Authors:

Xiangrui Kong

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About Paper:

This research aims to develop a non-destructive method for monitoring and predicting the strength of mortar during the curing process, which is crucial for ensuring structural safety and quality in civil infrastructure projects. The experimental approach utilizes ultrasound testing, piezoelectric impedance sensors (PZT sensors), and machine learning techniques. The PZT sensors detect changes in electromechanical impedance that correspond to the evolving mechanical properties of the mortar. Simultaneously, ultrasound testing captures internal microstructure that reflects material integrity. AI models were trained to analyze the PZT sensor data and estimate mortar strength continuously throughout the curing process. The developed models demonstrated decent predictive performance, showing high correlation with standard compressive strength tests across various curing durations and mixture designs. This method has significant implications for the construction industry, enabling continuous and real-time strength monitoring. It reduces reliance on traditional destructive testing and supports more informed decision-making during the construction process. The integration of smart sensing and AI has the potential to enhance quality control, safety, and efficiency on future job sites. Keywords: PZT Sensor; Electromechanical Impedance(EMI); Strength; Air- Entraining

Source:

Purdue University / 2025

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Co-authors:

Xiangrui Kong