Method, system and equipment for detecting compaction quality of glutenite roadbed and storage medium
The nonlinear mapping relationship was established through finite element simulation and on-site calibration tests, and the compaction degree of the conglomerate roadbed was determined by using multi-source information sensors, which solved the problem that traditional methods were difficult to detect the filling quality of the conglomerate roadbed, and achieved rapid and accurate compaction degree judgment.
Patent Information
- Application Number
- CN202510387414.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-03-31
- Publication Date
- 2025-07-18
AI Technical Summary
When using conglomerate as the roadbed filling material in the prior art, it is difficult to effectively detect the filling quality through traditional sand filling and water filling methods.
Through finite element simulation and on-site calibration test, a nonlinear mapping relationship between compaction degree and multi-source information is established, the roadbed data is obtained using the multi-source information sensor, and the nonlinear mapping relationship is used to determine whether the compaction degree reaches the preset value, and the undervoltage area is marked.
It quickly and accurately identify whether the roadbed compaction degree is qualified, improves construction efficiency and quality control level, and solves the problem that traditional methods are difficult to detect the quality of the sand and conglomerate roadbed filling.
Smart Images
Figure CN120334523A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the fields of geotechnical engineering and highway engineering, and particularly relates to a method, system, device and storage medium for detecting the compaction quality of a sandy conglomerate subgrade. Background Art
[0002] With the development of China's highway system, the construction of mountain highways has been increasing continuously. After mountain excavation in areas such as Guangdong, a large amount of sandy conglomerate materials are generated. In order to reduce transportation and disposal costs, this material is usually used as subgrade filling material. When using sandy conglomerate as subgrade filling material, since the filler particle size is usually large, it is difficult to use traditional sand filling and water filling methods to check its filling quality in actual engineering. Summary of the Invention
[0003] The present application provides a method, system, device and storage medium for detecting the compaction quality of a sandy conglomerate subgrade, which can solve the technical problem in the prior art that when using sandy conglomerate as subgrade filling material, since the filler particle size is usually large, it is difficult to use traditional sand filling and water filling methods to check its filling quality in actual engineering.
[0004] In a first aspect, an embodiment of the present application provides a method for detecting the compaction quality of a sandy conglomerate subgrade. The method for detecting the compaction quality of a sandy conglomerate subgrade includes: establishing a non-linear mapping relationship between the compaction degree and actual multi-source information through finite element simulation and on-site calibration test; judging whether the subgrade compaction degree reaches a preset value based on the non-linear mapping relationship; if so, not marking, otherwise, marking the under-compacted area.
[0005] In combination with the first aspect, in an implementation manner, the establishing a non-linear mapping relationship between the compaction degree and actual multi-source information through finite element simulation and on-site calibration test includes: performing finite element simulation analysis based on the multi-source information of the subgrade to establish a first model between the compaction degree and the multi-source information of the subgrade; obtaining the calibrated multi-source information under the on-site working conditions of the roller; substituting the calibrated multi-source information into the first model to obtain a second model; calculating the compaction degree under different working conditions, and substituting the calculation results into a deep neural network to fuse the time series signal training to obtain the non-linear mapping relationship between the compaction degree and the actual multi-source information.
[0006] In combination with the first aspect, in an implementation manner, before substituting the calibrated multi-source information into the first model, it includes: placing the initial detection device equipped with multi-source information sensors at a preset position of the subgrade; obtaining the calibrated multi-source information when the roller moves to the measurement point along the length direction of the subgrade.
[0007] In combination with the first aspect, in an implementation manner, before judging whether the subgrade compaction degree reaches a preset value based on the non-linear mapping relationship, it includes: measuring the distance between the initial detection device and the roller when the roller moves to the measurement point, and recording it as the first distance.
[0008] In combination with the first aspect, in one embodiment, determining whether the degree of compaction of the roadbed reaches a preset value based on the non-linear mapping relationship includes: driving the roller to move and keeping a first distance between the roller and the initial detection device; obtaining the actual multi-source information of each position of the roadbed, substituting the actual multi-source information into the non-linear mapping relationship and calculating; and determining whether the degree of compaction of the roadbed reaches the preset value according to the calculation result.
[0009] In combination with the first aspect, in one embodiment, the initial detection device is equipped with a radar sensor.
[0010] In combination with the first aspect, in one embodiment, after marking the under-compacted area, it includes: controlling the roller to re-compact the under-compacted area.
[0011] In the second aspect, an embodiment of the present application provides a system for detecting the compaction quality of a sandy conglomerate roadbed. The system for detecting the compaction quality of a sandy conglomerate roadbed includes: a calculation module, which is used to establish a non-linear mapping relationship between the degree of compaction and the actual multi-source information through finite element simulation and on-site calibration tests; a judgment module, which is used to judge whether the degree of compaction of the roadbed reaches a preset value based on the non-linear mapping relationship; if so, do not mark, otherwise, mark the under-compacted area.
[0012] In the third aspect, an embodiment of the present application provides a device for detecting the compaction quality of a sandy conglomerate roadbed. The device for detecting the compaction quality of a sandy conglomerate roadbed includes a processor, a memory, and a sandy conglomerate roadbed compaction quality detection program stored on the memory and executable by the processor. When the sandy conglomerate roadbed compaction quality detection program is executed by the processor, the steps of the sandy conglomerate roadbed compaction quality detection method as described above are implemented.
[0013] In the fourth aspect, an embodiment of the present application provides a storage medium, which is characterized in that a sandy conglomerate roadbed compaction quality detection program is stored on the storage medium. When the sandy conglomerate roadbed compaction quality detection program is executed by a processor, the steps of the sandy conglomerate roadbed compaction quality detection method as described above are implemented.
[0014] The beneficial effects brought by the technical solutions provided by the embodiments of the present application include:
[0015] By establishing a non-linear mapping relationship between the degree of compaction and the actual multi-source information through finite element simulation and on-site calibration tests, the actual multi-source information obtained during the actual operation of the roller can be substituted into the non-linear mapping relationship, and it can be judged whether the degree of compaction of the roadbed reaches the preset value according to the calculation result, so that it can quickly identify whether the degree of compaction of the roadbed is qualified, and solves the technical problem that it is difficult to use traditional sand filling and water filling methods to check the filling quality in actual projects when using sandy conglomerate as the roadbed filling material. Description of the Drawings
[0016] Figure 1 This is a schematic flow chart of the first embodiment of the gravel-sandstone roadbed compaction quality detection method of the present application;
[0017] Figure 2 This is a schematic flow chart of the second embodiment of the gravel-sandstone roadbed compaction quality detection method of the present application;
[0018] Figure 3 This is a schematic flow chart of the third embodiment of the gravel-sandstone roadbed compaction quality detection method of the present application;
[0019] Figure 4 This is a schematic diagram of the hardware structure of the gravel-sandstone roadbed compaction quality detection equipment involved in the embodiment solution of the present application. Detailed implementation manners
[0020] In order to enable those skilled in the art to better understand the solution of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.
[0021] First, some technical terms in the present application are explained to facilitate the understanding of the present application by those skilled in the art.
[0022] In a first aspect, an embodiment of the present application provides a gravel-sandstone roadbed compaction quality detection method.
[0023] In one embodiment, with reference to Figure 1 , Figure 1 This is a schematic flow chart of the first embodiment of the gravel-sandstone roadbed compaction quality detection method of the present application. As Figure 1 shown, the gravel-sandstone roadbed compaction quality detection method includes:
[0024] S1: Establish a non-linear mapping relationship between the compaction degree and actual multi-source information through finite element simulation and on-site calibration tests. The actual multi-source information may include information such as the vibration wave velocity, vibration acceleration, heat distribution of the roadbed, and the vibration of sound waves in the air.
[0025] S2: Determine whether the roadbed compaction degree reaches a preset value based on the non-linear mapping relationship. That is, determine whether the compaction degree of the roadbed meets the requirements.
[0026] S3: If so, do not mark; otherwise, mark the under-compacted area. Not being marked can indicate that the compaction degree meets the requirements and the roadbed is compacted; the under-compacted area is the area where the compaction degree does not meet the requirements.
[0027] In this embodiment, the embodiment of the present application establishes a non - linear mapping relationship between the degree of compaction and actual multi - source information through finite - element simulation and on - site calibration tests. The actual multi - source information obtained when the roller is actually working can be substituted into the non - linear mapping relationship, and whether the subgrade compaction degree reaches the preset value can be judged through the calculation result, so that whether the compaction degree of the subgrade is qualified can be quickly identified, solving the technical problem in the related art that when using conglomerate as the subgrade filling material, it is difficult to use the traditional sand - filling and water - filling methods to check its filling quality in actual projects.
[0028] See Figure 2 As shown, further, in one embodiment, the establishment of the non - linear mapping relationship between the degree of compaction and actual multi - source information through finite - element simulation and on - site calibration tests may include:
[0029] S11: Conduct finite - element simulation analysis based on the multi - source information of the subgrade to establish a first model of the degree of compaction and the multi - source information of the subgrade. It should be understood that the multi - source information of the subgrade may also include information such as the vibration wave velocity, vibration acceleration, heat distribution, and air - borne sound vibration of the subgrade. The vibration wave velocity, vibration acceleration, heat distribution, and air - borne sound vibration in the multi - source information of the subgrade can be common data or theoretical data selected from the related art. Specifically, the vibration wave velocity can be the Rayleigh wave velocity, the vibration acceleration can be the triaxial vibration acceleration, the heat distribution is the surface temperature gradient, and the air - borne sound vibration uses the environmental noise compensation term. Through finite - element simulation analysis, a finite - element simulation analysis model, that is, the first model, can be established.
[0030] S12: Obtain the calibrated multi - source information under the on - site working conditions of the roller. In the embodiment of the present application, the calibrated multi - source information refers to the information such as the vibration wave velocity, vibration acceleration, heat distribution, and air - borne sound vibration of the subgrade on the side of the roller obtained during the on - site calibration experiment. The vibration wave velocity can be obtained by a laser Doppler vibrometer, the vibration acceleration by an accelerometer, the heat distribution by an infrared thermal imager, and the air - borne sound vibration by a microphone array.
[0031] S13: Substitute the calibrated multi - source information into the first model to obtain a second model. That is, the data in the first model is calibrated by the obtained calibrated multi - source information to obtain the second model.
[0032] S14: Calculate the degree of compaction under different working conditions, and substitute the calculation results into the training of the deep - neural - network - fused time - series signal to obtain the non - linear mapping relationship between the degree of compaction and the actual multi - source information. The non - linear mapping relationship between the degree of compaction and the actual multi - source information can be denoted as: k = f(v, a, θ, γ)+ε
[0033] where k represents the degree of compaction, v is the Rayleigh wave velocity (m / s), a is the triaxial vibration acceleration (g), θ is the surface temperature gradient (°C / m), γ is the main frequency offset of the acoustic wave spectrum (Hz), and is the environmental noise compensation term.
[0034] In this embodiment, by establishing the first model, a theoretical basis is provided for subsequent calibration and calculation. The second model obtained through calibration can be closer to the actual working conditions of the subgrade, improving the applicability of the model in practical applications. After the neural network training is completed, the degree of compaction can be predicted quickly and accurately according to the new actual multi-source information, thus improving the construction efficiency and quality control level, and solving the problem of low construction efficiency in the related art.
[0035] Further, in one embodiment, before bringing the calibrated multi-source information into the first model, it may include: placing the initial detection device equipped with multi-source information sensors at a preset position on the subgrade; obtaining the calibrated multi-source information when the roller moves to the measurement point along the length direction of the subgrade. That is, in the embodiments of the present application, the multi-source information sensors are installed on the initial detection device, and the calibrated multi-source information of the subgrade around the roller is obtained by using the multi-source information sensors on the initial detection device. In the embodiments of the present application, the triaxial vibration acceleration, Rayleigh wave velocity, surface temperature gradient, and the main frequency offset of the γ acoustic wave spectrum can be obtained by a vibration accelerometer, a laser Doppler vibrometer, an infrared thermal imager, and a microphone array respectively. Preferably, the distance between the press and the initial detection device when it moves to the measurement point is less than or equal to two meters to enhance data accuracy.
[0036] Specifically, before determining whether the degree of compaction of the subgrade reaches the preset value based on the non-linear mapping relationship, it includes: measuring the distance between the initial detection device and the roller when the roller moves to the measurement point, and recording it as the first distance.
[0037] In this embodiment, the calibrated multi-source information of the subgrade around the roller is obtained through the initial detection device. Compared with directly installing each sensor on the roller, the influence of the roller on data accuracy can be reduced. Specifically, the engine, hydraulic system, and impact of the steel wheel on the ground of the roller itself will generate high-frequency mechanical vibrations (usually >500 Hz) and electromagnetic interference. If the sensors are directly installed on the roller body, these noises will seriously contaminate the target signals (such as Rayleigh waves and acoustic waves reflected by the subgrade), resulting in a decrease in the signal-to-noise ratio (SNR). When the roller runs without load, the vibration acceleration of the body can reach 5-8 g, while the effective vibration signal feedback from the subgrade is only 0.1-0.5 g. Therefore, in the embodiments of the present application, the calibrated multi-source information of the subgrade around the roller is obtained through the initial detection device, solving the problem that installing sensors on the roller itself in the related art will have a greater impact on data accuracy.
[0038] See Figure 3As shown, further, in one embodiment, determining whether the roadbed compaction degree reaches a preset value based on the non-linear mapping relationship may include:
[0039] S21: Drive the roller to move and keep a first distance between the roller and the initial detection device. That is, when actual multi-source information needs to be obtained during actual work, it is necessary to keep the distance between the initial detection device and the roller the same as that between the initial detection device and the roller when calibrating multi-source information.
[0040] S22: Obtain the actual multi-source information of each position of the roadbed and substitute the actual multi-source information into the non-linear mapping relationship for calculation. The initial detection device may have a calculation module for actual multi-source information, which has an edge computing unit and can realize data preprocessing and feature extraction through an embedded AI chip.
[0041] S23: Determine whether the roadbed compaction degree reaches the preset value according to the calculation result.
[0042] In this embodiment, by keeping the first distance between the initial detection device and the roller, it is possible to ensure that the actual multi-source information and the calibrated multi-source information are compared and analyzed under the same measurement conditions, so as to obtain a more accurate compaction degree judgment result. In addition, the calculation module in the initial detection device can preprocess and extract features from the acquired data to improve the quality and usability of the data, solving the problem that it is difficult to use traditional sand filling and water filling methods to check the filling quality in actual engineering in the related art.
[0043] In some alternative embodiments, the initial detection device is equipped with a radar sensor. By installing the radar sensor in the initial detection device, dynamic following between the roller and the initial detection device can be realized. Preferably, the distance between the initial detection device and the roller is less than or equal to 2 m, and the dynamic following in the embodiments of the present application may adopt UWB (Ultra-Wideband) positioning technology.
[0044] In some alternative embodiments, after the marked under-voltage area, it may include: controlling the road roller to re-compact the under-voltage area. In the embodiments of the present application, the calculation module in the initial detection device may be signal-connected to the cloud platform. The cloud platform may receive data through MQTT (Message Queuing Telemetry Transport), and call a pre-trained model to generate a compaction degree heat map; based on BIM (Building Information Modeling), overlay the compaction data and automatically mark the under-voltage area. The cloud platform may also be signal-connected to the main control module in the road roller. After the marked under-voltage area in the cloud platform is input into the main control module in the road roller, it may be displayed on the display screen in the road roller to remind the operator. The operator continues to compact the uncompacted area according to the display result. The cloud platform may also push a navigation path to the road roller cockpit.
[0045] In a second aspect, the embodiments of the present application further provide a gravel-sandstone roadbed compaction quality detection system, characterized in that the gravel-sandstone roadbed compaction quality detection system may include:
[0046] A calculation module, which is used to establish a non-linear mapping relationship between the compaction degree and actual multi-source information through finite element simulation and on-site calibration tests.
[0047] A judgment module, which is used to judge whether the roadbed compaction degree reaches a preset value based on the non-linear mapping relationship.
[0048] If so, do not mark; otherwise, mark the under-voltage area.
[0049] In a third aspect, the embodiments of the present application provide a gravel-sandstone roadbed compaction quality detection device. The gravel-sandstone roadbed compaction quality detection device may be a device with data processing functions such as a personal computer (PC), a laptop computer, a server, etc.
[0050] Refer to Figure 4 , Figure 4 which is a schematic diagram of the hardware structure of the gravel-sandstone roadbed compaction quality detection device involved in the solution of the embodiments of the present application. In the embodiments of the present application, the gravel-sandstone roadbed compaction quality detection device may include a processor, a memory, a communication interface, and a communication bus.
[0051] Among them, the communication bus may be of any type and is used to interconnect the processor, the memory, and the communication interface.
[0052] The communication interface includes interfaces such as input / output (I / O) interfaces, physical interfaces, and logical interfaces for implementing the interconnection of components inside the compaction quality detection device for sandy conglomerate subgrade, as well as interfaces for implementing the interconnection between the compaction quality detection device for sandy conglomerate 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, a keyboard, etc.
[0053] 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.
[0054] The processor can be a general-purpose processor, which can call the AAAA program stored in the memory and execute the AAAA method provided in 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 compaction quality detection program for sandy conglomerate subgrade is called can refer to the various embodiments of the compaction quality detection method for sandy conglomerate subgrade in the present application, which will not be elaborated here.
[0055] Those skilled in the art can understand that Figure 4 the hardware structure shown in
[0056] does not constitute a limitation to the present application, and may include more or fewer components than shown in the figure, or combine some components, or have different component arrangements.
[0057] A storage medium is further provided in the embodiments of the present application.
[0058] The compaction quality detection program for sandy conglomerate subgrade is stored on the storage medium of the present application. When the compaction quality detection program for sandy conglomerate subgrade is executed by a processor, the steps of the compaction quality detection method for sandy conglomerate subgrade as described above are implemented.
[0059] It should be noted that the serial numbers of the embodiments of the present application above are only for description and do not represent the superiority or inferiority of the embodiments.
[0060] The terms "including" and "having" and any variations thereof in the specification, claims and above-mentioned drawings of the present application 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. The descriptions of terms 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 of different types.
[0061] In the description of the embodiments of the present application, "exemplary", "for example" or "for instance" etc. are used to indicate examples, illustrations or explanations. Any embodiment or design solution 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 design solutions. Rather, the use of words such as "exemplary", "for example" or "for instance" is intended to present relevant concepts in a specific manner.
[0062] In the description of the embodiments of the present application, unless otherwise specified, " / " means "or". For example, A / B can mean A or B; "and / or" in the text is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can mean: 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.
[0063] 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 order or in parallel, and these operations or steps may be combined.
[0064] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-described 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. 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 (such as ROM / RAM, magnetic disk, optical disk) as described above and includes several instructions for causing a terminal device to execute the methods described in various embodiments of the present application.
[0065] 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 in other related technical fields, shall be equally included in the patent protection scope of the present application.
Claims
1. A method for detecting the compaction quality of a sandy conglomerate subgrade, characterized in that, The method for detecting the compaction quality of the sandy conglomerate subgrade includes: Establishing a non-linear mapping relationship between the compaction degree and actual multi-source information through finite element simulation and on-site calibration tests; Judging whether the subgrade compaction degree reaches a preset value based on the non-linear mapping relationship; If so, no marking is performed; otherwise, the under-compacted area is marked.
2. The method for detecting the compaction quality of a sandy conglomerate roadbed according to claim 1, characterized in that The establishment of the non-linear mapping relationship between the compaction degree and actual multi-source information through finite element simulation and on-site calibration tests includes: Conducting finite element simulation analysis based on the multi-source information of the subgrade to establish a first model between the compaction degree and the multi-source information of the subgrade; Obtaining the calibrated multi-source information under the on-site working conditions of the roller; Substituting the calibrated multi-source information into the first model to obtain a second model; Calculating the compaction degree under different working conditions and bringing the calculation results into the depth neural network for fusing time series signals for training to obtain the non-linear mapping relationship between the compaction degree and actual multi-source information.
3. The method for detecting the compaction quality of a sandy conglomerate subgrade according to claim 2, characterized in that Before substituting the calibrated multi-source information into the first model, it includes: Placing the initial detection device equipped with multi-source information sensors at a preset position on the subgrade; Obtaining the calibrated multi-source information when the roller moves to the measurement point along the length direction of the subgrade.
4. The method for detecting the compaction quality of a sandy conglomerate roadbed according to claim 3, characterized in that, Before judging whether the subgrade compaction degree reaches the preset value based on the non-linear mapping relationship, it includes: Measuring the distance between the initial detection device and the roller when the roller moves to the measurement point, denoted as the first distance.
5. The method for detecting the compaction quality of a sandy conglomerate roadbed according to claim 4, characterized in that, The judgment of whether the subgrade compaction degree reaches the preset value based on the non-linear mapping relationship includes: Driving the roller to move and keeping the distance between the roller and the initial detection device at the first distance; Obtaining the actual multi-source information of each position of the subgrade and substituting the actual multi-source information into the non-linear mapping relationship for calculation; Judging whether the subgrade compaction degree reaches the preset value according to the calculation result.
6. The method for detecting the compaction quality of a sandy conglomerate roadbed according to claim 5, wherein : The initial detection device is equipped with a radar sensor.
7. The method for detecting the compaction quality of a sandy conglomerate roadbed according to claim 1, characterized in that, After marking the under-compacted area, it includes: Controlling the roller to recompress the under-compacted area.
8. A compaction quality detection system for a sandy conglomerate subgrade, characterized in that, The sandy conglomerate subgrade compaction quality detection system includes: A calculation module, which is used to establish a non-linear mapping relationship between the compaction degree and actual multi-source information through finite element simulation and on-site calibration tests; A judgment module, which is used to judge whether the subgrade compaction degree reaches a preset value based on the non-linear mapping relationship; If so, no marking is performed; otherwise, the under-compacted area is marked.
9. A compaction quality detection device for a sandy conglomerate roadbed, characterized in that, The sandy conglomerate subgrade compaction quality detection device includes a processor, a memory, and a sandy conglomerate subgrade compaction quality detection program stored on the memory and executable by the processor. When the sandy conglomerate subgrade compaction quality detection program is executed by the processor, the steps of the sandy conglomerate subgrade compaction quality detection method described in any one of claims 1 to 7 are implemented.
10. A storage medium, characterized in that, The sandy conglomerate subgrade compaction quality detection program is stored on a storage medium. When the sandy conglomerate subgrade compaction quality detection program is executed by a processor, the steps of the sandy conglomerate subgrade compaction quality detection method described in any one of claims 1 to 7 are implemented.