CN111622274A - Method and system for predicting settlement of foundation of high-fill foundation of large grained soil in mountainous area - Google Patents
Method and system for predicting settlement of foundation of high-fill foundation of large grained soil in mountainous area Download PDFInfo
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- CN111622274A CN111622274A CN202010384839.5A CN202010384839A CN111622274A CN 111622274 A CN111622274 A CN 111622274A CN 202010384839 A CN202010384839 A CN 202010384839A CN 111622274 A CN111622274 A CN 111622274A
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- E—FIXED CONSTRUCTIONS
- E02—HYDRAULIC ENGINEERING; FOUNDATIONS; SOIL SHIFTING
- E02D—FOUNDATIONS; EXCAVATIONS; EMBANKMENTS; UNDERGROUND OR UNDERWATER STRUCTURES
- E02D33/00—Testing foundations or foundation structures
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- E—FIXED CONSTRUCTIONS
- E02—HYDRAULIC ENGINEERING; FOUNDATIONS; SOIL SHIFTING
- E02D—FOUNDATIONS; EXCAVATIONS; EMBANKMENTS; UNDERGROUND OR UNDERWATER STRUCTURES
- E02D1/00—Investigation of foundation soil in situ
- E02D1/08—Investigation of foundation soil in situ after finishing the foundation structure
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Abstract
The application discloses a method for predicting settlement of a mountain huge-grained soil high-fill foundation, which comprises the following steps: acquiring acquisition parameters corresponding to settlement monitoring of a position to be detected at different times; forming a measured sedimentation value curve graph by the acquisition parameters; preprocessing the acquisition parameters to obtain effective parameters; the effective parameters are used as the input of a Gompers growth curve prediction model; taking the effective parameters as the input of a Poisson curve prediction model; taking the effective parameters as the input of an exponential curve prediction model; taking the effective parameters as the input of a parabolic prediction model; comparing the settlement prediction result with the actually measured settlement value curve graph to determine the settlement prediction result with the minimum absolute error; and forming a final settlement prediction curve graph according to the settlement prediction result with the minimum absolute error.
Description
Technical Field
The application relates to the field of foundation settlement prediction, in particular to a method and a system for predicting settlement of a foundation of a mountain huge grained soil high-fill foundation.
Background
The settlement prediction can predict the stable condition of the building, and provide necessary information for safe operation diagnosis so as to find problems in time and take effective measures; meanwhile, the principle of deformation can be fundamentally understood, engineering theory design and feedback design are carried out, and an effective settlement trend prediction model is established.
At present, a settlement calculation method for a backfill stone foundation is not mature enough, the prediction precision is low, particularly, a simple and practical engineering calculation method does not exist, and the problem of post-construction settlement is most important.
Aiming at the problems of low prediction precision and complex prediction process of a settlement calculation method of a backfill stone foundation in the related art, an effective solution is not provided at present.
Disclosure of Invention
The main purpose of the present application is to provide a method and a system for predicting settlement of a foundation with high fill of large grained soil in a mountainous area, so as to solve the problems of low prediction accuracy and complex prediction process of a settlement calculation method of a backfill stone foundation in the related art.
In order to achieve the above object, the present application provides a method for predicting settlement of a mountain huge-grained soil high-fill foundation, which includes the following steps:
acquiring acquisition parameters corresponding to settlement monitoring of a position to be detected at different times;
forming a measured sedimentation value curve graph by the acquisition parameters;
preprocessing the acquisition parameters to obtain effective parameters;
the effective parameters are used as the input of a Gompers growth curve prediction model to obtain the first sedimentation prediction result;
taking the effective parameters as the input of a Poisson curve prediction model to obtain a second settlement prediction result;
taking the effective parameters as the input of an exponential curve prediction model to obtain a third settlement prediction result;
taking the effective parameters as the input of a parabolic prediction model to obtain a fourth settlement prediction result;
comparing the first settlement prediction result, the second settlement prediction result, the third settlement prediction result and the fourth settlement prediction result with the actually measured settlement value curve graph respectively to determine a settlement prediction result with the minimum absolute error;
and forming a final settlement prediction curve graph according to the settlement prediction result with the minimum absolute error.
Further, the acquisition parameters include time and a settling amount corresponding to the time.
Furthermore, the time is integral multiple of 10d, and d is unit days.
Further, in the Gompers growth curve prediction model and the Poisson curve prediction model, S is a predicted value corresponding to the time t; t is time; a. b and k are undetermined parameters.
Further, in the exponential curve prediction model, stThe predicted value corresponding to the time t is obtained; t is time; a. b is a parameter to be determined, s∞Is the sum of the final settlement values of the foundation.
Furthermore, in the parabolic prediction model, t is the settling time, s is the foundation settling amount at the time t, and a and b are undetermined parameters.
According to another aspect of the present application, there is provided a system for predicting settlement of foundation of high-fill foundation using mountain huge grained soil, comprising:
the acquisition module is used for acquiring acquisition parameters corresponding to settlement monitoring of the position to be detected at different times;
the preprocessing module is used for preprocessing the acquisition parameters to obtain effective parameters;
the prediction module is used for respectively inputting the effective parameters as the Gompers growth curve prediction model, the Poisson curve prediction model, the index curve prediction model and the parabola prediction model and respectively obtaining the prediction results of the Gompers growth curve prediction model, the Poisson curve prediction model, the index curve prediction model and the parabola prediction model;
the drawing module is used for drawing an actually measured sedimentation value curve graph according to the effective parameters and respectively drawing corresponding sedimentation prediction curve graphs according to a first sedimentation prediction result, a second sedimentation prediction result, a third sedimentation prediction result and a fourth sedimentation prediction result;
and the comparison module is used for comparing the settlement prediction curve graphs formed by the first settlement prediction result, the second settlement prediction result, the third settlement prediction result and the fourth settlement prediction result with the actually-measured settlement value curve graph respectively and determining the settlement prediction curve graph with the minimum absolute error.
Further, the method also comprises the following steps:
the database is used for storing the curve graph formed by the drawing module and the settlement prediction curve graph with the minimum absolute error formed by the comparison module;
and the query module is used for calling the contents stored in the database.
In the embodiment of the application, the collected actually measured data are compared by combining the construction engineering example of the mountain region giant grained soil high-fill foundation and applying four settlement prediction methods of a Gompers curve, a Poisson curve, an exponential curve and a parabola. Verification shows that when the calculated settlement value is compared with the actually collected actually-measured settlement value at the same time node, a settlement prediction curve graph with the minimum absolute error is selected, and the influence of the construction environment on a settlement prediction result is reduced, so that the method can accurately reflect the linear relation between the settlement value of the foundation of the high-fill foundation of the large-grained soil and the pre-settlement time t, achieve the purpose of accurately predicting the post-construction settlement, improve the accuracy of settlement prediction, and solve the problems of low prediction precision and complex prediction process of the settlement calculation method of the foundation of the backfill soil stone material in the related art
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, serve to provide a further understanding of the application and to enable other features, objects, and advantages of the application to be more apparent. The drawings and their description illustrate the embodiments of the invention and do not limit it. In the drawings:
FIG. 1 is a schematic diagram of a Gompers growth curve prediction model according to an embodiment of the present application;
FIG. 2 is a schematic diagram of a Poisson curve prediction model according to an embodiment of the present application;
FIG. 3 is a schematic diagram of a parabolic prediction model according to an embodiment of the present application;
FIG. 4 is a schematic diagram of an exponential curve prediction model according to an embodiment of the present application;
Detailed Description
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only partial embodiments of the present application, but 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 application.
Moreover, some of the above terms may be used to indicate other meanings besides the orientation or positional relationship, for example, the term "on" may also be used to indicate some kind of attachment or connection relationship in some cases. The specific meaning of these terms in this application will be understood by those of ordinary skill in the art as appropriate.
Furthermore, the terms "disposed," "provided," "connected," "secured," and the like are to be construed broadly. For example, "connected" may be a fixed connection, a detachable connection, or a unitary construction; can be a mechanical connection, or an electrical connection; may be directly connected, or indirectly connected through intervening media, or may be in internal communication between two devices, elements or components. The specific meaning of the above terms in the present application can be understood by those of ordinary skill in the art as appropriate.
In addition, the term "plurality" shall mean two as well as more than two.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
The embodiment of the application provides a method for predicting the settlement of a mountain huge-grained soil high-fill foundation, which comprises the following steps:
acquiring acquisition parameters corresponding to settlement monitoring of a position to be detected at different times;
forming a measured sedimentation value curve graph by the acquisition parameters;
preprocessing the acquisition parameters to obtain effective parameters;
the effective parameters are used as the input of a Gompers growth curve prediction model to obtain the first sedimentation prediction result;
taking the effective parameters as the input of a Poisson curve prediction model to obtain a second settlement prediction result;
taking the effective parameters as the input of an exponential curve prediction model to obtain a third settlement prediction result;
taking the effective parameters as the input of a parabolic prediction model to obtain a fourth settlement prediction result;
comparing the first settlement prediction result, the second settlement prediction result, the third settlement prediction result and the fourth settlement prediction result with the actually measured settlement value curve graph respectively to determine a settlement prediction result with the minimum absolute error;
and forming a final settlement prediction curve graph according to the settlement prediction result with the minimum absolute error.
In this embodiment, a plurality of settlement prediction models are established, and an optimal model is determined by comparison:
⑴ over-built Gompers growth curve prediction model
The post-construction settlement is subjected to predictive analysis,
s is a predicted value corresponding to the t moment; t is the time (1, 2, 3, 4 … …,
the same applies below); a. b and k are undetermined parameters, and predicted values s at different times t can be obtained by substituting three unknown parameters, so that a first settlement prediction result is formed.
The first step is as follows:
the prediction model is simplified by exponential correction: when S 'is lnS, the formula is modified to S' ═ k + abt
The second step is that:
for simplified model s' ═ k + abtSolving the medium parameters k, a and b. Wherein the solved number s is assumed to be a time value s multiplied by each stage1、s2、s3、s4、s5、s6......
In this embodiment, 9 time parameters are selected, and 3 values are set as a set of numerical sequences, which are respectively substituted into the modified simplified model gompers formula s' ═ k + abtIn the method, three unknown parameter values can be obtained by solving according to a ternary linear equation composition method.
⑵ forecasting model by establishing Poisson curve
The post-construction settlement is subjected to predictive analysis, the model is similar to a Gompers curve model, the Gompers curve model is monotonous and progressive, and the settlement value S is continuously increased along with the increase of time; as the time t approaches infinity, the settling value S will approach a steady value. In the embodiment, three undetermined parameters a, b and k are solved by mainly using a three-segment calculation method, and a predicted value s at different time t can be obtained by substituting three unknown parameters, so that a second settlement prediction result is formed.
The first step is as follows:
the original Poisson curve model is subjected to derivation, and the prediction model form is corrected to be as follows:
the second step is that:
for model
Solving the medium parameters k, a and b. Let l1、l2And l3The reciprocal sums of the numerical values in the 3 intervals are respectively shown as follows:
let l1And l2，l2And l3The parameters can be obtained by taking the difference between the two formulas:
⑶ forecasting model s by building exponential curvet＝(1-ae-bt)s∞Performing predictive analysis on the post-construction settlement, wherein stThe predicted value corresponding to the time t is obtained; t is time; a. b is a parameter to be determined, s∞The sum of the final settlement value of the foundation is substituted into the undetermined parameter to obtain the predicted value s at different time ttThereby forming a third sedimentation predictor.
The first step is as follows:
by modifying the exponential curve model, the prediction model is in the form of:
the second step is that:
for model
Solving the medium parameters a and b, s∞And summing, wherein the expressions are respectively:
⑷ is obtained by building a parabolic prediction model s ═ a (lgt)2And + blgt + c, performing predictive analysis on the post-construction settlement, wherein t is settlement time, s is foundation settlement amount at the time t, and a and b are undetermined parameters, and obtaining predicted values s at different times t by substituting the undetermined parameters, so as to form a fourth settlement prediction result.
The acquisition parameters related in the four prediction models of the embodiment all include time and settlement amount corresponding to the time, the selected time is integral multiple of 10d, d is unit days, and by combining with the mountain region giant grained soil high-fill foundation construction engineering example, four settlement prediction methods of a Gompers curve, a Poisson curve, an index curve and a parabola are used for comparing the collected actually measured data. Verification shows that when the calculated settlement value is compared with the actually collected actually-measured settlement value at the same time node, a settlement prediction curve graph with the minimum absolute error is selected, and the influence of the construction environment on a settlement prediction result is reduced.
As shown in fig. 1 to 4, taking the first-stage engineering layered settlement prediction of the new-generation operation area in the hong qing hong kong zhong chinese hong province as an example, the prediction curves of the gomper pahs growth curve prediction model, the poisson curve prediction model, the parabola prediction model and the index curve prediction model are established, and after comparing with the actually measured settlement value curve, the error value between the curve of the gomper pahs growth curve prediction model and the actually measured settlement value curve is the minimum, so that the gomper pahs growth curve prediction model is selected as the prediction curve of the foundation settlement.
In this embodiment, according to another aspect of the present application, a system for predicting settlement of a foundation of a high-fill foundation using mountain huge grained soil is provided, including:
the acquisition module is used for acquiring acquisition parameters corresponding to settlement monitoring of the position to be detected at different times;
the preprocessing module is used for preprocessing the acquisition parameters to obtain effective parameters;
the prediction module is used for respectively inputting the effective parameters as the Gompers growth curve prediction model, the Poisson curve prediction model, the index curve prediction model and the parabola prediction model and respectively obtaining the prediction results of the Gompers growth curve prediction model, the Poisson curve prediction model, the index curve prediction model and the parabola prediction model;
the drawing module is used for drawing an actually measured sedimentation value curve graph according to the effective parameters and respectively drawing corresponding sedimentation prediction curve graphs according to a first sedimentation prediction result, a second sedimentation prediction result, a third sedimentation prediction result and a fourth sedimentation prediction result;
the comparison module is used for comparing a settlement prediction curve graph formed by the first settlement prediction result, the second settlement prediction result, the third settlement prediction result and the fourth settlement prediction result with the actually-measured settlement value curve graph respectively and determining a settlement prediction curve graph with the minimum absolute error;
the database is used for storing the curve graph formed by the drawing module and the settlement prediction curve graph with the minimum absolute error formed by the comparison module;
and the query module is used for calling the contents stored in the database.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (8)
1. A method for predicting settlement of a foundation of a mountain huge-grained soil high-fill foundation is characterized by comprising the following steps:
acquiring acquisition parameters corresponding to settlement monitoring of a position to be detected at different times;
forming a measured sedimentation value curve graph by the acquisition parameters;
preprocessing the acquisition parameters to obtain effective parameters;
the effective parameters are used as the input of a Gompers growth curve prediction model to obtain the first sedimentation prediction result;
taking the effective parameters as the input of a Poisson curve prediction model to obtain a second settlement prediction result;
taking the effective parameters as the input of an exponential curve prediction model to obtain a third settlement prediction result;
taking the effective parameters as the input of a parabolic prediction model to obtain a fourth settlement prediction result;
comparing the first settlement prediction result, the second settlement prediction result, the third settlement prediction result and the fourth settlement prediction result with the actually measured settlement value curve graph respectively to determine a settlement prediction result with the minimum absolute error;
and forming a final settlement prediction curve graph according to the settlement prediction result with the minimum absolute error.
2. The method of claim 1, wherein the collection parameters include time and a settlement amount corresponding to the time.
3. The method of claim 1, wherein the time is an integer multiple of 10d, and d is a unit number of days.
4. The method for predicting mountain region giant grained soil high fill foundation base settlement according to claim 1, wherein S is a predicted value corresponding to time t in the gompers growth curve prediction model and the poisson curve prediction model; t is time; a. b and k are undetermined parameters.
5. The method of claim 1, wherein in the exponential curve prediction model, s istThe predicted value corresponding to the time t is obtained; t is time; a. b is a parameter to be determined, s∞Is the sum of the final settlement values of the foundation.
6. The method of claim 1, wherein t is a settlement time, s is a settlement amount of the foundation at the time t, and a and b are undetermined parameters.
7. A system for predicting settlement of foundation of mountain area megagrained soil high fill according to any one of claims 1 to 3, comprising:
the acquisition module is used for acquiring acquisition parameters corresponding to settlement monitoring of the position to be detected at different times;
the preprocessing module is used for preprocessing the acquisition parameters to obtain effective parameters;
the prediction module is used for respectively inputting the effective parameters as the Gompers growth curve prediction model, the Poisson curve prediction model, the index curve prediction model and the parabola prediction model and respectively obtaining the prediction results of the Gompers growth curve prediction model, the Poisson curve prediction model, the index curve prediction model and the parabola prediction model;
the drawing module is used for drawing an actually measured sedimentation value curve graph according to the effective parameters and respectively drawing corresponding sedimentation prediction curve graphs according to a first sedimentation prediction result, a second sedimentation prediction result, a third sedimentation prediction result and a fourth sedimentation prediction result;
and the comparison module is used for comparing the settlement prediction curve graphs formed by the first settlement prediction result, the second settlement prediction result, the third settlement prediction result and the fourth settlement prediction result with the actually-measured settlement value curve graph respectively and determining the settlement prediction curve graph with the minimum absolute error.
8. The system for predicting sedimentation of foundation foundations highly filled with mountain megasoil according to claim 7, further comprising:
the database is used for storing the curve graph formed by the drawing module and the settlement prediction curve graph with the minimum absolute error formed by the comparison module;
and the query module is used for calling the contents stored in the database.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112347536A (en) * | 2020-10-12 | 2021-02-09 | 中国路桥工程有限责任公司 | Prediction system for special foundation bridge settlement |
| CN113091600A (en) * | 2021-04-06 | 2021-07-09 | 长沙理工大学 | Monitoring method for monitoring deformation of soft soil foundation by utilizing time sequence InSAR technology |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107341328A (en) * | 2017-09-05 | 2017-11-10 | 中交第公路勘察设计研究院有限公司 | Based on the Ground Settlement method for improving Verhulst curves |
| CN108876918A (en) * | 2018-07-02 | 2018-11-23 | 长安大学 | A kind of high roadbed loess body sedimentation and deformation prediction technique |
| CN108960480A (en) * | 2018-05-18 | 2018-12-07 | 北京工业职业技术学院 | Settlement prediction method and device |
-
2020
- 2020-05-08 CN CN202010384839.5A patent/CN111622274A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107341328A (en) * | 2017-09-05 | 2017-11-10 | 中交第公路勘察设计研究院有限公司 | Based on the Ground Settlement method for improving Verhulst curves |
| CN108960480A (en) * | 2018-05-18 | 2018-12-07 | 北京工业职业技术学院 | Settlement prediction method and device |
| CN108876918A (en) * | 2018-07-02 | 2018-11-23 | 长安大学 | A kind of high roadbed loess body sedimentation and deformation prediction technique |
Non-Patent Citations (1)
| Title |
|---|
| 黄玮等: "高填方地基沉降变形计算及分析研究综述", 《山西建筑》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112347536A (en) * | 2020-10-12 | 2021-02-09 | 中国路桥工程有限责任公司 | Prediction system for special foundation bridge settlement |
| CN113091600A (en) * | 2021-04-06 | 2021-07-09 | 长沙理工大学 | Monitoring method for monitoring deformation of soft soil foundation by utilizing time sequence InSAR technology |
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Application publication date: 20200904 |