Proposal for the Rehabilitation of Houseshold in Seismic Vulnerability Zones
Bibliographic record
Abstract
Worldwide earthquakes have caused a total of 719,501 human victim and 1,344.8 billion dollars in economic losses between 2000-2021.The Santa Elena province (Ecuador) is classified as an area of high seismic danger, according to the Ecuadorian Construction Standard (NEC).This coastal province has 70% of informal housing construction, structural problems and poor construction processes can be seen with the occurrence of these natural phenomena.This study aims to propose technical-economic rehabilitation solutions in informal housing constructions, through modelling and structural analysis, that guarantee occupational safety and the recovery of the structural capacity of the housing, case study (pilot).The applied methodology consisted of four phases: i) information on the pilot case study and its environment; review of the building regulations, ii) laboratory tests on soil characteristics, iii) structural modelling and analysis using SAP2000 software, and iv) structural support and rehabilitation solutions.The results indicate that the columns of the first floor require reinforcement because the average cross-section of the columns is less than the local construction regulations and defined by the criteria of experts in the construction sector.The structural elements of the second floor also require repair to reduce bending moments and distribute loads to the ground.The idea of innovation is the configuration of a protocol with construction methods for existing and new buildings, as tools for local authorities to promote reinforcement in areas of high seismic vulnerability, and thus a territorial order in the construction sector.This research recommends structural rehabilitation with reinforcements and structural repairs to guarantee its ability to withstand an earthquake and ensure the occupational well-being of inhabitants.This methodology can be replicated in houses with similar structural characteristics in Ecuadorian coast, considering SDGs 3, 9, and 11, which address health and well-being, industry, innovation and infrastructure, and sustainability.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".