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Experimental image and range scanner datasets fusion in SHM for displacement detection
Author(s) -
RiveraCastillo Javier,
FloresFuentes Wendy,
RivasLópez Moisés,
Sergiyenko Oleg,
GonzalezNavarro Felix F.,
RodríguezQuiñonez Julio C.,
HernándezBalbuena Daniel,
Lindner Lars,
BásacaPreciado Luis C.
Publication year - 2017
Publication title -
structural control and health monitoring
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.587
H-Index - 62
eISSN - 1545-2263
pISSN - 1545-2255
DOI - 10.1002/stc.1967
Subject(s) - robustness (evolution) , computer vision , scanner , artificial intelligence , sensor fusion , structural health monitoring , displacement (psychology) , computer science , nondestructive testing , image fusion , image resolution , engineering , image (mathematics) , structural engineering , psychology , psychotherapist , medicine , biochemistry , chemistry , radiology , gene
Summary Optical images and signals can be used to detect displacement in civil engineering structures. This paper presents a technical experimentation of a vision‐based technology and artificial intelligence algorithms methodology for structural health monitoring of new and aging structures, by a noncontact and nondestructive system. The experimental study emphasis is on the outdoor urban environment, by the detection of spatial coordinate displacement on the structures, in order to perform a damage assessment. Also, the experimental study contains both theoretical and experimental aspects of the fusion of image and range scanner datasets created using intelligent algorithms. A camera and an optical scanning system were used to generate high resolution and quality images for 2D imaging, and 3D accuracy range data from optoelectronic sensor signals. Scans at a specific area of an engineering structure were performed to measure spatial coordinates displacements, successfully verifying the effectiveness and the robustness of the proposed non‐contact and non‐destructive monitoring approach.