CN113673624A - Bridge state monitoring method based on decision tree model - Google Patents
Bridge state monitoring method based on decision tree model Download PDFInfo
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- CN113673624A CN113673624A CN202111017465.4A CN202111017465A CN113673624A CN 113673624 A CN113673624 A CN 113673624A CN 202111017465 A CN202111017465 A CN 202111017465A CN 113673624 A CN113673624 A CN 113673624A
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- 238000003066 decision tree Methods 0.000 title claims abstract description 31
- 238000012544 monitoring process Methods 0.000 title claims abstract description 30
- 238000000034 method Methods 0.000 title claims abstract description 20
- 238000012549 training Methods 0.000 claims abstract description 4
- 238000013507 mapping Methods 0.000 claims description 11
- 238000011156 evaluation Methods 0.000 claims description 9
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Abstract
The invention discloses a bridge state monitoring method based on a decision tree model, which comprises the following steps: s1, acquiring historical state information of a bridge, and taking the historical state information of the bridge as a sample data set; s2, performing model training by using the sample data set to generate a decision tree model; s3, collecting real-time state information of the bridge; s4, classifying and judging the real-time state information of the bridge by using a decision tree model to obtain a classification result; and S5, carrying out grade division on the classification result to obtain a monitoring result of the bridge state. The bridge state monitoring method based on the decision tree model can be used for rapidly classifying the bridge states and obtaining accurate and reliable bridge state monitoring results.
Description
Technical Field
The invention relates to the field of bridge monitoring, in particular to a bridge state monitoring method based on a decision tree model.
Background
As a common traffic facility, a bridge is often affected by the surrounding environment and the bearing capacity, and further causes damage to the structure of the bridge to different degrees. In order to enable the bridge to operate normally and stably, the state of the bridge is often monitored.
At present, the bridge state is mainly monitored by processing and analyzing data and signals acquired by various sensors and judging the health condition and the working state of the bridge according to an analysis result, but the processing and analysis process generally consumes long time, and the analysis result obtained at the same time often has larger deviation with an actual result.
Disclosure of Invention
In view of the above, the present invention is to overcome the defects in the prior art, and provide a bridge state monitoring method based on a decision tree model, which can quickly classify bridge states and obtain an accurate and reliable bridge state monitoring result.
The invention discloses a bridge state monitoring method based on a decision tree model, which comprises the following steps:
s1, acquiring historical state information of a bridge, and taking the historical state information of the bridge as a sample data set;
s2, performing model training by using the sample data set to generate a decision tree model;
s3, collecting real-time state information of the bridge;
s4, classifying and judging the real-time state information of the bridge by using a decision tree model to obtain a classification result;
and S5, carrying out grade division on the classification result to obtain a monitoring result of the bridge state.
Further, in step S2, the decision tree model is generated by using the full set of the sample data set as a root node, using the feature attributes filtered in the sample data set as internal nodes, and using the classification result as a leaf node.
Further, the characteristic attribute with the largest information gain is selected as the screened characteristic attribute.
Further, the information gain is determined according to the following formula:
wherein, e (S) is the information entropy of the known classification sample data set S; gamma raymThe proportion of the mth type sample in the sample data set S is calculated; l is the number of classes in the sample data set S.
Further, the classification result is graded according to the following formula to obtain the monitoring result of the bridge state:
wherein G is a monitoring result; r is a damage evaluation value of the classification result; r0、R1、R2And R3Are measures of the degree of damage.
Further, the damage evaluation value of the classification result is determined according to the following formula:
wherein R isiA damage evaluation value which is a classification result i; i isiThe mapping score is a mapping score of the classification result i, and the value range of the mapping score is (1, lambda); k is the total number of classification results; a isiAnd the weight coefficient is corresponding to the classification result i.
The invention has the beneficial effects that: the invention discloses a bridge state monitoring method based on a decision tree model, which generates the decision tree model by carrying out node construction on a sample data set, and rapidly classifies the real-time state information of a target bridge through the decision tree model, so that the classification result is accurate and effective; and objective and reliable bridge state monitoring results are obtained by grading the classification results.
Drawings
The invention is further described below with reference to the following figures and examples:
FIG. 1 is a schematic flow chart of the method of the present invention.
Detailed Description
The invention is further described with reference to the accompanying drawings, in which:
the invention discloses a bridge state monitoring method based on a decision tree model, which comprises the following steps:
s1, acquiring historical state information of a bridge, and taking the historical state information of the bridge as a sample data set; the historical state information can be obtained by inquiring a historical database;
s2, performing model training by using the sample data set to generate a decision tree model;
s3, collecting real-time state information of the bridge; the method comprises the steps that real-time state information of a target bridge is collected through a sensor or an existing monitoring system, wherein the state information comprises environmental information of the bridge, bridge vibration information, bridge settlement information and bridge strain information;
s4, classifying and judging the real-time state information of the bridge by using a decision tree model to obtain a classification result; starting from a root node of a decision tree in a decision tree model, comparing real-time state information with internal nodes of the decision tree, when a comparison result does not meet requirements, continuing to perform downward comparison along branches of the internal nodes until reaching leaf nodes, further matching the leaf nodes to proper target leaf nodes, and taking a result value of the target leaf nodes as a classification result of the bridge;
and S5, carrying out grade division on the classification result to obtain a monitoring result of the bridge state.
In this embodiment, in step S2, the decision tree model is generated by using the full set of the sample data set as a root node, using the feature attributes filtered in the sample data set as internal nodes, and using the classification result as a leaf node.
In this embodiment, the feature attribute with the largest information gain is selected as the screened feature attribute. The attributes of each sample in the sample data set are generally multiple, the influence degrees of different attributes are different, and the characteristic attributes with high correlation with the classification result are obtained by performing characteristic screening on the attributes.
In this embodiment, the information gain is determined according to the following formula:
wherein, e (S) is the information entropy of the known classification sample data set S; gamma raymThe proportion of the mth type sample in the sample data set S is calculated; l is the number of classes in the sample data set S.
In this embodiment, the classification result is classified according to the following formula to obtain a monitoring result of the bridge state:
wherein G is a monitoring result; r is a damage evaluation value of the classification result; r0、R1、R2And R3Are measures of the degree of damage. Wherein R is0、R1、R2And R3The value of (b) can be set according to actual operating conditions.
In the present embodiment, the damage evaluation value of the classification result is determined according to the following formula:
wherein R isiA damage evaluation value which is a classification result i; i isiThe mapping score of the classification result i is in a value range of (1, lambda), and the lambda value is 10; k is the total number of classification results; a isiAnd setting the weight coefficient corresponding to the classification result i according to the actual working condition. The method comprises the steps of calculating the influence degree of a classification result i on bridge damage in a historical data record, and setting a mapping score for the classification result i, wherein generally, if the classification result i is displayed in the historical record, cracks and obvious vibration of a bridge can be caused, and the mapping score range of the classification result i can be set to be (8, 10); if the classification result i is displayed in the history record, the bridge will be enabledThe mapping sub-range of the classification result i can be set to (4,7) if the beam material is degraded and the like; if the classification result i is displayed in the history record to have no influence on the bridge basically, the mapping range of the classification result i can be set to (1, 3).
Finally, the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all of them should be covered in the claims of the present invention.
Claims (6)
1. A bridge state monitoring method based on a decision tree model is characterized in that: the method comprises the following steps:
s1, acquiring historical state information of a bridge, and taking the historical state information of the bridge as a sample data set;
s2, performing model training by using the sample data set to generate a decision tree model;
s3, collecting real-time state information of the bridge;
s4, classifying and judging the real-time state information of the bridge by using a decision tree model to obtain a classification result;
and S5, carrying out grade division on the classification result to obtain a monitoring result of the bridge state.
2. The bridge condition monitoring method based on the decision tree model as claimed in claim 1, wherein: in step S2, a decision tree model is generated by using the full set of the sample data set as a root node, using the feature attributes filtered in the sample data set as internal nodes, and using the classification results as leaf nodes.
3. The bridge condition monitoring method based on the decision tree model as claimed in claim 2, wherein: and selecting the characteristic attribute with the maximum information gain as the screened characteristic attribute.
4. The decision tree model-based bridge condition monitoring method of claim 3, wherein: the information gain is determined according to the following formula:
wherein, e (S) is the information entropy of the known classification sample data set S; gamma raymThe proportion of the mth type sample in the sample data set S is calculated; l is the number of classes in the sample data set S.
5. The bridge condition monitoring method based on the decision tree model as claimed in claim 1, wherein: and grading the classification result according to the following formula to obtain a monitoring result of the bridge state:
wherein G is a monitoring result; r is a damage evaluation value of the classification result; r0、R1、R2And R3Are measures of the degree of damage.
6. The decision tree model-based bridge condition monitoring method of claim 5, wherein: determining a damage evaluation value of the classification result according to the following formula:
wherein R isiA damage evaluation value which is a classification result i; i isiThe mapping score is a mapping score of the classification result i, and the value range of the mapping score is (1, lambda); k is the total number of classification results; a isiAnd the weight coefficient is corresponding to the classification result i.
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