Rock-filled roadbed compaction quality evaluation method, system and equipment and readable storage medium

Abstract

The invention discloses a rock-filled roadbed compaction quality evaluation method, system and equipment and a readable storage medium. The rock-filled roadbed compaction quality evaluation method comprises the following steps: constructing an interaction model of a road roller and filler; determining an arrangement mode of a sensor grid; calculating a vibration signal of each sensor under a preset compaction degree by using an interaction model; establishing a compaction degree prediction relational expression between the vibration signal and the compaction degree; and substituting the actually detected vibration signal into the compaction degree prediction relational expression, and judging whether the compaction degree is qualified or not according to a calculation result. The vibration transmission rule is quantified through the constructed interaction model, and the vibration response difference of different positions of the roadbed can be captured by the gridding arrangement sensor, so that the subsequent calculation result is more accurate; and the compaction degree of the roadbed at the corresponding position can be calculated by directly utilizing the vibration signals obtained by the sensors in actual detection through the compaction degree prediction relational expression.

Description

Technical Field

[0001] This application relates to the fields of geotechnical engineering and highway engineering, and particularly relates to a method, system, device and readable storage medium for evaluating the compaction quality of a rock-filled subgrade. Background Art

[0002] Due to the large hardness and particle size of the rock fill from mountain excavation, it is very difficult to use the traditional method of excavating test pits to test the filling quality of the subgrade. Summary of the Invention

[0003] This application provides a method, system, device and readable storage medium for evaluating the compaction quality of a rock-filled subgrade, which can solve the technical problem in the prior art that it is very difficult to use the traditional method of excavating test pits to test the filling quality of the subgrade due to the large hardness and particle size of the rock fill from mountain excavation.

[0004] In a first aspect, an embodiment of this application provides a method for evaluating the compaction quality of a rock-filled subgrade. The method for evaluating the compaction quality of a rock-filled subgrade includes: constructing an interaction model between a roller and the fill; determining the layout method of a sensor grid; calculating the vibration signals of each sensor at a preset compaction degree by using the interaction model; establishing a compaction degree prediction relationship between the vibration signals and the compaction degree; and bringing the actually detected vibration signals into the compaction degree prediction relationship to judge whether the compaction degree is qualified according to the calculation results.

[0005] In combination with the first aspect, in an implementation manner, the determining the layout method of the sensor grid includes: quantifying the spatial distribution law of the vibration signals at a preset compaction degree according to the interaction model; and determining the layout positions of the sensors by using the quantified spatial distribution law.

[0006] In combination with the first aspect, in an implementation manner, the determining the layout positions of the sensors by using the quantified spatial distribution law includes: determining the layout positions of the sensors and the layout method of the sensor grid according to the spatial distribution law and sensitivity analysis.

[0007] In combination with the first aspect, in an implementation manner, after calculating the vibration signals of each sensor at a preset compaction degree by using the interaction model, it includes: extracting the vibration signal features of the vibration signals and constructing a graph convolutional neural network model by using the vibration signal features.

[0008] In combination with the first aspect, in an implementation manner, the establishing the compaction degree prediction relationship between the vibration signals and the compaction degree includes: establishing the compaction degree prediction relationship between the vibration signals and the compaction degree by using the topological relationship, the graph convolutional neural network model and the vibration signal features.

[0009] In combination with the first aspect, in one embodiment, the vibration signal features include time-domain features, frequency-domain features, and spatial features.

[0010] In combination with the first aspect, in one embodiment, after determining whether the degree of compaction is qualified according to the calculation result, the following steps are included: if so, stop rolling; otherwise, control the roller to repeat rolling.

[0011] In a second aspect, an embodiment of the present application provides a system for evaluating the compaction quality of a rock-filled subgrade. The system for evaluating the compaction quality of a rock-filled subgrade includes: a model construction module for constructing an interaction model between a roller and a filler; a sensor optimal layout module for determining the layout method of a sensor grid; a calculation module for calculating the vibration signals of each sensor under a preset degree of compaction by using the interaction model; a signal preprocessing module for establishing a compaction degree prediction relationship between the vibration signal and the compaction degree; and a data processing module for substituting the actually detected vibration signal into the compaction degree prediction relationship and determining whether the compaction degree is qualified according to the calculation result.

[0012] In a third aspect, an embodiment of the present application provides a device for evaluating the compaction quality of a rock-filled subgrade. The device for evaluating the compaction quality of a rock-filled subgrade includes a processor, a memory, and a program for evaluating the compaction quality of a rock-filled subgrade stored on the memory and executable by the processor. When the program for evaluating the compaction quality of a rock-filled subgrade is executed by the processor, the steps of the method for evaluating the compaction quality of a rock-filled subgrade as described above are implemented.

[0013] In a fourth aspect, an embodiment of the present application provides a readable storage medium with a program for evaluating the compaction quality of a rock-filled subgrade stored thereon. When the program for evaluating the compaction quality of a rock-filled subgrade is executed by a processor, the steps of the method for evaluating the compaction quality of a rock-filled subgrade as described above are implemented.

[0014] The beneficial effects brought by the technical solution provided by the embodiment of the present application include:

[0015] By constructing the interaction model, the vibration transmission law is quantified, and the grid layout of sensors can capture the vibration response differences at different positions of the subgrade, making the subsequent calculation results more accurate. In addition, through the compaction degree prediction relationship, the compaction degree of the subgrade at the corresponding position can be directly calculated by using the vibration signals obtained by each sensor in actual detection, solving the technical problem that it is very difficult to use the traditional method of excavating test pits to test the filling quality of the subgrade in the related art. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a schematic flowchart of an embodiment of the method for evaluating the compaction quality of a rock-filled subgrade of the present application;

[0017] Figure 2 It is a schematic flow chart of another embodiment of the method for evaluating the compaction quality of the rock-filled subgrade in this application;

[0018] Figure 3 It is a schematic diagram of the hardware structure of the equipment for evaluating the compaction quality of the rock-filled subgrade involved in the embodiment solution of this application. Detailed implementation manners

[0019] In order to enable those skilled in the art to better understand the solution of this application, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all the embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of this application.

[0020] To make the purpose, technical solution and advantages of this application clearer, the following will further describe the embodiments of this application in detail in conjunction with the accompanying drawings.

[0021] In a first aspect, an embodiment of this application provides a method for evaluating the compaction quality of a rock-filled subgrade.

[0022] In one embodiment, with reference to Figure 1 , Figure 1 It is a schematic flow chart of the first embodiment of the method for evaluating the compaction quality of the rock-filled subgrade in this application. As Figure 1 shown, the method for evaluating the compaction quality of the rock-filled subgrade includes:

[0023] S1: Construct an interaction model between the roller and the filler. The interaction model can be constructed by using a combined simulation of discrete element - multibody dynamics (EDEM - ADAMS).

[0024] S2: Determine the arrangement method of the sensor grid. The sensor grid can include multiple sensors, and the multiple sensors are arranged in a grid pattern. Each sensor can be used to obtain the vibration signal of the subgrade during actual operation. In addition, the sensor can be an acceleration sensor.

[0025] S3: Calculate the vibration signals of each sensor under a preset compaction degree by using the interaction model. It should be understood that the preset compaction degree can be taken within the distribution range of the subgrade compaction degree in the related art. Preferably, the subgrade can select rock fillers with large hardness and large particles.

[0026] S4: Establish a compaction degree prediction relationship between the vibration signal and the compaction degree.

[0027] S5: Substitute the actually detected vibration signal into the compaction degree prediction relationship, and judge whether the compaction degree is qualified according to the calculation result.

[0028] In this embodiment, the vibration transmission law is quantified by constructing an interaction model, and the grid arrangement of sensors can capture the vibration response differences at different locations of the roadbed, preferably avoiding accidental errors caused by less detection data, and can more comprehensively reflect the overall compaction state of the roadbed, that is, can make subsequent calculation results more accurate; in addition, the compaction degree prediction relationship can directly use the vibration signal obtained by each sensor in the actual detection to calculate the compaction degree of the roadbed at the corresponding position. Even in a roadbed with stone fillers with large hardness and particles, the filling quality of each location of the roadbed can be tested, which solves the technical problem in the related art that the traditional method of excavating test pits to test the filling quality of the roadbed is very difficult.

[0029] join Figure 2 As shown, further, in one embodiment, determining the arrangement of the sensor grid may include:

[0030] S21: Quantify the spatial distribution of vibration signals at a preset compaction degree based on the interaction model. The spatial distribution of vibration signals can refer to the spatial attenuation of vibration signals. Specifically, the propagation of vibration waves in the filler at different compaction degrees is calculated using the roller-fill interaction model. The attenuation characteristics of the vibration acceleration or velocity signals at the subgrade depth, in the lateral direction, and in the longitudinal direction are analyzed to determine the effective detection range.

[0031] S22: Use the quantified spatial distribution pattern to determine the sensor placement. This identifies the area with the strongest correlation between vibration signal amplitude and compaction. This allows the determination of the frequency bands that require key monitoring, providing guidance for subsequent monitoring. Sensor placement can also be located in the area with the strongest correlation.

[0032] In this embodiment, the sensors are arranged with guidance of the interaction model, so that the sensors can be arranged more accurately. Compared with blind arrangement, this method can ensure detection reliability while reducing the number of sensors. It solves the problem in related technologies of excessive number of sensors, increased burden on detection device arrangement and waste of resources.

[0033] Preferably, determining sensor placement using a quantified spatial distribution pattern can include determining sensor placement based on spatial distribution patterns and sensitivity analysis, and determining a sensor grid layout. Specifically, the vibration signal distribution calculated using the interaction model can be used to determine sensor placement through sensitivity analysis. Sensitivity analysis can improve detection accuracy, reduce errors, and further reduce the number of deployed sensors.

[0034] Further, in one embodiment, after calculating the vibration signals of each sensor under the preset compaction degree by using the interaction model, it may include: extracting the vibration signal characteristics of the vibration signals from the vibration signals, and constructing a graph convolutional neural network model by using the vibration signal characteristics. By mining the spatial correlation between sensors through the graph convolutional neural network model, the error can be reduced, and the technical problem of large error in the related art is solved.

[0035] Further, in one embodiment, the establishment of the compaction degree prediction relationship between the vibration signal and the compaction degree may include: establishing the compaction degree prediction relationship between the vibration signal and the compaction degree by using the topological relationship, the graph convolutional neural network model and the vibration signal characteristics. The compaction degree prediction relationship can be recorded as: n = GCN({a i}, W) + λ·L spatial .

[0036] Where GCN is the graph convolutional neural network model, W is the sensor node adjacency matrix, L spatial is the spatial smoothing constraint term, λ is the weight coefficient, and a i are the vibration signal characteristics of each sensor node.

[0037] In this embodiment, by adopting the cooperation of topological relationship modeling, GCN feature extraction and spatial smoothing constraint, the robustness of the model is enhanced, and the GCN model still maintains a prediction error ≤ 3% under the condition of 30% missing nodes.

[0038] Further, in one embodiment, the vibration signal characteristics include time domain characteristics, frequency domain characteristics and spatial characteristics. It should be understood that the time domain characteristics (i.e., kurtosis) can reflect the vibration energy and impact characteristics, the frequency domain characteristics (1 / 3 octave energy) can be related to the filler stiffness and compaction state, and the spatial characteristics can be the collaborative analysis of multiple sensors. In the embodiment of the present application, by extracting various vibration information characteristics in the vibration signals, the evaluation of the compaction quality of the rock-filled subgrade is upgraded from single-point analysis to spatial collaborative perception, significantly improving the intelligent level.

[0039] Further, in one embodiment, after judging whether the compaction degree is qualified according to the calculation result, it includes: if so, stop rolling; otherwise, control the roller to repeat rolling. That is, after judging that the compaction degree of a region is qualified, the roller can continue to compact the next region, but if it is judged that the compaction degree of a region is unqualified, the operator needs to operate the roller to repeat rolling the unqualified region until it is qualified. After the roller completes each number of rolling passes, the cloud platform generates a compaction degree distribution cloud map and projects a three-dimensional under-compacted area positioning (accuracy ±0.3 m) to the operator through the AR glasses.

[0040] Preferably, the sensor grid can adopt the composite fixing technology of magnetic anchoring plus self-expanding airbag, and the laying of a 20m×6m area can be completed within 10 minutes; in addition, by adopting the sensor grid, the single detection coverage area is ≥100㎡, which is more than a hundred times higher than the traditional method; furthermore, the proposed detection method can be operated during the conventional rolling operation of the vibratory roller without specific vibration excitation equipment, and the vibration sensors are prefabricated in a mesh shape, which is convenient for on-site layout.

[0041] In a second aspect, an embodiment of the present application further provides a compaction quality evaluation system for a rock-filled subgrade. The compaction quality evaluation system for a rock-filled subgrade may include: a model construction module for constructing an interaction model between a roller and a filler; a sensor optimization layout module for determining the layout method of the sensor grid; a calculation module for calculating the vibration signals of each sensor at a preset compaction degree by using the interaction model; a signal preprocessing module for establishing a compaction degree prediction relationship between the vibration signal and the compaction degree; and a data processing module for substituting the actually detected vibration signal into the compaction degree prediction relationship and judging whether the compaction degree is qualified according to the calculation result.

[0042] In a third aspect, an embodiment of the present application provides a compaction quality evaluation device for a rock-filled subgrade. The compaction quality evaluation device for a rock-filled subgrade may be a device with data processing functions such as a personal computer (PC), a laptop computer, a server, etc.

[0043] Refer to Figure 3 , Figure 3 which is a schematic hardware structure diagram of the compaction quality evaluation device for a rock-filled subgrade involved in the solution of the embodiment of the present application. In the embodiment of the present application, the compaction quality evaluation device for a rock-filled subgrade may include a processor, a memory, a communication interface, and a communication bus.

[0044] Among them, the communication bus can be of any type and is used to interconnect the processor, the memory, and the communication interface.

[0045] The communication interface includes input / output (I / O) interfaces, physical interfaces, and logical interfaces, etc., which are used to implement the interconnection of internal devices of the compaction quality evaluation device for a rock-filled subgrade, and interfaces for implementing the interconnection between the compaction quality evaluation device for a rock-filled subgrade and other devices (such as other computing devices or user devices). The physical interface can be an Ethernet interface, an optical fiber interface, an ATM interface, etc.; the user device can be a display screen (Display), a keyboard (Keyboard), etc.

[0046] The memory can be various types of storage media, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), flash memory, optical memory, hard disk, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), etc.

[0047] The processor can be a general-purpose processor, which can call the AAAA program stored in the memory and execute the rock-filled subgrade compaction quality evaluation method provided by the embodiments of the present application. For example, the general-purpose processor can be a central processing unit (CPU). Among them, the method executed when the rock-filled subgrade compaction quality evaluation program is called can refer to the various embodiments of the rock-filled subgrade compaction quality evaluation method of the present application, which will not be elaborated here.

[0048] Those skilled in the art can understand that Figure 3 the hardware structure shown in does not constitute a limitation to the present application, and may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements.

[0049] In a fourth aspect, the embodiments of the present application further provide a readable storage medium.

[0050] The rock-filled subgrade compaction quality evaluation program is stored on the readable storage medium of the present application. When the rock-filled subgrade compaction quality evaluation program is executed by a processor, the steps of the rock-filled subgrade compaction quality evaluation method as described above are implemented.

[0051] Among them, the method implemented when the rock-filled subgrade compaction quality evaluation program is executed can refer to the various embodiments of the rock-filled subgrade compaction quality evaluation method of the present application, which will not be elaborated here.

[0052] It should be noted that the serial numbers of the above embodiments of the present application are only for description and do not represent the superiority or inferiority of the embodiments.

[0053] In the description of the specification, claims and the above-mentioned drawings of the present application, the terms "comprising", "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally further includes steps or units not listed, or optionally further includes other steps or units inherent to these processes, methods, products or devices. Descriptions such as "first", "second" and "third" are used to distinguish different objects, etc., and do not represent a sequence, nor do they limit that "first", "second" and "third" are different types.

[0054] In the description of the embodiments of the present application, words such as "exemplary", "for example" or "for instance" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "exemplary", "for example" or "for instance" in the embodiments of the present application should not be construed as being more preferred or having more advantages than other embodiments or designs. Rather, the use of words such as "exemplary", "for example" or "for instance" is intended to present relevant concepts in a specific manner.

[0055] In the description of the embodiments of the present application, unless otherwise specified, " / " means "or". For example, A / B may represent A or B; "and / or" in the text is merely a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, in the description of the embodiments of the present application, "a plurality of" means two or more than two.

[0056] In some processes described in the embodiments of the present application, a plurality of operations or steps appear in a specific order. However, it should be understood that these operations or steps may not be executed in the order in which they appear in the embodiments of the present application or may be executed in parallel. The serial numbers of the operations are only used to distinguish different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations or steps may be executed in sequence or in parallel, and these operations or steps may be combined.

[0057] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium as described above (such as ROM / RAM, magnetic disk, optical disk), and includes several instructions to enable a terminal device to execute the methods described in the various embodiments of the present application.

[0058] The above are only the preferred embodiments of the present application, and do not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made by using the content of the specification and drawings of the present application, or directly or indirectly applied to other related technical fields, shall be equally included in the patent protection scope of the present application.

Claims

1. A method for evaluating the compaction quality of a rock-filled subgrade, characterized in that The method for evaluating the compaction quality of a rock-filled subgrade includes: Constructing an interaction model between the roller and the filler; Determining the layout method of the sensor grid; Calculating the vibration signals of each sensor at a preset compaction degree by using the interaction model; Establishing a compaction degree prediction relationship between the vibration signal and the compaction degree; Substituting the actually detected vibration signal into the compaction degree prediction relationship and judging whether the compaction degree is qualified according to the calculation result.

2. The method for evaluating the compaction quality of a rock-filled subgrade according to claim 1, wherein The determination of the layout method of the sensor grid includes: Quantifying the spatial distribution law of the vibration signal at a preset compaction degree based on the interaction model; Determining the layout positions of the sensors by using the quantified spatial distribution law.

3. The method for evaluating the compaction quality of a rock-filled subgrade according to claim 2, characterized in that, The determination of the layout positions of the sensors by using the quantified spatial distribution law includes: Determining the layout positions of the sensors and the layout method of the sensor grid according to the spatial distribution law and sensitivity analysis.

4. The method for evaluating the compaction quality of a rock-filled subgrade according to claim 1, wherein, After calculating the vibration signals of each sensor at a preset compaction degree by using the interaction model, it includes: Extracting the vibration signal characteristics of the vibration signal from the vibration signal and constructing a graph convolutional neural network model by using the vibration signal characteristics.

5. The method for evaluating the compaction quality of a rock-filled subgrade according to claim 4, wherein The establishment of the compaction degree prediction relationship between the vibration signal and the compaction degree includes: Establishing a compaction degree prediction relationship between the vibration signal and the compaction degree by using the topological relationship, the graph convolutional neural network model and the vibration signal characteristics.

6. The method for evaluating the compaction quality of a rock-filled subgrade according to claim 5, wherein: The vibration signal characteristics include time domain characteristics, frequency domain characteristics and spatial characteristics.

7. The method for evaluating the compaction quality of a rock-filled subgrade according to claim 1, wherein After judging whether the compaction degree is qualified according to the calculation result, it includes: If so, stop rolling; Otherwise, control the roller to repeat rolling.

8. A compaction quality evaluation system for a rock-filled subgrade, characterized in that The compaction quality evaluation system of the rock-filled subgrade includes: A model construction module for constructing an interaction model between the roller and the filler; A sensor optimal layout module for determining the layout method of the sensor grid; A calculation module for calculating the vibration signals of each sensor at a preset compaction degree by using the interaction model; A signal preprocessing module for establishing a compaction degree prediction relationship between the vibration signal and the compaction degree; A data processing module for substituting the actually detected vibration signal into the compaction degree prediction relationship and judging whether the compaction degree is qualified according to the calculation result.

9. An evaluation device for the compaction quality of a rock-filled subgrade, characterized in that, The compaction quality evaluation device of the rock-filled subgrade includes a processor, a memory, and a compaction quality evaluation program of the rock-filled subgrade stored on the memory and executable by the processor. When the compaction quality evaluation program of the rock-filled subgrade is executed by the processor, the steps of the method for evaluating the compaction quality of the rock-filled subgrade according to any one of claims 1 to 7 are realized.

10. A readable storage medium, characterized in that, A compaction quality evaluation program of the rock-filled subgrade is stored on the readable storage medium. When the compaction quality evaluation program of the rock-filled subgrade is executed by the processor, the steps of the method for evaluating the compaction quality of the rock-filled subgrade according to any one of claims 1 to 7 are realized.

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