Vision‐based monitoring system for in‐plan seismic tests conducted on a shaking table: The case study of prefabricated concrete shells

Abstract

Computer vision enables a much more efficient monitoring system of structural behavior, compared to traditional methods. This derives from the fact that it allows the assessment of displacements in a vast number of points that can be processed to the estimate deformations, accelerations, and other key parameters at relevant cross‐sections. This paper proposes a computer vision‐based methodology, specifically designed for monitoring seismic tests conducted on a shaking table with a reduced‐scale model, using a single camera approach. This innovative methodology uses artificial targets with predefined color and geometry to optimize their detection and tracking. These targets are positioned at key points of the reduced model—“Moving Targets”—and at reference points of the seismic table—“Control Targets.” Videos are recorded during the seismic tests and the coordinates (in pixels) of the targets' centers are automatically detected and captured. Then, transformations are applied to each frame to compute the targets coordinates in millimeters and based on the differences between frames, to calculate the displacements of each target. The methodology was first calibrated with dynamic tests performed on a reduced‐scale model (1:33) of a prefabricated concrete shell, printed in acrylonitrile butadiene styrene (ABS), conducted on an educational shaking table. Afterwards, the methodology was validated with seismic tests performed on a 1:3 reduced model of a prefabricated concrete shell, produced according to the similitude theory to best reproduce the behavior of the corresponding prototype. It was demonstrated the ability of the methodology to monitoring seismic tests, enabling continuous tracking of the targets placed on a structure with complex geometry.