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AI-Driven Scheduling for Highway Construction - Coggle Diagram
AI-Driven Scheduling for Highway Construction
Data Ingestion and Preparation
1.1 Historical Weather Data
Temperature (max, avg, min)
Precipitation
Humidity
1.2 Project Production Data
Baseline Productivity Rates (e.g., m3/day)
Task Complexity Factors
Synthetic or Real Observations
Model Training and Validation
3.1 Data Splitting and Preprocessing
Train/Test Split (e.g., 70/30)
Data Cleaning and Feature Engineering
3.2 Model Fitting
Linear Regression (Training)
Random Forest (Training)
3.3 Performance Metrics
MAE (Mean Absolute Error)
RMSE (Root Mean Squared Error)
R2 Score
3.4 Model Selection
Compare Metrics on Test Data
Identify Overfitting or Underfitting
Potential Fine-Tuning (hyperparameters, retraining)
Scheduling Engine
5.1 Timeline Generation
Gantt Charts
Critical Path Identification
Resource Loading (e.g., labor, machinery)
5.2 Real-Time Updates
Incorporate new weather forecasts
On-site sensor data (if available)
Continuous model re-evaluation
5.3 Feedback Loop
Compare predicted vs. actual progress
Retrain or recalibrate model if necessary
Machine Learning Models
2.1 Linear Regression
Uses numerical regression to find a linear relationship
Simpler, more interpretable baseline
Often struggles with threshold-based or non-linear effects
2.2 Random Forest
Aggregation of multiple decision trees
Handles non-linear relationships and thresholds better
Typically higher computational load
Productivity Adjustments
4.1 Correction Coefficients
Derived from the chosen ML model
Adjust baseline productivity for predicted weather
4.2 Effective Daily Production
Baseline Rate x Correction Coefficients
Reflects expected performance under specific weather
4.3 Schedule Impact
Adverse Weather -> Lower Productivity -> Extended Task Durations
Output and Project Management
6.1 Enhanced Schedule
More accurate milestones
Reduced overruns due to realistic weather adjustments
6.2 Resource Allocation and Optimization
Better assignment of workforce and machinery
Clear forecasting for materials delivery
6.3 Reporting and Stakeholder Communication
Updated timelines, cost forecasts
Decision-making support for potential schedule acceleration or buffer time