This tool estimates concrete compressive strength based on user-defined mix proportions using a LightGBM model.
π Why LightGBM?
This tool uses a Light Gradient Boosting Machine(GBM) model selected after benchmarking 12 machine learning algorithms. LightGBM achieved the highest RΒ² (0.9126), lowest RMSE (4.747 MPa),highest accuracy (92.23%), and F1-score (89.74%), outperforming models like Random Forest, XGBoost, and Neural Networks.
Features:
β Superior handling of nonlinear feature
β Fast training with large tabular data
β Excellent generalization on unseen data
LightGBM's balance of accuracy, speed, and interpretability makes it ideal for predicting concrete compressive strength.
Please click below for PredictionThis interactive tool helps assess pavement condition using ASTM D6433 distress data and a trained XGBoost model.
Features:
β Uses standard distress metrics (cracking, potholes, patching)
β Designed for input from sample units β₯ 20 mΒ² (ASTM D6433 compliant)
β Automatically classifies PCI score (Good, Fair, Poor, etc.)
β Includes guidance, disclaimers, and live prediction
Use the tool below to estimate PCI score:
This real-time detection tool helps monitor compliance with helmet safety protocols using a YOLOv8-based computer vision model.
Features:
β Detects presence/absence of helmets on individuals in images, videos, or webcam feeds
β Trained on 5,000 diverse images using YOLOv8n with high precision and recall
β Ideal for construction sites and industrial safety monitoring
β Provides instant visual alerts and classification output
Download the desktop version of the app below:
β¬ Download Helmet Detection App (.exe)This tool replaces the slow, traditional workflow with a quick, easy, and modern way to calculate and visualize earthwork excavation cross-sections. Generate detailed cross-section plots in just a few clicks no AutoCADD software or complex tools needed!
π§ Key Features:
How to Use:
Launch the application below: