Issues and Challenges in the Philippine Construction Industry: An Opportunity for BIM Adoption
Bibliographic record
Abstract
The construction industry is widely regarded as the driving force behind global economic growth. The Philippines recorded a GDP increase of 11.8% in the second quarter of 2021. The construction industry is one of the main contributors, with a growth rate of 25.7%. However, the industry faces numerous challenges and issues, the most well-known of which involve the iron triangle of project management, attributed mainly to poor technology adoption, resulting in massive declines in productivity. Building Information Modeling (BIM) is a popular technology with proven benefits, as demonstrated by countries that have mandated its use. However, BIM is said to be in its infancy in the Philippines. Construction professionals have a low level of awareness, and BIM is primarily used by firms that are outsourcing their services for international projects. This study aims to identify construction industry issues and their degree of occurrence in Philippine construction. The study also seeks to determine the current state of BIM and identify the current BIM applications to resolve these issues. The study utilized a mixed-methods approach involving a literature review and a structured survey. Data analysis includes Cronbach’s alpha for reliability testing, descriptive statistics, the Relative Importance Index (RII), and Kendall’s W test.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".