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 μ ϵ / d a y . 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 μ ϵ r m s 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 μ ϵ peak strain, whereas the nearby sensors on a damage-free surface did not observe significant strain change.

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.

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The accuracy of thematic information extracted from remote sensing image is assessed recurrently using the confusion matrix method. But the accuracies have been criticized as a consequence of its aspatial nature. The work presented here describes a geographically weighted method combined with logistic regression for producing and visualizing the spatially distributed accuracy measures across the landscape. The outcomes compare the standard confusion matrix-based accuracy measures with those that have been permitted to differ locally. Furthermore, statistical parameters, i.e. Akaike information criterion, adjusted squared correlation coefficient (R 2 ) and residual sum of squares (RSS) were employed to compare the performance of geographically weighted logistic regression (GWLR) with global ordinary least square regression technique. The GWLR technique was found to provide more reliable performance in estimating spatially varying accuracy measures. The results demonstrated that the geographically weighted approach offers additional and valuable insights for examining spatial variation in the context of landscape mapping accuracy.