Investigating Stakeholders' Perceptions of Feasibility and Implications of Modular Construction-Based Post-Disaster Reconstruction
Bibliographic record
Abstract
Natural Disasters cause major adverse social and financial effects by destroying homes and infrastructures. For example, Hurricane Katrina in August 2005 damaged over 214,700 homes in New Orleans and forced over 800,000 citizens to live outside of their homes due to flooding. Thus, these disasters require a quick and efficient response to post-disaster housing issues and provide resources for temporary houses for short-term disaster relief and reconstruction of destroyed and damaged housing for full rehabilitation. Reconstruction of permanent housing for disaster victims is one of the most time-consuming activities in the post-disaster recovery process. However, time is a critical factor which should be minimized for the restoration of affected communities. Modularized construction is a promising solution for improving the process of post-disaster housing reconstruction because of its inherent characteristic of time-efficiency. This paper aimed to evaluate prefabricated modular construction potentials as an approach that can facilitate the design and construction phase of post-disaster reconstruction. An extensive literature review has been carried out to identify the features of modularized construction which can add value to the post-disaster recovery process. To investigate the suitability and feasibility of implementing modular construction for post-disaster reconstruction and also identify major barriers of its implementation, a survey has been conducted in 2018 among AEC experts who were experienced in the prefabricated construction industry and/or involved in post-disaster reconstruction projects. The results of the study indicate that prefabricated modular construction is a promising approach to improve time-efficiency of post-disaster reconstruction and tackle challenges of current practices by its unique benefits such as reduced demand for on-site labor (overcome local labor pool constraints impacted by the disaster) and resources (overcome shortage of equipment and materials), shorter schedule (due to concurrent & non-seasonal), reduced site congestion, and improved labor productivity (due to assembly line-like and controlled environment).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".