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The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
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Related Experiment Videos

Particle peak velocity prediction based on risk-oriented hybrid ensemble learning.

Lijie Ge^1,2, Jianhui He^1,2, Zhuang Zhang^1,2

¹Hebei University of Architecture, Zhangjiakou, 075000, Hebei, China.

Scientific Reports

|April 20, 2026

Summary

This summary is machine-generated.

This study introduces a novel hybrid ensemble model for predicting peak particle velocity (PPV) in blasting engineering. The model enhances safety by prioritizing the avoidance of hazardous underestimations, improving structural protection.

Keywords:

Asymmetric security assessment system Blasting vibration Integrated model Peak particle velocity Risk-oriented evaluation

Related Experiment Videos

Area of Science:

Engineering
Machine Learning
Risk Assessment

Background:

Accurate peak particle velocity (PPV) prediction is critical for structural safety in blasting engineering.
Symmetric loss functions in machine learning inadequately address the risks of underestimation in safety-critical applications.

Purpose of the Study:

To develop a risk-oriented hybrid ensemble model for enhanced PPV prediction accuracy and safety.
To implement an asymmetric safety assessment system to mitigate hazardous underestimations.

Main Methods:

Utilized a stacking ensemble framework integrating LightGBM, XGBoost, and CatBoost gradient-boosting models.
Employed Bayesian Optimisation (BO), Grey Wolf Optimiser (GWO), and Particle Swarm Optimisation (PSO) for hyperparameter tuning.
Introduced an asymmetric weighted mean squared error (W-MSE) and hazardous low-estimation rate (HLR) for performance evaluation.

Main Results:

The proposed hybrid ensemble model demonstrated strong overall prediction performance.
The model effectively suppressed hazardous underestimations, significantly enhancing safety and reliability.
The integrated model showed clear advantages in PPV prediction compared to traditional methods.

Conclusions:

The developed model offers a reliable solution for PPV prediction in blasting engineering.
It provides a reusable paradigm for incorporating engineering safety constraints into machine learning.
The approach offers valuable technical support for safety planning and risk minimization in blasting projects.