Soil compressibility judgment method and device, electronic equipment and medium

Abstract

The invention provides a soil compressibility judgment method and device, electronic equipment and a medium, and the method comprises the steps: obtaining type parameters and physical parameters of a plurality of soil samples, and constructing a data set of the soil samples; taking the physical parameters of the same type of soil body samples in the sample set as input, taking the compression modulus and / or the compression coefficient as output, and constructing a compressibility discrimination model of each type of soil body samples; obtaining type parameters and physical parameters of a to-be-tested soil body sample, inputting the physical parameters of the to-be-tested soil body sample into the compressibility discrimination model of the type of the to-be-tested soil body sample, and outputting the compression modulus and / or the compression coefficient of the to-be-tested soil body sample; and respectively comparing the compression modulus and / or the compression coefficient of the to-be-tested soil body sample with a preset compression modulus threshold and / or a compression coefficient threshold so as to determine the compressibility of the to-be-tested soil body sample. According to the method, the stratum compressibility can be automatically and efficiently judged, and the construction period of railway engineering is shortened.

Description

Technical Field

[0001] The present invention relates to the technical field of soil identification, and in particular to a method, device, electronic equipment and medium for determining soil compressibility. Background Art

[0002] The “millimeter-level” deformation control of high-speed railway foundation is an important guarantee for the safe operation of high-speed railway. In the early stage of high-speed railway construction, in order to control the post-construction settlement of high-speed railway foundation to no more than 15mm and the post-construction settlement of transition section to no more than 5mm, almost all soil foundations needed to be treated with pile foundations. However, the treatment cost of deep soil foundations was too high, which greatly increased the cost of engineering construction. After a lot of practical experience and long-term monitoring data, it was found that the compression coefficient a v Medium and low compressibility soil between 0.1 and 0.3 can converge quickly under load, and this medium and low compressibility soil can be directly used as the natural foundation of the roadbed or the bearing layer of the pile foundation. Therefore, directly using medium and low compressibility soil as the high-speed railway foundation can significantly reduce the cost of the high-speed railway foundation project.

[0003] Conventional soil compressibility identification requires tedious and complex on-site sampling and indoor testing, which is labor-intensive and time-consuming. During the long-distance linear engineering survey of railways, the use of a comprehensive survey method combining drilling and in-situ testing will generate massive test data, which makes it difficult for geological survey personnel to organize and analyze the data. Therefore, there is an urgent need for a method that can quickly, intelligently and accurately identify medium and low compressible soils to shorten the pre-construction survey period and quickly provide accurate geological data for railway engineering design and construction. Summary of the invention

[0004] The present invention provides a soil compressibility determination method, device, electronic equipment and medium, which can quickly and accurately identify the compressibility of soil and quickly provide accurate geological data for railway engineering design and construction.

[0005] According to one aspect of the present invention, a method for determining soil compressibility is provided, comprising:

[0006] Acquire type parameters and physical parameters of multiple soil samples to construct a data set of the soil samples; the type parameters represent the type of the soil samples, and the physical parameters represent the parameters of the physical properties of the soil samples;

[0007] Taking the physical parameters of the soil samples of the same type in the sample set as input and the compression modulus and / or compression coefficient as output, a compressibility discrimination model for each type of soil sample is constructed;

[0008] Acquire type parameters and physical parameters of the soil sample to be tested, input the physical parameters of the soil sample to be tested into the compressibility discrimination model of the type to which the soil sample to be tested belongs, and output the compression modulus and / or compression coefficient of the soil sample to be tested;

[0009] The compression modulus and / or compression coefficient of the soil sample to be tested are compared with a preset compression modulus threshold and / or compression coefficient threshold respectively to determine the compressibility of the soil sample to be tested.

[0010] Optionally, after obtaining the type parameters and physical parameters of soil samples of various roadbeds, the method further includes:

[0011] The correlation between the physical parameters of each soil sample is determined, and soil samples with a correlation greater than a preset threshold are selected.

[0012] Optionally, determining the correlation between the physical parameters of each soil sample includes:

[0013] A linear fitting or nonlinear function fitting method is used to determine the correlation function between the physical parameters in each type of soil sample, and the correlation between the physical parameters of the soil sample is determined by bringing the physical parameters of a soil sample into the correlation function.

[0014] Optionally, after obtaining the type parameters and physical parameters of soil samples of various roadbeds, the method further includes:

[0015] Abnormal data in the data set is removed.

[0016] Optionally, the compressibility discrimination model includes an empirical formula relating the compression modulus to the physical parameter, and / or an empirical formula relating the compression coefficient to the physical parameter, and / or a neural network prediction model taking the physical parameter as input and the compression modulus as output, and / or a neural network prediction model taking the physical parameter as input and the compression coefficient as output.

[0017] Optionally, the method of taking the physical parameters of the soil samples of the same type in the sample set as input and taking the compression modulus and / or compression coefficient as output to construct a compressibility discrimination model for each type of soil sample includes:

[0018] The first empirical formula of compression modulus and standard penetration number or specific penetration resistance is constructed by taking the standard penetration number or specific penetration resistance of multiple soil samples of the same type as input and the compression modulus as output;

[0019] A neural network prediction model is constructed by taking multiple physical parameters of multiple soil samples of the same type as input and the compression coefficient as output.

[0020] Optionally, the type parameters include at least the test point of the soil sample and the lithology of the soil layer;

[0021] The physical parameters include at least soil layer depth, standard penetration number, specific penetration resistance, lateral friction resistance, lateral pressure modulus, basic bearing capacity, and ultimate bearing capacity.

[0022] According to another aspect of the present invention, there is provided a soil compressibility determination device, comprising:

[0023] A data acquisition unit, used to acquire type parameters and physical parameters of various soil samples to construct a data set of the soil samples; the type parameters represent the type of the soil samples, and the physical parameters represent the parameters of the physical properties of the soil samples;

[0024] A model building unit, used to build a compressibility discrimination model for each type of soil sample by taking the physical parameters of the soil samples of the same type in the sample set as input and taking the compression modulus and / or compression coefficient as output;

[0025] A prediction unit, used for obtaining type parameters and physical parameters of the soil sample to be tested, inputting the physical parameters of the soil sample to be tested into the compressibility discrimination model of the type to which the soil sample to be tested belongs, and outputting the compression modulus and / or compression coefficient of the soil sample to be tested;

[0026] The compressibility judgment unit is used to compare the compression modulus and / or compression coefficient of the soil sample to be tested with a preset compression modulus threshold and / or compression coefficient threshold respectively to determine the compressibility of the soil sample to be tested.

[0027] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0028] at least one processor; and

[0029] a memory communicatively connected to the at least one processor; wherein,

[0030] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the soil compressibility determination method described in any embodiment of the present invention.

[0031] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the soil compressibility determination method described in any embodiment of the present invention when executed.

[0032] The technical solution of the embodiment of the present invention constructs a data set of soil samples by acquiring type parameters and physical parameters of multiple soil samples; uses the physical parameters of soil samples of the same type as input and compression modulus and / or compression coefficient as output to construct a compressibility discrimination model for each type of soil sample; and uses the constructed compressibility discrimination model to determine the compression modulus and / or compression coefficient of the soil sample to be tested, and discriminates the compressibility of the soil sample to be tested based on the obtained compression modulus and / or compression coefficient, thereby being able to automatically and efficiently discriminate the compressibility of the stratum, thereby shortening the railway project construction period.

[0033] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0035] Figure 1 is a flow chart of a soil compressibility determination method provided according to Embodiment 1 of the present invention;

[0036] Figure 2 This is a flow chart of a soil compressibility determination method provided according to Embodiment 2 of the present invention;

[0037] Figure 3 This is an architecture diagram of a compressibility discrimination model constructed by a neural network in Embodiment 2 of the present invention;

[0038] Figure 4 A structural diagram of a soil compressibility device provided in Embodiment 3 of the present invention;

[0039] Figure 5 It is a schematic diagram of the structure of an electronic device for implementing the soil compressibility method of an embodiment of the present invention. DETAILED DESCRIPTION

[0040] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0041] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0042] Embodiment 1

[0043] Figure 1 A flow chart of a method for determining soil compressibility is provided for the first embodiment of the present invention. Figure 1 As shown, the method includes:

[0044] S101, acquiring type parameters and physical parameters of a plurality of soil samples to construct a data set of the soil samples; the type parameters characterize the type of the soil samples, and the physical parameters characterize the parameters of the physical properties of the soil samples.

[0045] The soil samples can be soil samples from various sections of the planned railway roadbed or various places on non-planned railway sections. In-situ tests such as standard penetration tests, static penetration tests, spiral plate load tests, and flat plate load tests can be used to obtain various parameters of soil samples. Soil samples can also use the type parameters and physical parameters of soil samples collected during historical soil exploration to avoid the need to consume a lot of time and test work to obtain soil sample data.

[0046] In addition, the soil samples may include multiple ones, for example, thousands or more soil samples may be obtained, and these soil samples include high compressibility soil, medium-low compressibility soil, medium-high compressibility soil and low compressibility soil.

[0047] The type parameter of the soil sample includes the type of soil, for example, the soil can be clay type, sand type, etc. The physical parameters of the soil sample are parameters that characterize the physical properties of the soil sample, such as the specific penetration resistance, lateral friction resistance, lateral pressure modulus, basic bearing capacity, ultimate bearing capacity, etc. of the soil.

[0048] S102, using the physical parameters of the soil samples of the same type in the sample set as input and compression modulus and / or compression coefficient as output, to construct a compressibility discrimination model for each type of soil sample.

[0049] The same type of soil samples can be taken as a similar sample set, for example, clay type soil samples can be taken as a clay sample set. The physical parameters of a soil sample in the same type of soil samples are taken as the input of the compressibility discrimination model, and the compression modulus or compression coefficient measured for the soil sample is output. The above steps are repeated to tune the parameters of the compressibility discrimination model, and the compressibility discrimination model of each type of soil sample can be constructed. The compressibility discrimination model can include a linear formula, a quadratic formula, a multi-time formula, or a neural network prediction model. Different soil types correspond to different compressibility discrimination models.

[0050] The physical parameters input into the compressibility discrimination model may include one or more physical parameters, that is, only one physical parameter and compression modulus / compression coefficient relationship model may be established, or multiple physical parameters and compression modulus / compression coefficient relationship models may be established.

[0051] S103, obtaining type parameters and physical parameters of the soil sample to be tested, inputting the physical parameters of the soil sample to be tested into the compressibility discrimination model of the type to which the soil sample to be tested belongs, and outputting the compression modulus and / or compression coefficient of the soil sample to be tested.

[0052] The type parameters and physical parameters of the soil sample to be tested are obtained by performing in-situ tests such as standard penetration test, static penetration test, spiral plate load test, flat plate load test, etc. on the soil sample to be tested. The category of the soil sample to be tested is determined according to the type parameters, and the physical parameters of the soil sample to be tested are input into the compressibility discrimination model to which the soil sample to be tested belongs, so as to obtain the compression modulus and / or compression coefficient of the soil sample to be tested.

[0053] S104: Compare the compression modulus and / or compression coefficient of the soil sample to be tested with a preset compression modulus threshold and / or compression coefficient threshold, respectively, to determine the compressibility of the soil sample to be tested.

[0054] For different compressible soil types, the compression modulus threshold and the compression coefficient threshold are different. For example, the compressibility of the soil sample to be tested can be judged only by comparing the compression modulus of the soil sample with the compression modulus threshold: when the compression modulus of the soil sample to be tested is greater than the first compression modulus threshold and is less than or equal to the second compression modulus threshold, the soil sample to be tested can be judged as a high-compressibility soil; when the compression modulus of the soil sample to be tested is greater than the third compression modulus threshold and is less than or equal to the fourth compression modulus threshold, the soil sample to be tested can be judged as a medium-high-compressibility soil; when the compression modulus of the soil sample to be tested is greater than the fifth compression modulus threshold and is less than or equal to the sixth compression modulus threshold, the soil sample to be tested can be judged as a medium-low-compressibility soil. Or the compressibility of the soil sample to be tested can be judged only by comparing the compression coefficient of the soil sample with the compression coefficient threshold: when the compression coefficient of the soil sample to be tested is greater than or equal to the first compression coefficient threshold, the soil sample to be tested can be judged as a highly compressible soil; when the compression coefficient of the soil sample to be tested is less than the first compression coefficient threshold and greater than or equal to the second compression coefficient threshold, the soil sample to be tested can be judged as a highly compressible soil; when the compression coefficient of the soil sample to be tested is less than the third compression coefficient threshold and greater than or equal to the fourth compression coefficient threshold, the soil sample to be tested can be judged as a medium-low compressible soil. The compressibility of the soil sample to be tested can also be judged by combining the comparison results of the compression coefficient of the soil sample with the compression coefficient threshold, and the comparison results of the compression modulus of the soil sample with the compression modulus threshold.

[0055] The technical solution of the first embodiment of the present invention constructs a data set of soil samples by acquiring type parameters and physical parameters of multiple soil samples; uses the physical parameters of soil samples of the same type as input and compression modulus and / or compression coefficient as output to construct a compressibility discrimination model for each type of soil sample; and uses the constructed compressibility discrimination model to determine the compression modulus and / or compression coefficient of the soil sample to be tested, and discriminates the compressibility of the soil sample to be tested based on the obtained compression modulus and / or compression coefficient, thereby being able to automatically and efficiently discriminate the compressibility of the stratum, thereby shortening the railway project construction period.

[0056] Embodiment 2

[0057] Figure 2 A flow chart of a soil compressibility determination method provided in the second embodiment of the present invention is shown in FIG. Figure 2 As shown, the method includes:

[0058] S201, acquiring type parameters and physical parameters of a plurality of soil samples, and constructing a data set of the soil samples; the type parameters characterize the type of the soil samples, and the physical parameters characterize the parameters of the physical properties of the soil samples.

[0059] Among them, the type parameters of the soil sample at least include the test point of the soil sample and the lithology of the soil layer; the physical parameters of the soil sample at least include the soil layer depth, standard penetration number, specific penetration resistance, lateral friction resistance, lateral pressure modulus, basic bearing capacity, and ultimate bearing capacity.

[0060] S202: Determine the correlation between the physical parameters of each soil sample, and select soil samples with correlation greater than a preset threshold.

[0061] In one embodiment, a linear fitting or nonlinear function fitting method may be used to determine the correlation function between the physical parameters in each type of soil sample, and the size of the correlation between the physical parameters of the soil sample may be determined by bringing the physical parameters of a soil sample into the correlation function.

[0062] Since there must be correlation between the physical parameters of the same soil sample, that is, the physical parameters of a large number of soil samples of the same type can be linearly fitted or fitted with other functional relationships to obtain the correlation function between the physical parameters of the soil sample of the same type. Then the physical parameters of a soil sample are brought into the correlation function to compare the difference between the output value of the correlation function and the actual value. In one embodiment, the correlation between the physical parameters of the soil sample can be judged according to the ratio of the difference between the output value of the correlation function and the actual value to the actual value. For example, when the ratio of the difference between the output value of the correlation function and the actual value to the actual value is less than or equal to 5%, it can be judged that the correlation between the physical parameters of the soil sample is strong. If the ratio of the difference between the output value of the correlation function and the actual value to the actual value is greater than 30%, the correlation of the soil sample is poor, and the test method for obtaining the physical parameters of the soil sample may have an operational error, and it is necessary to retest to obtain the physical parameters of the soil sample.

[0063] S203: Eliminate abnormal data in the data set.

[0064] In one embodiment, a box plot, scatter plot or other method can be used to identify data outliers, and sample points that are obviously deviated from other data can be removed. For example, a scatter plot of the same physical parameter of a large number of soil samples of the same type is drawn. If there is a data that is obviously deviated from other data, then the data point is an outlier. The data point may be caused by data errors due to problems such as test methods, and the outlier data needs to be removed.

[0065] S204: Using the physical parameters of the soil samples of the same type in the sample set as input and the compression modulus and / or compression coefficient as output, construct a compressibility discrimination model for each type of soil sample.

[0066] After obtaining the physical parameters of soil samples with strong correlation and eliminating abnormal data, the physical parameters of soil samples of the same type are used as input data of the compressibility discrimination model, and the measured compression modulus and / or compression coefficient of each soil sample is used as output. A relationship model between the physical parameters and the compression modulus and / or compression coefficient can be constructed.

[0067] In one embodiment, the compressibility discrimination model includes an empirical formula relating the compression modulus to the physical parameter, and / or an empirical formula relating the compression coefficient to the physical parameter, and / or a neural network prediction model with the physical parameter as input and the compression modulus as output, and / or a neural network prediction model with the physical parameter as input and the compression coefficient as output.

[0068] The empirical formula may be a linear equation, a quadratic equation or a multi-order equation. When the compressibility discrimination model is a linear equation, the physical parameters of multiple soil samples of the same type and the measured compression modulus and / or compression coefficient may be input into the linear equation for linear fitting to obtain a linear equation relationship between the compression modulus and / or compression coefficient of the type of soil and the physical parameters. When the compressibility discrimination model is a neural network prediction model, the physical parameters of multiple soil samples of the same type may be used as input, and the measured values ​​of the compression modulus and / or compression coefficient of each soil sample may be used as output to train the neural network prediction model, and the trained neural network prediction model is the compressibility discrimination model.

[0069] Specifically, the standard penetration number or specific penetration resistance of multiple soil samples of the same type can be used as input, and the compression modulus can be used as output to construct a first empirical formula of compression modulus and standard penetration number or specific penetration resistance; multiple physical parameters of multiple soil samples of the same type can be used as input, and the compression coefficient can be used as output to construct a neural network prediction model.

[0070] For example, taking silty clay as an example, the SPF number of the soil sample of silty clay can be used as input, and the compression modulus can be used as output to construct the first empirical formula of compression modulus and SPF number. The first empirical formula can be set as a linear equation, that is:

[0071] Es=aN+b

[0072] Where Es is the compression modulus, N is the standard penetration number, and a and b are coefficients to be solved.

[0073] Taking the SPF of the silty clay sample as input and the compression modulus as output, the linear equation of silty clay can be obtained as follows:

[0074] Es=0.5107N+0.6299

[0075] Similarly, the first empirical formula of compression modulus and standard penetration number or specific penetration resistance can be constructed with the standard penetration number or specific penetration resistance of the silt soil sample as input and the compression modulus as output. The first empirical formula is set as a linear equation, that is:

[0076] Es=cPs+d

[0077] Where Ps is the standard penetration number, c and d are the coefficients to be solved.

[0078] Taking the standard penetration number or specific penetration resistance of the silt soil sample as input and the compression modulus as output, the first-order equation of silt soil can be obtained as follows:

[0079] Es=0.2762N+7.2198

[0080] Es=4.0361Ps+0.8422

[0081] For multiple soil samples of the same type, the standard penetration number N, the specific penetration resistance P s , lateral friction resistance f s , lateral pressure modulus E m , basic bearing capacity σ0, ultimate bearing capacity P u As input, the compression factor a v As the output of the neural network prediction model, the neural network prediction model is constructed. Figure 3 shown.

[0082] S205, obtaining type parameters and physical parameters of the soil sample to be tested, inputting the physical parameters of the soil sample to be tested into the compressibility discrimination model of the type to which the soil sample to be tested belongs, and outputting the compression modulus and / or compression coefficient of the soil sample to be tested.

[0083] S206: Compare the compression modulus and / or compression coefficient of the soil sample to be tested with a preset compression modulus threshold and / or compression coefficient threshold, respectively, to determine the compressibility of the soil sample to be tested.

[0084] For different types of soil samples, the compression modulus threshold and compression coefficient threshold are different. In a specific implementation, for example, the compression modulus threshold and compression coefficient threshold for distinguishing the compressibility of clay soil are shown in Table 1 below:

[0085] Table 1 Physical and mechanical parameters of clay soil

[0086] Soil type <![CDATA[Compression coefficient a v / MPa -1 > <![CDATA[Compression modulus Es 0.1-0.2 / MPa]]> High compressibility ≥0.5 1.3～4.7 Medium to high compression 0.3～0.5 3.6～6.2 Medium to low compression 0.1～0.3 7.0～11.6

[0087] After determining that the soil sample is clay, the physical parameters of the soil sample to be tested are input into the compressibility discrimination model of clay to obtain the compression coefficient and compression modulus of the soil sample to be tested. The values ​​of the compression coefficient and compression modulus are compared with the shrinkage modulus threshold and compression coefficient threshold of clay in Table 1 to determine the compressibility of clay.

[0088] The technical solution of the second embodiment of the present invention improves the quality of the database by determining the correlation between the test data and eliminating sample points that obviously deviate from other data, thereby improving the accuracy of compressibility prediction; adopts a massive test experience formula combined with a neural network prediction model to calculate the soil compression modulus and compression coefficient value respectively, thereby achieving accurate judgment of the soil compressibility, thereby being able to automatically and efficiently judge the stratum compressibility and shorten the railway project construction period.

[0089] Embodiment 3

[0090] Figure 4 This is a schematic diagram of the structure of a soil compressibility determination device provided in the third embodiment of the present invention. Figure 4 As shown, the device comprises:

[0091] The data acquisition unit 401 is used to acquire type parameters and physical parameters of various soil samples to construct a data set of the soil samples; the type parameters represent the type of the soil samples, and the physical parameters represent the parameters of the physical properties of the soil samples.

[0092] The model building unit 402 is used to build a compressibility discrimination model for each type of soil sample by taking the physical parameters of the soil samples of the same type in the sample set as input and taking the compression modulus and / or compression coefficient as output.

[0093] The prediction unit 403 is used to obtain the type parameters and physical parameters of the soil sample to be tested, input the physical parameters of the soil sample to be tested into the compressibility discrimination model of the type to which the soil sample to be tested belongs, and output the compression modulus and / or compression coefficient of the soil sample to be tested.

[0094] The compressibility determination unit 404 is used to compare the compression modulus and / or compression coefficient of the soil sample to be tested with a preset compression modulus threshold and / or compression coefficient threshold, respectively, to determine the compressibility of the soil sample to be tested.

[0095] In one embodiment, a correlation determination unit is further included, which is used to determine the correlation between the physical parameters of each soil sample and select soil samples with correlation greater than a preset threshold.

[0096] In one embodiment, the correlation determination unit is also used to determine the correlation function between the physical parameters in each type of soil sample by using a linear fitting method, and to judge the size of the correlation between the physical parameters of the soil sample by bringing the physical parameters of a soil sample into the correlation function.

[0097] In one embodiment, it further includes an abnormal data elimination unit, which is used to eliminate abnormal data in the data set.

[0098] In one embodiment, the model building unit 402 is also used to take the standard penetration number or specific penetration resistance of multiple soil samples of the same type as input, and take the compression modulus as output to construct a first empirical formula of compression modulus and standard penetration number or specific penetration resistance; take multiple physical parameters of multiple soil samples of the same type as input, and take the compression coefficient as output to construct a neural network prediction model.

[0099] The soil compressibility determination device provided in the embodiment of the present invention can execute the soil compressibility determination method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0100] Embodiment 4

[0101] Figure 5 A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0102] like Figure 5As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0103] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0104] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as a soil compressibility determination method.

[0105] In some embodiments, a soil compressibility determination method may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the soil compressibility determination method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to execute a soil compressibility determination method in any other appropriate manner (e.g., by means of firmware).

[0106] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0107] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0108] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0109] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0110] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0111] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0112] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0113] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for determining soil compressibility, characterized in that: include: Acquire type parameters and physical parameters of a plurality of soil samples and construct a data set of the soil samples; The type parameter represents the type of the soil sample, and the physical parameter represents the parameter of the physical property of the soil sample; Taking the physical parameters of the soil samples of the same type in the sample set as input and the compression modulus and / or compression coefficient as output, a compressibility discrimination model for each type of soil sample is constructed; Acquire type parameters and physical parameters of the soil sample to be tested, input the physical parameters of the soil sample to be tested into the compressibility discrimination model of the type to which the soil sample to be tested belongs, and output the compression modulus and / or compression coefficient of the soil sample to be tested; The compression modulus and / or compression coefficient of the soil sample to be tested are compared with a preset compression modulus threshold and / or compression coefficient threshold respectively to determine the compressibility of the soil sample to be tested.

2. The soil compressibility determination method according to claim 1, characterized in that: After obtaining the type parameters and physical parameters of soil samples of various roadbeds, the method further includes: The correlation between the physical parameters of each soil sample is determined, and soil samples with a correlation greater than a preset threshold are selected.

3. The soil compressibility determination method according to claim 2, characterized in that: Determining the correlation between the physical parameters of each soil sample includes: A linear fitting or nonlinear function fitting method is used to determine the correlation function between the physical parameters in each type of soil sample, and the correlation between the physical parameters of the soil sample is determined by bringing the physical parameters of a soil sample into the correlation function.

4. The soil compressibility determination method according to claim 1, characterized in that: After obtaining the type parameters and physical parameters of soil samples of various roadbeds, the method further includes: Abnormal data in the data set is removed.

5. The soil compressibility determination method according to claim 1, characterized in that: The compressibility discrimination model includes an empirical formula related to the compression modulus and the physical parameter, and / or an empirical formula related to the compression coefficient and the physical parameter, and / or a neural network prediction model with the physical parameter as input and the compression modulus as output, and / or a neural network prediction model with the physical parameter as input and the compression coefficient as output.

6. The soil compressibility determination method according to claim 5, characterized in that: The method uses the physical parameters of the soil samples of the same type in the sample set as input and the compression modulus and / or compression coefficient as output to construct a compressibility discrimination model for each type of soil sample, including: The first empirical formula of compression modulus and standard penetration number or specific penetration resistance is constructed by taking the standard penetration number or specific penetration resistance of multiple soil samples of the same type as input and the compression modulus as output; A neural network prediction model is constructed by taking multiple physical parameters of multiple soil samples of the same type as input and the compression coefficient as output.

7. The soil compressibility determination method according to claim 1, characterized in that: The type parameters at least include the test points of the soil samples and the lithology of the soil layers; The physical parameters include at least soil layer depth, standard penetration number, specific penetration resistance, lateral friction resistance, lateral pressure modulus, basic bearing capacity, and ultimate bearing capacity.

8. A soil compressibility determination device, characterized in that: include: A data acquisition unit, used to acquire type parameters and physical parameters of various soil samples and construct a data set of the soil samples; The type parameter represents the type of the soil sample, and the physical parameter represents the parameter of the physical property of the soil sample; A model building unit, used to build a compressibility discrimination model for each type of soil sample by taking the physical parameters of the soil samples of the same type in the sample set as input and taking the compression modulus and / or compression coefficient as output; A prediction unit, used for obtaining type parameters and physical parameters of the soil sample to be tested, inputting the physical parameters of the soil sample to be tested into the compressibility discrimination model of the type to which the soil sample to be tested belongs, and outputting the compression modulus and / or compression coefficient of the soil sample to be tested; The compressibility judgment unit is used to compare the compression modulus and / or compression coefficient of the soil sample to be tested with a preset compression modulus threshold and / or compression coefficient threshold respectively to determine the compressibility of the soil sample to be tested.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the soil compressibility determination method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the soil compressibility determination method according to any one of claims 1 to 7 when executed.

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