Crack disease is one of the most serious and common diseases in road detection. Traditional manual methods for measuring crack detection can no longer meet the needs of road crack detection. In previous work, the authors proposed a crack detection method for asphalt pavements based on an improved YOLOv5s model, which is a better model for detecting various types of cracks in asphalt pavements. However, most of the current research on automatic pavement crack detection is still focused on crack identification and location stages, which contributes little to practical engineering applications. Based on the shortcomings of the above work, and in order to improve its contribution to practical engineering applications, this paper proposes a method for segmenting and analyzing asphalt pavement cracks and identifying parameters based on image processing. The first step is to extract the crack profile through image grayscale, histogram equalization, segmented linear transformation, median filtering, Sauvola binarization, and the connected domain threshold method. Then, the magnification between the pixel area and the actual area of the calibration object is calculated. The second step is to extract the skeleton from the crack profile images of asphalt pavement using the Zhang–Suen thinning algorithm, followed by removing the burrs of the crack skeleton image using the connected domain threshold method. The final step is to calculate physical parameters, such as the actual area, width, segments, and length of the crack with images obtained from the crack profile and skeleton. The results show that (1) the method of local thresholding and connected domain thresholding can completely filter noise regions under the premise of retaining detailed crack region information. (2) The Zhang–Suen iterative refinement algorithm is faster in extracting the crack skeleton of asphalt pavement, retaining the foreground features of the image better, while the connected-domain thresholding method is able to eliminate the missed isolated noise. (3) In comparison to the manual calibration method, the crack parameter calculation method proposed in this paper can better complete the calculation of crack length, width, and area within an allowable margin of error. On the basis of this research, a windowing system for asphalt pavement crack detection, WSPCD1.0, was developed. It integrates the research results from this paper, facilitating automated detection and parameter output for asphalt pavement cracks.

Slope deformation monitoring is the prerequisite for disaster risk assessment and engineering control. Terrestrial laser scanning (TLS) is highly applicable to this field. Coarse registration method of point cloud based on scale-invariant feature transform (SIFT) feature points and fine registration method based on the k-dimensional tree (K-D tree) improved iterative closest point (ICP) algorithm were proposed. The results show that they were superior to other algorithms (such as speeded-up robust features (SURF) feature points, Harris feature points, and Levenberg-Marquardt (LM) improved ICP algorithm) when taking the Stanford Bunny as an example, and had high applicability in coarse and fine registration. In order to integrate the advantages of point measurement and surface measurement, an improved point cloud comparison method was proposed and the optimal model parameters were determined through model tests. A case study was conducted on the left side of the K146 + 150 point at S236 Boshan section, Shandong Province, and research results show that from 14 August 2018 and 9 November 2019, the overall deformation of the slope was small with a maximum value of 0.183 m, and the slope will continue to maintain a stable state without special inducing factors such as earthquake, heavy rainfall and artificial excavation.

Although embankment seismic damages are very complex, there has been little seismic fragility research yet. Researches on seismic fragility of bridges, dams and reinforced concrete (RC) structures have achieved fruitful results, which can provide references for embankment seismic fragility assessment. Meanwhile, the influencing degrees of retaining structures, such as retaining walls on the embankment seismic performances are still unclear. The K1025+470 embankment of the Xi’an-Baoji expressway was selected as the research object, and the finite difference models of the embankment fill-soil foundation system and embankment fill-soil foundation-retaining wall system were established. The ground-motion records for Incremental Dynamic Analysis (IDA) were selected and the dynamic response analysis were conducted. Probabilistic Seismic Demand Analysis (PSDA) was used to deal with the IDA results and the seismic fragility curves were generated. Based on the assessment results, the influences of the retaining wall on the embankment seismic fragility were further verified. The research results show that regardless of which seismic damage parameter is considered or the presence or absence of the retaining wall, larger PGAs always correspond to higher probabilities of each seismic damage grade. Seismic damages to the embankment fill-soil foundation-retaining wall system are always lower than those of the embankment fill-soil foundation system under the same PGA actions, thus, the retaining wall can decrease the embankment seismic fragility significantly.