CN113762378A - Rusted concrete column earthquake failure mode discrimination method based on decision tree algorithm - Google Patents
Rusted concrete column earthquake failure mode discrimination method based on decision tree algorithm Download PDFInfo
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- 230000002787 reinforcement Effects 0.000 claims description 6
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Abstract
The invention discloses a decision tree algorithm-based method for judging earthquake failure modes of rusted concrete columns, which comprises the following steps of: 1. firstly, collecting a large amount of test data of a rusted concrete column, wherein the test data comprises basic design information of a test piece, a rust rate and a failure mode under a reciprocating load; 2. taking the basic information parameters of each test piece as input variables, taking the failure mode as an output variable, and putting the output variable into a decision tree algorithm for training; 3. continuously adjusting parameters of the decision tree algorithm and training the model until the best classification accuracy is achieved, so that the trained decision tree model is obtained; 4. inputting new parameters of the rusted concrete column into the trained decision tree model to obtain an earthquake failure mode of the concrete column; the method avoids a large amount of theoretical calculation or numerical calculation of the traditional method, has extremely high calculation efficiency and accuracy, and is beneficial to the earthquake resistance evaluation of the concrete structure in the corrosion environment.
Description
Technical Field
The invention relates to a method for analyzing an earthquake failure mode of a rusted concrete column, in particular to a method for judging the earthquake failure mode of the rusted concrete column based on a decision tree algorithm.
Background
The failure mode of the concrete column under the action of earthquake directly affects the earthquake-resistant performance of the column and the earthquake-resistant performance of the whole structure. Generally speaking, it is expected that concrete columns will bend and break under earthquake load, and the concrete columns have better deformation capability, and the whole structure has better deformation capability under earthquake. However, when the concrete column is subjected to bending shear or shearing failure, the concrete column has poor deformation capability, and the whole structure is easy to collapse. On the other hand, under the offshore and other corrosion environments, the diameter of the stirrup inside the concrete column is smaller and is closer to the external corrosion environment, so that the corrosion condition of the stirrup is more serious than that of a longitudinal bar, more severe shear performance degradation is caused, and further the concrete column subjected to bending damage design can be subjected to bending shear or shear damage, so that the corrosion influence is considered, and the method has very important significance for accurately judging the damage mode of the corroded concrete column under the earthquake load.
The existing concrete column failure mode judging method is low in precision and cannot consider the influence of corrosion only according to the shear-span ratio; or according to the comparison between the bending bearing capacity and the shearing bearing capacity, the method needs to calculate the bending bearing capacity and the shearing bearing capacity of the concrete column respectively, is very complex and tedious, and currently, the influence of steel bar corrosion is difficult to consider. The invention provides a method for judging earthquake failure modes of a rusted concrete column based on a decision tree algorithm in artificial intelligence, which can directly give out possible failure modes under an earthquake according to basic information of the concrete column, has extremely high accuracy and is very convenient and fast to implement, and has very important guiding value for earthquake resistance evaluation of the concrete column under a rusty environment.
Disclosure of Invention
The invention aims to provide a decision tree algorithm-based method for judging earthquake failure modes of rusted concrete columns, which can directly give out possible failure modes according to basic information of concrete columns, avoids a large amount of theoretical calculation or numerical calculation of the traditional method, has extremely high calculation efficiency and accuracy, and is beneficial to earthquake resistance evaluation of concrete structures in rusty environments.
The technical scheme adopted by the invention is as follows: a method for judging earthquake failure modes of rusted concrete columns based on a decision tree algorithm comprises the following steps:
(1) collecting N groups of test data of the rusted concrete columns under reciprocating load, wherein the test data comprises basic parameter information and failure modes of the test piece;
(2) taking basic information parameters of the N groups of test pieces as input variables X, and putting a failure mode as an output variable y into a decision tree algorithm for training;
(3) continuously adjusting parameters of a decision tree algorithm and training until the best classification accuracy is achieved, so that a trained decision tree model F (X) is obtained;
(4) inputting new parameters of the rusted concrete column into the trained decision tree model F (X), and obtaining the earthquake failure mode of the concrete column.
Further, the basic parameter information of the test piece in the step (1) comprises: the method comprises the following steps of test piece length l, section width b, section height h, section effective height d, concrete strength fc, steel bar yield strength fy, longitudinal bar reinforcement ratio pl, stirrup reinforcement ratio pt, longitudinal bar diameter dl, stirrup diameter dt, longitudinal bar corrosion ratio Xl, stirrup corrosion ratio Xt, stirrup spacing s and axial compression ratio v.
Further, the test piece in the step (1) should include three failure modes of bending failure, bending shear failure and shearing failure.
Further, the input variables in the step (2) are: x ═ l, b, h, d, fc, fy, pl, pt, dl, dt, Xl, Xt, s, v ], the output variable y is failure mode.
Further, the parameters to be adjusted of the decision tree algorithm in the step (3) are as follows: maximum tree depth max _ dep, minimum number of samples required for node partitioning min _ sa _ split, minimum number of samples for leaf nodes min _ sa _ leaf, maximum number of leaf nodes max _ le _ nodes.
Further, the parameter setting method in the step (3) is as follows: presetting a maximum tree depth max _ dep, node division requiring 4 possible values of the minimum sample number min _ sa _ split, the minimum sample number min _ sa _ leaf of a leaf node, and the maximum leaf node number max _ le _ nodes, and obtaining k parameter combination schemes according to the possible value combinations of the 4 parameters:
Z＝[max_dep,min_sa_split,min_sa_leaf,max_le_nodes]k。
further, the decision tree training method in the step (3) is as follows: and traversing the k parameter combination schemes, wherein each time one parameter combination scheme is obtained, the parameter combination scheme is as follows:
Zj＝[max_dep,min_sa_split,min_sa_leaf,max_le_nodes]j∈k，
setting parameters in the decision tree algorithm as the combination, and training to obtain the classification accuracy under the combination of the parameters; continuously repeating the above processes until k rounds of training are performed;
and (5) taking the parameter combination with the highest classification accuracy in the k rounds as the final decision tree model parameters, and finishing the model training.
Has the advantages that: the invention provides a decision tree algorithm-based failure mode discrimination scheme of a rusty concrete column under the action of earthquake load, the scheme is that firstly, test data of the rusty concrete column are collected in large quantity, then basic information parameters of each test piece are used as input variables, and the failure mode is used as an output variable and is put into a decision tree algorithm for training; continuously adjusting parameters of the decision tree algorithm and training until the best classification accuracy is achieved, so that a trained decision tree model is obtained; according to the trained decision tree model, the basic information of the new rusty concrete column to be analyzed is input, so that the possible failure mode of the concrete column under the earthquake load can be directly given, a large amount of theoretical calculation or numerical calculation of the traditional method is avoided, the calculation efficiency and accuracy are high, and the evaluation of the earthquake resistance of the concrete structure in the rusty environment is facilitated.
Drawings
FIG. 1 is a schematic flow diagram of the present invention.
FIG. 2 is a schematic diagram of decision tree growth.
Detailed Description
The invention is further described with reference to the following figures and detailed description.
As shown in FIG. 1, a method for judging earthquake failure modes of rusted concrete columns based on a decision tree algorithm comprises the following steps:
the method comprises the following steps: collecting N groups of test data of the rusted concrete columns under reciprocating load, wherein the test data comprises basic parameter information and failure modes of the test piece;
the basic information of the test piece in the first step mainly comprises the following steps:
the basic parameter information of the test piece includes: the method comprises the following steps of test piece length l, section width b, section height h, section effective height d, concrete strength fc, steel bar yield strength fy, longitudinal bar reinforcement ratio pl, stirrup reinforcement ratio pt, longitudinal bar diameter dl, stirrup diameter dt, longitudinal bar corrosion ratio Xl, stirrup corrosion ratio Xt, stirrup spacing s and axial compression ratio v. The rusted concrete column test piece collected in the first step should include three failure modes, namely bending failure, bending shear failure and shearing failure.
Step two: taking basic information parameters of the N groups of test pieces as input variables X, and putting a failure mode as an output variable y into a decision tree algorithm for training;
in the second step, the input variables are as follows: x ═ l, b, h, d, fc, fy, pl, pt, dl, dt, Xl, Xt, s, v ], for a total of 14 concrete column basic information parameters, as shown in table 1.
The output variable y is the failure mode for each concrete column, including flexural failure (F), flexural shear Failure (FS) and shear failure (S), as shown in table 1.
Thus, the input and output data sets may be represented as:
φ＝[φ1,φ2,...φN]＝[(X1,y1),(X2,y2)…(XN,yN)]
wherein Xi＝[x1,x2,...x14]i＝[l,b,h,d,fc,fy,pl,pt,dl,dt,Xl,Xt,s,v]i i∈N，
TABLE 1 input and output variable settings
Step three: adjusting parameters of a decision tree algorithm and training until the best classification accuracy is achieved, so that a trained decision tree model F (X) is obtained;
the parameters to be adjusted of the decision tree algorithm in the third step are as follows: maximum tree depth max _ dep, minimum number of samples required for node partitioning min _ sa _ split, minimum number of samples for leaf nodes min _ sa _ leaf, maximum number of leaf nodes max _ le _ nodes.
The parameter setting method in the third step comprises the following steps: presetting a maximum tree depth max _ dep, node division requiring 4 possible values of the minimum sample number min _ sa _ split, the minimum sample number min _ sa _ leaf of a leaf node, and the maximum leaf node number max _ le _ nodes, and obtaining k parameter combination schemes according to the possible value combinations of the 4 parameters:
Z＝[max_dep,min_sa_split,min_sa_leaf,max_le_nodes]k。
the decision tree training method in the third step comprises the following steps: and traversing the k parameter combination schemes, wherein each time one parameter combination scheme is obtained, the parameter combination scheme is as follows:
Zj＝[max_dep,min_sa_split,min_sa_leaf,max_le_nodes]j∈k，
setting parameters in the decision tree algorithm as the combination, and training to obtain the classification accuracy under the combination of the parameters;
as shown in fig. 2, the specific training process of the decision tree under each parameter combination is as follows:
(1) starting from a root node, the root node contains all data samples;
(2) if the samples in the current node belong to the same destruction mode, the current node becomes a leaf node, otherwise;
(3) selecting an attribute with the most classification capability as a current node, and dividing sample data into a plurality of subsets according to the attribute value of the node;
(4) repeating the steps (2) and (3) for each subset obtained in the last step;
(5) the above steps are stopped when the following conditions are satisfied: the current node samples belong to the same failure mode, and redundant attribute values are not available for division.
Continuously repeating the above processes until k rounds of training are performed;
and (5) taking the parameter combination with the highest classification accuracy in the k rounds as the final decision tree model parameters, and finishing the model training.
Step four: inputting new parameters of the rusted concrete column into the trained decision tree model F (X), and obtaining the earthquake failure mode of the concrete column.
The embodiments of the present invention are described in detail with reference to the drawings and the specific embodiments, but the present invention is not limited to the described embodiments. It will be apparent to those skilled in the art that various changes, modifications, substitutions and alterations can be made on the embodiments without departing from the spirit and scope of the inventive concept.
Claims (7)
1. A method for judging earthquake failure modes of rusted concrete columns based on a decision tree algorithm is characterized by comprising the following steps: the method comprises the following steps:
(1) collecting N groups of test data of the rusted concrete columns under reciprocating load, wherein the test data comprises basic parameter information and failure modes of the test piece;
(2) taking basic information parameters of the N groups of test pieces as input variables X, and putting a failure mode as an output variable y into a decision tree algorithm for training;
(3) continuously adjusting parameters of a decision tree algorithm and training until the best classification accuracy is achieved, so that a trained decision tree model F (X) is obtained;
(4) inputting new parameters of the rusted concrete column into the trained decision tree model F (X), and obtaining the earthquake failure mode of the concrete column.
2. The decision tree algorithm-based rusted concrete column earthquake failure mode discrimination method according to claim 1, characterized in that: the basic parameter information of the test piece in the step (1) comprises: the method comprises the following steps of test piece length l, section width b, section height h, section effective height d, concrete strength fc, steel bar yield strength fy, longitudinal bar reinforcement ratio pl, stirrup reinforcement ratio pt, longitudinal bar diameter dl, stirrup diameter dt, longitudinal bar corrosion ratio Xl, stirrup corrosion ratio Xt, stirrup spacing s and axial compression ratio v.
3. The decision tree algorithm-based rusted concrete column earthquake failure mode discrimination method according to claim 1, characterized in that: the test piece in the step (1) should include three failure modes of bending failure, bending shear failure and shearing failure.
4. The decision tree algorithm-based rusted concrete column earthquake failure mode discrimination method according to claim 1, characterized in that: the input variables in the step (2) are as follows: x ═ l, b, h, d, fc, fy, pl, pt, dl, dt, Xl, Xt, s, v ], the output variable y is failure mode.
5. The decision tree algorithm-based rusted concrete column earthquake failure mode discrimination method according to claim 1, characterized in that: the parameters to be adjusted of the decision tree algorithm in the step (3) are as follows: maximum tree depth max _ dep, minimum number of samples required for node partitioning min _ sa _ split, minimum number of samples for leaf nodes min _ sa _ leaf, maximum number of leaf nodes max _ le _ nodes.
6. The decision tree algorithm-based rusted concrete column earthquake failure mode discrimination method according to claim 1, characterized in that: the parameter setting method in the step (3) comprises the following steps: presetting a maximum tree depth max _ dep, node division requiring 4 possible values of the minimum sample number min _ sa _ split, the minimum sample number min _ sa _ leaf of a leaf node, and the maximum leaf node number max _ le _ nodes, and obtaining k parameter combination schemes according to the possible value combinations of the 4 parameters:
Z＝[max_dep,min_sa_split,min_sa_leaf,max_le_nodes]k。
7. the decision tree algorithm-based rusted concrete column earthquake failure mode discrimination method according to claim 1, characterized in that: the decision tree training method in the step (3) comprises the following steps: and traversing the k parameter combination schemes, wherein each time one parameter combination scheme is obtained, the parameter combination scheme is as follows:
Zj＝[max_dep,min_sa_split,min_sa_leaf,max_le_nodes]j∈k，
setting parameters in the decision tree algorithm as the combination, and training to obtain the classification accuracy under the combination of the parameters; continuously repeating the above processes until k rounds of training are performed;
and (5) taking the parameter combination with the highest classification accuracy in the k rounds as the final decision tree model parameters, and finishing the model training.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116432380A (en) * | 2023-01-13 | 2023-07-14 | 中国海洋大学 | Method for judging three-point bending failure mode of double-steel-plate-concrete composite beam |
| CN116612841A (en) * | 2023-05-31 | 2023-08-18 | 中国海洋大学 | Asymmetric double-steel-plate-concrete composite beam failure mode judging method |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106650807A (en) * | 2016-12-20 | 2017-05-10 | 东南大学 | Method for predicting and evaluating concrete strength deterioration under ocean environment |
| CN108460173A (en) * | 2017-11-29 | 2018-08-28 | 西安建筑科技大学 | A kind of existing industry building structure durability damage alarming method |
| US20180260531A1 (en) * | 2017-03-10 | 2018-09-13 | Microsoft Technology Licensing, Llc | Training random decision trees for sensor data processing |
| CN110414580A (en) * | 2019-07-19 | 2019-11-05 | 东南大学 | Reinforced concrete deep beam bearing capacity evaluation method based on random forests algorithm |
| CN110737967A (en) * | 2019-09-10 | 2020-01-31 | 上海交通大学 | concrete structure durability prediction method, system and terminal |
| AU2020101854A4 (en) * | 2020-08-17 | 2020-09-24 | China Communications Construction Co., Ltd. | A method for predicting concrete durability based on data mining and artificial intelligence algorithm |
| CN112668078A (en) * | 2020-12-24 | 2021-04-16 | 青岛理工大学 | Method for identifying damage of rusted reinforced concrete beam after fire disaster |
-
2021
- 2021-09-06 CN CN202111037576.1A patent/CN113762378A/en not_active Withdrawn
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106650807A (en) * | 2016-12-20 | 2017-05-10 | 东南大学 | Method for predicting and evaluating concrete strength deterioration under ocean environment |
| US20180260531A1 (en) * | 2017-03-10 | 2018-09-13 | Microsoft Technology Licensing, Llc | Training random decision trees for sensor data processing |
| CN108460173A (en) * | 2017-11-29 | 2018-08-28 | 西安建筑科技大学 | A kind of existing industry building structure durability damage alarming method |
| CN110414580A (en) * | 2019-07-19 | 2019-11-05 | 东南大学 | Reinforced concrete deep beam bearing capacity evaluation method based on random forests algorithm |
| CN110737967A (en) * | 2019-09-10 | 2020-01-31 | 上海交通大学 | concrete structure durability prediction method, system and terminal |
| AU2020101854A4 (en) * | 2020-08-17 | 2020-09-24 | China Communications Construction Co., Ltd. | A method for predicting concrete durability based on data mining and artificial intelligence algorithm |
| CN112668078A (en) * | 2020-12-24 | 2021-04-16 | 青岛理工大学 | Method for identifying damage of rusted reinforced concrete beam after fire disaster |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116432380A (en) * | 2023-01-13 | 2023-07-14 | 中国海洋大学 | Method for judging three-point bending failure mode of double-steel-plate-concrete composite beam |
| CN116432380B (en) * | 2023-01-13 | 2024-03-05 | 中国海洋大学 | Method for judging three-point bending failure mode of double-steel-plate-concrete composite beam |
| CN116612841A (en) * | 2023-05-31 | 2023-08-18 | 中国海洋大学 | Asymmetric double-steel-plate-concrete composite beam failure mode judging method |
| CN116612841B (en) * | 2023-05-31 | 2024-01-09 | 中国海洋大学 | Asymmetric double-steel-plate-concrete composite beam failure mode judging method |
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