Experimental Study on Monitoring Damage Progression of Basalt-FRP Reinforced Concrete Slabs Using Acoustic Emission...

Area of Science:

• Civil Engineering
• Materials Science
• Structural Health Monitoring

Background:

• Basalt fiber-reinforced polymer (BFRP) offers superior tensile strength and corrosion resistance compared to steel reinforcement in concrete.
• The brittle nature of BFRP necessitates robust structural health monitoring (SHM) to prevent unexpected failures in BFRP-reinforced concrete structures.
• Acoustic emission (AE) is a promising non-destructive technique for detecting and analyzing damage mechanisms in materials.

Purpose of the Study:

• To evaluate the effectiveness of the acoustic emission (AE) method for monitoring damage initiation and progression in BFRP-reinforced concrete slabs.
• To identify and differentiate various damage mechanisms, including tensile and shear cracking, using AE data and machine learning.
• To quantify the relationship between AE features and crack width for improved structural health assessment.

Main Methods:

• Two simply supported BFRP-reinforced concrete slabs were subjected to loading until failure, instrumented with AE sensors, cameras, and strain/displacement gauges.
• Unsupervised machine learning (clustering) was applied to AE data from the first slab to classify damage mechanisms.
• K-nearest neighbors (KNN) supervised learning model was used to validate AE data clusters from the second slab, achieving 99.2% accuracy.
• Statistical SHapley Additive exPlanations (SHAP) analysis was employed to determine the correlation between AE features and crack width.

Main Results:

• The dominant damage mechanism observed was concrete cracking, attributed to over-reinforced design and strong BFRP-concrete bonding.
• AE monitoring successfully identified damage initiation, progression from tensile to shear cracks, and crack width evolution.
• AE duration was identified as the most significant feature correlating with crack width, with cumulative AE duration near cracks showing nearly 100% accuracy in tracking width.
• The KNN model accurately predicted three damage clusters: tensile cracks, shear cracks, and noise.

Conclusions:

• Acoustic emission sensors, particularly when placed at the mid-span, provide an effective SHM solution for BFRP-reinforced concrete slabs.
• AE monitoring can reliably detect tensile crack initiation, significant structural response changes, damage evolution, and crack width progression.
• The integration of AE data with machine learning and SHAP analysis enhances the understanding and quantification of damage in BFRP-reinforced concrete structures.