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Motivated by the low efficiency and poor accuracy of the existing techniques for measuring the depth of a pavement structure, we examine the characteristics of laser speckle and speckle measurement methods in relation to the depth of a pavement structure and propose a set of methods for the 3D measurement of such structures based on laser speckle and digital image processing. First, we analyze the relevant characteristics of laser speckle measurement, and then based on the relevant region and temporal sequence correlation, a faster digital speckle correlation method is used to establish a mathematical relationship between the 3D image and the structural depth. The erosion and dilation technique and mean optimization algorithm are proposed to eliminate the noise and burr of primeval 3D reconstruction. After this, a 3D point cloud of information on the depth of the pavement structure is obtained. Finally, through fitting, a good linear relationship was found between the texture depth obtained by the sand paving method and the system. From measurements using a laboratory calibration platform, it was found that the accuracy of measurement of our method can reach the subpixel level, and the running speed is high, meaning that it can be used for highly efficient and accurate detection of the depth of a road structure.

Aiming at the defects of routine settlement measurement methods, such as complicated procedures, time-consuming and labor-intensive, high cost and low measurement accuracy, based on the analysis of existing engineering measurement technical requirements and specifications, a multi-point high precision and high efficiency based on laser reference is proposed. The automatic building settlement real-time monitoring system program gives the principle and system model of single-point settlement observation. The model of multi-point scanning settlement monitoring system and the model of multi-point network settlement monitoring system are designed, and their advantages and disadvantages are analyzed. We focus on the networked multi-point settlement monitoring system for network cumulative error analysis, and propose related evaluation and correction methods. The hardware schematic and software block diagram of the laser reference measurement and measurement system of the single point settlement acquisition system are given. Finally, the risk of subsidence state is quantitatively evaluated based on multi-point settlement monitoring data. The measurement error of this method is less than 300 µm, which can realize the monitoring and evaluation of the overall settlement.

The detection of bridge bottom cracks is required for bridge maintenance. In order to realise the requirement of automatic real-time detection of a bridge structure, an image detection method for cracks in the bottom of the bridge structure using a tethered creeping unmanned aerial vehicle (UAV) is proposed. A high-precision unlimited endurance detection plan based on the tethered creeping UAV is designed to use for the bottom cracks of the bridge structure. The detection scheme applies a high-precision image stitching measurement algorithm for cracks at the bottom of the beam body, which is able to restore a panoramic image. All mainstream filtering methods were evaluated, and it turned out that they are practicable/applicable in various crack images of different shapes. The method is applied to the crack detection in the bottom of bridge structures to ensure the accuracy and efficiency of the system measurement. According to the actual measurement by the laboratory platform, the measurement error using this method is less than 0.1 mm, which meets the requirements of measurement automation. The results of the research represent an initial step towards developing an automatic bridge health monitoring and evaluating system.