| CPC G01M 5/0008 (2013.01) [G01M 7/08 (2013.01); G01S 17/86 (2020.01); G01S 17/894 (2020.01); G01S 17/931 (2020.01); G06T 7/521 (2017.01); G06V 20/56 (2022.01); G08G 1/0112 (2013.01); G08G 1/0125 (2013.01); G08G 1/0129 (2013.01); H04N 13/243 (2018.05); G01S 7/4802 (2013.01); G06T 2200/04 (2013.01); G06T 2207/10028 (2013.01); G06T 2207/30252 (2013.01)] | 9 Claims |
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1. An integrated automatic detection equipment for a highway network structure group, comprising
a tractor, a first test vehicle and a second test vehicle,
wherein a central control system, a geometric linear detection system, a road three-dimensional (3D) detection system, and a laser 3D scanning system are arranged on the tractor;
wherein the geometric linear detection system comprises a vehicle inertial unit configured to record driving trajectories, an accelerometer sensor configured to record vehicle vibration and turbulence, and a portable body scanning equipment configured to detect a center of gravity and geometric parameters of logistic transportation vehicles;
a front end of the first test vehicle is detachably connected to a rear end of the tractor, and a rear end of the first test vehicle is detachably connected to a front end of the second test vehicle;
a drop hammer loading system is arranged on the first test vehicle;
a bridge dynamic detection system is arranged on the second test vehicle;
the geometric linear detection system, the road 3D detection system, and the drop hammer loading system are used for a road detection, and are specifically used for indoor and outdoor experiments and data acquisition by using a semi-automated 3D data acquisition system, the determination of the establishment of the data model of road surface detection and the extraction of macro texture, fine texture and micro texture by analyzing 3D data from different scales of the road; in terms of parameterization, the automatic identification of the pavement type, the maximum particle size of mixtures, the pavement damage type and the rut type through deep learning of texture features and two-dimensional (2D) image data, the quantization and determination of the severity of damage, and the determination of the key damage affecting the structure; in terms of flatness, the determination of the longitudinal flatness of different strips positions by calculation and analysis of 3D deformation; in terms of the anti-sliding model, the establishment of an anti-sliding coefficient model under the influence of texture, gradation combination and old and new pavement; the analysis of the interaction between the tire and pavement by mechanical simulation software such as ANSYS, so as to verify the texture indexes for anti-sliding performance evaluation; the determination of the standard axial load type, the establishment of a theoretical model of vehicle-road dynamics safety analysis based on geometric parameters, and the establishment of a driving safety analysis model, including linear parameters and key volume parameters of special vehicles, by using Carsim driving simulation software, and the comparison and verification of the relevant conclusions of theoretical analysis; based on the professional transportation standard axial load and drop hammer curved detection technology, the establishment of a load capacity evaluation model; based on the driving safety model and structural load capacity, the research of a speed limit model and the design of a special vehicle driving safety speed limit query table, and the determination of the road professional transportation level; the quick evaluation of the amount of backfilling of large-scale damage such as the craters by using the 3D scanning technology, the detection of the residual load capacity of the structure after backfilling, and the traffic capacity of professional transport vehicles by using deflection loading equipment; in view of the usual times, combined with professional transportation demand, the provision of restrictions on vehicle passage in relevant roads, and the setting of the regular detection standards; the accumulation of daily detection and evaluation data, and the improvement of the knowledge database and facility model in the data management system;
the bridge dynamic detection system is used for a bridge detection, and is specifically used for the mastery of the main control parameters through the vehicle-bridge coupling mechanical model and theoretical evolution analysis, the consideration of the actual vehicle-bridge damping, pavement roughness, temperature difference, and cross-section change, and the derivation of the feasibility signal processing method of the bridge power parameters; through numerical analysis and simulation of the whole vehicle-bridge dynamic test system, the establishment of the vehicle-bridge coupling model of Abaqus software and the vehicle-bridge coupling model of MATLAB for bidirectional verification; then, the improvement of the actual vehicle-bridge coupling model being made for meeting the engineering accuracy requirements; the further selection of medium and small span bridges for modeling analysis being made for studying the relationship between non-contact components, contact components, nodes and test signals of the automatic bridge detection vehicles; the realization of parameter correction of the finite element model by calling MATLAB platform with secondary development of Abaqus software; finding out the influence of design optimization parameters of automatic bridge detection vehicles by dynamic analysis methods, so that a more reliable reference for actual operations is provided; studying concrete medium and small span bridges under conventional strength, and the analysis of the dynamic test effects of T-beam, box girders, hollow plates and variable cross-sections under different supports; based on the dynamic test damage simulation of beam components under different roughness levels and different temperature changes and with the foundation of probability density evolution and Bayesian theory, the establishment of quantitative relationships between uncertain parameters such as bridge damping, excitation amplitude, spectrum, and time and damage of beam component, the analysis of the damage mechanism, the clarification of the damage mechanism of the bridge structure dynamic test under uncertain parameters, the establishment of the extraction principle of damage indexes based on automatic bridge detection vehicles, the execution of parallel and weight evidence fusion for related information and damage index, and the analysis of different damage index sensitivity and noise sensitivity under uncertain parameter;
the laser 3D scanning system is used for a tunnel detection, and is specifically used for the execution of indoor tests and on-site tests by using the panoramic cloud platform equipped with 3D laser sensors and test model vehicles, so as to determine the instrument placement parameters and detection process of 3D laser scanning technology and multi-sensor data fusion analysis technology for the tunnel boundary detection; based on the improved differential method, the development of the program modules of tunnel point cloud boundary extraction, tunnel boundary limitation and central axis establishment; the studying of the minimization scheme of point cloud stitching error based on the two-end stitching algorithm and the global stitching algorithm; according to the least squares principle, the development of a cross-section curve fitting program module for common tunnel cross-section shapes such as straight wall arch cross-section, multi-lane cross-section of three-center circular tank arch with inverted arch, rectangular cross-section and circular cross-section; the development of point cloud denoising algorithms and programs for “hybrid point” type noise points of tunnel auxiliary facilities such as lighting equipment, ventilation pipes, bolts and power facilities by the mean error method; through the structural dynamics theory, the derivation of the influence of the vibration generated by the vehicle in the tunnel on the measurement data and the development of the vibration error correction program module by using the vibration data of the acceleration sensor of the measurement system; based on the geometric relationship, the derivation of the calculation formula of the influence of the tunnel section change and the tunnel bend on the measurement results by using the data of the tilt sensor of the measurement system, and the compilation of the corresponding error correction program; based on the representative point method and regular grid method, aiming at the problem of rapid evaluation of tunnel professional transport capacity, the studying of the simplification method of massive point cloud data; the scanning of The tunnel spatial changes, large diseases, deformation and settlement; combined with the rapid detection device of vehicle geometric characteristics, the determination of instant traffic capacity; in view of the usual times, completing the mathematical modeling of typical professional transport vehicles based on the data investigation of spatial geometric dimensions and driving speed of the typical professional transport vehicles; based on theoretical derivation, the determination of the traffic flow parameter model, which considers the parameters such as vehicle parameters, vehicle components, driving speed, tunnel limit, and the number of lanes, and the derivation of the evaluation formula of the professional transport capacity of the tunnel; the execution of the traffic simulation of the traffic capacity of the professional transport tunnel under different numbers of lanes, different tunnel cross-section shapes and cross-section sizes by using the vehicle simulation software CarSim, and the determination of the calculation formulas of the traffic capacity of the corresponding lanes and corresponding sections, and the design of the optimized traffic scheme of the professional transport tunnel; and
the geometric linear detection system, the road 3D detection system, the laser 3D scanning system, the drop hammer loading system, and the bridge dynamic detection system are electrically connected to the central control system;
wherein the central control system is equipped with a human-computer interaction interface platform including two different working modes of real-time traffic detection mode and short-term safety detection mode, the real-time traffic detection model mainly serving professional transport pilot vehicles, including 4 functions of detection data management, traffic capacity evaluation, traffic scheme decision and quick maintenance methods, the short-term safety detection mode being mainly for professional transportation periodic detection requirements, and including short-term safety evaluation except for the above functions;
wherein the tunnel scan is set to the same cross section as the road sweep surface section, and the detection range has a certain degree of overlapping for data fusion calibration. The road load loading system may assist the dynamic deflection measurement of the bridge in addition to curved measurement. The roughness index obtained from road texture/flatness detection may be used for signal noise reduction during bridge detection.
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