Integrating expert insights and data analytics for enhanced construction productivity monitoring and control: a machine learning approach
Bibliographic record
Abstract
Purpose This study aims to enhance productivity monitoring and control in the construction industry by integrating data-driven analytics with expert insights. Design/methodology/approach A novel framework combines expert knowledge and data analysis to identify productivity trends and devise improvement strategies. A machine learning model predicts productivity ranges using historical data and project-specific factors’ evaluated by surveys, supported by a warning dashboard for proactive decision-making. Findings The findings reveal that integrating expert insights with data analytics significantly enhances the ability to monitor and control productivity, leading to proactive strategies for construction stakeholders. The machine learning model demonstrates robust accuracy in forecasting productivity ranges, allowing for early identification of potential issues. The dashboard system proves invaluable, offering semi-real-time alerts and facilitating swift action to prevent productivity lapses. These results highlight the effectiveness of the proposed approach in detecting trends, predicting outcomes and enabling timely interventions, thereby contributing to the overall productivity improvement of construction projects. Research limitations/implications There are also limitations to consider, including potential data availability, constraints in the expert pool, implementation challenges and the need for long-term evaluation; these factors should be considered when interpreting the study’s findings and applying the proposed framework to construction projects. Future research can focus on expanding the application of this framework to different types of construction projects and evaluating its scalability. Practical implications This study introduces a framework with a warning dashboard for early detection of issues, combining expert insights and data analysis for improved project outcomes. This research suggests a shift toward more expert, data-driven, insightful decision-making in construction, aiming for enhanced performance and reduced disruptions. An important implication of this research is the need to balance digital tools and expert judgment. Project managers are advised to use a holistic strategy that ensures informed and comprehensive decision-making. Originality/value This research introduces a unique methodology that blends traditional expertise with modern analytics to address construction productivity challenges. It offers a practical solution for stakeholders to enhance decision-making, resource allocation and project planning, marking a significant contribution to construction management literature.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".