Artificial Intelligence and Non-Destructive Testing Data to Assess Concrete Sustainability of Civil Engineering Infrastructures
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View abstract on PubMed
Summary
This summary is machine-generated.This study uses artificial intelligence to predict infrastructure health using sensor data. Random Forest models showed superior performance over Artificial Neural Networks in predicting concrete properties for better civil engineering maintenance.
Area Of Science
- Civil Engineering
- Materials Science
- Data Science
- Artificial Intelligence
Background
- Effective maintenance of civil engineering infrastructure is crucial for sustainable development and resource preservation.
- Advancements in sensor technology and data digitization allow for real-time data acquisition from structures.
- Corrosion significantly impacts infrastructure integrity, necessitating advanced predictive maintenance strategies.
Purpose Of The Study
- To explore the application of artificial intelligence (AI) in optimizing civil engineering maintenance actions.
- To investigate the predictive capabilities of supervised Machine Learning (ML) regression models for structural properties.
- To compare the performance of Random Forest (RF) and Artificial Neural Networks (ANNs) using Non-Destructive Testing (NDT) data.
Main Methods
- Utilized supervised ML regression techniques, specifically Random Forest (RF) and Artificial Neural Networks (ANNs).
- Employed a dataset comprising various NDT measurements (ultrasonic, electromagnetic, electrical) from concrete samples.
- Applied SHapley Additive exPlanation (SHAP) for model interpretability and transparency.
Main Results
- Both RF and ANN models demonstrated strong predictive accuracy for concrete characteristics (compressive strength, elastic modulus, porosity, density, saturation rate).
- Random Forest models generally outperformed Artificial Neural Networks across most performance metrics.
- SHAP analysis provided valuable insights into the decision-making processes of the predictive models.
Conclusions
- Machine Learning integration, particularly RF, offers a robust approach to enhance predictive maintenance in civil engineering.
- The study provides a framework for combining ML with empirical and mechanical methods for improved infrastructure management.
- AI-driven predictive maintenance can significantly contribute to the longevity and safety of critical infrastructure.
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