Failure and Collapse of Building Structures in the Cities of Yaoundé and Douala, Cameroon from 2010 to 2014
Bibliographic record
Abstract
The failure and collapse of buildings in most cases result in loss of lives and properties. The incessant collapses of buildings nowadays are so enormous that it has become a serious concern to the professionals in the building industry, clients, governments, and general public. In most cases, the architects and engineers who are directly involved in the construction of such building are held responsible for building collapse. The purpose of this study was to elaborate various cases of building structures collapsed that occurred in Yaoundé and Douala, Cameroon between 2010 and 2014 and to investigate the factors causing such incidents. The methods employed in the collection of data include the administration of questionnaire to professionals in the building industry (professional engineers, architects and construction professionals), site inspections and case studies for the sites. The data collected were analysed using descriptive and analytical statistics. The findings show that the collapse of some buildings in major cities in Cameroon (Yaoundé and Douala) can be attributed to absence of soil investigation and foundation, structural design, detailing, degradation due to environmental factors, use of poor quality materials and concrete processing. In the two case studies considered, the study revealed that the major causes of building failures were excessive loading, structural design, degradation due to environmental factors and other causes. The paper concludes by recommending possible measures to be undertaken by government and other regulatory bodies in the building industry to avert this.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".