A scoping review of fire safety on building construction sites: current measures, practices and future research directions
Bibliographic record
Abstract
Purpose Construction sites are inherently prone to fire hazards due to the frequent use of flammable materials, dynamic environments and large workforces. However, research on fire safety in building construction remains fragmented, making it difficult to identify trends and challenges and resulting in knowledge gaps that limit effective strategies and innovations. This paper bridges these gaps through a comprehensive review that establishes the current state of knowledge and categorizes existing studies. Design/methodology/approach A scoping review following the Joanna Briggs Institute (JBI) methodology was conducted. Scientific literature was collected from the Web of Science and Scopus, with 52 studies selected. Additionally, grey literature was sourced through web searches and relevant organization websites, with 16 documents selected. Findings Four key categories were identified: (1) fire incident investigation, (2) fire risk assessment, (3) fire risk response planning and (4) monitoring and detection. The qualitative analysis highlights future research directions to advance this field, including (1) developing near-miss incident data collection platforms, (2) integrating digital twins for dynamic risk assessment, (3) integrating extended reality for fire safety training and (4) deploying robots and UAVs for flexible detection methods. Practical implications The proposed conceptual map illustrates the interconnections among different measures, offering practitioners a holistic understanding of this field. Identified gaps and research directions can enhance awareness in this field and foster collaboration between researchers and other stakeholders. Originality/value This study provides the first structured synthesis of fragmented research in this field, serving as a valuable reference for researchers and laying the groundwork for future research.
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 | 0.039 | 0.132 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.031 | 0.033 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".