Forecasting Construction Equipment Productivity Based on Weather Conditions: A Data-Driven Time-Series Machine Learning Approach
Bibliographic record
Abstract
Accurate forecasting of construction equipment productivity is crucial for effectively planning and controlling construction resources. Traditional approaches often rely on data from previous projects and the subjective experience of project managers, which can result in inaccurate estimates. Moreover, previous studies in forecasting construction equipment productivity often tend to overlook the time dependencies in the data which is inherent to the nature of construction productivity. This study presents a generic data-driven framework for forecasting productivity data from ongoing construction field activities and weather conditions, utilizing daily report logs. The framework combines machine learning (ML) models with time-series analysis to forecast, over time, the productivity of construction equipment while considering weather conditions as exogenous factors. Modeling construction productivity as a time series enables the reflection of temporal dependencies in the data. The framework was evaluated through a case study involving excavation activities for an infrastructure project over eight months. The ML-based time-series model yielded a model fit (R2) of 0.89, outperforming the classical Seasonal Autoregressive Integrated Moving Average (SARIMAX) time-series model, which achieved an R2 of 0.71. Moreover, the ML-based time-series model showed a 63% reduction in the estimation error compared to the project manager’s approach. Once the construction field productivity is accurately estimated, project managers can leverage the estimates to make data-driven resource allocation decisions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".