Two different nondestructive test (NDT) methods, air-coupled impact-echo (IE) and infrared (IR) thermography are evaluated on a full-scale simulated reinforced concrete bridge deck containing simulated delamination and cracking defects. The IE data are presented as two-dimensional frequency maps and spectral B-scan lines. The IR data are presented as temperature maps on the concrete surface. The lateral boundaries of the detected delaminations are also indicated in the images. The results obtained from each of the individual NDT methods show reasonably good agreement with most of the actual defects. The advantages and limitations of each method to characterize defects are discussed. The consistency and sensitivity of each method are also investigated. Finally, a simple data fusion technique is proposed to improve effectiveness of the individual test data. The findings from this study demonstrate that the combination of air-coupled IE and IR thermography tests is a practical option for consistent a...

The utility of micro-electro-mechanical sensors (MEMS) for application in air-coupled (contactless or noncontact) sensing to concrete nondestructive testing (NDT) is studied in this paper. The fundamental operation and characteristics of MEMS are first described. Then application of MEMS sensors toward established concrete test methods, including vibration resonance, impact-echo, ultrasonic surface wave, and multi-channel analysis of surface waves (MASW), is demonstrated. In each test application, the performance of MEMS is compared with conventional contactless and contact sensing technology. Favorable performance of the MEMS sensors demonstrates the potential of the technology for applied contactless NDT efforts. Objective: To illustrate the utility of air-coupled MEMS sensors for concrete NDT, as compared with conventional sensor technology.

Dynamic non-classical nonlinear analyses show promise for improved damage diagnostics in materials that exhibit such structure at the mesoscale, such as concrete. In this study, nonlinear non-classical dynamic material behavior from standard vibration test data, using pristine and frost damaged cement mortar bar samples, is extracted and quantified. The procedure is robust and easy to apply. The results demonstrate that the extracted nonlinear non-classical parameters show expected sensitivity to internal damage and are more sensitive to changes owing to internal damage levels than standard linear vibration parameters.

The characterization of in-place material properties is important for quality control and condition assessment of the built infrastructure. Although various methods have been developed to characterize structural materials in situ, many suffer limitations and cannot provide complete or desired characterization, especially for inhomogeneous and complex materials such as concrete and rock. Recent advances in machine learning and artificial neural networks (ANN) can help address these limitations. In particular, physics-informed neural networks (PINN) portend notable advantages over traditional physics-based or purely data-driven approaches. PINN is a particular form of ANN, where physics-based equations are embedded within an ANN structure in order to regularize the outputs during the training process. This paper reviews the fundamentals of PINN, notes its differences from traditional ANN, and reviews applications of PINN for selected material characterization tasks. A specific application example is presented where mechanical wave propagation data are used to characterize in-place material properties. Ultrasonic data are obtained from experiments on long rod-shaped mortar and glass samples; PINN is applied to these data to extract inhomogeneous wave velocity data, which can indicate mechanical material property variations with respect to length.