Abstract

Timely identification of collision damage, especially in aging bridges, is critical for the safety of commuters. However, there is no efficient, cost-effective, in-situ technique to serve this purpose. Wave propagation-based structural health monitoring (SHM) using piezoelectric material is a promising alternative for remote sensing. To that end, this study aims to develop a wave propagation-based monitoring technique using surface-bonded smart piezoelectric modules (SPM) to determine the impact force, location, and projectile properties of low-velocity impacts on concrete panels. An impact source localization algorithm used in composite structures is adapted and simplified for concrete structures. This technique is validated using a combined experimental and numerical investigation, which shows good agreement with the actual impact source location. The impact force, projectile mass, and velocity are determined using a semi-theoretical-experimental technique based on Reed contact model. A special contact-SPM is fabricated and calibrated to determine the contact force at the impact location. The relationship between contact-SPM response and distributed-SPM response is determined using a drop-weight test with steel sphere. The peak contact force and contact duration are in good agreement with Reed contact model, although the latter overpredicts the given parameters. A simplified formula based on Reed contact model is used to inversely estimate the projectile velocity of a known mass and vice versa. Then, using the pre-calibrated data, the impact force, projectile properties, and impact force-time distribution are determined using the response of distributed-SPM system. The technique is validated using an arbitrary steel sphere mass. As demonstrated in the combined experimental, theoretical, and numerical study, the proposed surface-bonded SPM system is capable of effectively identifying low-velocity impact incidents on concrete structures, which could potentially facilitate inexpensive, in-situ, real-time condition assessment.

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