Machine Learning Algorithms for Construction Projects Delay Risk Prediction
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
Projects delays are among the most pressing challenges faced by the construction sector attributed to the sector’s complexity and its inherent delay risk sources’ interdependence. Machine learning offers an ideal set of techniques capable of tackling such complex systems; however, adopting such techniques within the construction sector remains at an early stage. The goal of this study was to identify and develop machine learning models in order to facilitate accurate project delay risk analysis and prediction using objective data sources. As such, relevant delay risk sources and factors were first identified, and a multivariate data set of previous projects’ time performance and delay-inducing risk sources was then compiled. Subsequently, the complexity and interdependence of the system was uncovered through an exploratory data analysis. Accordingly, two suitable machine learning models, utilizing decision tree and naïve Bayesian classification algorithms, were identified and trained using the data set for predicting project delay extents. Finally, the predictive performances of both models were evaluated through cross validation tests, and the models were further compared using machine-learning-relevant performance indices. The evaluation results indicated that the naïve Bayesian model provides a better predictive performance for the data set examined. Ultimately, the work presented herein harnesses the power of machine learning to facilitate evidence-based decision making, while inherent risk factors are active, interdependent, and dynamic, thus empowering proactive project risk management strategies.
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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 | 0.001 |
| 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