Total Quality Management’s Critical Role in Resolving Delay Issue of Construction Projects Submission
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Despite considerable advancements and innovations in the construction industry, it continues to grapple with challenging obstacles that potentially impede the timely delivery of projects.Among these impediments, project delays are particularly detrimental.Over the past decades, the industry has seen the design and implementation of various intelligent methodologies aimed at alleviating this issue, with Total Quality Management (TQM) being a notable example.This study was conducted to investigate the beneficial impacts of TQM on addressing delays in diverse construction projects.Three research methodologies were employed in this study: 1) a quantitative approach, 2) a qualitative cross-sectional descriptive approach, and 3) a numerical analysis, which explored the role of Building Information Modeling (BIM) in facilitating quantity takeoff with increased accuracy and reduced time.The latter thereby mitigates the delay in construction projects due to the substantial effort, cost, and duration required to estimate the quantity of construction materials.The numerical analysis was carried out using the REVIT software tool.The findings from these three methodologies demonstrated the substantial importance of TQM principles for project managers and senior engineers.These principles can aid in streamlining project delivery and reducing delays.Furthermore, the implementation of TQM resulted in a reduction in quality costs, improved client satisfaction, decreased remedial work, mitigated delays, and fostered a closer relationship between suppliers and subcontractors.In addition, the use of BIM technology was found to enhance the accuracy of construction project cost calculations.Consequently, it reduced the time and cost of estimation and minimized errors, thereby contributing to resolving the delay problem in construction.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it