16 results about "Pavement management" patented technology

The invention belongs to the technical field of asphalt pavement maintenance management, and particularly relates to abnormal value identification according to characteristics of maintenance data and a quartile spacing method to obtain a normal data set and an abnormal data set after abnormal maintenance measures are removed; constructing a long short-term memory network model, and finding a group of optimal hyper-parameters by using a quintuple cross validation grid search method; and training by using the LSTM model and the collected normal data set to obtain the optimal weight and offset parameter of the model, and obtaining an asphalt pavement maintenance engineering data prediction model for predicting and filling the vacancy in the data set. The invention provides a pavement maintenance data abnormal value cleaning method considering time sequence characteristics. According to the method, reference is provided for asphalt pavement management data cleaning research, the availability of data in a pavement management system is improved, and a decision maker can be helped to formulate a scientific and reasonable pavement maintenance scheme.

The invention belongs to the technical field of pavement condition data monitoring, and particularly relates to a pavement maintenance and condition data abnormal value comprehensive identification method based on data driving. The method comprises the following steps: respectively training a maintenance measure probability prediction model based on an artificial neural network and a pavement condition prediction model based on a Bayesian neural network by using a normal data set; the method comprises the following steps: performing abnormal maintenance data identification and correction by using a trained maintenance measure probability prediction model based on an artificial neural network, and performing abnormal pavement condition index data identification and correction by using a pavement condition prediction model based on a Bayesian neural network. And dynamic adjustment of maintenance data abnormal threshold values and road condition index abnormal threshold values of different road sections is carried out in combination with a 2 sigma principle. The method can be used for improving the data quality of the pavement management system and helping managers to make scientific and reasonable maintenance decisions.

The present application belongs to the technical field of road surface condition data prediction, and particularly relates to a road surface condition degradation prediction method fusing road age perception and physical constraints, collecting road surface management data of an un-maintained road section, and performing multi-source feature preprocessing on the road surface management data to obtain a road surface feature vector; introducing a physical information constraint module on the basis of a Transformer model, the physical information constraint module introducing physical laws for the Transformer model through a road surface condition degradation equation and interval constraints, thereby constructing and training a physical information Transformer model for road surface condition degradation prediction; and introducing a SHAP explainability framework to perform explainability analysis on the optimal physical information Transformer model. The present application can ensure that road surface condition prediction conforms to physical laws and has clear explainability.

The invention relates to the technical field of data processing, and discloses an asphalt pavement green maintenance decision-making method and system based on long-term performance monitoring, which are used for solving the problems of low decision-making efficiency and insufficient resource utilization caused by the fact that global optimum is difficult to solve by multi-objective optimization under budget constraint in a traditional method. The method comprises the following steps: firstly, acquiring and sorting pavement performance data through a data acquisition and storage module, establishing a performance attenuation model and a green index system, and forming a multi-objective function framework; setting a budget hard constraint and a target weight, and performing initial solution space division and iterative adjustment, including boundary expansion and cross validation, on a function; and finally, outputting a decision scheme and applying the decision scheme to pavement management to realize balance optimization of cost, performance and environmental influence. The decision-making efficiency is improved, and sustainable maintenance is supported.

The invention belongs to the technical field of pavement condition data prediction, and particularly relates to a pavement condition decay prediction method fusing road age perception and physical constraint, which comprises the following steps: acquiring pavement management data of an unmaintained road section, and carrying out multi-source feature preprocessing on the pavement management data to obtain a pavement feature vector; a physical information constraint module is introduced on the basis of a Transform model, and the physical information constraint module introduces a physical rule for the Transform model through a pavement condition attenuation equation and interval constraint, so that a physical information Transform model is constructed and trained for pavement condition decay prediction; and an SHAP interpretability framework is introduced to carry out interpretability analysis on the optimal physical information Transform model. The method can ensure that the pavement condition prediction accords with the physical law and the interpretability is clear.

The invention discloses an asphalt pavement compaction uniformity evaluation method based on a ground penetrating radar, and relates to the field of data detection, and the method comprises the following steps: 1, obtaining a uniformity parameter set through a ground penetrating radar sensor through a signal processing algorithm, comparing the uniformity parameter set with a preset uniformity attribute index threshold value, and determining the uniformity attribute index threshold value; judging whether an event exceeding a threshold value or a low threshold value exists or not, when an event not meeting the threshold value is detected, identifying the amplitude of data violating the threshold value, and preliminarily classifying the compaction uniformity state of the current pavement as normal, light anomaly or heavy anomaly; 2, constructing a state evaluation model based on a deep learning algorithm; according to the method, the compaction uniformity variation trend of the pavement in a future period can be predicted, factors causing abnormity can be accurately identified, targeted maintenance suggestions are provided, the cost effectiveness of a repair scheme is improved, real-time monitoring and data sharing are provided, the pavement management efficiency is improved, and the service life of the pavement is prolonged.

A road surface management device according to an aspect of the present disclosure includes: at least one memory configured to store instructions; and at least one processor configured to execute the instructions to: identify a section in which an image is captured, among a plurality of sections obtained by dividing a traveling road of an airport, based on positional relationships between the plurality of sections and signs, and signs in the image included in sensor information collected on the traveling road; detect an abnormality of the traveling road in the identified section based on the collected sensor information; and output the detected abnormality in association with the identified section.

A computer-implemented paving management system and operation utilizes an autonomous mobile compactor to perform a subordinate compaction operation on a paving mat laid by a paver. To activate the autonomous mobile compactor, one or more worksite sensors obtain thermal data regarding the paving mat. Thermal data obtained from the worksite sensor is converted to an evaluated temperature value associated with the paving mat, and the evaluated temperature value is compared to a compaction temperature setting. The paving management system and operations start and / or destart the autonomous mobile compactor based on a comparison between the evaluated temperature value and the compaction temperature setting.

The application discloses a kind of asphalt pavement material strain rate variation law with road age determination method and device, wherein method is carried out by indoor multi-sequence local loading test to asphalt surface layer wheel trace band and road shoulder core sample, obtains the strain rate value of material under multiple stress, first determines the strain rate value of performance starting point and performance turning point of material in entire service period, then assumes the road age corresponding to performance turning point, carries out two-section fitting to the performance development law of strain rate of material under different road age, different stress, with the value of left and right side guide function at road age is closest as determining basis, determines the road age value of performance turning point, finally obtains the segmented performance development law of material strain rate. Through the above method, the strain rate development law of actual asphalt pavement can be comprehensively and effectively determined according to the traffic characteristics of the asphalt pavement, helping the pavement management unit to develop scientific and fine maintenance strategies for pavement rutting diseases, and saving maintenance funds.

In this road surface management system, a work vehicle, a water sprinkler vehicle, and a management terminal are connected via a wireless communication line. The management terminal calculates, on the basis of sprinkled water information relating to the water sprinkler vehicle, a cumulative sprinkled water parameter representing the cumulative amount of water sprinkled from a predetermined time before the time at which the sprinkled water information was acquired to the time at which the sprinkled water information was acquired, at each of a plurality of positions on a traveled road surface, and extracts the cumulative sprinkled water parameter, with the time at which the rate of change in a degree of deterioration of the traveled road surface exceeds a deterioration degree threshold as a deterioration determination time. On the basis of the cumulative sprinkled water parameter, one of the management terminal and a travel control device executes at least one of travel suppression control for outputting a travel suppression signal for suppressing travel of the work vehicle and water sprinkling suppression control for outputting a water sprinkling suppression signal for suppressing water sprinkling by the water sprinkler vehicle.

The present invention relates to the technical field of tunnel maintenance, and in particular to a high-speed tunnel maintenance detection method based on Beidou positioning, comprising: installing a Beidou positioning system on a maintenance detection vehicle, uploading electronic design drawings of the high-speed tunnel, obtaining structural parameters of the high-speed tunnel in the electronic design drawings of the high-speed tunnel, and constructing a three-dimensional model of the high-speed tunnel according to the structural parameters of the high-speed tunnel; selecting a maintenance detection path in the three-dimensional model of the high-speed tunnel, feeding the maintenance detection path back to the maintenance detection vehicle, the maintenance detection vehicle uses the maintenance detection path as navigation, travels in the high-speed tunnel, and perceives the state parameters of the high-speed tunnel in real time; the present invention obtains tunnel pavement defects that need to be processed urgently, and assists tunnel pavement management personnel to obtain tunnel pavement defect information more quickly, thereby more conveniently and orderly repairing tunnel pavement defects and ensuring tunnel pavement safety.

The invention relates to the field of airfield pavement disease identification, and provides an airfield pavement management system based on AI and three-dimensional scanning, which comprises three-dimensional laser scanning equipment and an AI server, the three-dimensional laser scanning equipment is deployed on an airport pavement and is used for collecting point cloud data of a target area; the AI server comprises a filtering unit, a flattening processing unit and a crack identification unit; the filtering unit is used for performing multi-dimensional filtering on the point cloud data to generate a target point cloud without noise points; the flattening processing unit is used for generating a flatness cloud picture of the target area according to the target point cloud and positioning a standard exceeding area; wherein the flatness cloud picture comprises a two-dimensional cloud picture and a three-dimensional cloud picture; and the crack identification unit is used for inputting the exceeding area into the U-Net model, extracting a crack mask through an RGB space and an HSV space, and outputting a crack geometric parameter according to the crack mask.

The invention relates to the technical field of tunnel maintenance, in particular to a Beidou positioning-based expressway tunnel maintenance detection method, which comprises the following steps of: installing a Beidou positioning system on a maintenance detection vehicle, uploading an expressway tunnel electronic design drawing, acquiring structural parameters of an expressway tunnel in the expressway tunnel electronic design drawing, and determining the structural parameters of the expressway tunnel. Constructing a three-dimensional model of the expressway tunnel according to the structural parameters of the expressway tunnel; a maintenance detection path is selected in the three-dimensional model of the expressway tunnel, the maintenance detection path is fed back to a maintenance probe vehicle, and the maintenance probe vehicle takes the maintenance detection path as navigation, runs in the expressway tunnel and senses state parameters of the expressway tunnel in real time; according to the method, the tunnel pavement defects urgently needing to be processed are acquired, and tunnel pavement management personnel are assisted to acquire tunnel pavement defect information more quickly, so that the tunnel pavement defects are repaired in order more conveniently, and the tunnel pavement safety is guaranteed.

This invention discloses a method and apparatus for determining the high-temperature performance development stage of asphalt pavement, belonging to the field of road maintenance. The method involves conducting indoor multi-sequence local loading tests on core samples from the wheel track zone and shoulder of the asphalt pavement to obtain strain rate values ​​of different layer materials under multiple stresses. Combined with the axle load spectrum of the cored sections, the average strain rate of different layer materials under light, medium, and heavy traffic is calculated. The ratio of the average strain rate of the wheel track zone to that of the shoulder material is used to ultimately determine the high-temperature performance development stage of the pavement material at different depths. This method allows for a comprehensive and effective determination of the high-temperature performance development trend of actual asphalt pavements based on their traffic characteristics, helping road management units to formulate scientific and refined maintenance strategies for rutting damage and saving maintenance costs.

The utility model relates to a prefabricated bridge floor drainage blind ditch which comprises a concrete integral pouring layer, drainage ditches are arranged on at least one side of the concrete integral pouring layer, and a waterproof layer is arranged on the upper surface of the concrete integral pouring layer. A plurality of longitudinal prefabricated drainage boxes are arranged in the drainage ditch, each prefabricated drainage box comprises an outer frame, the top of each outer frame is open and provided with a cover plate, and each outer frame is filled with a gravel layer; water permeable holes are formed in side plates on the periphery of the outer frame, the bottom plate and the cover plate; the prefabricated drainage box further comprises an asphalt layer, and the prefabricated drainage box is covered with the asphalt layer. The prefabricated drainage box is manufactured in a prefabricated mode, the production efficiency is high, the quality is controllable, the construction progress is not affected, gravel does not need to be tamped, vibrated and compacted, asphalt does not need to be cut, excavated and removed, the construction period is shortened, the drainage effect is good, and the road surface is convenient to manage and maintain.

The present invention belongs to the technical field of road surface condition data monitoring, and specifically relates to a comprehensive identification method for road surface maintenance and abnormal values of condition data based on data-driven. This method uses normal data sets to train a maintenance measure probability prediction model based on an artificial neural network and a road surface condition prediction model based on a Bayesian neural network; first, the trained maintenance measure probability prediction model based on the artificial neural network is used to identify and correct abnormal maintenance data, and then the road surface condition prediction model based on the Bayesian neural network is used to identify and correct abnormal road surface condition index data, and the 2σ principle is combined to dynamically adjust the abnormal thresholds of maintenance data and road surface condition index for different road sections. The present invention can be used to improve the data quality of the road surface management system and help managers make scientific and reasonable maintenance decisions.