Numerical and Experimental Verification of a Multiple-Variable Spatiotemporal Regression Model for Grout Defect...

Area of Science:

• Civil Engineering
• Structural Health Monitoring
• Non-destructive Testing

Background:

• Civil structures are prone to local defects from aging, corrosion, and construction issues, compromising structural integrity.
• Identifying grout defects in precast concrete frames with sleeve-spliced rebars presents significant challenges due to complexity.

Purpose of the Study:

• To develop and validate a robust algorithm for identifying local defects in precast reinforced concrete frame structures.
• To utilize structural vibration responses for defect detection, specifically focusing on grout integrity in sleeve splices.

Main Methods:

• Development of a multiple-variable spatiotemporal regression model.
• Numerical simulations comparing single-variable, two-variable spatial, and two-variable spatiotemporal regression models.
• Experimental validation using a half-scaled, two-floor precast concrete frame with known grout defects.

Main Results:

• The multiple-variable spatiotemporal regression model demonstrated superior performance in identifying local defects.
• Successful detection of grout defects in column rebar sleeve splices was achieved through vibration analysis.
• Robustness analysis confirmed the reliability of the damage indicator.

Conclusions:

• The proposed multiple-variable spatiotemporal regression model is effective for local defect identification in precast concrete frames.
• Structural vibration response analysis offers a viable non-destructive method for assessing grout quality in sleeve-spliced connections.