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Compared to the traditional anchor cable, the constant resistance and large deformation anchor cable (constant resistance and large deformation anchor cable) has good applications in many fields of geotechnical engineering. Through the indoor static tension test, this study reveals the variation law of constant resistance, axial strain, the outer diameter of the sleeve, and the thermal effect of constant resistance and large deformation anchor cables during static tension. The ANSYS software was used for the first time to establish the nonlinear thermomechanical coupling analysis model of the finite element structure of constant resistance and large deformation anchor cables for the numerical calculation and analysis of static tension mechanical properties of constant resistance anchor cables. The experimental results that the average elongation of this batch of constant resistance anchor cables is 905 mm with an average elongation rate of 45.2% and an average constant resistance of 650 kN prove that constant resistance anchor cables are characterized by good constant resistance and large deformation, which can meet the requirements of deep soft rock roadway support and advanced landslide monitoring. The numerical simulation results show that the elongation of this type of anchor cables is 902 mm with an elongation rate of 45.1% and a constant resistance of 660 kN, which are basically consistent with the experimental results, indicating that numerical simulation is relatively accurate for testing the mechanical property of constant resistance and large deformation anchor cables, and the combination of the indoor test and numerical simulation provides the reference for engineering practice and design optimization of constant resistance and large deformation anchor cables.

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With the rapid development of autonomous vehicles and mobile robotics, the desire to advance robust light detection and ranging (Lidar) detection methods for real world applications is increasing. However, this task still suffers in degraded visual environments (DVE), including smoke, dust, fog, and rain, as the aerosols lead to false alarm and dysfunction. Therefore, a novel Lidar target echo signal recognition method, based on a multi-distance measurement and deep learning algorithm is presented in this paper; neither the backscatter suppression nor the denoise functions are required. The 2-D spectrogram images are constructed by using the frequency-distance relation derived from the 1-D echo signals of the Lidar sensor individual cell in the course of approaching target. The characteristics of the target echo signal and noise in the spectrogram images are analyzed and determined; thus, the target recognition criterion is established accordingly. A customized deep learning algorithm is subsequently developed to perform the recognition. The simulation and experimental results demonstrate that the proposed method can significantly improve the Lidar detection performance in DVE.