CN115098914B - Intelligent extraction method and system for sensitive elements of urban road collapse hidden danger - Google Patents
Intelligent extraction method and system for sensitive elements of urban road collapse hidden danger Download PDFInfo
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Abstract
The invention relates to an intelligent extraction method and system for sensitive factors of urban road collapse hidden danger, belonging to the technical field of road collapse factor identification, and being capable of unifying the sensitivity of various pregnant disaster and disaster-causing factors to the compressive strength of a disease soil body and realizing the unification and quantification of the characterization indexes of various road collapse sensitive factors; the method comprises the following steps: s1, determining the types of hidden danger factors of urban road collapse; s2, acquiring actual measurement data of each element to form an element database; s3, establishing a polynomial regression model of a single independent variable and a single dependent variable; s4, calibrating the polynomial regression model by using data in the element database, and determining parameters in the polynomial regression model; s5, calculating a change value of a dependent variable caused by a unit independent variable according to the calibrated polynomial regression model; s6, calculating a discrete standardized processing value of the change value; and S7, extracting elements according to the calculated discrete normalized processing value.
Description
Technical Field
The invention relates to the technical field of road collapse factor identification, in particular to an intelligent extraction method and system for urban road collapse hidden danger sensitive factors.
Background
In recent years, urban road collapse accidents occur frequently, and the safety of people in trip is threatened seriously.
According to incomplete statistics, urban road collapse inducement is complex, collapse accidents are often caused by multiple factors, and accurate extraction of road collapse sensitive elements has important scientific value and engineering significance for road collapse risk early warning and adoption of targeted prevention and control measures, but no research on extraction of road collapse sensitive elements exists at present.
Therefore, in the face of the serious trend of road collapse accidents, it is necessary to research an intelligent extraction method and system for sensitive elements of urban road collapse risks to address the deficiencies of the prior art, so as to solve or alleviate one or more of the above problems.
Disclosure of Invention
In view of the above, the invention provides an intelligent extraction method and system for sensitive factors of hidden danger of urban road collapse, which can unify the sensitivity of various pregnant disaster and disaster-causing factors to the compressive strength condition of a diseased soil body, and realize the unification and quantification of characterization indexes of various road collapse sensitive factors.
On one hand, the invention provides an intelligent extraction method for sensitive elements of hidden danger of urban road collapse, which is characterized by comprising the following steps:
s1, determining the types of hidden danger factors of urban road collapse;
s2, acquiring actual measurement data of each element to form an element database;
s3, establishing a polynomial regression model of a single independent variable and a single dependent variable; the independent variable and the dependent variable are both element types in S1;
s4, calibrating the polynomial regression model by using data in the element database, and determining parameters in the polynomial regression model;
s5, calculating unit independent variable x according to the calibrated polynomial regression model i Cause variable y i Of the variation value σ i ；
S6, calculating a variation value sigma i Is a discrete normalized processing value σ bi ；
S7, according to the calculated discrete normalization processing value sigma bi And (5) extracting elements.
As for the above-mentioned aspect and any possible implementation manner, there is further provided an implementation manner, and the specific content of step S7 includes: processing the value sigma according to a discrete normalization bi Determining the sensitivity level of the elements, and extracting the elements or element combinations according to the sensitivity level of the elements.
As for the above-mentioned aspects and any possible implementation manner, an implementation manner is further provided, and the division manner of the sensitivity level specifically includes: processing the value σ with discrete normalization bi And dividing the sensitivity level of the elements by adopting a four-fold breakpoint method as a basis.
As for the above-mentioned aspect and any possible implementation manner, there is further provided an implementation manner, where the category of the element in step S1 includes:
the diameter and burial depth of hidden danger of road collapse;
density, water content, cohesion, internal friction angle and uniaxial compressive strength of the soil body;
structural stress and pavement settlement value; and
and (4) the engineering disturbance dynamic load generates tensile stress.
In the above aspects and any possible implementation manner, an implementation manner is further provided, and the independent variable in step S3 is any one of the diameter and burial depth of the hidden danger of road collapse, the density, water content, cohesion, internal friction angle of a soil body, structural stress, pavement settlement value, and tensile stress generated by engineering disturbance dynamic load;
the dependent variable is the uniaxial compressive strength of the soil body.
As for the above-mentioned aspects and any possible implementation manner, there is further provided an implementation manner, where the polynomial regression model established in step S3 is:
y i ＝β i0 +β i1 x i +β i2 x i 2 +ε i
in the formula, x i Is an independent variable, y i Is a dependent variable;
i is an independent variable mark, i =1, 2, 3 \8230, and n is the total number of independent variables;
β i0 、β i1 、β i2 are in turn independent variable x i Zeroth order coefficient, first order coefficient, and second order coefficient;
ε i is an independent variable x i The error term of (c).
As for the above-mentioned aspect and any possible implementation manner, there is further provided an implementation manner, and the manner of acquiring the actual measurement data of the element in step S2 includes:
the method for acquiring the measured data of the diameter and the burial depth of the hidden danger of the road collapse comprises the following steps: the method comprises the following steps of obtaining by a ground penetrating radar detection mode;
the actual measurement data acquisition mode of density, moisture content, cohesion, internal friction angle and unipolar compressive strength of the soil body is: firstly, detecting by adopting a micro-core penetration test device to obtain a coring soil body standard test piece with hidden road collapse danger, and then carrying out a physical mechanical test on the coring soil body standard test piece to obtain the coring soil body standard test piece;
the actual measurement data acquisition mode of structural stress and road surface settlement is as follows: monitoring and obtaining are carried out by arranging MEMS sensing equipment at the junction of the soil body and the roadbed right above the collapse hidden danger.
As to the above aspect and any possible implementation manner, an implementation manner is further provided, and a manner of acquiring the actual measurement data of the tensile stress generated by the engineering disturbance dynamic load in step S2 is:
distributing more than two groups of vibration pickers in an engineering disturbance area, and acquiring an initial peak vibration speed of engineering disturbance dynamic load by adopting the vibration pickers;
and calculating the tensile stress according to the obtained initial peak value vibration speed of the engineering disturbance dynamic load.
The above-described aspect and any possible implementation further provide an implementation, where the formula for calculating the tensile stress is:
in the formula, v s The method is characterized in that the initial peak value vibration speed of the dynamic load of the engineering disturbance is obtained, L is the distance between an engineering operation point and a collapse hidden danger, delta L is the distance between vibration pickups, delta t is the wave travel time difference, m is the peak value vibration speed attenuation coefficient, and rho is the soil mass density.
On the other hand, the invention provides an intelligent extraction system for sensitive elements of hidden danger of urban road collapse, which comprises the following steps:
an element database for storing element types and element data;
the data acquisition module is used for acquiring the measured data of each element and storing the measured data in the element database;
the model building module is used for building a polynomial regression model of a single independent variable and a single dependent variable; the independent variable and the dependent variable are both element types in an element database;
the calibration module is embedded with a model calibration algorithm and is used for realizing the calibration of element data in the element database to the polynomial regression model and determining parameters in the polynomial regression model;
a result calculation module for calculating the unit independent variable x according to the calibrated polynomial regression model i Cause variable y i Variation value σ of i (ii) a Then calculates the variation value sigma i Is a discrete normalized processing value σ bi ；
An element extraction module for processing the value sigma according to the discrete normalization bi And (5) extracting elements.
Compared with the prior art, one of the technical schemes has the following advantages or beneficial effects: the method is based on the detection and monitoring means with mature technology and wide application range at present to construct a road collapse hidden danger disaster pregnancy and disaster causing element database, and the feasibility of data acquisition is high;
another technical scheme among the above-mentioned technical scheme has following advantage or beneficial effect: the invention adopts the method of actual measurement data calibration to establish a multinomial regression model, and the reliability of the model in field application is high;
another technical scheme in the above technical scheme has the following advantages or beneficial effects: the four-fold breakpoint method is adopted to divide the element sensitivity levels into four levels of special sensitivity, relatively sensitivity, general sensitivity and weak sensitivity, the element sensitivity levels are classified comprehensively, meticulously, accurately and pertinently, the sensitivity levels of various pregnant disaster and disaster causing elements are unified to the condition of compressive strength of a diseased soil body, the unification and quantification of the characterization indexes of various road collapse sensitive elements are realized, and a basis is provided for monitoring and early warning of road collapse and taking of targeted prevention and control measures.
Of course, it is not necessary for any product to achieve all of the above-described technical effects simultaneously in the practice of the invention.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flow chart of an intelligent extraction method for sensitive elements of urban road collapse according to an embodiment of the invention;
fig. 2 is a block diagram of an intelligent extraction system for sensitive elements of urban road collapse according to an embodiment of the present invention.
Wherein, in the figure:
1. an element database; 2. a data acquisition module; 3. a model building module; 4. a calibration module; 5. a result calculation module; 6. and an element extraction module.
Detailed Description
For better understanding of the technical solutions of the present invention, the following detailed descriptions of the embodiments of the present invention are provided with reference to the accompanying drawings.
It should be understood that the described embodiments are only some embodiments of the invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Aiming at the defects of the prior art, the invention provides an intelligent extraction method of sensitive elements of hidden danger of urban road collapse, which comprises the following steps as shown in figure 1:
the element types comprise attribute characteristic detection data, mechanical parameter detection data, stress deformation monitoring data and engineering disturbance monitoring data; specifically, the attribute characteristic detection data comprise road collapse hidden danger diameter and burial depth, the mechanical parameter detection data comprise soil body density, water content, cohesion, internal friction angle and uniaxial compressive strength, the stress deformation monitoring data comprise structural stress and pavement settlement value, and the engineering disturbance monitoring data comprise tensile stress generated by engineering disturbance dynamic load;
step 2.1, determining the diameter and the burial depth of the hidden danger of road collapse by using a ground penetrating radar;
step 2.2, obtaining a standard core soil test piece with hidden road collapse danger by intelligent micro-core penetration equipment according to soil density, water content, cohesion, internal friction angle and uniaxial compressive strength, and obtaining the core soil standard test piece through a core soil standard physical mechanical test;
step 2.3, monitoring and acquiring structural stress of the soil body and a pavement settlement value through MEMS sensing equipment arranged at the junction of the soil body and the roadbed right above the collapse hidden danger, wherein the MEMS sensing equipment is an existing device;
step 2.4, surveying the construction disturbance situation around the hidden danger of road collapse, and measuring the distance between an engineering operation point and the hidden danger of collapse based on a total station; at least 2 groups of vibration pickups are distributed in an engineering disturbance area, and the vibration pickups are adopted to obtain the initial peak vibration speed of the dynamic load of engineering disturbance; the vibration pickup is an existing device;
step 2.5, calculating the tensile stress sigma generated by the engineering disturbance dynamic load at the hidden danger of road collapse t The relation between the initial peak value vibration speed of the engineering disturbance dynamic load and the tensile stress is as follows:
in the above formula, v s The method comprises the steps of calculating the peak vibration speed of initial particles caused by engineering disturbance dynamic load, wherein L is the distance between an engineering operation point and collapse hidden danger, delta L is the distance between vibration pickups, delta t is the wave travel time difference, m is the peak vibration speed attenuation coefficient, and rho is the soil density; wherein v is s L, Δ t, m and ρ are known values.
The number of the same type of elements in the element database is more than or equal to 10, and the same type of elements refers to any one of road collapse hidden danger diameter, buried depth, disease soil density, water content, cohesion, internal friction angle, uniaxial compressive strength, structural stress, pavement settlement value and tensile stress generated by engineering disturbance dynamic load; the purpose is to ensure that the polynomial regression model has reliable goodness of fit.
y i ＝β i0 +β i1 x i +β i2 x i 2 +ε i (2)
in the above formula, x i Is an independent variable, y i I =1, 2, 3 \8230asa dependent variable, n is the number of independent variables, beta i0 、β i1 、β i2 In order of x i Of zero order, first order and second order coefficients, epsilon i Is x i The error term of (2).
And 4, step 4: calibrating the undetermined coefficient beta of a polynomial regression model i0 、β i1 、β i2 And error term epsilon i ；
The specific implementation steps of the step 4 comprise:
4.1, importing the road collapse hidden danger pregnancy disaster and disaster causing element database established in the step 2 and the polynomial regression model established in the step 3 into a model calibration algorithm, wherein the model calibration algorithm is established based on any one programming language of PYTHON, R language or MATLAB;
4.2, fitting a polynomial regression relation of the single independent variable and the single dependent variable based on a model calibration algorithm;
4.3, determining a waiting coefficient beta of the polynomial regression model based on the fitting result i0 、β i1 、β i2 And ε i And (4) calibrating the value to finish the parameter calibration of the polynomial regression model.
The polynomial regression model represents the unified quantitative relation between the road collapse sensitive elements and the compressive strength of the soil body after the measured data calibration.
σ i ＝β i0 +β i1 +β i2 +ε i (3)
in the above formula, σ i Is a unit independent variable x i Cause variable y i Of change value of, beta i0 、β i1 、β i2 And ε i Are all known values.
in the above formula, σ bi Is σ i Is a discrete normalized processing value of imin Is σ i Minimum value of, σ imax Is σ i Is measured.
Step 7, dividing the sensitivity levels of hidden danger and disaster-causing factors of road collapse by adopting a four-fold breakpoint method:
grade a sensitive (particularly sensitive): sigma 0.75 bi ≤1，
Level B sensitive (more sensitive): sigma is more than 0.5 bi ≤0.75，
Class C hazards (generally sensitive): sigma is more than 0.25 bi ≤0.5，
Grade D hazard (hypo-sensitivity): sigma is more than or equal to 0 bi ≤0.25；
Step 8, according to the sigma bi Calculating a value, and determining the sensitive grade of the factors of road collapse hidden danger, pregnancy disaster and disaster causing;
and 9, extracting road collapse sensitive elements or element combinations according to different sensitivity levels.
Another objective of the present invention is to provide a system for implementing an intelligent extraction method of sensitive elements of hidden danger of urban road collapse, where a structural block diagram of the system is shown in fig. 2, and the method includes:
an element database 1 for storing element types and element data;
the data acquisition module 2 is used for acquiring the measured data of each element and storing the measured data in the element database 1;
the model building module 3 is used for building a polynomial regression model of a single independent variable and a single dependent variable; the independent variable and the dependent variable are both element types in an element database;
the calibration module 4 is embedded with a model calibration algorithm and is used for calibrating the polynomial regression model by the element data in the element database and determining parameters in the polynomial regression model;
a result calculating module 5 for calculating the unit independent variable x according to the calibrated polynomial regression model i Cause variable y i Variation value σ of i (ii) a Then calculates the variation value sigma i Is a discrete normalized processing value σ bi ；
An element extraction module 6 for processing the value σ according to the discrete normalization bi And (5) extracting elements.
Example 1:
the following clearly and completely describes the technical scheme of the present invention with reference to the specific embodiment 1 of the present invention, and the specific steps include:
(1) Radar detection is carried out on hidden danger of road collapse on a certain road, 8 parts of loose diseases, 2 parts of water-rich diseases, 1 part of void diseases and 3 parts of void diseases are found together, and the positions, the diameters and the burial depths of the diseases are recorded; acquiring a standard core soil test piece at a collapse hidden danger position by adopting micro-core penetration equipment, and testing the density, the water content, the cohesion, the internal friction angle and the uniaxial compressive strength of a soil body in a laboratory; arranging MEMS sensing equipment at the junction of the road collapse hidden danger and a roadbed soil body, and monitoring the structural stress and the road surface deformation; and (3) investigating the surrounding environment, determining that one engineering disturbance point exists, and arranging two vibration pickers at the periphery of a construction area at an interval of 80m. Establishing a road collapse hidden danger pregnant disaster and disaster causing database based on the detection and monitoring data;
(2) Calculating the tensile stress sigma generated at the collapse hidden danger position by the disturbance dynamic load of each engineering t ；
In the above formula, the maximum dynamic load due to field monitoring is 10 6 J, according to the corresponding relation between the dynamic load and the peak speed, taking v s The ratio of the delta L to the delta t is 2917m/s according to the monitoring data, and the L and the rho are the actual measured values of 14 collapse hidden parts of the road.
(3) Establishing a polynomial regression model in which the single independent variable of the collapse hidden danger at the 14 position of the road is the diameter, the burial depth, the soil body density, the water content, the cohesion, the internal friction angle, the tensile stress generated by the dynamic load of engineering disturbance, the soil body structural stress, the pavement settlement value and the single dependent variable are the uniaxial compressive strength of the soil body;
y i ＝β i0 +β i1 x i +β i2 x i 2 +ε i (2)
in the above formula, x i Is an independent variable, y i Is a dependent variable, i =1, 2, 3 \8230;, 9, beta i0 、β i1 、β i2 In order of x i Of zero order, first order and second order coefficients, epsilon i Is x i The error term of (2).
(4) Calibrating the coefficients to be determined and the error terms of the 9 groups of polynomial regression models;
(5) Calculating the variation value sigma of the dependent variable caused by the unit independent variable of 9 groups of polynomial regression models i ；
σ i ＝β i0 +β i1 +β i2 +ε i (3)
In the above formula, σ i Is a unit independent variable x i Cause variable y i Change value of beta i0 、β i1 、β i2 And ε i Are all known values.
(6) To sigma i Value is enteredLine discrete normalization, 9 sets of processed σ i The value is present in the interval [0, 1]]Internal;
in the above formula, σ bi Is σ i Is a discrete normalized processing value of imin Is σ i Minimum value of, σ imax Is σ i Of (c) is calculated.
(7) A four-fold breakpoint method is adopted to divide the sensitivity levels of hidden danger, pregnancy disaster and disaster causing elements of road collapse:
grade a sensitive (particularly sensitive): sigma is more than 0.75 bi ≤1，
Grade B sensitive (more sensitive): sigma is more than 0.5 bi ≤0.75，
Class C hazards (generally sensitive): sigma is more than 0.25 bi ≤0.5，
Grade D hazard (hypo-sensitivity): sigma is more than or equal to 0 bi ≤0.25；
(8) According to the diameter of the hidden trouble, the buried depth, the soil density, the water content, the cohesive force, the internal friction angle, the stretching stress generated by the dynamic load of the engineering disturbance, the soil structure stress and the sigma corresponding to the pavement settlement value bi The method comprises the steps of dividing a value and sensitivity grade into standards, determining that the density and the water content of a road soil body are A-grade sensitivity (particularly sensitivity), the buried depth, the cohesive force and the internal friction angle of the hidden danger of road collapse are C-grade sensitivity (general sensitivity), and the diameter of the hidden danger of road collapse, the tensile stress generated by the dynamic load of engineering disturbance, the structural stress of the soil body and the settlement value of a road surface are D-grade sensitivity (weak sensitivity);
(9) And extracting the element combination with density and water content which are the most sensitive relative to the road collapse according to the sensitive grades of the hidden danger and disaster causing elements of the road collapse.
Due to the adoption of the technical scheme, compared with the prior art, the invention has the following advantages:
(1) Quantitatively representing the sensitivity of pregnant disaster and disaster-causing factors
At present, a technical scheme for quantitatively representing the susceptibility of the hidden danger of road collapse and disaster-causing elements is lacked, a polynomial regression model of the hidden danger and disaster-causing elements and the uniaxial compressive strength of a soil body is calibrated through field actual measurement data and test data, and the sensitivity degrees of different hidden danger and disaster-causing elements are quantitatively represented.
(2) Quantitatively dividing the sensitivity level of pregnant disaster and disaster-causing elements
At present, the technical scheme for quantitatively dividing the sensitive grades of the elements causing the hidden danger and the disaster of the collapse of the road is lacked, the invention innovatively develops a four-fold breakpoint method for dividing the sensitive grades of the elements, and the sensitive grades are determined to be weak sensitive, general sensitive, more sensitive and particularly sensitive according to [0,0.25], (0.25, 0.5], (0.5, 0.75) and (0.75, 1), and the grading indexes of the sensitive grades of the elements have strong pertinence.
(3) Proposes a road collapse sensitive element or combination of elements
At present, the technical scheme of providing road collapse sensitive elements or element combinations is lacked, the invention specifically analyzes the environment according to actual measured data of roads and provides the most sensitive factors or element combinations of pregnancy and disaster based on the element sensitivity grade, thereby providing theoretical basis for early warning and comprehensive prevention and treatment of the road collapse risk.
The method and the system for intelligently extracting the sensitive elements of the hidden danger of urban road collapse provided by the embodiment of the application are introduced in detail. The above description of the embodiments is only for the purpose of helping to understand the method of the present application and its core ideas; meanwhile, for a person skilled in the art, according to the idea of the present application, the specific implementation manner and the application scope may be changed, and in summary, the content of the present specification should not be construed as a limitation to the present application.
It is also noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a good or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such good or system. Without further limitation, an element defined by the phrases "comprising one of \8230;" does not exclude the presence of additional like elements in an article or system comprising the element. "substantially" means within an acceptable error range, and a person skilled in the art can solve the technical problem within a certain error range to substantially achieve the technical effect.
The terminology used in the embodiments of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the examples of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. The term "and/or" as used herein is merely an associative relationship that describes an associated object, meaning that three relationships may exist, e.g., a and/or B, may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" herein generally indicates that the former and latter related objects are in an "or" relationship.
Claims (8)
1. An intelligent extraction method for sensitive elements of hidden danger of urban road collapse is characterized by comprising the following steps:
s1, determining the types of hidden danger factors of urban road collapse;
s2, acquiring actual measurement data of each element to form an element database;
s3, establishing a polynomial regression model of a single independent variable and a single dependent variable; the independent variable and the dependent variable are both element types in S1; the dependent variable is the uniaxial compressive strength of the soil body;
s4, calibrating the polynomial regression model by using data in the element database, and determining parameters in the polynomial regression model;
s5, calculating unit independent variable x according to the calibrated polynomial regression model i Cause variable y i Variation value σ of i ；
S6, calculating a variation value sigma i Is a discrete normalized processing value σ bi ；
S7, according to the calculated discrete normalization processing value sigma bi Extracting elements;
the specific content of step S7 includes: processing the value sigma according to a discrete normalization bi Determining the sensitivity level of the elements, and extracting the elements or element combinations according to the sensitivity level of the elements;
the method for dividing the sensitivity level specifically comprises the following steps: processing the value σ by discrete normalization bi And dividing the sensitivity level of the elements by adopting a four-fold breakpoint method as a basis.
2. The method for intelligently extracting the urban road collapse hidden danger sensitive elements according to claim 1, wherein the types of the elements in the step S1 comprise:
the diameter and burial depth of the hidden danger of road collapse;
density, water content, cohesion, internal friction angle and uniaxial compressive strength of the soil body;
structural stress and pavement settlement value; and
and (4) the engineering disturbance dynamic load generates tensile stress.
3. The method for intelligently extracting the sensitive elements of the hidden danger of urban road collapse according to claim 1, wherein the independent variables in the step S3 are any one of the diameter and the burial depth of the hidden danger of road collapse, the density, the water content, the cohesive force, the internal friction angle of a soil body, the structural stress, the pavement settlement value and the tensile stress generated by the dynamic load of engineering disturbance.
4. The method for intelligently extracting the urban road collapse hidden danger sensitive elements according to claim 1, wherein the polynomial regression model established in the step S3 is as follows:
y i ＝β i0 +β i1 x i +β i2 x i 2 +ε i
in the formula, x i Is an independent variable, y i Is a dependent variable;
i is an independent variable mark, i =1, 2, 3 \8230, and n is the total number of independent variables;
β i0 、β i1 、β i2 are in turn independent variable x i Coefficient of order zero, oneA second order coefficient and a third order coefficient;
ε i is an independent variable x i The error term of (2).
5. The method for intelligently extracting the urban road collapse hidden danger sensitive elements according to claim 2, wherein the mode for acquiring actually measured element data in the step S2 comprises the following steps:
the method for acquiring the measured data of the diameter and the burial depth of the hidden danger of the road collapse comprises the following steps: the method comprises the following steps of obtaining by a ground penetrating radar detection mode;
the actual measurement data acquisition mode of density, moisture content, cohesion, internal friction angle and unipolar compressive strength of the soil body is: firstly, detecting by adopting a micro-core penetration test device to obtain a coring soil body standard test piece with hidden road collapse danger, and then carrying out a physical mechanical test on the coring soil body standard test piece to obtain the coring soil body standard test piece;
the actual measurement data acquisition mode of structural stress and road surface settlement is as follows: monitoring and obtaining are carried out by arranging MEMS sensing equipment at the junction of the soil body and the roadbed right above the hidden danger of collapse.
6. The method for intelligently extracting the urban road collapse hidden danger sensitive elements according to claim 2, wherein the mode of acquiring the actually measured data of the tensile stress generated by the engineering disturbance dynamic load in the step S2 is as follows:
distributing more than two groups of vibration pickers in an engineering disturbance area, and acquiring an initial peak vibration speed of engineering disturbance dynamic load by adopting the vibration pickers;
and calculating the tensile stress according to the obtained initial peak value vibration speed of the engineering disturbance dynamic load.
7. The intelligent urban road collapse hidden danger sensitive element extraction method according to claim 6, wherein a formula for calculating the tensile stress is as follows:
in the formula, v s Dynamic load initialization for engineering disturbanceThe peak value vibration speed, L is the distance between an engineering operation point and the collapse hidden danger, delta L is the distance between the vibration pickers, delta t is the wave travel time difference, m is the peak value vibration speed attenuation coefficient, and rho is the soil mass density.
8. An intelligent urban road collapse hidden danger sensitive element extraction system is characterized by being used for realizing the intelligent urban road collapse hidden danger sensitive element extraction method of any one of claims 1 to 7; the system comprises:
an element database for storing element types and element data;
the data acquisition module is used for acquiring the measured data of each element and storing the measured data in the element database;
the model building module is used for building a polynomial regression model of a single independent variable and a single dependent variable; the independent variable and the dependent variable are both element types in an element database;
the calibration module is embedded with a model calibration algorithm and is used for realizing the calibration of element data in the element database to the polynomial regression model and determining parameters in the polynomial regression model;
a result calculation module for calculating the unit independent variable x according to the calibrated polynomial regression model i Cause variable y i Variation value σ of i (ii) a Then calculates the variation value sigma i Is a discrete normalized processing value σ bi ；
An element extraction module for processing the value sigma according to the discrete normalization bi And (5) extracting elements.
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