Model updating method for detect and localize structural damage using generalized flexibility matrix and improved...

Area of Science:

• Civil Engineering
• Structural Health Monitoring
• Optimization Algorithms

Background:

• Strategic implementation of structural health monitoring (SHM) systems is crucial for critical civil engineering infrastructures.
• A primary challenge in SHM is the timely and automatic identification and localization of structural damage.
• Optimization algorithms are increasingly employed for structural model updating to enhance damage detection capabilities.

Purpose of the Study:

• To evaluate the effectiveness of combining structural frequency and generalized flexibility matrix for damage assessment.
• To develop an advanced optimization approach for precise damage identification in civil structures.
• To compare the performance of the Improved Grey Wolf Optimizer (I-GWO) against other optimization techniques for SHM.

Main Methods:

• Utilizing a combination of the structure's natural frequencies and the generalized flexibility matrix to pinpoint damage.
• Applying the Improved Grey Wolf Optimizer (I-GWO) to process structural data under various noise conditions.
• Validating the method on diverse structural models, including a 15-story shear frame, a 2D truss bridge, and a 2D frame.

Main Results:

• The I-GWO approach demonstrated high accuracy in locating and quantifying damage across all tested structural models.
• Average errors between simulated and estimated results were consistently below 3% across multiple damage scenarios and runs.
• The proposed method significantly outperformed traditional modal flexibility techniques and other optimizers like GWO and PSO.

Conclusions:

• The developed SHM technique using I-GWO precisely identifies the location and extent of damage in various civil structures.
• This method enhances the overall effectiveness and reliability of damage identification in structural systems.
• I-GWO provides a dependable and accurate approach for damage detection in structural health monitoring applications.