An integrated framework to improve waste management practices and environmental awareness in the Saudi construction industry
Bibliographic record
Abstract
There are concerns that the rapid expansion of the Saudi construction industry is contributing to substantial waste production, resulting in significant environmental impacts. Despite global efforts to improve sustainability, the Saudi construction industry faces challenges due to the high levels of construction waste, a limited focus on managing environmental impacts beyond physical waste (i.e., solid or hazardous waste), and the lack of comprehensive waste management strategies. This research introduces a novel integrated framework that combines lean construction principles with environmental management systems to support efficient waste management in Saudi construction projects. The framework integrates the Define, Measure, Analyse, Improve, Control (DMAIC) model from Lean Six Sigma with the Aspect and Impact Analysis (AIA) from environmental management to simultaneously manage both production and environmental wastes. To develop this framework, the current state of waste management practices in Saudi Arabia was investigated through semi-structured interviews with industry practitioners, revealing 44 factors contributing to waste generation. Poor planning emerged as the most frequently cited factor, followed by poor coordination among stakeholders, leftover materials on-site and frequent design changes. These findings underscore the need for a comprehensive and structured approach to address waste management. The proposed framework guides practitioners through defining and measuring waste, analysing root causes, prioritising waste-generating activities based on their impact, and implementing improvement strategies across strategic, tactical, and operational levels. The framework's application is demonstrated through a case example of piling operations and is validated through expert interviews. The integrated framework contributes to knowledge by offering a holistic approach to addressing both production and environmental waste, which aligns with Saudi Arabia's sustainability goals. It equips organisations with a practical tool to optimize resources, reduce environmental impacts, and enhance overall project efficiency. • Novel framework addresses production and environmental waste in Saudi construction. • DMAIC and Aspect Impact Analysis combined to manage diverse waste types. • Framework's applicability demonstrated through piling operations case study.
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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 | 0.011 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| 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".