Foundation pit seepage risk evaluation method based on fuzzy analytic hierarchy process and cloud model

Abstract

The invention provides a foundation pit seepage risk evaluation method based on fuzzy analytic hierarchy process and a cloud model, and the method comprises the following steps: obtaining foundation pit basic data which comprises the definition, type and evaluation index system of foundation pit seepage risk; analyzing the foundation pit basic data to carry out foundation pit seepage risk evaluation calculation, including calculating preliminary and comprehensive fuzzy weights of evaluation indexes at all levels based on a fuzzy analytic hierarchy process, and determining a normal cloud comprehensive certainty degree of foundation pit seepage risks by using a cloud model; and finally, determining a risk level corresponding to the current foundation pit seepage by utilizing a representative principle. According to the foundation pit seepage risk evaluation method based on the fuzzy analytic hierarchy process and the cloud model, the problems of fuzziness, uncertainty and mutual coupling conversion of the fuzziness and the uncertainty in seepage risks can be solved, the seepage risk, induced by underground water, of a foundation pit is accurately predicted, the seepage risk level is evaluated, seepage control measures are adopted, and the foundation pit seepage risk evaluation accuracy is improved. And foundation pit construction seepage safety is ensured.

Description

Technical Field

[0001] The invention belongs to the technical field of data processing, and in particular relates to a foundation pit seepage risk assessment method based on fuzzy hierarchical analysis and cloud model. Background Art

[0002] Foundation pit construction risk refers to the potential possibility of accidents or adverse events during the foundation pit excavation and construction process, as well as the adverse consequences for the project. According to statistics on foundation pit accident cases in the past decade, foundation pit construction risks can be divided into six types of risk accidents according to the form of damage: seepage damage, support instability, pit bottom sliding, kick damage, sudden surge damage, and mechanical injury. Among them, in soft soil areas, seepage damage is the main cause of frequent foundation pit accidents, accounting for "62%".

[0003] Therefore, the risk accidents induced by groundwater seepage during foundation pit construction are becoming more and more prominent. How to accurately predict and evaluate the risk level of foundation pits, take seepage control measures, and ensure the safety of construction seepage is one of the major problems that need to be solved urgently. Summary of the invention

[0004] In view of the above-mentioned shortcomings of the prior art, the purpose of the present invention is to provide a foundation pit seepage risk assessment method based on fuzzy hierarchical analysis and cloud model to solve the above-mentioned problems.

[0005] In a first aspect, the present invention provides a foundation pit seepage risk assessment method based on fuzzy hierarchical analysis and cloud model, the method comprising the following steps:

[0006] Acquire foundation pit basic data, wherein the foundation pit basic data includes the definition and type of foundation pit seepage risk and a foundation pit seepage risk evaluation index system;

[0007] Analyzing the foundation pit basic data to carry out foundation pit seepage risk assessment calculation, including calculating preliminary and comprehensive fuzzy weights of evaluation indicators at each level based on fuzzy analytic hierarchy process, and determining the normal cloud comprehensive certainty of foundation pit seepage risk using cloud model;

[0008] Based on the comprehensive fuzzy weights of evaluation indicators at all levels in the foundation pit seepage risk assessment model and the comprehensive certainty of the normal cloud, the risk level corresponding to the current foundation pit seepage is determined using the representativeness principle.

[0009] In a possible implementation of the present application, the obtaining of foundation pit basic data specifically includes:

[0010] Obtaining a user-defined foundation pit seepage risk definition to obtain the foundation pit seepage risk type, wherein the foundation pit seepage risk type includes foundation pit sudden surge, seepage damage, water and sand leakage of the enclosure structure, and contact scour damage;

[0011] The foundation pit seepage risk evaluation index system is obtained based on the evaluation index and value range defined by the user, wherein the foundation pit seepage risk evaluation index system includes a target layer, a criterion layer and a factor layer.

[0012] In a possible implementation of the present application, the foundation pit seepage risk evaluation index system is obtained based on the evaluation index and value range defined by the user, specifically including:

[0013] Obtaining first-level evaluation indicators to form the criterion layer, wherein the first-level evaluation indicators include hydrogeological conditions, soil physical and mechanical characteristics, foundation pit design conditions, and foundation pit construction factors;

[0014] Acquire second-level evaluation indicators to form the factor layer, wherein the second-level evaluation indicators include quantitative indicators and qualitative indicators;

[0015] The value range corresponding to the second evaluation index is obtained, wherein the interval range of the qualitative index is divided into four value ranges, corresponding to four foundation pit seepage risk levels respectively.

[0016] In a possible implementation of the present application, the calculation of preliminary and comprehensive fuzzy weights of evaluation indicators at each level based on the fuzzy analytic hierarchy process specifically includes:

[0017] Obtain the triangular fuzzy number established by the user, and obtain the complementary judgment matrix of the evaluation index based on the triangular fuzzy number;

[0018] Calculating preliminary fuzzy weights of evaluation indicators based on the complementary judgment matrix;

[0019] Obtaining a preliminary possibility matrix based on the preliminary fuzzy weight calculation;

[0020] Converting the preliminary possibility matrix into a fuzzy consistent possibility matrix;

[0021] The comprehensive fuzzy weight is calculated by utilizing a ranking formula in combination with the fuzzy consistency possibility matrix.

[0022] In a possible implementation of the present application, the method of determining the comprehensive determination degree of the normal cloud of the foundation pit seepage by using the cloud model specifically includes:

[0023] Selecting the number of cloud droplets to generate a target cloud and calculating characteristic values ​​of the target cloud, wherein the characteristic values ​​include expectation, entropy and super entropy;

[0024] Calculating the target certainty of the target cloud using a normal cloud forward generator in combination with the eigenvalue;

[0025] The normal cloud comprehensive certainty is calculated based on the target certainty combined with the comprehensive fuzzy weight of the evaluation index.

[0026] In a possible implementation of the present application, when calculating the eigenvalue, the method also includes boundary judgment, wherein the calculation method of the bilateral boundary is different from the calculation method of the unilateral boundary in terms of the expectation and the entropy, and the super entropy is a constant value.

[0027] In a possible implementation of the present application, the method further includes calling the level of other calculation results to compare with the risk level corresponding to the current foundation pit seepage, and outputting the comparison result based on a preset visualization method.

[0028] In a second aspect, the present invention provides a foundation pit seepage risk assessment system based on fuzzy hierarchical analysis and cloud model, the system comprising:

[0029] An acquisition module is used to acquire foundation pit basic data, wherein the foundation pit basic data includes the definition and type of foundation pit seepage risk and a foundation pit seepage risk evaluation index system;

[0030] An analysis module is used to carry out foundation pit seepage risk assessment calculation, which is used to analyze the foundation pit basic data, including calculating the preliminary and comprehensive fuzzy weights of each level of evaluation indicators based on the fuzzy analytic hierarchy process, and determining the normal cloud comprehensive certainty of the foundation pit seepage risk using a cloud model;

[0031] The determination module is used to determine the risk level corresponding to the current foundation pit seepage based on the comprehensive fuzzy weights of the evaluation indicators at all levels in the foundation pit seepage risk assessment model and the normal cloud comprehensive certainty using the representative principle.

[0032] In a third aspect, the present invention provides an electronic device, the electronic device comprising: a processor and a memory;

[0033] The memory is used to store computer programs;

[0034] The processor is used to execute the computer program stored in the memory so that the electronic device executes the above-mentioned foundation pit seepage risk assessment method based on fuzzy hierarchical analysis and cloud model.

[0035] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by an electronic device, implements the above-mentioned foundation pit seepage risk assessment method based on fuzzy hierarchical analysis and cloud model.

[0036] As described above, the foundation pit seepage risk assessment method based on fuzzy hierarchical analysis and cloud model described in the present invention has the following beneficial effects:

[0037] (1) The concept of foundation pit seepage risk and control was proposed for the first time, filling the relevant gaps. The types of foundation pit seepage risks and risk assessment indicators were summarized. With reference to relevant standards, regulations and literature, the classification and value range of quantitative and qualitative indicators of seepage risk were clarified.

[0038] (2) An evaluation method model that couples the fuzzy hierarchical analysis method and the cloud model was constructed, which comprehensively reflects the uncertainty and fuzziness of the foundation pit seepage risk evaluation indicators, realizes the conversion between randomness and fuzziness, and objectively shows the mutual importance of various indicators. The comprehensive certainty of the normal cloud of each risk level calculated by the cloud model reflects the possibility of foundation pit seepage disaster accidents, and uses the maximum representativeness principle to determine the risk level. The evaluation results are more reasonable and credible. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 Shown is a schematic diagram of a scene of an electronic device in an embodiment of the present invention;

[0040] Figure 2 Shown is a flow chart of a foundation pit seepage risk assessment method based on fuzzy hierarchical analysis and cloud model in one embodiment of the present invention;

[0041] Figure 3 A schematic diagram showing the types of foundation pit seepage risk in one embodiment of a foundation pit seepage risk assessment method based on fuzzy hierarchical analysis and cloud model of the present invention;

[0042] Figure 4 Shown is a schematic diagram of the structure of a foundation pit seepage risk assessment index system in an embodiment of a foundation pit seepage risk assessment method based on fuzzy hierarchical analysis and cloud model of the present invention;

[0043] Figure 5 Shown is a schematic diagram of the comprehensive fuzzy weights of the first-level evaluation indexes in one embodiment of the foundation pit seepage risk evaluation method based on fuzzy hierarchical analysis and cloud model of the present invention;

[0044] Figure 6 Shown is a schematic diagram of cloud model characteristics in one embodiment of a foundation pit seepage risk assessment method based on fuzzy hierarchical analysis and cloud model of the present invention;

[0045] Figure 7 Shown is a schematic structural diagram of a foundation pit seepage risk assessment system based on fuzzy hierarchical analysis and cloud model in one embodiment of the present invention;

[0046] Figure 8 It is a schematic structural diagram of an electronic device of the present invention in one embodiment. DETAILED DESCRIPTION

[0047] The following describes the embodiments of the present invention by specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.

[0048] It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present invention, and thus the drawings only show components related to the present invention rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

[0049] The following embodiments of the present invention provide a foundation pit seepage risk assessment method based on fuzzy hierarchical analysis and cloud model, which can be applied to Figure 1 The electronic device shown. The electronic device described in the present invention may include a mobile phone 11 with a wireless charging function, a tablet computer 12, a laptop computer 13, a wearable device, a vehicle-mounted device, an augmented reality (AR) / virtual reality (VR) device, an ultra-mobile personal computer (UMPC), a netbook, a personal digital assistant (PDA), etc. The embodiment of the present invention does not impose any restrictions on the specific type of the electronic device.

[0050] For example, the electronic device may be a station (STAION, ST) in a WLAN with a wireless charging function, a cellular phone, a cordless phone, a Session Initiation Protocol (SIP) phone, a Wireless Local Loop (WLL) station, a Personal Digital Assistant (PDA) device, a handheld device with a wireless charging function, a computing device or other processing device, a computer, a laptop computer, a handheld communication device, a handheld computing device, and / or other devices for communicating on a wireless system and a next generation communication system, such as a mobile terminal in a 5G network, a mobile terminal in a future evolved Public Land Mobile Network (PLMN), or a mobile terminal in a future evolved Non-terrestrial Network (NTN), etc.

[0051] Specifically, the risk assessment methods of foundation pits can be mainly divided into three categories: fuzzy set theory, machine learning methods, and coupled models. Fuzzy set theory refers to the use of fuzzy set theory to evaluate and analyze risks from a qualitative and quantitative perspective. It can be specifically divided into hierarchical analysis method, fuzzy hierarchical analysis method, approximate ideal solution method, etc. Although the above fuzzy calculation methods have been widely used in the risk assessment of foundation pits, dams, and tunnel construction, they still have certain limitations, such as reliance on expert judgment, complex calculation process of eigenvalues ​​and eigenvectors, and large workload of data statistics. As for machine learning methods, they are a method of using computers to simulate human learning. It allows machines to identify and acquire knowledge and experience from the real world, including artificial neural networks, Bayesian networks, support vector machines, etc. Machine learning methods can solve nonlinear problems well, with accurate classification and fast processing speed, but they also have problems such as overfitting, difficulty in algorithm development, and many and complex model parameters. Compared with a single evaluation model, a coupled model based on two or more methods often has better nonlinear adaptability, robustness, and data processing capabilities. As for the new coupling model (cloud model) for risk assessment, the superiority of the normal cloud model in the conversion process between qualitative and quantitative indicators is discussed, and the nonlinear data processing capability and robustness of the coupling method are demonstrated. Among them, the formation mechanism of foundation pit seepage risk is very complex, and there are many influencing factors, such as hydrogeological conditions, design schemes, construction excavation factors, etc., involving a large number of qualitative and quantitative evaluation indicators, and these factor indicators themselves have great uncertainty and fuzziness, which makes these indicator data have strong nonlinearity and imbalance. Therefore, how to propose a suitable coupling evaluation model to characterize the fuzziness of seepage risk and solve the uncertainty and randomness of seepage risk is a research content that needs to be carried out urgently, so the foundation pit seepage risk evaluation method based on fuzzy hierarchical analysis and cloud model of the present invention is proposed.

[0052] The technical solutions in the embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0053] Specifically, see Figure 2 In one embodiment of the invention, the foundation pit seepage risk assessment method based on fuzzy hierarchical analysis and cloud model of the present invention comprises the following steps:

[0054] Step S202, obtaining foundation pit basic data, wherein the foundation pit basic data includes the definition and type of foundation pit seepage risk and a foundation pit seepage risk evaluation index system;

[0055] Step S204, analyzing the foundation pit basic data to carry out foundation pit seepage risk assessment calculation, including calculating preliminary and comprehensive fuzzy weights of evaluation indicators at each level based on fuzzy analytic hierarchy process, and determining the normal cloud comprehensive certainty of foundation pit seepage risk using cloud model;

[0056] Step S206, based on the comprehensive fuzzy weights of the evaluation indicators at each level in the foundation pit seepage risk assessment model and the comprehensive certainty of the normal cloud, the risk level corresponding to the current foundation pit seepage is determined using the representativeness principle.

[0057] It should be noted that, in this embodiment, the foundation pit basic data is first obtained, specifically, the user-defined foundation pit seepage risk definition is obtained to obtain the foundation pit seepage risk type, wherein the foundation pit seepage risk type includes foundation pit sudden surge, seepage damage, water and sand leakage of retaining structure, and contact scour damage; and the foundation pit seepage risk evaluation index system is obtained based on the user-defined evaluation index and value range, wherein the foundation pit seepage risk evaluation index system includes a target layer, a criterion layer, and a factor layer.

[0058] Specifically, Figure 3 As shown in the figure, it is a schematic diagram of the seepage risk types of foundation pits, among which, the seepage risk of foundation pits refers to the risk of damage accidents on the foundation pit excavation surface directly induced by groundwater seepage. Specifically, the sudden surge of foundation pits refers to the phenomenon that the gravity generated by the thickness of the viscous impermeable layer at the bottom of the pit is less than the pressure of the pressurized water due to improper foundation pit dewatering or too fast earth excavation, thereby causing shear failure of the soil; seepage failure refers to the Terzaghi classic foundation pit seepage failure mode, in which the sand and soil layer at the bottom of the pit undergoes pipe bursts or overall jacking failure; water and sand leakage of the retaining structure refers to the leakage of the anti-seepage curtain, which leads to water leakage, sand flow or pipe bursts on the slope excavation surface or the side wall of the foundation pit; contact scouring failure refers to the contact scouring of groundwater with the soil layer along the pile structure or dewatering well, thereby inducing pipe bursts, soil and sand flow and other damage accidents.

[0059] Furthermore, in one embodiment of the invention, the foundation pit seepage risk evaluation index system is obtained based on the user-defined evaluation indicators and value ranges, specifically including: obtaining the first-level evaluation indicators to form the criterion layer, wherein the first-level evaluation indicators include hydrogeological conditions, soil physical and mechanical characteristics, foundation pit design conditions and foundation pit construction factors; obtaining the second-level evaluation indicators to form the factor layer, wherein the second-level evaluation indicators include quantitative indicators and qualitative indicators; obtaining the value range corresponding to the second evaluation indicator, wherein the interval range of the qualitative indicator is divided into four value ranges, which correspond to different foundation pit seepage risk levels.

[0060] It should be noted that, in this embodiment, Figure 4As shown, it is a structural schematic diagram of the foundation pit seepage risk assessment index system, wherein the foundation pit seepage risk assessment system is composed of a target layer, a criterion layer (first-level evaluation index) and a factor layer (second-level evaluation index) from top to bottom, wherein the first-level evaluation index includes four categories: hydrogeological conditions, soil physical and mechanical characteristics, foundation pit design conditions and foundation pit construction factors, and the second-level evaluation index specifically includes "17" indicators, including "9" quantitative indicators and "8" qualitative indicators, wherein the quantitative indicators are shown in Table 1.

[0061] Table 1. Quantitative indicators list

[0062]

[0063]

[0064] Furthermore, the interval range of the qualitative indicator is specifically divided into four value ranges, corresponding to different foundation pit seepage risk levels. Specifically, the foundation pit seepage risk level is divided into four categories: extremely high risk, high risk, medium risk and low risk, as shown in Table 2.

[0065] Table 2. Detailed list of qualitative indicators

[0066]

[0067] Among them, the value range of qualitative indicators is divided into "4" levels, namely [0, 0.25), [0.25, 0.5), [0.5, 0.75), [0.75, 1.0), and the cloud digital eigenvalues ​​are (0.125, 0.042, 0.01), (0.375, 0.042, 0.01), (0.625, 0.042, 0.01), (0.875, 0.042, 0.01).

[0068] Furthermore, in one embodiment of the invention, the basic data of the foundation pit is analyzed, including calculating the comprehensive fuzzy weights of the evaluation indicators at each level based on the fuzzy hierarchical analysis method, and determining the comprehensive certainty of the normal cloud of the foundation pit seepage using a cloud model, wherein the comprehensive fuzzy weights of the evaluation indicators at each level are calculated based on the fuzzy hierarchical analysis method, specifically including: obtaining triangular fuzzy numbers established by the user, and obtaining a complementary judgment matrix of the evaluation indicators based on the triangular fuzzy numbers; calculating preliminary fuzzy weights of the evaluation indicators based on the complementary judgment matrix; obtaining a preliminary possibility matrix based on the preliminary fuzzy weights; converting the preliminary possibility matrix into a fuzzy consistent possibility matrix; and obtaining the comprehensive fuzzy weights by using a sorting formula combined with the fuzzy consistent possibility matrix.

[0069] It should be noted that in this embodiment, the fuzzy hierarchy analysis can be divided into interval fuzzy hierarchy analysis method, triangular fuzzy hierarchy analysis method, and trapezoidal fuzzy hierarchy analysis method. The triangular fuzzy hierarchy analysis method uses triangular fuzzy numbers to express the relative importance of evaluation indicators. The median of the triangular fuzzy number represents the most likely value, and the upper and lower limit values ​​represent the minimum possible value and the maximum possible value, respectively, which is more suitable for risk assessment during the actual construction of the foundation pit. The process of calculating the initial and comprehensive fuzzy weights of the evaluation indicators is as follows:

[0070] Step 1: Calculate the preliminary fuzzy weights of the evaluation index based on the complementary judgment matrix, specifically including: Invite four experts to establish a triangular fuzzy number a by scoring the questionnaire ij =(l ij ,m ij ,u ij ), construct the triangular fuzzy number complementary judgment matrix of the first-level evaluation index Among them, l ij , m ij ,u ij Respectively represent the upper limit, most likely value and lower limit of triangular fuzzy numbers, and satisfy a ij +a ji =1,l ij +u ji =1,l ji +u ij =1, and m ij +m ji =1;u ij -l ij Represents the degree of ambiguity. When the value is 0, it means that there is no ambiguity in the expert judgment table, which is an accurate judgment. ij =(a ij1 +a ij2 +…+a ijd ) / d, d is the number of evaluation experts, and each expert has the same weight. The fuzzy importance scale is shown in Table 3.

[0071] Table 3. Fuzzy importance scale

[0072]

[0073] Step 2: Calculate the preliminary fuzzy weights of the evaluation indicators based on the complementary judgment matrix. Taking the first-level evaluation indicators as an example, the following steps are specifically performed: Calculate the preliminary fuzzy weights of the first-level evaluation indicators and

[0074] Step 3: Based on the preliminary fuzzy weights, a preliminary possibility matrix is ​​calculated. Taking the first-level evaluation index as an example, the following steps are specifically performed: Perform pairwise comparisons, i.e. calculate The initial possibility matrix Here, λ is set to 0.5, which means risk neutrality.

[0075] Step 4: Convert the preliminary possibility matrix into a fuzzy consistent possibility matrix. Taking the first-level evaluation index as an example, the specific steps include: converting the preliminary possibility matrix p ij Transformed into fuzzy consistent possibility matrix in

[0076] Step 5: Calculate the comprehensive fuzzy weight using the ranking formula combined with the fuzzy consistency possibility matrix. Taking the first-level evaluation index as an example, the following specifically includes: Calculate the comprehensive fuzzy weight of each factor using the ranking formula

[0077] Among them, Figure 5 As shown in Table 4, it shows the comprehensive fuzzy weight of the first-level evaluation index. Among them, the foundation pit construction factor (F4) in the triangular fuzzy judgment matrix of the first-level evaluation index of foundation pit seepage risk has the largest weight in the criterion layer, as shown in Table 4.

[0078] Table 4. Detailed list of comprehensive fuzzy weights of first-level evaluation indicators

[0079]

[0080] Correspondingly, the comprehensive fuzzy weight of the second-level evaluation index is also obtained based on the above steps one to five, which will not be elaborated here.

[0081] Furthermore, in one embodiment of the invention, a cloud model is used to determine the comprehensive certainty of a normal cloud of foundation pit seepage, specifically including: selecting the number of cloud droplets to generate a target cloud, and calculating characteristic values ​​of the target cloud, wherein the characteristic values ​​include expectation, entropy, and super entropy; calculating the target certainty of the target cloud using a preset normal cloud forward generator in combination with the characteristic values; and calculating the comprehensive certainty of the normal cloud based on the target certainty combined with the comprehensive fuzzy weight of the evaluation index.

[0082] It should be noted that in this embodiment, the cloud model is a qualitative and quantitative interchange model based on traditional fuzzy mathematics and probability statistics, which organically combines randomness and fuzziness to achieve a natural conversion between qualitative concepts and quantitative values. Assume that U is a domain, C is a qualitative concept on U, and x is both a value in U and an occurrence value of C. If μ(x)∈[0,1] is the degree of certainty of x to C, and the distribution of μ(x) satisfies μ:X→[0,1], The distribution of X is called a cloud, and x is a cloud droplet. The characteristics of the cloud model can be represented by three parameters (Ex, En, He), such as Figure 6 As shown, Ex is called expectation, that is, the expectation of the domain interval where the cloud droplet is located, which is the point that best represents the qualitative concept. En is called entropy, which represents the uncertainty measure of the qualitative concept, which is jointly determined by the randomness and fuzziness of the concept. On the one hand, it is a measure of the randomness of the qualitative concept, reflecting the discrete degree of the cloud droplets that can represent this qualitative concept. He is called super entropy, which represents the uncertainty of entropy, which is jointly determined by the randomness and fuzziness of entropy. The size of super entropy reflects the discrete degree and thickness of the cloud. And the cloud generator can realize the mapping relationship between qualitative concepts and quantitative features through computers, which can be divided into forward cloud generators and reverse cloud generators, and the normal cloud generator can convert qualitative concepts into quantitative features to generate the required cloud droplets.

[0083] Specifically, when using the cloud model to determine the normal cloud comprehensive certainty of foundation pit seepage, the corresponding process steps are as follows: ① According to the definition of the cloud model μ:X→[0,1], Select the number of cloud droplets x "5000" to generate cloud X; ②, calculate the three digital eigenvalues ​​(Ex, En, He) of the cloud model; ③, use the normal cloud forward generator to calculate the degree of certainty of cloud droplet x ④. Combine the comprehensive fuzzy weights of the evaluation indicators to calculate the degree of certainty of the comprehensive cloud Where M j is the degree of certainty at level j, μ(x j ) is the j-th level certainty function, w i is the weight of the i-th influencing factor of foundation pit seepage risk.

[0084] Furthermore, in one embodiment of the invention, when calculating the normal cloud eigenvalue, the method also includes boundary judgment of the eigenvalue, wherein the calculation method of the bilateral boundary is different from that of the unilateral boundary in the expectation and the calculation method of the entropy, the super entropy is a constant value, and the level of other calculation results is called to compare with the risk level corresponding to the current foundation pit seepage, and the comparison result is output based on a preset visualization method.

[0085] It should be noted that, in this embodiment, for quantitative indicator variables with bilateral boundaries, such as [B max ,B min ], the calculation formulas for digital eigenvalues ​​are He = k, and for a single boundary quantitative indicator variable, such as [-∞, B min ] or [-∞,B min ], the calculation formulas for digital eigenvalues ​​are The value k of super entropy is a constant, and in this embodiment, it is set to "0.01".

[0086] Furthermore, a table visualization form is selected to output the comparison results, and the results are shown in Table 5. Among them, the normal cloud certainty is combined with the fuzzy comprehensive weight, and the normal cloud comprehensive certainty of each seepage risk level can be calculated through step ④, and when applied, all calculations are performed in MATLAB.

[0087] Table 5. Comparison results

[0088]

[0089] Among them, the seepage risk level of the foundation pit is determined based on the representative principle, that is, the risk level with the highest comprehensive certainty. In this embodiment, the seepage risk level is Level I, which is an extremely high risk. It is verified and compared with other evaluation methods and actual excavation conditions to verify the correctness of this evaluation system method.

[0090] An embodiment of the present application also provides a foundation pit seepage risk assessment system based on fuzzy hierarchical analysis and cloud model. The foundation pit seepage risk assessment system based on fuzzy hierarchical analysis and cloud model can implement the foundation pit seepage risk assessment method based on fuzzy hierarchical analysis and cloud model described in the present application. However, the implementation device of the foundation pit seepage risk assessment method based on fuzzy hierarchical analysis and cloud model described in the present application includes but is not limited to the structure of the foundation pit seepage risk assessment system based on fuzzy hierarchical analysis and cloud model listed in the present embodiment. All structural deformations and replacements of the prior art made according to the principles of the present application are included in the protection scope of the present application.

[0091] See also Figure 7 In one embodiment, this embodiment provides a foundation pit seepage risk assessment system 70 based on fuzzy hierarchical analysis and cloud model, the system comprising:

[0092] An acquisition module 71 is used to acquire foundation pit basic data, wherein the foundation pit basic data includes the definition and type of foundation pit seepage risk and a foundation pit seepage risk evaluation index system;

[0093] An analysis module 72 is used to analyze the foundation pit basic data to carry out foundation pit seepage risk assessment calculation, including calculating preliminary and comprehensive fuzzy weights of evaluation indicators at each level based on fuzzy analytic hierarchy process, and determining the normal cloud comprehensive certainty of foundation pit seepage risk using cloud model;

[0094] The determination module 73 is used to determine the risk level corresponding to the current foundation pit seepage based on the comprehensive fuzzy weights of the evaluation indicators at each level in the foundation pit seepage risk assessment model and the normal cloud comprehensive certainty, using the representative principle.

[0095] Since the specific implementation of this embodiment corresponds to the aforementioned method embodiment, the same details will not be repeated here, and those skilled in the art should also understand that Figure 7 The division of the modules in the embodiment is merely a division of logical functions, and in actual implementation, all or part of them can be integrated into one or more physical entities, and these modules can be implemented entirely in the form of software called by processing elements, or entirely in the form of hardware, or some modules can be implemented in the form of software called by processing elements, and some modules can be implemented in the form of hardware.

[0096] In the several embodiments provided by the present invention, it should be understood that the disclosed system, device or method can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of modules / units is only a logical function division, and there may be other division methods in actual implementation, such as multiple modules or units can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules or units, which can be electrical, mechanical or other forms.

[0097] The modules / units described as separate components may or may not be physically separated, and the components displayed as modules / units may or may not be physical modules, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules / units may be selected according to actual needs to achieve the purpose of the embodiments of the present invention. For example, the functional modules / units in the various embodiments of the present invention may be integrated into one processing module, or each module / unit may exist physically separately, or two or more modules / units may be integrated into one module / unit.

[0098] Those of ordinary skill in the art should further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0099] The embodiment of the present invention also provides a computer-readable storage medium. A person of ordinary skill in the art can understand that all or part of the steps in the method for implementing the above embodiment can be completed by instructing the processor through a program, and the program can be stored in a computer-readable storage medium, and the storage medium is a non-transitory medium, such as a random access memory, a read-only memory, a flash memory, a hard disk, a solid-state hard disk, a magnetic tape, a floppy disk, an optical disc, and any combination thereof. The above storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or a data center that includes one or more available media integrated. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a digital video disc (DVD)), or a semiconductor medium (e.g., a solid-state disk (SSD)), etc.

[0100] An embodiment of the present invention further provides an electronic device, which includes a processor and a memory.

[0101] The memory is used to store computer programs.

[0102] The memory includes: ROM, RAM, disk, USB flash drive, memory card or CD and other media that can store program codes.

[0103] The processor is connected to the memory and is used to execute the computer program stored in the memory so that the electronic device executes the above-mentioned foundation pit seepage risk assessment method based on fuzzy hierarchical analysis and cloud model.

[0104] Preferably, the processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components.

[0105] like Figure 8As shown, the electronic device of the present invention is in the form of a general computing device. The components of the electronic device may include but are not limited to: one or more processors or processing units 81, a memory 82, and a bus 83 connecting different system components (including the memory 82 and the processing unit 81).

[0106] Bus 83 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor or a local bus using any of a variety of bus architectures. For example, these architectures include but are not limited to Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MAC) bus, Enhanced ISA bus, Video Electronics Standards Association (VESA) local bus and Peripheral Component Interconnect (PCI) bus.

[0107] Electronic devices typically include a variety of computer system readable media. These media can be any available media that can be accessed by the electronic device, including volatile and non-volatile media, removable and non-removable media.

[0108] The memory 82 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 821 and / or cache memory 822. The electronic device may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the storage system 823 may be used to read and write non-removable, non-volatile magnetic media ( Figure 8 not shown, usually called a "hard drive"). Although Figure 8 Not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk"), and an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, a DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to the bus 83 via one or more data medium interfaces. The memory 82 may include at least one program product having a set (e.g., at least one) of program modules that are configured to perform the functions of the various embodiments of the present invention.

[0109] A program / utility 824 having a set (at least one) of program modules 8241 may be stored, for example, in the memory 82, such program modules 8241 including but not limited to an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment. The program modules 8241 generally perform the functions and / or methods of the embodiments described herein.

[0110] The electronic device may also communicate with one or more external devices (e.g., keyboards, pointing devices, displays, etc.), one or more devices that enable a user to interact with the electronic device, and / or any device that enables the electronic device to communicate with one or more other computing devices (e.g., network cards, modems, etc.). Such communication may be performed via input / output (I / O) interface 84. Furthermore, the electronic device may also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via network adapter 85. Figure 8 As shown, the network adapter 85 communicates with other modules of the electronic device via the bus 83. It should be understood that, although not shown in the figure, other hardware and / or software modules can be used in conjunction with the electronic device, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0111] The above embodiments are merely illustrative of the principles and effects of the present invention, and are not intended to limit the present invention. Anyone familiar with the art may modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by a person of ordinary skill in the art without departing from the spirit and technical concept disclosed by the present invention shall still be covered by the claims of the present invention.

Claims

1. A foundation pit seepage risk assessment method based on fuzzy hierarchical analysis and cloud model, characterized in that: The method comprises the following steps: Acquire foundation pit basic data, wherein the foundation pit basic data includes the definition and type of foundation pit seepage risk and a foundation pit seepage risk evaluation index system; Analyzing the foundation pit basic data to carry out foundation pit seepage risk assessment calculation, including calculating preliminary and comprehensive fuzzy weights of evaluation indicators at each level based on fuzzy analytic hierarchy process, and determining the normal cloud comprehensive certainty of foundation pit seepage risk using cloud model; Based on the comprehensive fuzzy weights of evaluation indicators at all levels in the foundation pit seepage risk assessment model and the comprehensive certainty of the normal cloud, the risk level corresponding to the current foundation pit seepage is determined using the representativeness principle.

2. The foundation pit seepage risk assessment method based on fuzzy hierarchical analysis and cloud model according to claim 1 is characterized in that: The obtaining of foundation pit basic data specifically includes: Obtaining a user-defined foundation pit seepage risk definition to obtain the foundation pit seepage risk type, wherein the foundation pit seepage risk type includes foundation pit sudden surge, seepage damage, water and sand leakage of the enclosure structure, and contact scour damage; The foundation pit seepage risk evaluation index system is obtained based on the evaluation index and value range defined by the user, wherein the foundation pit seepage risk evaluation index system includes a target layer, a criterion layer and a factor layer.

3. The foundation pit seepage risk assessment method based on fuzzy hierarchical analysis and cloud model according to claim 2 is characterized in that: The foundation pit seepage risk evaluation index system is obtained based on the evaluation index and value range defined by the user, specifically including: Obtaining first-level evaluation indicators to form the criterion layer, wherein the first-level evaluation indicators include hydrogeological conditions, soil physical and mechanical characteristics, foundation pit design conditions, and foundation pit construction factors; Acquire second-level evaluation indicators to form the factor layer, wherein the second-level evaluation indicators include quantitative indicators and qualitative indicators; The value range corresponding to the second evaluation index is obtained, wherein the interval range of the qualitative index is divided into four value ranges, corresponding to four foundation pit seepage risk levels respectively.

4. The foundation pit seepage risk assessment method based on fuzzy hierarchical analysis and cloud model according to claim 3 is characterized in that: The preliminary and comprehensive fuzzy weights of evaluation indicators at all levels are calculated based on the fuzzy analytic hierarchy process, specifically including: Obtain the triangular fuzzy number established by the user, and obtain the complementary judgment matrix of the evaluation index based on the triangular fuzzy number; Calculating preliminary fuzzy weights of evaluation indicators based on the complementary judgment matrix; Obtaining a preliminary possibility matrix based on the preliminary fuzzy weight calculation; Converting the preliminary possibility matrix into a fuzzy consistent possibility matrix; The comprehensive fuzzy weight is calculated by using the sorting formula combined with the fuzzy consistency possibility matrix.

5. The foundation pit seepage risk assessment method based on fuzzy hierarchical analysis and cloud model according to claim 4 is characterized in that: The method of using the cloud model to determine the comprehensive degree of normal cloud determination of foundation pit seepage specifically includes: Selecting the number of cloud droplets to generate a target cloud and calculating characteristic values ​​of the target cloud, wherein the characteristic values ​​include expectation, entropy and super entropy; Calculating the target certainty of the target cloud using a normal cloud forward generator in combination with the eigenvalue; The normal cloud comprehensive certainty is calculated based on the target certainty combined with the comprehensive fuzzy weight of the evaluation index.

6. The foundation pit seepage risk assessment method based on fuzzy hierarchical analysis and cloud model according to claim 5 is characterized in that: When calculating the normal cloud eigenvalue, the method also includes boundary judgment of the eigenvalue, wherein the calculation method of the bilateral boundary is different from that of the unilateral boundary in the expectation and the calculation method of the entropy, and the super entropy is a constant value.

7. The foundation pit seepage risk assessment method based on fuzzy hierarchical analysis and cloud model according to claim 6 is characterized in that: The method also includes calling the level of other calculation results to compare with the risk level corresponding to the current foundation pit seepage, and outputting the comparison result based on a preset visualization method.

8. A foundation pit seepage risk assessment system based on fuzzy hierarchical analysis and cloud model, characterized in that: include: An acquisition module is used to acquire foundation pit basic data, wherein the foundation pit basic data includes the definition and type of foundation pit seepage risk and a foundation pit seepage risk evaluation index system; An analysis module is used to analyze the foundation pit basic data to carry out foundation pit seepage risk evaluation calculation, including calculating the preliminary and comprehensive fuzzy weights of each level of evaluation indicators based on the fuzzy analytic hierarchy process, and determining the normal cloud comprehensive certainty of the foundation pit seepage risk using a cloud model; The determination module is used to determine the risk level corresponding to the current foundation pit seepage based on the comprehensive fuzzy weights of the evaluation indicators at all levels in the foundation pit seepage risk assessment model and the normal cloud comprehensive certainty using the representative principle.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, it implements the foundation pit seepage risk assessment method based on fuzzy hierarchical analysis and cloud model as described in any one of claims 1 to 7.

10. An electronic device, characterized in that: The electronic device includes: a processor and a memory; wherein the memory is used to store computer programs, and the processor is used to execute the computer programs stored in the memory, so that the electronic device executes the foundation pit seepage risk assessment method based on fuzzy hierarchical analysis and cloud model as described in any one of claims 1 to 7.

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