Strategies for Prioritizing Needs for Accelerated Construction after Hazard Events

There is a need for rapid and responsive infrastructure repair and construction after natural disaster events such as hurricanes, wildfires, and tornadoes. These natural disasters often shut down basic infrastructure systems, as experienced recently in several Region 6 states as well as in other states around the country. Accelerated construction practices are often used in these situations to speed up the traditional, and often slow, project delivery process. However, after a natural disaster, several and different types of transportation infrastructure components are in need of inspection, rehabilitation or reconstruction, and transportation agencies are challenged with the task of prioritizing these accelerated projects. This study conducted an extensive literature review of current accelerated methods, infrastructure prioritization practices, and institutional barriers. Interviews with professionals from the transportation industry, including both private and public services, were conducted. Significant input from the railroad industry was used to compare private and public transportation systems responses after disasters. The results of this survey were used to quantify the importance of the accelerate methods and prioritization criteria, and which are the barriers to implement a prioritization model. Lastly, a decision support tool for prioritizing needs for accelerated construction after disaster events, specifically hurricanes and flooding, which commonly affect Region 6, was developed using the data collected

Deep Reinforcement Learning-based Project Prioritization for Rapid Post-Disaster Recovery of Transportation Infrastructure Systems

Among various natural hazards that threaten transportation infrastructure, flooding represents a major hazard in Region 6\u27s states to roadways as it challenges their design, operation, efficiency, and safety. The catastrophic flooding disaster event generally leads to massive obstruction of traffic, direct damage to highway/bridge structures/pavement, and indirect damages to economic activities and regional communities that may cause loss of many lives. After disasters strike, reconstruction and maintenance of an enormous number of damaged transportation infrastructure systems require each DOT to take extremely expensive and long-term processes. In addition, planning and organizing post-disaster reconstruction and maintenance projects of transportation infrastructures are extremely challenging for each DOT because they entail a massive number and the broad areas of the projects with various considerable factors and multi-objective issues including social, economic, political, and technical factors. Yet, amazingly, a comprehensive, integrated, data-driven approach for organizing and prioritizing post-disaster transportation reconstruction projects remains elusive. In addition, DOTs in Region 6 still need to improve the current practice and systems to robustly identify and accurately predict the detailed factors and their impacts affecting post-disaster transportation recovery. The main objective of this proposed research is to develop a deep reinforcement learning-based project prioritization system for rapid post-disaster reconstruction and recovery of damaged transportation infrastructure systems. This project also aims to provide a means to facilitate the systematic optimization and prioritization of the post-disaster reconstruction and maintenance plan of transportation infrastructure by focusing on social, economic, and technical aspects. The outcomes from this project would help engineers and decision-makers in Region 6\u27s State DOTs optimize and sequence transportation recovery processes at a regional network level with necessary recovery factors and evaluating its long-term impacts after disasters

Optimizing the Prioritization of Natural Disaster Recovery Projects

Prioritizing reconstruction projects to recover a base from a natural disaster is a complicated and arduous process that involves all levels of leadership. The project prioritization phase of base recovery has a direct affect on the allocation of funding, the utilization of human resources, the obligation of projects, and the overall speed and efficiency of the recovery process. The focus of this research is the development of an objective and repeatable process for optimizing the project prioritization phase of the recovery effort. This work will focus on promoting objectivity in the project prioritizing process, improving the communication of the overall base recovery requirement, increasing efficiency in utilizing human and monetary resources, and the creation of a usable and repeatable decision-making tool based on Value-Focused Thinking and integer programming methods

Development of a Conceptual Model for Accelerated Project Prioritization after Disaster Event

There is a need for rapid and responsive infrastructure repair and construction after natural disaster events such as hurricanes, wildfires, and tornadoes. These natural disasters often shut down basic infrastructure systems, including roads, bridges, water supply, and power supply, as experienced recently in several Region 6 states as well as in other states around the country. Accelerated construction practices are often used in these situations to speed up the traditional, and often slow, project delivery process. However, after a natural disaster, several and different types of transportation infrastructure components are in need of inspection, rehabilitation or reconstruction, and transportation agencies are challenged with the task of prioritizing these accelerated projects. This paper studied the current practices and institutional barriers to identify the critical decision criteria and to develop a conceptual model for prioritizing needs for accelerated construction after disaster events, specifically hurricanes and flooding which commonly affect Region 6

Natural disasters : what is the role for social safety nets?

This paper makes the case for why safety nets are an important tool for managing the risk of natural hazards. The use of safety nets is advocated both ex ante, to prevent and mitigate the impact of natural disaster and ex post, to cope with the impacts of natural shocks. Firstly, the paper explores the implications of contextual factors to be taken into account in the design of an effective safety net system to respond to the needs generated by natural disasters. Learning from the responses to a number of recent natural disasters, a typology of the different types of natural hazards which require different approaches to reduce their risk is introduced. Secondly, the paper considers some'guidelines'for improving the design and implementation of safety nets either to prevent and/or to recover from natural disasters. Finally, some conclusions and recommendations for more effective safety net and suggestions for addressing key issues are outlined.Safety Nets and Transfers,Hazard Risk Management,Food&Beverage Industry,Labor Policies,Natural Disasters

A systematic review on MIVES: a sustainability-oriented multi-criteria decision-making method

Sustainability has practically become a mandatory concept to be considered in every decision, and multiple decision-making methods have been adapted to take it into account. Among them, sustainability centred methods are also known as sustainability assessments, which provides sustainability indexes to select and prioritize alternatives. One of these most recent presented techniques is MIVES, a multi attribute utility theory multi-criteria decision-making value function-based method initially developed to introduce environmental and social indicators in civil engineering design decisions and later adapted for general evaluation and prioritization of homogenous and heterogeneous alternatives. Over the last decade, it has been widely studied and applied to specific situations, but a MIVES summary is currently lacking. Therefore, in this paper MIVES literature is reviewed with a deep bibliometric analysis carried out to provide on multiple data about MIVES state-of-the-art. Furthermore, a thematic clusters categorisation is done to reveal the usefulness of MIVES as design and decision-making tool, cataloguing the wide applications of MIVES as sustainability index. Finally, a MIVES characteristics discussion is carried out to help researchers deepen their knowledge towards the method and highlight potential future research pathways.The first author acknowledges the Goverment of Spain: Ministry of Education, Culture and Sports [grant number FPU18/01471]. The second and last author wishes to recognize the support from Serra Hunter programme. Finally, this work was supported by Catalan agency AGAUR trough their research groups support program (2017SGR00227). This research is part of the R&D project IAQ4EDU, reference no. PID2020-117366RB-I00, funded by MCIN/AEI/10.13039/501100011033.Peer ReviewedPostprint (published version

Lessons Learned from Chile, Evaluating Strategic Reconstruction Master Plans in Post-Disaster Scenarios

Natural and manmade disasters have been increasing in the last decade all over the world affecting millions of people and generating high costs to recover from them. There is a need to implement policies that will foster mitigation and prevention measures and that will address recovery as a long-term process rather than short-term solutions. Chile has overcome several disasters in the last decade and has designed innovative tools to rebuild from the aftermath. The purpose of this research is to evaluate one of these tools, Strategic Reconstruction Master Plans, and what lessons have the different government periods have learned from past disasters in order to apply them for future ones. The research compares and contrasts two case studies in which master plans were the main tool implemented for recovery purposes, these are Pelluhue and Santa Olga, and both are located in the Maule region. The study evaluates the methodology that each project implemented to recover from the disaster period and what lessons are drawn from each case for future policy applications. The paper incorporates three main research methods. First, archival research on post-disaster efforts; second, technical documentation from each case study and local media from each period; and third, in depth interviews to experts and stakeholders involved in each process together with field observations

Optimization-based decision-making models for disaster recovery and reconstruction planning of transportation networks

The purpose of this study is to analyze optimization-based decision-making models for the problem of Disaster Recovery Planning of Transportation Networks (DRPTN). In the past three decades, seminal optimization problems have been structured and solved for the critical and sensitive problem of DRPTN. The extent of our knowledge on the practicality of the methods and performance of results is however limited. To evaluate the applicability of those context-sensitive models in real-world situations, there is a need to examine the conceptual and technical structure behind the existing body of work. To this end, this paper performs a systematic search targeting DRPTN publications. Thereafter, we review the identified literature based on the four phases of the optimization-based decision-making modeling process as problem definition, problem formulation, problem-solving, and model validation. Then, through content analysis and descriptive statistics, we investigate the methodology of studies within each of these phases. Eventually, we detect and discuss four research improvement areas as [1] developing conceptual or systematic decision support in the selection of decision attributes and problem structuring, [2] integrating recovery problems with traffic management models, [3] avoiding uncertainty due to the type of solving algorithms, and [4] reducing subjectivity in the validation process of disaster recovery models. Finally, we provide suggestions as well as possible directions for future research.TU Berlin, Open-Access-Mittel - 202

Disaster management in industrial areas: perspectives, challenges and future research

Purpose: In most countries, development, growth, and sustenance of industrial facilities are given utmost importance due to the influence in the socio-economic development of the country. Therefore, special economic zones, or industrial areas or industrial cities are developed in order to provide the required services for the sustained operation of such facilities. Such facilities not only provide a prolonged economic support to the country but it also helps in the societal aspects as well by providing livelihood to thousands of people. Therefore, any disaster in any of the facilities in the industrial area will have a significant impact on the population, facilities, the economy, and threatens the sustainability of the operations. This paper provides review of such literature that focus on theory and practice of disaster management in industrial cities. Design/methodology/approach: In the paper, content analysis method is used in order to elicit the insights of the literature available. The methodology uses search methods, literature segregation and developing the current knowledge on different phases of industrial disaster management. Findings: It is found that the research is done in all phases of disaster management, namely, preventive phase, reactive phase and corrective phase. The research in each of these areas are focused on four main aspects, which are facilities, resources, support systems and modeling. Nevertheless, the research in the industrial cities is insignificant. Moreover, the modeling part does not explicitly consider the nature of industrial cities, where many of the chemical and chemical processing can be highly flammable thus creating a very large disaster impact. Some research is focused at an individual plant and scaled up to the industrial cities. The modeling part is weak in terms of comprehensively analyzing and assisting disaster management in the industrial cities. Originality/value: The comprehensive review using content analysis on disaster management is presented here. The review helps the researchers to understand the gap in the literature in order to extend further research for disaster management in large scale industrial cities.Peer Reviewe