The intelligent crack detection method is an important guarantee for the realization of intelligent operation and maintenance, and it is of great significance to traffic safety. In recent years, the recognition of road pavement cracks based on computer vision has attracted increasing attention. With the technological breakthroughs of general deep learning algorithms in recent years, detection algorithms based on deep learning and convolutional neural networks have achieved better results in the field of crack recognition. In this paper, deep learning is investigated to intelligently detect road cracks, and Faster R-CNN and Mask R-CNN are compared and analyzed. The results show that the joint training strategy is very effective, and we are able to ensure that both Faster R-CNN and Mask R-CNN complete the crack detection task when trained with only 130+ images and can outperform YOLOv3. However, the joint training strategy causes a degradation in the effectiveness of the bounding box detected by Mask R-CNN.

Terrestrial laser scanning (TLS) is capable to be a reliable deformation monitoring device with high-precision for concrete composite structures. Measurements based on TLS for an arch structure with monotonic loading is carried out. In this paper, comparison between original and optimized extraction of point clouds are presented. Surface approximation is implemented, where the vacant measurement area is also covered and the uncertainties of different-order surfaces are investigated. The results of surface approximation based on TLS measurement have certain relation with surface roughness of specimen, which will be eliminated by subtraction in deformation calculation

In this article, the Carrera Unified Formulation and full Green–Lagrange nonlinear relations are employed to investigate the postbuckling behavior of laminated composite panels subjected to in-plane shear and combined loadings. The layerwise refined plate models are implemented by the efficient Lagrange expansion functions through the laminate thickness. An artificial surrounding area is assumed to model shear conditions. The accuracy of the presented model is validated based on the available literature. Then, the effects of stiffeners on the nonlinear response of panels are evaluated, and comprehensive assessments are provided for the equilibrium curves and stress distributions.

Architectural structures today are increasingly complex and structural health monitoring plays an important role in guaranteeing their safety. How to improve the reliability of deformation analysis is, thus, one of the key problems. This article combines laser-based measurement technology and the Carrera unified formulation (CUF) method to investigate the deformation of engineering structures. Within this article, we simulate architectural structures using the CUF geometric model, which is consistent with the results of the laser tracker experiment. We aimed at constructing an intelligent and efficient CUF model which can be applied extensively in the monitoring of various constructs, such as tunnels and bridges. The innovation of this article is that high-accuracy laser tracker technology is integrated with an effective CUF model to investigate the load-displacement relationship considering lateral displacement.