Knowledge Agora

Title	Revamping Sustainability Efforts Post-Disaster by Adopting Circular Economy Resilience Practices
ID_Doc	35
Authors	Pradhananga, P; ElZomor, M
Published	Sustainability, 15, 22
Structure	I will provide the analysis of the article in sections with two sentences each. Abstract Section The article discusses the importance of revamping sustainability efforts post-disaster by adopting circular economy resilience practices. The authors aim to investigate the factors that impact the reusability of buildings post-disaster and recommend strategies that align with circular economy goals. Introduction Section The article highlights the increasing frequency and severity of natural disasters worldwide, resulting in significant damage to infrastructure and human settlements. The authors review existing literature on post-disaster reconnaissance and debris management to identify gaps in current practices and propose a new approach. Methods Section The authors employed a three-step research methodology to investigate the impact of building damage on reusability. They used thematic analysis to evaluate types of damages reported in reconnaissance reports, machine-learning algorithms to analyze datasets, and concept mapping to identify strategies for reducing disaster debris. Thematic Analysis Section The authors identified common types of damage to buildings, including whole structures, roof, wall, and foundation components. They found that roof structure damage has the highest impact on building reusability, while minor damage to roofs and walls has less impact. Machine-Learning Analysis Section The authors developed a regression model using a supervised machine-learning algorithm to predict building reusability. They found that gradient-boosting regression model has the highest accuracy, with an R-squared value of 0.667. Semi-Structured Interviews Section The authors conducted 109 semi-structured interviews with stakeholders to identify strategies for reducing disaster debris. They found that adopting deconstruction methods and design for disassembly principles can help maximize the reuse and recycling of building components post-disaster. Discussion Section The authors discussed the results of the study, highlighting the importance of adopting circular economy resilience practices post-disaster. They found that deconstruction methods and design for disassembly principles can help reduce waste and maximize resource efficiency in post-disaster recovery. Limitations and Future Work Section The authors identified limitations in the study, including the limited dataset and lack of information on deconstruction feasibility. They proposed future research directions, including collecting more data on deconstruction feasibility and validating the model using a larger dataset. Conclusions Section The authors concluded that adopting circular economy resilience practices post-disaster can help reduce waste and maximize resource efficiency in recovery efforts. They highlighted the importance of adopting deconstruction methods and design for disassembly principles to achieve sustainable post-disaster recovery. References Section The article references 60 studies and publications, including academic papers, conference proceedings, and reports. Acknowledgments Section The authors acknowledged funding from the National Science Foundation Innovation Corps (NSF I-CORPS) and the support of a FIU University Graduate School Dissertation Year Fellowship. Disclaimer/Publisher’s Note Section The publisher notes that the statements, opinions, and data contained in the article are solely those of the authors and not of MDPI and/or the editor(s).
Summary	The article discusses the importance of revamping sustainability efforts post-disaster by adopting circular economy resilience practices. The study investigates the factors that impact the reusability of buildings post-disaster and recommends strategies that align with circular economy goals. The study used a three-step research methodology: thematic analysis of reconnaissance reports, machine-learning analysis of datasets, and semi-structured interviews with stakeholders. The thematic analysis revealed common damage patterns observed in buildings after hurricane impact, while the machine-learning analysis identified the most effective regression model for predicting building reusability. The semi-structured interviews highlighted the importance of adopting circular economy practices, such as deconstruction methods and design for disassembly principles. The study found that buildings with significant damage to the roof structure are not considered reusable, and therefore, demolition is a common practice. However, the study also found that adopting deconstruction methods can reduce waste disposal in landfills and maximize resource efficiency. The study recommends training deconstruction workforce personnel and educating them about proper deconstruction and recycling practices. The findings of the study contribute to disaster management and sustainable construction bodies of knowledge, highlighting the impact of different factors on building reusability and promoting circular economy resilience. The study also highlights the importance of assessing post-disaster recovery sites for the feasibility of building deconstruction and recovering salvage materials for reuse in the reconstruction process.
Scientific Methods	The article "Revamping Sustainability Efforts Post-Disaster by Adopting Circular Economy Resilience Practices" by Piyush Pradhananga and Mohamed ElZomor presents a research study that aims to investigate existing reconnaissance reports and datasets to identify the factors that impact the reusability of buildings post-disaster and to recommend strategies that align with circular economy goals. The research methods used in this study are: 1. Thematic analysis: The study conducted a thematic analysis of available Structural Extreme Events Reconnaissance (StEER) reconnaissance reports to identify different types of damages observed in the aftermath of different hurricanes. 2. Machine-learning algorithm: The study used a machine-learning algorithm to analyze reconnaissance datasets and identify factors that impact building reusability. 3. Semi-structured interviews: The study conducted semi-structured interviews with 109 stakeholders, including homeowners, consulting engineers, contractors, forensic engineers, and project managers, to identify solutions to increase the reusability potential of buildings and salvage materials in disaster-prone zones. The study used the following tools and techniques: 1. NVIVO data analysis software: The study used NVIVO software to analyze the thematic analysis of the StEER reports. 2. Design Safe cyberinfrastructure: The study used the Design Safe cyberinfrastructure, an open-source database, to investigate damages observed in the aftermath of different hurricanes and identify factors that impact building reusability. 3. Concept map: The study used concept mapping to analyze the qualitative data from the interviews and identify linkages, reduce data volume, and provide a complete picture of the solution. The study found that: 1. The roof structure has the highest impact on the predicted building reusability. 2. Buildings with significant damage to the roof structure are not considered reusable. 3. Deconstruction is an effective solution to reduce disaster debris disposal in landfills and promote resource efficiency. 4. The adoption of a design for disassembly (DfD) principle can maximize circular economy practices. The study's findings contribute to disaster management and sustainable construction bodies of knowledge by highlighting the impact of different factors on building reusability and creating awareness of circular economy practices among construction stakeholders and policymakers.
Article contribution	The article "Revamping Sustainability Efforts Post-Disaster by Adopting Circular Economy Resilience Practices" presents a comprehensive framework for promoting sustainability and resilience in the aftermath of natural disasters. The authors' main objective is to investigate the factors that impact the reusability of buildings post-disaster and to recommend strategies that align with circular economy goals. The article's contributions to regenerative economics and sustainability are significant in several ways: 1. Circular Economy Resilience Framework: The study proposes a circular economy resilience framework that integrates strategies for reducing waste, promoting resource efficiency, and enhancing the resilience of communities to natural disasters. This framework provides a holistic approach to disaster recovery and has the potential to become a best practice in the field. 2. Building Deconstruction as a Sustainable Option: The article highlights the benefits of building deconstruction as a sustainable option for post-disaster recovery. Deconstruction involves the careful disassembly of buildings to recover reusable materials, reducing waste and the environmental impacts associated with traditional demolition methods. 3. Quantifying Building Reusability: The study presents a machine-learning model that can predict the reusability of buildings post-disaster with high accuracy. This model has the potential to inform decision-making in the disaster recovery process, enabling policymakers and stakeholders to prioritize the most efficient and effective recovery strategies. 4. Emphasis on Low-Income Communities: The article acknowledges the disproportionate impact of natural disasters on low-income communities and highlights the need for strategies that prioritize their needs and resilience. By promoting circular economy practices and building deconstruction, the study suggests that these communities can benefit from more sustainable and equitable disaster recovery approaches. 5. Interdisciplinary Approach: The study demonstrates an interdisciplinary approach by integrating insights from engineering, architecture, economics, and social sciences. This interdisciplinary approach is essential for developing comprehensive solutions to the complex challenges posed by natural disasters. Overall, the article makes a significant contribution to regenerative economics and sustainability by providing a novel framework for promoting circular economy resilience in the aftermath of natural disasters. The study's recommendations and insights have the potential to inform policy and practice, enabling communities to better prepare for and respond to natural disasters in a more sustainable and resilient manner. Rating: 9.5/10 Strengths: 1. Comprehensive framework: The study presents a comprehensive framework for promoting circular economy resilience in the aftermath of natural disasters. 2. Interdisciplinary approach: The article demonstrates an interdisciplinary approach by integrating insights from engineering, architecture, economics, and social sciences. 3. Practical recommendations: The study provides practical recommendations for policymakers and stakeholders, including the development of a machine-learning model to predict building reusability. Weaknesses: 1. Limited dataset: The study is based on a limited dataset, which may not be representative of the full range of disaster recovery experiences. 2. Lack of empirical evidence: While the article presents a theoretical framework, it could benefit from more empirical evidence to support its recommendations. 3. Potential for biases: The study's reliance on qualitative data from stakeholder interviews may be subject to biases and limitations in terms of representation and generalizability.

Knowledge Agora

Scientific Article analysis using AI

Similar Articles