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• Developed a computer vision-based method for structural displacement measurement. • Employed fully convolutional network (FCN), conditional random field (CRF) and digital image correlation (DIC) related techniques. • Performed validation experiments on a simply supported beam. • Verified illumination-robust and multi-point simultaneous measurements. Computer vision-based techniques for structural displacement measurement are rapidly becoming popular in civil structural engineering. However, most existing computer vision-based displacement measurement methods require man-made targets for object matching or tracking, besides usually the measurement accuracies are seriously sensitive to the ambient illumination variations. A computer vision-based illumination robust and multi-point simultaneous measuring method is proposed for structural displacement measurements. The method consists of two part, one is for segmenting the beam body from its background, the segmentation is perfectly carried out by fully convolutional network (FCN) and conditional random field (CRF); another is digital image correlation (DIC)-based displacement measurement. A simply supported beam is built in laboratory. The accuracy and illumination robustness are verified through three groups of elaborately designed experiments. Due to the exploitation of FCN and CRF for pixel-wise segmentation, numbers of locations along with the segmented beam body can be chosen and measured simultaneously. It is verified that the method is illumination robust since the displacement measurements are with the smallest fluctuations to the illumination variations. The proposed method does not require any man-made targets attached on the structure, but because of the exploitation of DIC in displacement measurement, the regions centered on the measuring points need to have texture feature. [ABSTRACT FROM AUTHOR]

• Developed a method for distributed detection of microcracks. • Employed robust principal analysis and related techiques. • Performed validation experiments. • Detected opening of microcracks of less than 23 microns. Development of a method for distributed detection of microcracks on structural elements with very small crack opening displacements is described in this study. Robust principal component analysis (RPCA) and support vector machine (SVM) techniques were employed for denoising and classification of the signals. The objective was to detect microcracks in structural elements less than 30 µm in size. The viability of the method was accomplished by experiments involving a 15-meter steel beam with known microcracks. A distributed optical fiber sensor system based on the Brillouin scattering technology was employed for distributed measurement of strains along the length of the 15-m long beam. Distributed strain signals based on Brillouin based sensors possess inherent system noise and ambient perturbations which in turn reduce the signal-to-noise ratio of the measurements. Therefore, it is not possible to detect the smaller microcracks with small crack opening displacements. Smaller CODs are lost within the noisy distributed strain signal acquired by the Brillouin system. Undetected microcracks result in larger cracks, corrosion, and other anomalies with severe economical and safety ramifications. The method introduced for denoising and enhancement of the signal in the present study enables manifestation of the singularities on the distributed strain data and detection of microcracks. The significant component containing those singularities is effectively separated from the noise component by RPCA-based matrix decomposition. An SVM classifier with Gaussian kernel function was designed, through which the crack detections are realized by singular and nonsingular binary classification. The experimental results demonstrated that it was possible to detect microcracks with CODs as low as 23 µm without errors. [ABSTRACT FROM AUTHOR]