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Record W4405467937 · doi:10.1016/j.clwas.2024.100195

An integrated framework to improve waste management practices and environmental awareness in the Saudi construction industry

2024· article· en· W4405467937 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCleaner Waste Systems · 2024
Typearticle
Languageen
FieldEngineering
TopicRecycled Aggregate Concrete Performance
Canadian institutionsUniversity of Alberta
FundersMinistry of Education – Kingdom of Saudi Arabi
KeywordsBusinessConstruction industryEnvironmental planningEnvironmental resource managementConstruction engineeringEngineeringProcess managementEnvironmental science

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.482
Threshold uncertainty score0.664

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.243
Teacher spread0.233 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it