How the choice of temporary housing impacts on widespread displacement after large-scale flooding? A disaster recovery simulation in Tokyo metropolitan area

Komatsuzaki, Nobuhiko; Otsuyama, Kensuke; Hiroi, U

doi:10.1016/j.ijdrr.2022.103243

Cited by 3 publications

(1 citation statement)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The problem is that all these requirements are intended for construction under normal conditions, such as scheduled urban development, the implementation of a government housing policy, commercial investment projects, etc. There are a number of studies in the scientific community dedicated to the construction of prefabricated buildings and temporary housing for the resettlement of people who have lost their homes [4][5][6][7][8]; however, rebuilding urban infrastructure and cities itself is a problem that is not sufficiently studied. The rebuilding of cities after destruction is complicated by a number of factors, so it has a longer duration, which adversely affects the population [9].…”

Section: Introductionmentioning

confidence: 99%

A Decision Support System for Organizing Quality Control of Buildings Construction during the Rebuilding of Destroyed Cities

Lapidus,

Makarov,

Kozlova

2023

Buildings

View full text Add to dashboard Cite

Natural disasters and warfare lead to the destruction of city buildings and infrastructure, leaving large numbers of people homeless. The rebuilding of destroyed cities needs to be carried out promptly while maintaining a balance between construction quality and duration. Rework due to defects and the lengthy approval of non-conformances significantly increases the duration of construction. This study aimed to develop a decision support system to fix or negotiate strategies to address construction defects, depending on their level of risk. The paper addresses the following objectives: classifying defects by the quality of construction that they affect; building a tree of construction defect risks; and developing an artificial neural network (ANN) to assess the defect risk. The weights of the links are represented by posterior probabilities of damage calculated using the Bayes’ theorem in the pre-training stage. The ANN has been adapted to cast-in-place reinforced concrete structures. When training the resulting ANN on a sample of precedents, the test sample demonstrated convergence and low errors. The resulting model will accelerate construction by automating assessments of defect severity and reducing the time spent on reworking defects with low quality risk.

show abstract