Damage significantly influences response of a strain sensor only if it occurs in the proximity of the sensor. Thus, two-dimensional (2D) sensing sheets covering large areas offer reliable early-stage damage detection for structural health monitoring (SHM) applications. This paper presents a scalable sensing sheet design consisting of a dense array of thin-film resistive strain sensors. The sensing sheet is fabricated using flexible printed circuit board (Flex-PCB) manufacturing process which enables low-cost and high-volume sensors that can cover large areas. The lab tests on an aluminum beam showed the sheet has a gauge factor of 2.1 and has a low drift of 1.5 $\mu ϵ/day$ . The field test on a pedestrian bridge showed the sheet is sensitive enough to track strain induced by the bridge’s temperature variations. The strain measured by the sheet had a root-mean-square (RMS) error of 7 $\mu {ϵ}_{rms}$ compared to a reference strain on the surface, extrapolated from fiber-optic sensors embedded within the bridge structure. The field tests on an existing crack showed that the sensing sheet can track the early-stage damage growth, where it sensed 600 $\mu ϵ$ peak strain, whereas the nearby sensors on a damage-free surface did not observe significant strain change.

Estimating variations in material properties over space and time is essential for the purposes of structural health monitoring (SHM), mandated inspection, and insurance of civil infrastructure. Properties such as compressive strength evolve over time and are reflective of the overall condition of the aging infrastructure. Concrete structures pose an additional challenge due to the inherent spatial variability of material properties over large length scales. In recent years, nondestructive approaches such as rebound hammer and ultrasonic velocity have been used to determine the in situ material properties of concrete with a focus on the compressive strength. However, these methods require personnel expertise, careful data collection, and high investment. This paper presents a novel approach using ground penetrating radar (GPR) to estimate the variability of in situ material properties over time and space for assessment of concrete bridges. The results show that attributes (or features) of the GPR data such as raw average amplitudes can be used to identify differences in compressive strength across the deck of a concrete bridge. Attributes such as instantaneous amplitudes and intensity of reflected waves are useful in predicting the material properties such as compressive strength, porosity, and density. For compressive strength, one alternative approach of the Maturity Index (MI) was used to estimate the present values and compare with GPR estimated values. The results show that GPR attributes could be successfully used for identifying spatial and temporal variation of concrete properties. Finally, discussions are presented regarding their suitability and limitations for field applications.

We present here a laboratory-based experimental protocol that seeks to establish and characterize the relationship between ground-penetrating radar attributes and the mechanical properties (density, porosity, and compressive strength) of typical industry concrete mixes. The experimental data consist of ground-penetrating radar attributes from 900 MHz radargrams that correspond to simultaneously measured physical properties of Portland cement concrete, alkali-activated concrete, and cement mortar. Appropriate regression models are trained and tested on this data set to predict each physical property from ground-penetrating radar attributes. From a small selection of individual attributes, including total phase and intensity, trained random forest regression models predict porosity ( R² = 0.83 from the instantaneous amplitude), density ( R² = 0.67 from the intensity attribute), and compressive strength ( R² = 0.51 from instantaneous amplitude). These novel relationships between physical properties and ground-penetrating radar attributes indicate that material properties could be predicted from the attributes of ordinary ground-penetrating radar scans of concrete.

Estimating material properties of natural materials is a goal shared between near-surface exploration, infrastructure management, and even conservation of historic sites. Existing methods (e.g., seismic, ultrasonics, and direct testing) for characterizing these properties of interest are not yet available at the desired scale or configuration. GPR is an established method for characterizing the physical configuration of these sites by mapping subsurface features. A new analysis method applying seismic and image processing attribute analysis to GPR data has promising results for material identification and possibly characterization. To further explore this capacity, an experiment to characterize concrete laboratory specimens using GPR was performed. Creating a unique data set, GPR scans and attribute analyses are paired with direct testing of the samples. A variety of attributes and regression analyses have been able to predict porosity of the samples from the mean instantaneous GPR amplitudes with some success (R2 = 0.8). Density and compressive strength are more difficult to predict from the GPR data, but porosity is an excellent proxy property that can be related to other physical properties using secondary relationships. By continuing to explore these relationships and test them on GPR data for other materials (stone, brick, etc.), the results could be extended and refined to provide fine-scale estimation of important properties to characterize reservoirs, basins, and other geological or anthropogenic features of interest.