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Quantitative prediction of differential settlement based on machine learning techniques
Shaista Jabeen Abbasi1, Hu Minqjie2, Xiaolin Weng3
1School of Highway, Chang'an University, Xi'an, 710064, Shaanxi, People's Republic of China. 2019021903@chd.edu.cn.
Scientific Reports
|July 6, 2026
Summary
This study introduces a machine learning (ML) framework to predict differential settlement in road widening projects. The Gradient Boosting model achieved high accuracy, offering a faster, more efficient alternative to traditional simulations for pavement engineering.
Area of Science:
- Civil Engineering
- Geotechnical Engineering
- Data Science
Background:
- Differential settlement at existing and new embankment junctions poses a significant challenge in road widening.
- Traditional finite element methods (FEM) are computationally intensive and time-consuming for settlement prediction.
Purpose of the Study:
- To develop and validate a data-driven machine learning (ML) framework for accurate prediction of differential settlement.
- To overcome the limitations of conventional simulation-based approaches in pavement engineering.
Main Methods:
- Generated a comprehensive dataset using finite element simulations (iSight-ABAQUS).
- Trained and evaluated eight ML algorithms, including Gradient Boosting.
- Implemented the best-performing model in a Python-based framework for rapid forecasting.
Main Results:
- The Gradient Boosting model demonstrated superior performance with an R² value approaching 1.0.
- The ML framework enables rapid prediction of road behavior and settlement.
- Identified key influencing parameters such as the modulus of elasticity.
Conclusions:
- The developed ML framework offers a transformative, data-driven paradigm for pavement engineering.
- This approach significantly outperforms conventional methods in speed and efficiency for performance prediction and damage assessment.
- Facilitates accelerated design optimization and proactive maintenance planning.