<p>Post-disaster temporary housing has been a significant
challenge for the emergency management group and industries for many years. According
to reports by the Department of Homeland Security (DHS), housing in
states and territories is ranked as the second to last proficient in 32 core
capabilities for preparedness.The number of temporary housing required in a
geographic area is influenced by a variety of factors, including social issues,
financial concerns, labor workforce availability, and climate conditions. Acknowledging
and creating a balance between these interconnected needs is considered as one
of the main challenges that need to be addressed. Post-disaster temporary
housing is a multi-objective process, thus reaching the optimized model relies
on how different elements and objectives interact, sometimes even conflicting,
with each other. This makes decision making in post-disaster construction more
restricted and challenging, which has caused ineffective management in post-disaster
housing reconstruction.</p>
<p>Few researches have studied the use of Artificial
Intelligence modeling to reduce the time and cost of post-disaster sheltering.
However, there is a lack of research and knowledge gap regarding the selection
and the magnitude of effect of different factors of the most optimized type of Temporary
Housing Units (THU) in a post-disaster event.</p>
The proposed framework
in this research uses supervised machine learing to maximize certain design
aspects of and minimize some of the difficulties to better support creating
temporary houses in post-disaster situations. The outcome in this study is the
classification type of the THU, more particularly, classifying THUs based on
whether they are built on-site or off-site. In order
to collect primary data for creating the model and evaluating the magnitude of
effect for each factor in the process, a set of surveys were distributed
between the key players and policymakers who play a role in providing temporary
housing to people affected by natural disasters in the United States. The
outcome of this framework benefits from tacit knowledge of the experts in the
field to show the challenges and issues in the subject. The result of this
study is a data-based multi-objective decision-making tool for selecting the
THU type. Using this tool, policymakers who are in charge of selecting and
allocating post-disaster accommodations can select the THU type most responsive
to the local needs and characteristics of the affected people in each natural
disaster.
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/14417132 |
| Date | 07 May 2021 |
| Creators | Mahdi Afkhamiaghda (10647542) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/DEVELOPING_A_DECISION_SUPPORT_SYSTEM_FOR_CREATING_POST_DISASTER_TEMPORARY_HOUSING/14417132 |
Page generated in 0.0026 seconds