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The object of this work is to study and predict the tensile properties (tensile strength, elastic modulus, and elongation at break) of ternary nanocomposites based on epoxy/ glass fiber/nanosilica using the fuzzy logic (FL). Two factors in three levels including glass fiber at 0, 5, and 10 wt% and nanosilica at 0, 0.5, and 1 wt% were chosen for adding to an epoxy matrix. From FL surfaces, it was found that the glass fiber content had a main role in the tensile properties of nanocomposites. The high levels of glass fiber content led to a significant increase in the elastic modulus and generally, the presence of glass fiber decreased the tensile strength and elongation at break. Also, addition of the nanosilica content resulted in an increased elastic modulus but decreased the elongation at break of nanocomposites. Finally, an FL model was obtained for each tensile property.

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Human inspection of defects of concrete tunnel lining is usually slow, laborious, and disruptive to traffic. This necessitates automated alternatives using sensors and computer-aided processing. The conventional image-matching methods only use the cost value of the pixel being processed based on similarity metric to estimate an image-matching location. To improve the image-matching efficiency, this paper proposes an image-matching method that relies on the curvatures of the cost curve at candidate matching points. This is followed by applying a median filter to mitigate the matching errors. Moreover, experimental results for an actual tunnel demonstrate that the curvature measurement can select the matching points accurately. The authors have developed an image-stitching software to create a high-resolution panorama of the tunnel lining surface for assisting in defect inspection.