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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. [ABSTRACT FROM AUTHOR]

Post-disaster reconstruction of the built environment represents a key global challenge that looks set to remain for the foreseeable future, but it also offers significant implications for the future sustainability and resilience of the built environment. The purpose of this research is to explore the current applications of advanced digital/Industry 4.0 technologies in the post-disaster reconstruction (PDR) process with a view to improving its effectiveness and efficiency and the sustainability and resilience of the built environment. The extant research literature from the Scopus database on built environment reconstruction is identified and described. In a novel literature review approach, small language models are used for the classification and filtering of technology-related articles. A qualitative content analysis is then carried out to understand the extent to which Industry 4.0 technologies are applied in current reconstruction practice, mapping their applications to specific phases of the PDR process and identifying dominant technologies and key trends in technology deployment. The study reveals a rapidly evolving landscape of technological innovation with transformative potential in enhancing the efficiency, effectiveness, and sustainability of rebuilding efforts, with dominant technologies including GIS, remote sensing, AI, and BIM. Key trends include increasing automation and data-driven decision-making, integration of multiple Industry 4.0/digital technologies, and a growing emphasis on incorporating community needs and local knowledge into reconstruction plans. The study highlights the need for future research to address key challenges, such as developing interoperable platforms, addressing the ethical implications of using AI and big data, and exploring the contribution of Industry 4.0/digital technologies to sustainable reconstruction practices. [ABSTRACT FROM AUTHOR]

Post-disaster reconstruction (PDR) is a complex and unpredictable process, especially concerning the construction sector, where understanding associated risks is increasingly vital. This study investigates and evaluates the present condition of post-disaster reconstruction risk (RoPDR) and discerns research trends and deficiencies in the domain via a systematic literature review (SLR) and bibliometric analysis. The Web of Science (WoS) was preferred for its extensive repository of pivotal research publications and its integrated analytical capabilities for producing representative data. This study performed a comprehensive bibliometric analysis of 204 peer-reviewed journal articles regarding the risks associated with post-disaster reconstruction from 1993 to 2024, utilizing the R statistical programming package RStudio Bibliometrix R version 4.3.1 to map the research landscape, identify literature gaps, and analyze rising trends. As a result of the analyses, the risks of post-disaster reconstruction were classified into four main clusters. Despite numerous studies exploring post-disaster reconstruction through diverse perspectives and methodologies, the associated risks of these projects remain inadequately analyzed. This inaugural bibliometric study in the realm of RoPDR utilizes novel techniques, such as the h-index, thematic mapping, and trend topic analysis, to attain a comprehensive understanding. Hence, the outcome of this study will aid scholars and practitioners in thoroughly comprehending the present condition and identifying prospective research directions. [ABSTRACT FROM AUTHOR]

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Abstract Solving complex post-disaster reconstruction challenges requires the altruistic involvement of heterogeneous stakeholder groups. However, small, more organized groups, such as government parastatals, private developers, and contractors often exploit large, unorganized groups, such as affected communities, leaving them more vulnerable to future disasters. Based on data collected from a case study in Pakistan, this study proposed a framework to assess, anticipate, and mitigate the exploitation of vulnerable stakeholders in post-disaster reconstruction projects. The framework draws on influential management theories and utilizes reciprocal relationships between stakeholder attributes (power, legitimacy, and urgency), participation, and exploitation. The study also argued for non-binary treatment of stakeholder attributes. The framework will allow practitioners to address issues around the exploitation of stakeholder interests in future post-disaster reconstruction projects.

Solving complex post-disaster reconstruction challenges requires the altruistic involvement of heterogeneous stakeholder groups. However, small, more organized groups, such as government parastatals, private developers, and contractors often exploit large, unorganized groups, such as affected communities, leaving them more vulnerable to future disasters. Based on data collected from a case study in Pakistan, this study proposed a framework to assess, anticipate, and mitigate the exploitation of vulnerable stakeholders in post-disaster reconstruction projects. The framework draws on influential management theories and utilizes reciprocal relationships between stakeholder attributes (power, legitimacy, and urgency), participation, and exploitation. The study also argued for non-binary treatment of stakeholder attributes. The framework will allow practitioners to address issues around the exploitation of stakeholder interests in future post-disaster reconstruction projects. [ABSTRACT FROM AUTHOR]

In post-earthquake reconstruction, the rapid and effective utilization of pre-disaster and post-disaster remote sensing data is crucial. In such scenarios, remote sensing satellite technology demonstrates its unique advantages by quickly and dynamically acquiring high-resolution imagery of extensive areas in earthquake-affected regions, efficiently capturing the immediate post-disaster situation. Change detection technology has become a key tool through these remote sensing images. This technology can automatically identify changes in damaged areas, buildings, and other critical infrastructure through the analysis of pre-disaster and post-disaster remote sensing imagery data, aiding in post-disaster reconstruction efforts. Existing remote sensing change detection methods mainly rely on Convolutional Neural Network (CNN) or Transformer for construction. Still, these methods often fail to fully balance the advantages and disadvantages of these two technologies. They are not specifically optimized for the features of the change detection task (extracting and learning features of the changed area). To address this issue, this paper fully leverages the global information processing capabilities of the Transformer and the local information capture capabilities of CNN, proposing a multi-level feature guided aggregation network model composed of multiple branches that fully integrate the respective strengths of both. The model initially captures global information from the images using the Transformer-based main network. Subsequently, it extracts local information from the images employing a custom multi-scale strip convolution module based on CNN. Subsequently, the global and local information extracted during the encoding phase is further integrated through the feature aggregation network, and the final prediction map is generated using an attention fusion module. In the experimental section, the effectiveness of the proposed algorithm is further validated through comparative experiments conducted on multiple publicly available datasets.

Post-disaster reconstruction remains problematic despite the increasing number of disasters worldwide. This research calls for a systematic approach to pre-earthquake planning, which focuses on Build-Back-Better principles and its application in post-earthquake housing reconstruction.The study uses the sequential methodology approach to provide a framework for defining the key aspects of Build-Back-Better and its practical application. First, a qualitative approach is used to identify and interpret the main dimensions of providing permanent housing with a Build-Back-Better approach in District 3 of Sari city. Data from semi-structured interviews and field observations is analysed through qualitative content analysis, with results quantitatively analysed in the second part of the research using survey method by receiving two rounds of feedback from experts.The Build-Back-Better framework for post-earthquake housing consists of four categories and fourteen sub-categories. In order to emphasise the importance of pre-earthquake measures, this framework begins with earthquake risk reduction in physical, economical, socio-cultural and legal dimensions. Then, in the category of community participation, it defines the purpose of participation, the key participants, the type of participation, and the needed resources. Following that, the reconstruction of financial and organisational arrangements is discussed in two parts; resource procurement and organisational design. Finally, the paper presents the requirements for the design and implementation of permanent housing in the form of four subcategories: executive requirements, design approach, economic efficiency, and socio-cultural considerations. The output of this research is a unique framework that can help planners and implementers of recovery projects in risk reduction before the earthquake and in accelerating the provision of safe and affordable housing after the earthquake by predicting the needs and guiding executive actions.

Post-disaster reconstruction of the built environment represents a key global challenge that looks set to remain for the foreseeable future, but it also offers significant implications for the future sustainability and resilience of the built environment. The purpose of this research is to explore the current applications of advanced digital/Industry 4.0 technologies in the post-disaster reconstruction (PDR) process with a view to improving its effectiveness and efficiency and the sustainability and resilience of the built environment. The extant research literature from the Scopus database on built environment reconstruction is identified and described. In a novel literature review approach, small language models are used for the classification and filtering of technology-related articles. A qualitative content analysis is then carried out to understand the extent to which Industry 4.0 technologies are applied in current reconstruction practice, mapping their applications to specific phases of the PDR process and identifying dominant technologies and key trends in technology deployment. The study reveals a rapidly evolving landscape of technological innovation with transformative potential in enhancing the efficiency, effectiveness, and sustainability of rebuilding efforts, with dominant technologies including GIS, remote sensing, AI, and BIM. Key trends include increasing automation and data-driven decision-making, integration of multiple Industry 4.0/digital technologies, and a growing emphasis on incorporating community needs and local knowledge into reconstruction plans. The study highlights the need for future research to address key challenges, such as developing interoperable platforms, addressing the ethical implications of using AI and big data, and exploring the contribution of Industry 4.0/digital technologies to sustainable reconstruction practices.

Post-disaster reconstruction remains problematic despite the increasing number of disasters worldwide. This research calls for a systematic approach to pre-earthquake planning, which focuses on Build-Back-Better principles and its application in post-earthquake housing reconstruction. The study uses the sequential methodology approach to provide a framework for defining the key aspects of Build-Back-Better and its practical application. First, a qualitative approach is used to identify and interpret the main dimensions of providing permanent housing with a Build-Back-Better approach in District 3 of Sari city. Data from semi-structured interviews and field observations is analysed through qualitative content analysis, with results quantitatively analysed in the second part of the research using survey method by receiving two rounds of feedback from experts. The Build-Back-Better framework for post-earthquake housing consists of four categories and fourteen sub-categories. In order to emphasise the importance of pre-earthquake measures, this framework begins with earthquake risk reduction in physical, economical, socio-cultural and legal dimensions. Then, in the category of community participation, it defines the purpose of participation, the key participants, the type of participation, and the needed resources. Following that, the reconstruction of financial and organisational arrangements is discussed in two parts; resource procurement and organisational design. Finally, the paper presents the requirements for the design and implementation of permanent housing in the form of four subcategories: executive requirements, design approach, economic efficiency, and socio-cultural considerations. The output of this research is a unique framework that can help planners and implementers of recovery projects in risk reduction before the earthquake and in accelerating the provision of safe and affordable housing after the earthquake by predicting the needs and guiding executive actions. [ABSTRACT FROM AUTHOR]

Still too often, technical reconstruction assistance does not lead to adoption of hazard-resistant construction techniques by disaster-affected households. Ideally, effective interactions should be framed and communicated in such a way that it appeals to different intentions of individual households to build back safer. Currently, there is no model for humanitarian interactions that indicates what household characteristics reveal intention differences. To develop audience-specific interactions, this study adapted the three stages used in marketing: segmentation, targeting, and positioning, and the established social scientific model motivation, ability, and opportunity. Our strategic targeted interaction design (STID) method segments the affected population based on intentions to build back safer and provides tailored interactions. Based on similarities, 27 target groups are identified, requiring different interaction approaches. To test viability of the STID method, data were collected through 865 open-ended household interviews in earthquake affected communities in Nepal. Cluster analysis shows the presence of different target groups within the same communities. Descriptive statistics of the target groups led to a proposal of audience-specific interaction strategies to enhance safer construction practices and reduce disaster risk. Our analysis indicates a combination of multiple interaction strategies, prioritizing the learning process and interactions within communities over interactions with external actors. This study presents a highly promising method toward personalization of aid, tailored to perceptions of target groups over general community characteristics. [ABSTRACT FROM AUTHOR]