SHM-Based Probabilistic Fatigue Life Prediction for Bridges Based on FE Model Updating

Area of Science:

• Civil Engineering
• Structural Engineering
• Reliability Engineering

Background:

• Accurate fatigue life prediction for bridges is crucial for safety and maintenance.
• Uncertainties in material properties, loads, and environmental conditions complicate traditional prediction methods.
• Structural Health Monitoring (SHM) systems offer data to assess in-service bridge condition and degradation.

Purpose of the Study:

• To develop and demonstrate a novel probabilistic approach for bridge fatigue life prediction.
• To integrate finite element (FE) model updating with SHM data for improved accuracy.
• To assess the impact of FE model updating on fatigue life predictions.

Main Methods:

• Ambient vibration testing under passing vehicles to identify modal properties (mode shapes, natural frequencies).
• Updating initial FE model parameters using identified modal properties.
• Probabilistic fatigue life prediction utilizing the updated FE model.

Main Results:

• The proposed method successfully updated the FE model of a bridge using SHM data.
• FE model updating demonstrated a significant impact on the predicted fatigue life of the bridge.
• The approach provides a more realistic assessment of bridge fatigue life by incorporating current structural condition.

Conclusions:

• FE model updating based on SHM data is an effective strategy for enhancing probabilistic fatigue life prediction in bridges.
• This integrated approach can lead to more reliable infrastructure management and maintenance planning.
• The methodology offers a robust framework for assessing the remaining fatigue life of bridges under operational conditions.