For acquired properties, historic housing polices, disaster risk reduction strategies, and the social construction of the land have all accumulated over time on the post-buyout landscape and influence contemporary land management practices. To understand the influence of power and social capital on post-buyout land management, this study analyzes land management practices and compares them with the socioeconomic characteristics of buyout neighborhoods in Harris County, Texas, USA, a county with a fifty-year buyout history. Results indicate that homeownership status, race, and ethnicity were related to post-buyout land management to varying degrees, thus reflecting differing degrees of social capital in buyout neighborhoods and therefore power to shape the management of post-buyout open space. Full article

The growing adoption of wind energy has resulted in an increasing number of decommissioned wind turbine blades, which pose significant disposal challenges due to their size, material composition, and environmental impact. Recycling these blades has thus become essential. To this aim, this study explores the potential of using recycled wind turbine blades in post-disaster housing applications and examines the feasibility of re-purposing these durable composite materials to create robust, cost-effective, and sustainable building solutions for emergency housing. A case study of a post-earthquake relief camp in Hatay, Türkiye, affected by the 2023 earthquake, is used for analysis. First, the energy consumption of thirty traditional modular container-based post-disaster housing units is simulated with a dynamic building simulation tool. Then, the study introduces novel wind turbine blade-based housing (WTB-bH) designs developed using the same simulation tool. The energy consumption of these (WTB-bH) units is compared to that of traditional containers. The results indicate that using recycled wind turbine blades for housing not only contributes to waste reduction but also achieves 27.3% energy savings compared to conventional methods. The novelty of this study is in demonstrating the potential of recycled wind turbine blades to offer durable and resilient housing solutions in post-disaster situations and to advocate for integrating this recycling method into disaster recovery frameworks, highlighting its ability to enhance sustainability and resource efficiency in construction. Overall, the output of this study may help to present a compelling case for the innovative reuse of decommissioned wind turbine blades, providing an eco-friendly alternative to traditional waste disposal methods while addressing critical needs in post-disaster scenarios. Full article

The design of post-emergency shelters is critical for mitigating the impacts of the numerous disasters occurring globally. Unfortunately, these shelters are frequently constructed with insufficient attention to comfort levels and minimum standards for temporary refuge. This study examines wooden post-emergency shelters, which have become increasingly common in various refugee camps and regions worldwide. Using the Active House Protocol, a comprehensive comparative analysis of indoor comfort is conducted to propose innovative approaches to global design challenges, particularly in developing countries. To minimize the negative effects of disasters and increase sustainability, it is essential to explore the feasibility of designing comfortable shelters despite numerous obstacles, such as the limited availability of low-cost materials and the lack of skills of the local workforce. Various shelter configurations are analyzed, revealing that structures made from natural materials and designed with careful consideration of air circulation yielded the highest comfort values. Additionally, the study finds that wooden structures do not always provide the best solution, contrary to common practice. Inadequate comfort standards can lead to distress and psychological stress among occupants, highlighting the necessity to improve design and construction practices to ensure the well-being of disaster-affected populations. Finally, this study provides guidelines on the minimum requirements for the development of post-disaster shelters, advocating for solutions that are both simple and effective in rapidly addressing the critical situations that arise following disasters. Full article

Post-disaster resettlement often faces abandonment and modification; yet, the factors influencing long-term residential satisfaction, especially within diverse communities, remain underexplored. This study examines how satisfaction evolves over time in relation to socio-economic status and community participation comparing the underprivileged Thami community in Panipokhari and the privileged Brahmin community in Jillu Integrated Settlement of Nepal. Using a mixed-method case study approach, this research integrates quantitative satisfaction scores with qualitative insights from surveys, interviews, and observations over three years. Findings reveal diverging satisfaction trends: Jillu’s satisfaction remained stable (3.55 to 3.43 from 2021 to 2023), whereas Panipokhari’s declined (3.27 to 2.33) due to unmet housing needs and limited participation. Correlation tests and qualitative interviews confirmed that while all five key factors—housing design, thermal comfort, water, cultural appropriateness, and architectural aesthetics—influenced satisfaction, their importance varied. These findings challenge “one size fits all” top-down resettlement models, demonstrating that housing adaptability and participatory decision-making are more critical than structural adequacy alone. The study underscores the need for flexible, community-driven housing strategies within the Build Back Better (BBB) framework. By integrating the housing satisfaction theory, the housing mobility theory, and the BBB framework, it advances understanding of socio-economic agency in shaping post-disaster housing outcomes, providing insights for sustainable and inclusive resettlement policies. Full article

This study aims to understand the impacts of climate change and extreme climate events on residential buildings and explore how existing buildings can be adapted to resist these negative impacts. A bibliometric and scientometric analysis was conducted on resilient residential retrofits to highlight the prevalent themes, critical directions, and gaps in the literature, which can inform future research directions. The resilient residential retrofit publications from 2012 to 2023 were retrieved and analyzed using text-mining software. In all, 4011 publications and 2623 patents were identified. The analysis revealed an average annual publication growth rate of 11%, indicating increasing interest in resilient residential retrofits. Four central topics were explored specifically throughout the study, as they are known to be the most prevalent climate risks for residential buildings: Overheating, Flooding, Wind, and Wildfires. The research trends analysis reveals that emerging interests in resilient residential retrofit encompass nature-based solutions, energy efficiency, thermal comfort, microclimates, durability, post-disaster recovery, and extreme events. Nearly half of the publications reference urban context and over one-third mention costs. The building envelope is the most frequently discussed housing component. Although energy retrofit was not the primary focus of this study and was not specifically searched for, energy concerns were still prevalent in the dataset, highlighting the critical importance of energy efficiency and management in resilient residential retrofits. The analysis of R&D momentum revealed several research gaps. Despite high growth rates, there are low publication rates on key topics such as durability, holistic approaches, microclimates, nature-based solutions, and traditional homes, to name a few. These areas could benefit from further research in the context of climate-resilient residential retrofits. Additionally, the analysis indicates a lack of publications on cross-themed research specific to rural and suburban settings. There are also few studies addressing combinations of themes, such as overheating in high-rise buildings, wildfires in Nordic climates, and flooding risk in smart homes within the scope of resilient residential retrofits. The United States leads in publication output, followed by China and the UK, with China dominating the patent landscape. This scientometric analysis provides a comprehensive overview of the research landscape in resilient residential retrofit, systematically maps and analyzes the vast amount of research output, and identifies the key trends and gaps, enabling us to see a type of quantitative snapshot of the research in a field at a certain point in time and thus providing a unique point of view. This study helps stakeholders prioritize efforts and resources effectively for guiding future research, funding decisions, informing policy decisions, and ultimately enhancing the resilience of residential buildings to climate-related challenges. Full article

Loneliness, a significant public health issue, was exacerbated during the COVID-19 pandemic, particularly in disaster-prone regions like the U.S. Gulf Coast. This study examined how social and built environmental factors were associated with pandemic-related disruptions and loneliness among respondents from the third wave of the Survey of Trauma, Resilience, and Opportunity among Neighborhoods in the Gulf (STRONG). Using a retrospective measure of loneliness (pre-pandemic vs. during pandemic), we found that loneliness increased significantly during the pandemic. Using a measure of routine behavior disruptions and measures of both objective (e.g., parks, walkability, etc.) and subjective (e.g., neighborhood safety, social cohesion, etc.) environmental factors, we found that disruptions to daily routines strongly predicted higher loneliness, and subjective measures, such as neighborhood safety, social cohesion, and lacking post-disaster social support, were more salient predictors of loneliness than objective factors such as the number of parks in one’s neighborhood. Difficulty accessing green spaces and housing distress were linked to greater COVID-19 disruptions, indirectly contributing to loneliness. These findings highlight the importance of safe, supportive, and accessible social and physical environments in mitigating loneliness and enhancing community resilience during crises. Full article

Climate-induced migration is increasingly affecting communities, disrupting livelihoods, and intensifying socio-economic inequalities, particularly in disaster-prone regions. Despite the prevalence of recurring flood hazards, there remains limited research on the multi-dimensional impacts of migration particularly in socio-culturally sensitive and resource-constrained settings like Khyber Pakhtunkhwa (KPK), Pakistan. This study seeks to bridge this gap by exploring the post-migration challenges of flood-affected communities in District Nowshera. Using a qualitative methodology, we conducted in-depth interviews with 25 diverse key informants. The study results revealed profound socioeconomic instability, inadequate access to essential services, and cultural disruptions. Key findings include significant challenges such as inadequate housing, the loss of traditional livelihoods, persistent financial hardships, health issues, and the breakdown of social support networks. Moreover, displaced families face marginalization and language barriers, which hinder integration into host communities, amplifying feelings of isolation and identity loss. Environmental degradation in resettlement areas further intensifies these challenges, prolonging poor living conditions and heightened vulnerability. To address these issues, the study recommends community-based interventions such as developing resilient, culturally appropriate housing, implementing targeted skills training programs to restore livelihoods, promoting climate-smart agricultural practices, and enacting inclusive social policies to promote integration and cohesion to address climate-induced migration in disaster-prone regions. Full article

An earthquake and tsunami on 1 January 2024, caused extensive damage across Ishikawa Prefecture, Japan. This study systematically examined the recovery process. It aimed to analyze the initial stages of recovery and highlight the lessons learned from these efforts. By collecting data from Ishikawa’s post-disaster records, including government reports, reconstruction plans, local news, and observational records, we compiled the first “Early Reconstruction Timeline for Ishikawa”. This timeline divided the recovery process into four phases: disaster occurrence and emergency response (January), reconstruction preparation (February–April), the beginning of early reconstruction (May–September), and the beginning of mid-reconstruction to the present (October–Present). Analysis of this timeline revealed several significant findings. First, Ishikawa’s reconstruction efforts were structured into three key phases: ‘Post-Disaster Relief and Emergency Response’, ‘Post-Disaster Reconstruction Preparation Period’, and ‘Infrastructure Reconstruction and Emergency Housing Construction Period’. This phased approach highlights an efficient and organized recovery process, distinguishing Ishikawa from other disaster-affected regions. Additionally, the housing reconstruction model showcased an innovative balance between emergency housing as a public resource and the specific needs of displaced residents. These findings not only establish a timeline-based framework for Ishikawa’s reconstruction but also provide practical insights for guiding early post-disaster recovery efforts in various disaster-affected contexts worldwide. Full article