4358results about "Material testing goods" patented technology

The invention provides a technical condition prediction method for a tunnel civil engineering structure, and relates to the technical field of traffic control monitoring systems.A biological acoustic composite sensing system comprising a distributed optical fiber acoustic sensing system and microorganism sample collection and analysis is constructed, and acoustic depth mode characteristics are mined by applying a deep learning model; the microbial dynamic biomarker is analyzed and identified by using bioinformatics; multi-modal information, a domain knowledge graph and a causal inference algorithm are fused, and a dynamic causal network model for revealing an internal driving relation in the degradation process is constructed; further, training and applying an adaptive neural network prediction model based on causal driving and fusing physical and biochemical mechanism constraints, and carrying out probabilistic prediction on future technical conditions of the tunnel; and executing anti-fact reasoning by using the causal network and the prediction model, and generating and optimizing an intervention strategy of cooperation of physical measures and biochemical measures. According to the method, the accuracy and advance of tunnel structure state prediction can be remarkably improved.

The invention discloses a wharf armor block quality detection method, and belongs to the technical field of concrete structure detection.The method comprises the steps that a surface model is constructed through a movable three-dimensional laser scanning device, and a high-risk defect area is recognized; performing encryption scanning and impact echo detection in the high-risk area by adopting a frequency-adjustable dual-mode flaw detection device to generate a three-dimensional defect distribution diagram; a core sample is drilled in the defect area for compressive strength testing, and the material performance is analyzed in combination with a non-contact infrared spectrum; and establishing a dynamic evaluation model, calculating a safety coefficient by integrating surface defects, internal defects and material discreteness data, and generating a three-dimensional visual report. The problems that a traditional detection method is low in efficiency, insufficient in internal defect positioning precision, one-sided in material performance evaluation and the like are solved, full-dimensional detection from the surface to the interior and from the macroscopic level to the microscopic level is achieved through a multi-source data fusion and closed-loop feedback mechanism, and the reliability of structural safety evaluation and the scientificity of maintenance decision are remarkably improved.

The invention discloses an asphalt pavement fatigue damage model calibration method based on AI and digital twinning, and relates to the technical field of damage detection. Compared with the prior art, the problems that traditional asphalt pavement design depends on empirical formulas and static parameters, material aging, environmental coupling and load uncertainty are difficult to dynamically reflect, and cross-scale correlation between microscopic interface behaviors and macroscopic structure performance is lacked are solved; the method comprises the following steps: collecting and preprocessing multi-source heterogeneous data in real time through a multi-modal data fusion and dynamic sensing system, simulating and accurately quantifying asphalt-aggregate interface binding energy and adhesion work in combination with molecular dynamics, and constructing a cross-scale damage evolution model; a physical information neural network is used for embedding an improved Paris formula, actually measured data and a physical rule are deeply fused, and the locality hypothesis of a traditional model is effectively corrected.

The invention belongs to the technical field of concrete crack detection, and particularly discloses and provides a high-strength concrete construction crack detection system and method.The system comprises a multi-parameter cooperative sensing module, a construction crack recognition module, a crack risk analysis module and a crack early warning processing module. According to the invention, the temperature, humidity, strain and multispectral image data from a plasticity stage to a hardening stage are collected in real time through the multi-parameter cooperative sensing module, and crack characteristics are extracted by combining staged differential signal processing and a deep convolutional neural network, so that the detection precision of micro cracks and internal defects is remarkably improved. Meanwhile, multi-physics field data are fused, a crack evolution risk rate model is innovatively constructed, dynamic probability prediction before crack initiation is achieved, a grading disposal scheme is automatically triggered through risk grade division and a progressive early warning mechanism, drying shrinkage and temperature crack causes are effectively distinguished, the prevention and control efficiency is remarkably improved, and the method is suitable for large-scale popularization and application. And full-life-cycle accurate monitoring of the high-strength concrete is supported.

The invention relates to a concrete temperature-stress two-parameter cooperative monitoring and early warning method and system, and belongs to the technical field of concrete structure health monitoring, and the method comprises the steps: activating a composite sensor network disposed at a preset position of a concrete structure, and executing a sensor self-calibration mode; the method comprises the following steps: synchronously acquiring temperature and stress original monitoring data of each node, processing based on an altitude air pressure compensation algorithm, and outputting a temperature gradient field matrix and a stress tensor sequence with aligned time domains; the method comprises the following steps: separately calculating a thermal stress component and an effective stress component, performing integral calculation on hydration reactivity, generating a multi-dimensional feature vector set, inputting a pre-constructed prediction model, executing time sequence evolution prediction, calculating a crack probability value, performing environment correction in combination with real-time environment parameters, outputting a risk level identifier, and matching a preset regulation and control strategy. And generating an equipment control instruction set and executing corresponding regulation and control actions. According to the invention, the accuracy and timeliness of crack risk prediction can be improved.

The invention relates to a concrete chloride ion gradient monitoring and durability enhancing method and system based on MXene, and solves the problems that a traditional drilling sampling method is high in operation process destructiveness, high in cost and low in detection efficiency, test results are remarkably influenced by environmental conditions, and field real-time performance and universal applicability are lacked. The method comprises the following steps: embedding a plurality of prefabricated sensing units containing MXene-Cl composite electrode layers and toughening protection layers into concrete at different depths according to a vertical gradient to form a monitoring network; potential signals are collected through a sensing unit, after correction, the potential signals are introduced into a Nernst equation for inversion to obtain the chloride ion concentration, and osmotic distribution imaging is achieved by combining a preset vertical and transverse gradient estimation method. The intercalation adsorption characteristic of MXene on chloride ions is utilized to capture and retard migration of the chloride ions to the reinforcing steel bar. The method has the advantages that chloride ions are monitored in real time, migration of the chloride ions to the steel bars is blocked, the concrete degradation state is evaluated in combination with multi-parameter data, and early warning and durability improvement are achieved.

A method for extracting a wear particle feature signal based on segmentation entropy is provided, including obtaining a raw signal to be processed by performing real-time data acquisition using a lubricating oil wear particle monitoring system; obtaining a preprocessed signal by performing low-pass filtering and harmonic interference suppression on the raw signal to be processed; dividing the preprocessed signal into a plurality of time domain sequence segments with a sliding window; calculating segmentation entropy corresponding to each time domain sequence segment, normalizing a segmentation entropy set to obtain normalized segmentation entropy; obtaining an adaptive threshold through curve fitting based on empirical cumulative distribution of normalized segmentation entropy, obtaining a plurality of non-zero discrete time domain signal segments by segmenting the preprocessed signal by the adaptive threshold; and obtaining final extraction results of the wear particle feature signal by excluding residual noise interference through target signal feature recognition indices.

The invention discloses a rubber product quality detection method and system, and relates to the related technical field of rubber quality detection.The method comprises the steps that rubber products are conveyed to a target detection table, and data acquisition of the rubber products is executed; inputting the wave impedance distribution data and the surface heat distribution gradient field into a defect causal atlas channel to execute phase inversion fusion processing, establishing a cross-modal coupling phase tensor, and identifying deep defect response characteristics by solving a phase difference matrix; activating a pressure spectrum sensing unit to apply control pressure, reading a local mechanical response spectrum, sending the local mechanical response spectrum to a cross-modal calibration layer, and executing joint error authentication; and performing quality detection report according to a self-calibration defect mapping result. The technical problems that in the prior art, multi-modal sensing data fusion efficiency is low, deep defects are difficult to comprehensively recognize, and consequently the quality detection precision and reliability of rubber products are insufficient are solved, and the technical effects that cross-modal cooperative detection is achieved, and the precision and reliability of defect recognition results are improved are achieved.

Provided herein are methods and compositions for determining and reporting carbon dioxide sequestered and / or carbon dioxide avoided in operations involving concrete, including concrete raw material transport, concrete production, and concrete use.

The invention belongs to the technical field of roadbed engineering, and particularly relates to a construction uniformity evaluation method for a clay improved copper tailing roadbed. The method comprises the following specific steps: S1, determining the proportion of the clay improved copper tailing roadbed filler; s2, applying the blended clay improved copper tailings to roadbed construction; and S3, carrying out construction detection on the clay-improved copper tailing roadbed, calculating a mixing uniformity coefficient, and comparing the mixing uniformity coefficient with a uniformity coefficient in a laboratory to evaluate the construction uniformity of the clay-improved copper tailing roadbed. According to the method, after roadbed construction is completed, a working project of measuring the compactness through a field sand filling method is combined, the mixing uniformity coefficient is calculated with the deviation between the composition proportion of roadbed filler sampled through the sand filling method and a proportion design result as a parameter, and the construction quality can be effectively evaluated conveniently.

The invention relates to the field of concrete detection, and discloses a concrete structure health real-time monitoring system based on multi-source sensing fusion, and the system comprises a data collection unit which carries out the collection of the physical field data of a concrete structure through a sensor array, and obtains the multi-source sensing data; performing structure response difference analysis on the multi-source sensing data to obtain concrete structure response difference data; according to the response difference data of the concrete structure, performing spatial and temporal distribution density analysis on a microcrack propagation path to obtain microcrack propagation spatial and temporal distribution density data; a data evaluation unit; multi-scale damage evolution path simulation is carried out through the structural damage time-space correlation characteristic data to obtain multi-scale damage evolution path data, the sensor array is used for collecting multi-source sensing data, structural response difference analysis is carried out, tiny changes of a concrete structure can be accurately captured, potential structural problems can be found in time, and the construction efficiency is improved. And a high-quality data basis is provided for subsequent analysis.

The invention relates to the field of construction temperature regulation and control, in particular to an intelligent temperature control method and device for fan foundation construction in an alpine region. The method comprises the following steps: collecting real-time concrete temperature monitoring parameters based on a distributed temperature sensor, carrying out time sequence temperature discrete trend analysis and spatial temperature fluctuation distribution evolution, and constructing a temperature fluctuation distribution evolution diagram; concrete surface environment parameters are extracted, heat exchange simulation processing is carried out on the temperature fluctuation distribution evolution diagram, multivariate coupling correlation analysis is carried out, and a temperature-environment quantitative relation model is constructed; and obtaining historical meteorological logs and meteorological environment data of the construction area, carrying out similar meteorological condition matching calculation, carrying out short-term meteorological trend prediction, and generating short-term meteorological trend prediction features. According to the dynamic temperature regulation and control requirements, safe construction of the fan foundation concrete in the high and cold environment is guaranteed, and the construction quality is improved.

The invention belongs to the technical field of hydroelectric generating set monitoring, and relates to a water turbine bearing temperature and lubricating oil state monitoring and fault diagnosis method and system. According to the method, triple-redundancy temperature measurement values of a water turbine bearing are adopted, multiple parameters are fused, redundancy design is adopted, sensor failure diagnosis is executed, a temperature-vibration-oil pressure combined feature space is constructed according to the triple-redundancy temperature measurement values and online oil quality parameters, a fault propagation map is established, and the fault propagation map is analyzed. And finally, based on the multi-level diagnosis model and in combination with the fault propagation atlas, composite fault mode recognition is realized, a fault classification result is obtained, and the composite fault mode recognition is realized based on the multi-level diagnosis model, so that the problems of low reliability, single monitoring dimension and insufficient fault recognition capability of a sensor in the prior art are effectively solved. And the equipment reliability and the operation safety are obviously improved.

The invention discloses a method for predicting the damage of a prestressed concrete cylinder pipe in a complex environment, and relates to the technical field of pipeline structure health monitoring, and the method comprises the steps: collecting multi-physical field data through deploying multiple types of sensors, transmitting the data to a central monitoring system in a wireless manner, carrying out the standardized preprocessing, and obtaining a multi-source fusion data set; a salt migration driving index dsa, a stress disturbance induction index yry and an electrochemical activity potential index che are constructed, a comprehensive corrosion risk index SCR is obtained, and corrosion behavior evaluation is carried out. And when the corrosion behavior is evaluated as a disturbance sign, a corrosion change prediction index CAR is further calculated according to time sequence evolution forming a corrosion risk index SCR, grading evaluation and transition identification of the corrosion state are realized, an evaluation result is stored in a database, and a corrosion risk evolution trend is presented through a visual chart. The method supports early warning and emergency response decision making, and has high adaptability and engineering practicability without adding hardware.

The invention relates to the technical field of concrete curing, in particular to a concrete crack risk prediction and curing system based on thermo-acoustic coupling indexes. According to the scheme, a temperature field and an acoustic emission signal are synchronously acquired through the multi-modal data acquisition module; a thermal diffusion shear index and an acoustic emission energy density are calculated by a multi-modal data fusion module, and are fused into a unified thermo-acoustic coupling factor to quantify a cross-scale risk; the structural constraint compensation module corrects the factor by using pre-stored constraint information to generate a final risk index; the self-adaptive maintenance decision-making module maps a dynamic maintenance strategy according to the indexes; and finally, the maintenance robot executes precise maintenance operation. The technical problems that macroscopic thermal stress and microcosmic damage signals cannot be evaluated in a unified mode, different-source data are difficult to fuse, and high-constraint area risk identification is inaccurate are solved, and continuous prediction and self-adaptive cooperative control of cracks from a potential stage to an initiation stage are achieved. And the accuracy and the intelligent level of early-age crack prevention and control of the concrete are remarkably improved.

The purpose of the present invention is to provide a technique for optically quantifying a compound of which a color does not change due to oxidative degradation.　Another aspect of the present invention provides a method for diagnosing oil characteristics, the method being characterized by: using an optical sensor having a light source and a detector that detects light emitted from the light source to acquire an absorption spectrum in a wavelength range of 800 nm to 3000 nm of an oil containing an additive having a maximum molar absorption coefficient value of 50 or less in the ultraviolet visible wavelength range of 250 nm to 800 nm; and inputting data based on the absorption spectrum into an estimation model implemented in an information processing device to predict the concentration of the additive.

The invention relates to the technical field of lubricant analysis, in particular to a gearbox lubricating oil product state intelligent recognition system and method, and the system comprises a lubricating oil state parameter collection module, a multi-dimensional feature fitting degree classification module, a spectrum pollutant dynamic detection module, a state change trend storage module and a lubricating oil abnormal deviation early warning module. According to the method, the lubricating oil state features are accurately extracted by constructing a multi-dimensional feature matrix and high-dimensional space mapping, the fitting degree weight and the data scale are combined to adjust, optimize and classify, the state recognition precision is improved, the pollution change is recognized through trend analysis in dynamic spectrum pollutant detection, and the monitoring sensitivity is improved. A data storage strategy driven by parameter change ensures the integrity of high-value data and reduces invalid storage, an offset calculation method is combined with data trend comparison to improve the recognition accuracy of an abnormal state, and misinformation and missing report are reduced.

The invention relates to a concrete pouring monitoring system and method. The system comprises an acquisition unit, an analysis unit and an execution unit, the analysis unit can predict a concrete quality range based on initial data information acquired by the acquisition unit, and sends corresponding pouring construction parameters as a construction strategy to the execution unit when the predicted concrete quality meets a preset standard, when the execution unit executes the pouring operation, the analysis unit can calculate the corresponding evaluation indexes according to the partial monitoring data acquired by the acquisition unit for the pouring operation in different stages of the current pouring link, so as to realize the real-time prediction of the concrete quality. The method comprises the following steps: collecting initial data information; predicting a concrete quality range to determine a pouring strategy to be executed; and when the pouring operation is executed, corresponding evaluation indexes are calculated according to different stages in the current pouring link, so that the real-time prediction of the concrete quality is realized.

The invention discloses an intelligent road surface recognition method based on force perception and image analysis. The method comprises the following steps: S1, acquiring image data and contact force data in the advancing process of a mobile robot; s2, inputting the image into a two-channel feature network, and extracting structural features and fuzzy region information; s3, processing through an unwrapping model to form an image semantic vector; s4, constructing a contact time sequence, extracting behavior characteristics, and generating a contact event code; s5, performing alignment and weighted fusion on the image semantic vector and the contact code, and outputting a combined pavement state vector; s6, inputting a trafficability scoring model, identifying a ground type and generating a trafficability score; and S7, transmitting the result to a navigation module for path planning and motion control. Visual and force sense information is combined, and the complex terrain recognition accuracy and the robot traffic capacity are improved.

The invention discloses a pavement quality evaluation method and system fused with pavement data, and relates to the technical field of pavement quality evaluation. The invention discloses a pavement quality evaluation system fused with pavement data. The pavement quality evaluation system comprises a pavement data acquisition module and a pavement quality evaluation module, by integrating the historical pavement disease data, the historical construction data, the environmental parameters, the traffic load data and the bump data and the noise data which are acquired when the vehicle passes, all-directional dynamic perception of the pavement state is realized, the limitation of single data source and lack of historical evolution modeling and dynamic response association in the prior art is overcome, and the reliability of the system is improved. The comprehensiveness and precision of pavement quality evaluation are effectively improved; by establishing the performance attenuation prediction model and combining the regional dynamic state variable and the performance label data, the residual life and the performance glide speed of each pavement region can be quantitatively evaluated, and the scientificity of maintenance decision is improved.

The invention provides a constructional engineering concrete compressive strength detection method and system.The method comprises the steps that multi-modal information of a to-be-detected concrete member for constructional engineering is collected, and the multi-modal information comprises rebound data, ultrasonic data, resistivity data and temperature data; constructing a hybrid prediction model; and outputting compressive strength data according to the mixed prediction model, and visually displaying the compressive strength data. According to the method and the system for detecting the compressive strength of the constructional engineering concrete, disclosed by the invention, multi-modal information such as ultrasonic, rebound, resistivity and temperature of the to-be-detected concrete member is input into the mixed prediction model for compressive strength prediction, and the model can be used for more accurately processing nonlinear and high-dimensional characteristics in data; a transfer learning mechanism is introduced, so that the model adapts to changes of different regions and materials under limited training data, and the generalization ability and accuracy of prediction are improved. And the compressive strength data is visually displayed, so that the analysis efficiency of engineers is improved.

The invention relates to the technical field of recycled asphalt mixture recovery, in particular to a mixing uniformity monitoring method adaptive to a recycled asphalt mixture, which comprises the following steps: S101, performing time-space fusion on infrared thermal radiation field dynamic capture and microwave dielectric property synchronous monitoring bimodal sensing to jointly perform mixing defect alarm; s102, based on the dynamic sequence of the infrared thermal radiation field, judging the particle motion consistency through the standard deviation of the motion direction angle, integrating the infrared temperature field and microwave dielectric data, constructing a three-dimensional feature vector, comprehensively evaluating the uniformity through a weighting function, judging defects according to the maximum contribution term, and generating an optimization instruction; and S103, setting a grading standard based on the multi-modal features, and feeding back and adjusting the process according to a grading result. According to the invention, infrared thermal radiation and microwave dielectric characteristics are combined, temperature uniformity and material component distribution are evaluated synchronously, and the problem of monitoring that the thermal uniformity of the recycled asphalt is not equal to the component uniformity is solved.

Apparatus for real time water content and water cement monitoring on a ready-mix concrete truck includes at least one real time moisture measurement module or sensor configured tosense continuous real time moisture measurements of a batch of wet cement loaded into a rotating drum of a ready-mix concrete truck; andprovide signaling containing information about the continuous real time moisture measurements sensed.The apparatus may include either a hatch door of the rotating drum of the ready-mix concrete truck, or the rotating drum of the ready-mix concrete truck, or the ready-mix concrete truck itself.

The invention relates to the technical field of concrete defect identification, and discloses a concrete defect state identification and passive detection combined control method under a super-tonnage block type spherical hinge, which is used for solving the problem that defect prediction cannot be realized during concrete defect identification, and comprises the following steps: setting sampling points in a concrete structure in a layered manner, acquiring strain monitoring data of each sampling point, judging whether the sampling points have defects or not, if so, performing grouting repair intervention treatment, if not, acquiring risk parameters of concrete defects, and evaluating according to the risk parameters to obtain defect risk indexes; whether the sampling points have the risk of concrete defects or not is judged, if the sampling points have the risk of concrete defects, early warning and vibration equipment parameter adjustment are carried out on the sampling points according to the defect risk indexes, the structure quality safety and the construction efficiency are effectively improved, and the engineering field intelligent management and control degree is improved.

The invention discloses a novel concrete pavement hardness detection device, and belongs to the technical field of detection. The invention discloses a novel concrete pavement hardness detection device, and belongs to the technical field of road engineering detection. The device comprises a springback tester, an ultrasonic tester and an analysis system, and equipment credibility parameters are generated through a springback confidence model (quantitative impact energy / angular deviation) and an ultrasonic confidence model (quantitative frequency / probe centering error); combining a pavement characteristic model (analyzing water content / temperature / steel bar density) to output an environment adaptation coefficient; further dynamically calculating a springback risk coefficient and an ultrasonic risk coefficient based on a risk model (fused vibration / carbonization depth); and finally, outputting a calibration intensity value through the intensity correction model. The method solves the defects of weak anti-interference capability and insufficient precision of a traditional method, effectively improves the detection efficiency, and is suitable for high-precision scenes such as expressways and airport runways.

The invention relates to the technical field of quality detection of building materials, in particular to a quality detection method and system for mirror-surface bare concrete. The quality detection method for the mirror-surface bare concrete comprises the following steps: S100, starting three-dimensional detection; s200, carrying out spatial dimension multi-index cooperative detection; s300, time dimension dynamic attenuation analysis is carried out; s400, carrying out environmental adaptability calibration; s500, carrying out digital dimension topological modeling; and S600, carrying out comprehensive quality evaluation. The invention further discloses a system for detecting the quality of the mirror-surface bare concrete. The system comprises a three-dimensional holographic detection module; a dynamic tracking and environment calibration module; a digital modeling engine; a comprehensive evaluation platform; and a data interface module. According to the method, through four-dimensional fusion of space-time-number-environment, multi-dimensional features such as flatness, glossiness, color uniformity and surface and near-surface bubbles are synchronously captured, and quality misjudgment caused by index omission is avoided.

The invention relates to the field of concrete crack monitoring, in particular to a solid waste aggregate concrete crack real-time monitoring method and system, and the method comprises the steps: collecting sensor data in real time; calculating a deviation value between the current data and the initial reference data; when the deviation value exceeds a threshold value, the deviation value is used for compensating the subsequently collected sensor data; updating the initial reference data by using the mean value of the compensated data in the previous period; processing the compensated data in different channels, extracting strain accumulation trend characteristics in a long-term channel through a large time window, and extracting acoustic emission instantaneous characteristics in a short-term channel through a small time window; setting a long-term feature weight to be higher than a short-term feature weight, calculating a long-term feature change rate, and when the change rate is stable, improving the long-term weight and reducing the short-term weight, and filtering features lower than a noise threshold in a short-term channel; and fusing the weighted long-term features with the effective short-term features, and inputting the fused features into a pre-training model to obtain a fracture state prediction result in a future predetermined period.

The invention discloses a high-precision ink path bubble detection and rapid circulation compensation method and a related device. The method comprises the following steps: inputting visual sensor data, ultrasonic data and pressure fluctuation data into a trained ink path bubble semantic segmentation network model for segmentation; calculating the equivalent diameter, the bubble volume, the bubble density, the bubble flow velocity and the category of the bubbles according to the binary mask sequence; inputting the equivalent diameter, the bubble volume, the bubble density, the bubble flow velocity and the category of the bubbles into the trained multi-agent collaborative decision network model, and outputting dynamic cyclic compensation and prevention actions; and according to the dynamic circulation compensation and prevention action, the ink path pumping pressure, the valve opening degree and the ink temperature are adjusted in real time, and a high-frequency micro-pulse exhausting, reverse suction or vibration bubble removing strategy is triggered. Through high-precision detection and intelligent compensation, bubbles are effectively removed, nozzle blockage and cleaning frequency are reduced, and therefore the downtime of equipment is shortened, and waste of ink and consumables is reduced.

The invention discloses a thermal shock failure prediction and laser repair device for a thermal barrier coating and an application method. The device comprises a multi-physics field coupling simulation analysis module, a thermal shock experiment loading module, an online damage monitoring and failure diagnosis module, a robot laser repair execution module and a central control and data processing core unit. A thermal-mechanical-chemical coupling model is established to predict a coating failure behavior, a high-frequency induction heating and gas quenching system is adopted to simulate an extreme thermal shock environment, a multi-band thermal infrared imager, an ultrasonic probe and a digital image related system are integrated to monitor damage evolution in real time, and in-situ repair of a damaged area is realized based on a six-degree-of-freedom industrial robot. According to the method, the whole-process closed-loop control from failure prediction to repair regeneration is realized, the problems of single function and lack of multi-field coupling analysis and real-time repair capability in the prior art are solved, and the service reliability evaluation precision and the remanufacturing efficiency of the thermal barrier coating are remarkably improved.

The invention discloses a building solid waste road performance index and multi-path utilization scene intelligent evaluation method. The method comprises the steps that the mass content of red bricks, the mass content of needle-sheet-shaped particles and the particle gradation in building solid waste particles are obtained through a machine vision system; the pavement performance indexes, namely CBR value, water absorption rate and crushing value, of the building solid waste are obtained through tests; according to the influence rules of the mass content of the red bricks, the mass content of the needle-sheet-shaped particles and the grain composition in the building solid waste particles on the building solid waste road performance indexes, an estimation model of the building solid waste road performance indexes is established. According to the method, the mass content of the red bricks, the mass content of the needle-sheet-shaped particles and the grain composition in the building solid waste particles are obtained in real time on the basis of machine vision, the road performance indexes of the building solid waste are predicted through the prediction model of the road performance indexes of the building solid waste, and the detection efficiency and accuracy are improved.