CN112200478B - Method and system for processing frost heaving risk information of cohesive soil field - Google Patents
Method and system for processing frost heaving risk information of cohesive soil field Download PDFInfo
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Abstract
The invention belongs to the technical field of frozen swelling risk grade evaluation of frozen soil fields, and discloses a frozen swelling risk information processing method and system for cohesive soil fields, wherein the frozen swelling risk information processing system suitable for the cohesive soil fields comprises the following steps: the system comprises a sampling module, an evaluation index determining module, an evaluation standard determining module, an evaluation factor weighting module, a correlation degree calculating module, an evaluation model constructing module and a risk level determining module. The extension cloud frost heaving risk evaluation model based on the entropy theory considers the fuzziness, randomness and actual distribution rule in the cohesive soil evaluation index information, avoids information loss caused by data normalization, and meanwhile, weights are given to original data by using the entropy method so that the weights of all indexes are more scientific and reasonable, and scientific and effective references can be provided for evaluation of frost heaving risk levels of foundation soil of a foundation pit field in a frozen soil area. The extension cloud model has greater superiority in the aspect of evaluation uncertainty, so the evaluation result of the extension cloud model is more consistent with the actual situation.
Description
Technical Field
The invention belongs to the technical field of frozen soil field frozen swelling risk grade evaluation, and particularly relates to a frozen swelling risk information processing method and system for a cohesive soil field.
Background
At present, when subway engineering construction is carried out in a frozen soil area, the phenomenon of overwintering of a foundation pit is very common. Under low temperature environment, on the one hand, the inside two-way heat dissipation of soil body, free water in the soil body on the other hand freezes concretion, makes different degree frost heaving appear in foundation ditch top surface and lateral wall, and its frost heaving ratio calculated value is more a lot more. In order to ensure the safety of foundation pit engineering in a frozen soil area, the frost heaving risk of foundation soil of the site is judged in advance, and corresponding measures are taken for a high-risk area within the excavation depth range of the foundation pit so as to control the frost heaving of the foundation pit. Foreign scholars Einstein firstly carry out underground engineering construction risk research on the basis of risk management. And (3) performing site frost heaving risk evaluation, namely determining a frost heaving risk evaluation system which is closely related to frost heaving property of soil. A large amount of researches are carried out on frost heaviness and classification of soil at home and abroad, and various frost heaving mathematical models are established to predict the frost heaving deformation of the soil body. Establishing a frost heaving prediction model by the aid of the Gilpin according to the relationship between the frost heaving rate of the soil and the thermal conductivity, particle size and external boundary conditions of the soil; dan Yang et al utilize frost heaving numerical model to carry out frost heaving trend prediction on the silty clay on the basis of centrifugal model; sally and the like predict the frost heaving amount of the frozen soil through a hydrothermal coupling model; establishing a saturated clay frost heaving deformation estimation model by a gunn by adopting an increment nonlinear elasticity theory; the frozen soil heat diffusion equation based on the water migration principle is deduced through a saturated powder soil frost heaving test in Zhou Jia do and the like, and a numerical simulation method for predicting the change of the frost heaving amount is provided; wu Ziwang proposes the limit value of frozen soil engineering classification and frost heaving grade division; chenaijun proposes a method for grading the frost heaving of the railway roadbed filling by taking the frost height as a standard; plum rain concentration and the like provide superplastic water content frost heaviness grade limit values of cohesive soil, silt and fine soil; the boundary water content of the coarse-grained soil corresponding to different frost heaving grades and categories is provided in Zhang-Kung-Cheng and the like; and the cheng zhup peak and the like provide the frost heaving grade limit water content of the clay when the frost heaving rate is less than 6%. The research mainly uses single indexes such as water content, plastic limit water content and frost heaving rate as threshold values to carry out frost heaving grade division, and does not consider other frost heaving influence factors. In addition, the frost heaving grade threshold value adopted by the specification is often larger, and the soil body frost heaving property evaluation result based on the standard is unsafe.
The current risk evaluation methods mainly use a fuzzy analytic hierarchy process, a fault tree analytic process, a grey fuzzy theory and the like, although the methods can predict and evaluate through measured data, the methods cannot accurately and effectively reflect the characteristics of evaluation indexes in actual engineering, the conversion situation among the indexes cannot be quantitatively described, and the obtained judgment result cannot evaluate and classify site risk levels. In recent years, the application of the extension theory in the stability evaluation of geotechnical engineering is increasingly wider. However, because there are many index factors affecting frost heaviness, there are some ambiguity, randomness and discreteness between these indexes, and the discretization problem cannot be solved only by using the extension theory to perform quantitative calculation through the correlation function. The cloud model can solve the problems just according to the randomness relation between the measured data and the evaluation level, some scholars develop extensive study evaluation research based on the cloud model, and the cloud model is applied to the aspects of tunnel surrounding rock stability, construction safety evaluation, site settlement risk, road performance and the like, so that a good effect is achieved.
In conclusion, relatively few researches on the evaluation of the frost heaving risk of the overwintering foundation pit site in the frozen soil area are carried out in China, the evaluation index is too single, the threshold value is set to be larger in the existing classification standard and evaluation method of the frost heaving level, the uncertainty relation among various frost heaving influence factors cannot be reflected in the evaluation result, and the accuracy of the evaluation result of the frost heaving risk level of the site is influenced.
Through the above analysis, the problems and defects of the prior art are as follows:
(1) the current risk evaluation method cannot accurately and effectively reflect the characteristics of evaluation indexes in actual engineering, cannot quantitatively describe the conversion situation among the indexes, and cannot evaluate and classify site risk levels according to the obtained judgment result.
(2) Because the index factors influencing the frost heaviness are many, the indexes have certain ambiguity, randomness and discreteness, and the discretization problem cannot be solved only by carrying out quantitative calculation through an association function by using an extension theory.
(3) The research on the frost heaving risk evaluation of the overwintering foundation pit site in the frozen soil area is relatively less in China, the evaluation index is too single, the threshold value is set to be larger in the existing frost heaving grade classification standard and evaluation method, the uncertainty relation among various frost heaving influence factors cannot be reflected in the evaluation result, and the accuracy of the site frost heaving risk grade evaluation result is influenced.
The difficulty in solving the above problems and defects is:
the traditional grading of frost heaving of a site is to measure basic physical indexes of a soil sample and perform a frost heaving test through reconnaissance sampling to obtain the frost heaving rate, and further determine the grading of frost heaving property of the soil according to the existing standard. However, the frost heaviness of the soil is considered to be the frost heaviness of the site, which is a result of the joint influence of multiple factors, because the frost heaviness index of the soil only reflects the frost heaviness of the test soil sample. Therefore, the site frost heaviness grading cannot be accurately obtained by adopting the traditional soil frost heaviness grading.
The significance of solving the problems and the defects is as follows:
the extension cloud model can solve the problems of ambiguity, randomness and discreteness existing among factors influencing the frost heaviness of the site according to the randomness relation between the measured data and the evaluation level, and the entropy weighting can objectively and accurately determine the weight among the factors according to the measured data. The technology obtains frost heaving risk grade of the site through multiple physical and mechanical indexes of the soil sample, has multiple evaluation indexes and high accuracy of evaluation results, provides basis for preventing the frost heaving risk of the site in actual engineering, reduces the risk and damage of the site to the engineering caused by frost heaving, and has important significance for engineering building design, construction and frost damage prevention and control of frozen soil areas.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a method and a system for processing frost heaving risk information of a cohesive soil field, so as to realize frost heaving risk grade evaluation on actually measured data of a cohesive soil sample.
The invention is realized in this way, a frost heaving risk information processing system suitable for a cohesive soil field comprises:
the sampling module is used for sampling the cohesive soil within the support depth range through the drill hole;
the physical index testing module is used for carrying out related physical index testing after sampling;
the evaluation index determining module is used for determining frost heaving risk evaluation indexes according to the influence factors of the frost heaving of the cohesive soil and the geological conditions of the area to be evaluated;
the evaluation standard determining module is used for carrying out standard division on the site frost heaving risk according to the engineering experience, the relevant documents, the specifications and the design requirements of the area to be evaluated;
the evaluation factor weighting module is used for weighting the evaluation factors according to the measured data of each index by an entropy method;
the correlation degree calculation module is used for calculating the correlation degree between the sample to be evaluated and each index of the frost heaving grade based on the composite cloud model;
the evaluation model building module is used for building an extension cloud model based on an extension cloud theory;
and the risk grade determining module is used for determining the evaluation grade of the sample according to the maximum certainty matrix and the maximum certainty principle by integrating the certainty matrix.
Further, in the evaluation index determination module, a sampling depth H, a water content w and a dry density gamma are selected d Porosity e and superplastic water content w-w p And the five indexes are used as evaluation indexes of the frost heaving grade of the cohesive soil field.
Further, in the evaluation criterion determining module, the site frost heaving risk is divided into five grades according to engineering experience, related documents, specifications and design requirements of the area to be evaluated, wherein the five grades are I grade (non-frost heaving), II grade (weak frost heaving), III grade (frost heaving), IV grade (strong frost heaving) and V grade (extra strong frost heaving).
Furthermore, the weighting of the evaluation factors according to the measured data of each index by the entropy method through the evaluation factor weighting module comprises the following steps:
suppose that m samples to be measured constitute an evaluation object set { A } i N index data form an index set { X ═ 1, 2, …, m j J ═ 1, 2, …, n), where x ij And the original value of the j index of the ith sample to be tested is shown. The proportion y occupied by different quantities in different indexes is obtained after standardization treatment ij The formed standard matrix Y and the proportion Y of the quantity value j in the index i ij And the formula for calculating the entropy e of any index is as follows:
Y＝(y ij ) m×n ,(i＝1,2,…,m；j＝1,2,…,n)；
in the formula, k is a constant number related to the number m of samples in the system. When the degree of order is 0, its entropy value is maximum, i.e., e ═ 1. When m samples are in a completely disordered distribution, y ij At 1/m, the entropy value at this time is zero for the utility value of the overall evaluation, and therefore, the difference coefficient g of the j-th index j Determined by the difference between the entropy of the indicator and 1, the coefficient of difference g j The calculation formula of (c) is as follows:
g j ＝1-e j (j＝1,2,…,n)；
the essence of estimating the index weight by the entropy method is to calculate by using a value coefficient of the index information, and the higher the value coefficient is, the greater the importance is. Weight w of j-th index j The calculation formula of (a) is as follows:
further, the calculating, by the association degree calculating module, the association degree between the sample to be evaluated and each index of the frost heaving level based on the composite cloud model includes:
assuming that the evaluation category is T, a calculation formula of an n-dimensional composite cloud model R with m evaluation categories obtained by actually measuring data in an object element form is as follows:
in the formula, T j (j ═ 1, 2, …, m) for the j-th assessment category; mu.s ij (x ij ) Is a corresponding magnitude x ij (i ═ 1, 2, …, n; j ═ 1, 2, …, m) membership; j and i are respectively the serial number and the physical dimension of the evaluation index and the corresponding characteristic value.
Calculating the degree of correlation between the sample to be evaluated and each index of the frost heaving grade based on a composite cloud model, namely, generating a normal random number En' by using Matlab and taking En as a mean value and He as a standard deviation to make a deterministic value in the sample to be evaluated be x i Cloud entropy of (x) i ，μ i ). The calculation formula of the correlation degree is as follows:
the calculation formula for constructing the extension cloud matrix Q according to the relevance is as follows:
further, the establishing of the extension cloud model through the evaluation model establishing module based on the extension cloud theory includes:
representing the cloud model by cloud digital feature values of expected Ex, entropy En and super entropy He; the entropy En is used for measuring the uncertainty degree of the qualitative concept, is determined by the ambiguity and the randomness of the qualitative concept, and reflects the discreteness of cloud droplets and the value interval of the cloud droplets approved by the qualitative concept. The super-entropy He is the uncertainty of the entropy measure, reflecting the thickness of the cloud droplets.
The calculation formulas for expected Ex, entropy En, and super-entropy He are as follows:
Ex＝(C max +C min )/2；
En＝(C max -C min )/6；
He＝s；
in the formula, C max 、C min S is a constant, and is the maximum and minimum boundary values of a certain class of criteria.
The expression of the extension cloud model constructed based on the extension cloud theory is as follows:
in the formula, R j Is a unit element, N j Is the jth evaluation category, c j Is the jth feature index, x j ＝(E xj ，E nj ，H ej ) Is N j About feature c j A specified magnitude interval.
Further, the determining the evaluation level of the sample by the risk level determining module by using the comprehensive certainty matrix and according to the maximum certainty principle includes:
when the risk level is determined, the comprehensive certainty matrix B is calculated by multiplying the index weight vector W and the extension cloud matrix Q. And each sample has corresponding certainty factor for each grade, the certainty factors of the same grade are accumulated, and the evaluation grade of the sample is determined according to the maximum certainty factor. The comprehensive certainty matrix is as follows:
in the formula, b ij The component of the integrated certainty matrix B is the integrated certainty that a sample corresponds to an index.
Another object of the present invention is to provide a method for processing frost heaving risk information of a cohesive soil field, using the system for processing frost heaving risk information of a cohesive soil field, where the method for processing frost heaving risk information of a cohesive soil field includes the following steps:
step one, sampling cohesive soil within a supporting depth range by using a drill hole through a sampling module.
And step two, determining frost heaving risk evaluation indexes according to the influence factors of the frost heaving of the cohesive soil and the geological conditions of the area to be evaluated by an evaluation index determination module.
And thirdly, performing standard division on the site frost heaving risk through an evaluation standard determining module according to engineering experience, relevant documents, specifications and design requirements of the area to be evaluated.
And step four, weighting the evaluation factors according to the measured data of each index by an entropy method through an evaluation factor weighting module.
And fifthly, calculating the correlation degree between the sample to be evaluated and each index of the frost heaving grade through a correlation degree calculation module based on the composite cloud model.
And step six, constructing an extension cloud model based on an extension cloud theory through an evaluation model construction module.
And seventhly, determining the evaluation level of the sample by using the comprehensive certainty matrix through a risk level determination module according to a maximum certainty principle.
It is a further object of the invention to provide a computer device comprising a memory and a processor, the memory storing a computer program which, when executed by the processor, causes the processor to perform the steps of:
sampling cohesive soil within the support depth range by using the drill hole through a sampling module;
determining frost heaving risk evaluation indexes according to the influence factors of the frost heaving of the cohesive soil and the geological conditions of the area to be evaluated by an evaluation index determination module;
performing standard division on the site frost heaving risk according to engineering experience, relevant documents, specifications and design requirements of the area to be evaluated by an evaluation standard determination module;
weighting the evaluation factors according to the measured data of each index by an entropy method through an evaluation factor weighting module;
calculating the association degree of the sample to be evaluated and each index of the frost heaving level through an association degree calculation module based on a composite cloud model;
constructing an extension cloud model based on an extension cloud theory through an evaluation model construction module;
and determining the evaluation grade of the sample by a risk grade determination module by using the comprehensive certainty matrix according to the maximum certainty principle.
It is another object of the present invention to provide a computer-readable storage medium storing a computer program which, when executed by a processor, causes the processor to perform the steps of:
sampling cohesive soil within the support depth range by using the drill hole through a sampling module;
determining frost heaving risk evaluation indexes according to the influence factors of the frost heaving of the cohesive soil and the geological conditions of the area to be evaluated by an evaluation index determination module;
performing standard division on the site frost heaving risk according to engineering experience, relevant documents, specifications and design requirements of the area to be evaluated by an evaluation standard determination module;
weighting the evaluation factors according to the measured data of each index by an evaluation factor weighting module by using an entropy method;
calculating the association degree of the sample to be evaluated and each index of the frost heaving level through an association degree calculation module based on a composite cloud model;
constructing an extension cloud model based on an extension cloud theory through an evaluation model construction module;
determining the evaluation level of the sample by a risk level determination module by utilizing a comprehensive certainty matrix according to a maximum certainty principle
By combining all the technical schemes, the invention has the advantages and positive effects that: the method and the system for processing the frost heaving risk information of the cohesive soil field are based on an entropy theory and an extensible cloud theory, and provide a field frost heaving risk grade classification standard suitable for cohesive soil and a comprehensive evaluation model thereof by utilizing the advantages of a cloud model in the aspect of uncertainty.
(1) Aiming at the problems that the evaluation indexes in the grade classification of frost heaving are single, the threshold value is large, the evaluation result is uncertain and the like at present, an entropy theory is combined with an extension cloud model, a frost heaving risk grade classification standard suitable for cohesive soil based on five evaluation indexes such as sampling depth, water content, dry density, porosity ratio and overspeed water content is provided, and an extension cloud frost heaving risk comprehensive evaluation model is constructed on the basis.
(2) Through the deep foundation pit engineering case in Changchun city, frost heaving risk grade evaluation is carried out on 12 cohesive soil samples by adopting an extension cloud model, and the frost heaving risk grade evaluation is compared with a standard evaluation result, wherein the evaluation results of non-frost heaving cohesive soil, weak frost heaving cohesive soil and frost heaving cohesive soil are consistent, and the extension cloud evaluation result of strong frost heaving cohesive soil is higher than the standard evaluation result by a first grade. The extension cloud model has greater superiority in the aspect of evaluation uncertainty, so the evaluation result of the extension cloud model is more consistent with the actual situation.
(3) The extension cloud frost heaving risk evaluation model based on the entropy theory considers the fuzziness, randomness and actual distribution rule in the cohesive soil evaluation index information, avoids information loss caused by data normalization, and meanwhile, weights of all indexes are more scientific and reasonable by weighting the original data through the entropy method, and scientific and effective references can be provided for evaluation of frost heaving risk levels of foundation soil of a foundation pit field in a frost region.
Technical effect or experimental effect of comparison. The method comprises the following steps:
drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the embodiments of the present application will be briefly described below, and it is obvious that the drawings described below are only some embodiments of the present application, and it is obvious for those skilled in the art that other drawings can be obtained from the drawings without creative efforts.
Fig. 1 is a block diagram of a frost heaving risk information processing system suitable for a cohesive soil field according to an embodiment of the present invention;
in the figure: 1. a sampling module; 2. an evaluation index determination module; 3. an evaluation criterion determining module; 4. an evaluation factor weighting module; 5. a relevance calculating module; 6. an evaluation model construction module; 7. a risk level determination module; 8. and a physical index testing module.
Fig. 2 is a flowchart of a frost heaving risk information processing method for a cohesive soil field according to an embodiment of the present invention.
Fig. 3 is an effect diagram of cloud drop map calculation performed on each evaluation index by using Matlab according to an embodiment of the present invention;
in the figure: graph (a) is a cloud drop plot of the sampling depth; the graph (b) is a cloud drop graph of water content; graph (c) is a dry density cloud drop graph; graph (d) is a pore ratio cloud drop graph; and (e) is a cloud drop diagram of the superplastic water content.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Aiming at the problems in the prior art, the invention provides a method and a system for processing frost heaving risk information of a cohesive soil field, and the invention is described in detail below with reference to the accompanying drawings.
As shown in fig. 1, an embodiment of the present invention provides a frost heaving risk information processing system for a cohesive soil site, including: the system comprises a sampling module 1, an evaluation index determining module 2, an evaluation standard determining module 3, an evaluation factor weighting module 4, a correlation degree calculating module 5, an evaluation model constructing module 6, a risk level determining module 7 and a physical index testing module 8.
The sampling module is used for sampling the cohesive soil within the support depth range through the drill hole;
the physical index testing module 8 is used for carrying out related physical index tests after sampling;
the evaluation index determining module is used for determining a frost heaving risk evaluation index according to the influence factors of the frost heaving of the cohesive soil and the geological conditions of the area to be evaluated;
the evaluation standard determining module is used for carrying out standard division on the site frost heaving risk according to the engineering experience, the relevant documents, the specifications and the design requirements of the area to be evaluated;
the evaluation factor weighting module is used for weighting the evaluation factors according to the measured data of each index by an entropy method;
the correlation degree calculation module is used for calculating the correlation degree between the sample to be evaluated and each index of the frost heaving grade based on the composite cloud model;
the evaluation model building module is used for building an extension cloud model based on an extension cloud theory;
and the risk grade determining module is used for determining the evaluation grade of the sample according to the maximum certainty matrix and the maximum certainty principle by integrating the certainty matrix.
As shown in fig. 2, the method for processing frost heaving risk information of a cohesive soil field according to an embodiment of the present invention includes the following steps:
s101, sampling the cohesive soil in the supporting depth range by the aid of the drill holes through the sampling module.
And S102, determining frost heaving risk evaluation indexes according to the influence factors of the frost heaving of the cohesive soil and the geological conditions of the area to be evaluated through an evaluation index determining module.
And S103, performing standard division on the site frost heaving risk according to engineering experience, relevant documents, specifications and design requirements of the area to be evaluated through an evaluation standard determining module.
And S104, weighting the evaluation factors according to the measured data of each index by an entropy method through an evaluation factor weighting module.
And S105, calculating the association degree of the sample to be evaluated and each index of the frost heaving level through the association degree calculation module based on the composite cloud model.
And S106, constructing an extension cloud model based on an extension cloud theory through an evaluation model construction module.
And S107, determining the evaluation level of the sample by using the comprehensive certainty matrix through a risk level determination module according to the maximum certainty principle.
The present invention will be further described with reference to the following examples.
The invention provides a site frost heaving risk grade classification standard suitable for cohesive soil and a comprehensive evaluation model thereof based on an entropy theory and an extensible cloud theory and by utilizing the advantages of a cloud model in the aspect of uncertainty, and carries out frost heaving risk grade evaluation on actually measured data of a cohesive soil sample by taking a railway station in Changchun city as an example for building deep foundation pit engineering, and the accuracy and the reliability of the model are verified through the example.
1. Extension evaluation method
1.1 basic theory
The extension theory is a theory of researching the matter elements and transformation thereof based on the matter element theory and the extension set, is used for evaluating the advantages and disadvantages and feasibility of a research object, and carries out qualitative and quantitative analysis on the research object by introducing the matter element R. The extension evaluation method is one of the main applications of the extension theory, and the theoretical framework of the method is constructed by the matter element theory and the extension mathematics, wherein the matter elements are logical cells and basic units of the extension theory and are represented in the form of ordered triad of R ═ { N, c, x }. Wherein N is an evaluation object, c is an index of the evaluation object, and x is a characteristic value of c. The expression of the basic matter element is shown in formula 1.
1.2 cloud theory of prolongation
The extended cloud theory is a method for coupling an object model and a cloud model and analyzing the cloud model by using the object model theory. The cloud model is an uncertainty mathematical model proposed by Leideresol, which is used for qualitative and quantitative conversion of objective things and is generally expressed by cloud digital feature values such as expected Ex, entropy En and super entropy He. Where Ex is expected to be the most representative point in the domain space, i.e., the best sample. The entropy En is used for measuring the uncertainty degree of the qualitative concept, is determined by the ambiguity and the randomness of the qualitative concept, and reflects the discreteness of cloud droplets and the value interval of the cloud droplets approved by the qualitative concept. The super-entropy He is the uncertainty of the entropy measure, reflecting the thickness of the cloud droplets. It is desirable that Ex and entropy En are calculated as shown in equations 2 to 3.
Ex＝(C max +C min )/2 (2)
En＝(C max -C min )/6 (3)
He＝s (4)
In the formula: c max 、C min S is a constant, and is the maximum and minimum boundary values of a certain class of criteria.
1.3 evaluation model
An expression of an extensible cloud model constructed based on an extensible cloud theory is shown as a formula 5.
In the formula: r j Is a unit element, N j Is the jth evaluation category, c j Is the jth feature index, x j ＝(E xj ，E nj ，H ej ) Is N j About feature c j A specified magnitude interval.
Assuming that the evaluation category is T, obtaining an n-dimensional composite cloud model R with m evaluation categories in an object element form through actually measured data, wherein the n-dimensional composite cloud model R is shown as a formula 6.
In the formula: t is j (j ═ 1, 2, …, m) for the j-th assessment category; mu.s ij (x ij ) Is a corresponding magnitude x ij (i ═ 1, 2, …, n ═ 1, 2, …, m) degrees of membership; j and i are evaluation indexes andthe serial number of the corresponding characteristic value and the dimension of the object element.
Calculating the degree of correlation between the sample to be evaluated and each index of the frost heaving grade based on the composite cloud model, namely, generating a normal random number En' by using Matlab and calculating by taking En as a mean value and He as a standard deviation, and making a deterministic value in the sample to be evaluated be x i Cloud entropy of (x) i ，μ i ). The correlation calculation is shown in equation 7.
And constructing a scalable cloud matrix Q according to the relevance, as shown in a formula 8.
1.4 Risk level determination
When the risk level is determined, the comprehensive certainty matrix B is calculated by multiplying the index weight vector W and the extension cloud matrix Q. And each sample has corresponding certainty factor for each grade, the certainty factors of the same grade are accumulated, and the evaluation grade of the sample is determined according to the maximum certainty factor. The comprehensive certainty matrix is shown in equation 9.
In the formula, b ij The component of the integrated certainty matrix B is the integrated certainty that a sample corresponds to an index.
Determining the weight of each evaluation factor is a core problem of extension evaluation and is a key for evaluating accuracy or not. Different weighting methods and different weight calculation results lead to different evaluation results. The evaluation factor is weighted according to the measured data of each index by adopting an entropy method, so that the subjectivity of other weighting methods is avoided, the calculation result is more objective and accurate, and the calculation result accords with the actual rule.
2. Theory of entropy
2.1 basic theory
Entropy is a physical quantity for characterizing the effective utilization degree of heat energy in thermodynamics, and is a physical quantity for counting the degree of disorder of medium-weight molecular motion in physics, and is a measure of the degree of disorder of a system in an information theory. When the entropy value of a certain measurement index is smaller as the value of the index varies to a greater extent, the amount of information provided by the index is larger, and the weight is larger. The entropy method is a method for determining weights according to entropy of each observation index, calculates each index weight through information entropy, and provides a basis for comprehensive evaluation.
2.2 method of empowerment
Suppose that there are m samples to be measured to form an evaluation object set { A i N index data form an index set { X ═ 1, 2, …, m j J ═ 1, 2, …, n), where x ij And the original value of the j index of the ith sample to be tested is shown. The proportion y occupied by different quantities in different indexes is obtained after standardization treatment ij The formed standard matrix Y is shown as a formula 10, and the proportion Y of a quantity value j in an index i ij The entropy e of any index is calculated as shown in equation 12, as shown in equation 11.
Y＝(y ij ) m×n ,(i＝1,2,…,m；j＝1,2,…,n) (10)
K in equation 10 is a constant number related to the number of samples m of the system. When the degree of order is 0, its entropy value is maximum, i.e., e ═ 1. When m samples are in a completely disordered distribution, y ij At 1/m, the entropy value at this time is zero for the utility value of the overall evaluation, and therefore, the difference coefficient g of the j-th index j Determined by the difference between the entropy of the indicator and 1, as shown in equation 13.
g j ＝1-e j (j＝1,2,…,n) (13)
The essence of estimating the index weight by the entropy method is to calculate by using a value coefficient of the index information, and the higher the value coefficient is, the greater the importance is. Weight w of j-th index j As shown in equation 14.
The entropy method is an objective weighting method capable of reflecting index variation degree, and the influence of subjective factors on the evaluation result is avoided to a certain extent by judging the utility value of the index by utilizing the inherent information of the evaluation index.
3. Case analysis
3.1 determining the evaluation indexes and the evaluation standards (FIG. 3 is an effect graph obtained by computing a cloud drop map on each evaluation index by using Matlab provided by the embodiment of the invention, in FIG. 3, a graph (a) is a sampling depth cloud drop map, a graph (b) is a water content cloud drop map, a graph (c) is a dry density cloud drop map, a graph (d) is a pore ratio cloud drop map, and a graph (e) is a superplastic water content cloud drop map.)
Taking a foundation pit project of a Changchun subway station under construction as an example, the frost heaving risk evaluation is carried out on cohesive soil within the range of the supporting depth through drilling and sampling. Selecting a sampling depth H, a water content w and a dry density gamma according to the frozen swelling influence factors of the cohesive soil and the geological conditions of the Changchun region d Porosity e and superplastic water content w-w p And the five indexes are used as evaluation indexes of the frost heaving grade of the cohesive soil field. The engineering safety grade of the foundation pit is one grade, and the frost heaving risk of the foundation pit is divided into five grades according to engineering experience, related documents, specifications and design requirements of a Changchun region, wherein the five grades are respectively grade I (non-frost heaving), grade II (weak frost heaving), grade III (frost heaving), grade IV (strong frost heaving) and grade V (extra strong frost heaving). The classification criteria of the evaluation indexes are shown in table 1, and the measured values of the evaluation indexes obtained by drilling are shown in table 2.
TABLE 1 Classification Standard of evaluation indexes for frost heaving level of cohesive soil
TABLE 2 borehole soil sample test data
3.2 weight and degree of certainty of evaluation index
Calculating sample parameters by the formulas 10 to 14 to determine the entropy e of each evaluation index j Coefficient of difference g j And entropy weight w j The calculation results are shown in table 3. Expected Ex, entropy En and super-entropy He of the frost heaving boundary of the cohesive soil field are calculated according to the grade classification standard of the frost heaving evaluation index of the cohesive soil shown in the table 1 and the formulas 2 to 4. By combining the characteristics of evaluation index parameters of the case, 0.001 is taken as the super-entropy He, and an extensible evaluation cloud model R is constructed according to the frost heaving classification standard and the matter element theory 0 As shown in equation 15.
Computing a cloud drop map of each evaluation index by using Matlab, as shown in FIG. 1, and constructing an extension cloud matrix as shown in Table 4.
TABLE 3 evaluation index entropy value empowerment calculation results
Table 4 evaluation index extension cloud matrix
3.3 evaluation of site frost heaving Risk
According to the weight determined by the entropy method, a comprehensive extension cloud evaluation matrix is calculated according to the formula 9, as shown in the table 5, the degrees of certainty of the same grade are accumulated to obtain the degrees of certainty of the sample under each grade, the frost heaving risk grade of the sample is judged according to the maximum degree of certainty principle, and the evaluation result is shown in the table 6.
Table 5 comprehensive extension cloud evaluation matrix
TABLE 6 extensive cloud evaluation results and comparison
The evaluation results of the present invention were compared with those obtained using the specifications, and the evaluation results of samples 1, 4, 7, 8, 11, and 12 were consistent. According to the data, the samples belong to clay and powdery clay with low plasticity index, and belong to non-frost heaving, weak frost heaving and frost heaving cohesive soil according to the frost heaving rate classification. The evaluation results of the samples 2, 3, 5, 6, 9 and 10 are inconsistent with the standard evaluation results, the cloud-based evaluation results are higher than the standard results by one frost heaving grade, and the samples belong to silty clay and belong to cohesive soil with strong frost heaving as known by grading according to the frost heaving rate.
In the winter period, the soil body behind the foundation pit retaining wall is influenced by low temperature and can generate frost heaving within a certain excavation depth range. Along with the increase of the depth, the temperature is gradually decreased towards the interior of the soil body according to a certain gradient. The Changchun region is mostly made of powdery clay and clay, and is usually frost heaving or strong frost heaving in the deep freezing range. The strong frost heaving cohesive soil sample in the case is greatly influenced by factors such as water content, dry density, plasticity index and temperature. Because the current standard is insufficient in the aspect of frost heaving grade classification, if the frost heaving rate measured by an indoor frost heaving simulation test is used as a judgment basis for the frost heaving risk grade, when the position of a sample exceeds a certain depth below a freezing line and groundwater supply is not influenced by low temperature, the evaluation result is easily lower than the actual condition, and the adopted preventive measures have considerable risk. The cloud extension model can reflect the uncertainty of the cohesive soil and the distribution characteristics within the excavation depth range of the foundation pit, and the preventive measures taken according to the cloud extension evaluation result are safer and more reliable. Therefore, the application of the extension cloud evaluation model based on the entropy method in the frozen swelling risk evaluation of the cohesive soil is effective and feasible, and has certain superiority.
The data processed by the scheme of the invention also comprises a table 7-
TABLE 7
TABLE 8
TABLE 9
TABLE 11
| A degree of comprehensive certainty | Depth of sample | Water content ratio | Dry density | Void ratio | Superplastic water content |
| 1 | 0.0041 | 0.0068 | 0.0000 | 0.0017 | 0.0837 |
| 2 | 0.0139 | 0.0033 | 0.0049 | 0.0109 | 0.0449 |
| 3 | 0.1795 | 0.0043 | 0.0023 | 0.0368 | 0.3394 |
| 4 | 0.1052 | 0.0050 | 0.0002 | 0.0140 | 0.0459 |
| 5 | 0.0050 | 0.0581 | 0.0064 | 0.0011 | 0.0836 |
| 6 | 0.1323 | 0.0056 | 0.0035 | 0.0003 | 0.2436 |
| 7 | 0.0113 | 0.0563 | 0.0106 | 0.0028 | 0.6478 |
| 8 | 0.0295 | 0.0403 | 0.0010 | 0.0175 | 0.6478 |
| 9 | 0.0981 | 0.0026 | 0.0100 | 0.0037 | 0.2528 |
| 10 | 0.2082 | 0.0045 | 0.0000 | 0.0002 | 0.2223 |
| 11 | 0.0216 | 0.0582 | 0.0036 | 0.0000 | 0.0387 |
| 12 | 0.1300 | 0.0039 | 0.0084 | 0.0032 | 0.1354 |
TABLE 12
Watch 13
The above description is only for the purpose of illustrating the present invention and the appended claims are not to be construed as limiting the scope of the invention, which is intended to cover all modifications, equivalents and improvements that are within the spirit and scope of the invention as defined by the appended claims.
Claims (10)
1. The utility model provides a frost heaving risk information processing system suitable for stickness soil place, a serial communication port, frost heaving risk information processing system suitable for stickness soil place includes:
the sampling module is used for sampling the cohesive soil within the support depth range through the drill hole;
the physical index testing module is used for carrying out related physical index testing after sampling;
the evaluation index determining module is used for determining frost heaving risk evaluation indexes according to the influence factors of the frost heaving of the cohesive soil and the geological conditions of the area to be evaluated;
the evaluation standard determining module is used for carrying out standard division on the site frost heaving risk according to the engineering experience, the relevant documents, the specifications and the design requirements of the area to be evaluated;
the evaluation factor weighting module is used for weighting the evaluation factors according to the measured data of each index through an entropy method;
the correlation degree calculation module is used for calculating the correlation degree between the sample to be evaluated and each index of the frost heaving grade based on the composite cloud model;
the evaluation model building module is used for building an extension cloud model based on an extension cloud theory;
and the risk grade determining module is used for determining the evaluation grade of the sample according to the maximum certainty matrix and the maximum certainty principle by integrating the certainty matrix.
2. The frost heaving risk information processing system for a cohesive soil site as claimed in claim 1, wherein the evaluation index determination module selects a sampling depth H, a water content w, and a dry density γ d Porosity e and superplastic water content w-w p And the five indexes are used as evaluation indexes of the frost heaving grade of the cohesive soil field.
3. The frost heaving risk information processing system suitable for cohesive soil sites as claimed in claim 1, wherein in the evaluation criterion determining module, the frost heaving risk of the site is divided into five grades according to the engineering experience, related documents, specifications and design requirements of the region to be evaluated, and the grades are respectively grade i: no frost heaving, grade II: weak frost heaving, grade iii: frost heaving, grade IV: strong frost heaving and grade V: very strong frost heaving.
4. The frost heaving risk information processing method suitable for the cohesive soil field is characterized by comprising the following steps of:
sampling cohesive soil within a support depth range by using a drill hole through a sampling module;
determining frost heaving risk evaluation indexes according to the frost heaving influence factors of the cohesive soil and the geological conditions of the area to be evaluated by an evaluation index determination module;
thirdly, performing standard division on the site frost heaving risk through an evaluation standard determining module according to engineering experience, relevant documents, specifications and design requirements of the area to be evaluated;
weighting the evaluation factors according to the measured data of each index by an entropy method through an evaluation factor weighting module;
calculating the correlation degree of the sample to be evaluated and each index of the frost heaving grade through a correlation degree calculation module based on the composite cloud model;
step six, constructing an extension cloud model based on an extension cloud theory through an evaluation model construction module;
and seventhly, determining the evaluation level of the sample by using the comprehensive certainty matrix through a risk level determination module according to a maximum certainty principle.
5. The frost heaving risk information processing method for the cohesive soil site as claimed in claim 4, wherein in step four, the weighting of the evaluation factor according to the measured data of each index by using the entropy method includes:
there are m samples to be measured to form an evaluation object set { A i 1, 2, …, m; there are n index data to form index set { X j 1, 2, …, n; wherein x is ij Representing the original value of the jth index of the ith sample to be tested;
the proportion y occupied by different quantities in different indexes is obtained after standardization treatment ij The formed standard matrix Y and the proportion Y of the quantity value j in the index i ij And the formula for calculating the entropy value e of any index is as follows:
Y＝(y ij ) m×n ，i＝1，2，…，m；j＝1，2，…，n；
in the formula, k is related to the number m of samples of the system and is constant;
when the degree of order is 0, the entropy value is maximum, namely e is 1; when m samples are in a completely disordered distribution, y ij At 1/m, the entropy value at this time is zero for the utility value of the overall evaluation, and therefore, the difference coefficient g of the j-th index j Determined by the difference between the entropy of the indicator and 1, the coefficient of difference g j The calculation formula of (a) is as follows:
g j ＝1-e j ；j＝1，2，…，n；
the essence of estimating the index weight by the entropy method is to calculate by using a value coefficient of the index information, wherein the higher the value coefficient is, the greater the importance of the value coefficient is; weight w of j-th index j The calculation formula of (a) is as follows:
6. the frost heaving risk information processing method suitable for the cohesive soil field as claimed in claim 4, wherein in step five, the correlation degree between the sample to be evaluated and each index of the frost heaving grade is calculated by the correlation degree calculating module based on the composite cloud model, and the correlation degree includes:
assuming that the evaluation category is T, a calculation formula of an n-dimensional composite cloud model R with m evaluation categories obtained by actually measuring data in an object element form is as follows:
in the formula, T j J is 1, 2, …, m is the jth evaluation category; mu.s ij (x ij ) Are of corresponding magnitude x ij I ═ 1, 2, …, n; j is 1, 2, …, degree of membership of m; j and i are respectively an evaluation index and a serial number and a matter element dimension of a corresponding characteristic value of the evaluation index;
calculating the degree of correlation between the sample to be evaluated and each index of the frost heaving grade based on the composite cloud model, namely, generating a normal random number En' by using Matlab and calculating by taking En as a mean value and He as a standard deviation, and making a deterministic value in the sample to be evaluated be x i Cloud entropy of (x) i ，μ i ) (ii) a The calculation formula of the correlation degree is as follows:
the calculation formula for constructing the extension cloud matrix Q according to the relevance is as follows:
7. the frost heaving risk information processing method for the cohesive soil site as claimed in claim 4, wherein in step six, the building of the scalable cloud model based on the scalable cloud theory by the evaluation model building module includes:
representing the cloud model by cloud digital feature values of expected Ex, entropy En and super entropy He; the entropy En is used for measuring the uncertainty degree of the qualitative concept, is determined by the fuzziness and randomness of the qualitative concept, and reflects the discreteness of cloud droplets and the value interval of the cloud droplets approved by the qualitative concept; the super entropy He is uncertainty of the measurement entropy and reflects the thickness of the cloud droplets;
the calculation formulas for expected Ex, entropy En, and super-entropy He are as follows:
Ex＝(C max +C min )/2；
En＝(C max -C min )/6；
He＝s；
in the formula, C max 、C min The maximum and minimum boundary values of a certain grade standard are obtained, and s is a constant;
the expression of the extension cloud model constructed based on the extension cloud theory is as follows:
in the formula, R j Is a unit element, N j Is the jth evaluation category, c j Is the jth feature index, x j ＝(E xj ，E nj ，H ej ) Is N j About feature c j A specified magnitude interval.
8. The frost heaving risk information processing method applicable to the cohesive soil site as claimed in claim 4, wherein in step seven, determining the evaluation grade of the sample by the risk grade determination module using the comprehensive certainty matrix and according to the maximum certainty principle comprises:
when the risk grade is determined, a comprehensive certainty matrix B is calculated by multiplying an index weight vector W by an extension cloud matrix Q; each sample has corresponding certainty factor for each grade, the certainty factors of the same grade are accumulated, and the evaluation grade of the sample is determined according to the maximum certainty factor principle; the comprehensive certainty matrix is as follows:
9. a computer device, characterized in that the computer device comprises a memory and a processor, the memory storing a computer program which, when executed by the processor, causes the processor to carry out the steps of:
sampling cohesive soil within the range of the supporting depth by using the drill hole through a sampling module;
determining frost heaving risk evaluation indexes according to the influence factors of the frost heaving of the cohesive soil and the geological conditions of the area to be evaluated by an evaluation index determination module;
performing standard division on the site frost heaving risk according to engineering experience, relevant documents, specifications and design requirements of the area to be evaluated by an evaluation standard determination module;
weighting the evaluation factors according to the measured data of each index by an entropy method through an evaluation factor weighting module;
calculating the association degree of the sample to be evaluated and each index of the frost heaving level through an association degree calculation module based on a composite cloud model;
constructing an extension cloud model based on an extension cloud theory through an evaluation model construction module;
and determining the evaluation level of the sample by a risk level determination module by utilizing the comprehensive certainty matrix according to the maximum certainty principle.
10. A computer-readable storage medium storing a computer program which, when executed by a processor, causes the processor to perform the steps of:
sampling cohesive soil within the support depth range by using the drill hole through a sampling module;
determining frost heaving risk evaluation indexes according to the influence factors of the frost heaving of the cohesive soil and the geological conditions of the area to be evaluated by an evaluation index determination module;
performing standard division on the site frost heaving risk according to engineering experience, relevant documents, specifications and design requirements of an area to be evaluated by an evaluation standard determination module;
weighting the evaluation factors according to the measured data of each index by an entropy method through an evaluation factor weighting module;
calculating the association degree of the sample to be evaluated and each index of the frost heaving level through an association degree calculation module based on a composite cloud model;
constructing an extension cloud model based on an extension cloud theory through an evaluation model construction module;
and determining the evaluation level of the sample by a risk level determination module by utilizing the comprehensive certainty matrix according to the maximum certainty principle.
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