CN114894619B - Method for predicting axial compressive load-strain curve of concrete filled steel tube column based on long-short-term memory network - Google Patents
Method for predicting axial compressive load-strain curve of concrete filled steel tube column based on long-short-term memory network Download PDFInfo
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Abstract
The invention provides a method for predicting a steel tube concrete column axial compressive load-strain curve based on a long-term and short-term memory network. For design parameters, axial pressure load and axial strain data, a design data preprocessing and data forming method is used for training an off-line model of a long-period memory network; constructing an axial strain data sequence, inputting the axial strain data sequence and design parameters into a trained offline model, and obtaining axial pressure load data output by the model; and restoring the axle pressure load-strain curve by using the axial strain data and the axle pressure load data, so as to realize the axle pressure load-strain curve prediction of the system.
Description
Technical Field
The invention relates to the field of construction and civil engineering, in particular to a method for predicting a steel tube concrete column axial compressive load-strain curve based on a long-short-term memory network.
Background
Concrete filled steel tubular columns are recognized as structural members that undergo compression and bending and are expected to be substitutes for conventional reinforced concrete and steel columns in high-rise buildings and urban overhead girder bridge construction. Compared with the traditional structural column, the steel tube concrete column has higher bending, axial bearing capacity and shock resistance. In addition, the mutual protection of the steel pipe and the filling concrete ensures that the steel pipe concrete column has better fire resistance, impact resistance and corrosion resistance.
The axial compressive load-strain curve is one of the most fundamental and important features in the design of structural column safety. Once the load-strain curve is known, critical design parameters including modulus of elasticity, ultimate load bearing capacity, and ductility of the column can be readily obtained. Thus, the axial compressive load-strain curve has been the basic research goal of concrete-filled steel tubular columns during the past three decades. Theoretical derivation and finite element model analysis are two main methods for calculating the load-strain curve of the concrete filled steel tubular column at present. In research based on theoretical methods, it is common to first assume the stress-strain relationship of the outer steel pipe and the inner concrete, and then gradually deduce the load-strain curve of the column according to the equilibrium conditions and the predetermined interaction relationship. However, with the update of materials, the applicability of this method gradually decreases, and the theoretical method based on empirical parameters is inevitably limited to the material strength and design concept at that time.
Finite element model analysis is another method for obtaining the axial load-strain curve of the concrete filled steel tubular column. In finite element analysis, concrete and steel pipes should be modeled using appropriate cell types, typically concrete solid cells and steel pipe shell cells, while contact models or spring models are required to define interactions between the concrete and steel surfaces. By reasonably adopting constitutive relation and proper grid, the axial compression load-strain curve of the concrete filled steel tube column can be obtained.
In recent years, soft computing methods and intelligent techniques have been remarkably developed and widely used in civil engineering. However, the application of the machine learning algorithm in the concrete filled steel tubular column research is still in a starting and exploring stage. Most of the existing research has focused on predicting performance metrics (such as axial load capacity and stiffness) based on a collected experimental database. There is currently no study on calculating the complete axial compressive load-strain curve of a concrete filled steel tubular member using a machine learning method.
A long and short term memory network is one type of recurrent neural network, commonly used to process time series. The long-short-period memory network is characterized in that the long-short-period dependency relationship of data is considered. In order to solve the above-mentioned problems, it is necessary to propose a technical solution that can consider points in an axle load-strain curve as a time series having a long-short-term dependency relationship, and implement curve prediction by designing a data construction method using a long-short-term memory network.
Disclosure of Invention
Aiming at the problem of predicting the axial pressure load-strain curve of the concrete-filled steel tube column, the invention provides a method for predicting the axial pressure load-strain curve of the concrete-filled steel tube column based on a long-short-period memory network, points in the axial pressure load-strain curve can be regarded as a time sequence with long-short-period dependency, and the curve prediction is realized by using the long-short-period memory network through a design data construction method.
In order to achieve the above purpose, the technical scheme adopted by the invention is as follows:
the method for predicting the axial compressive load-strain curve of the concrete filled steel tube column based on the long-short-term memory network is characterized by comprising the following steps of: the method comprises the following steps:
s1: collecting experimental data;
collecting experimental data of axial compression load-strain curves of steel tube concrete columns with different materials and sizes, and establishing a load-strain curve and five design parameters, wherein the five design parameters are respectively as follows: diameter D of steel pipe and wall thickness t of steel pipe s Column height H, steel strength f s And concrete strength f c Constructing a load-strain curve prediction training sample through design parameters according to the corresponding system database;
s2: preprocessing data;
setting 25000 mu epsilon as a limit value for collecting axial strain for all axial pressure load-strain curve experimental data of the concrete filled steel tube column, if the experimental data curve is shorter and does not reach the axial strain limit value of 25000 mu epsilon, executing compensation operation, and extending the axial pressure load-strain curve to the set axial strain limit value according to the final slope of the load-strain curve; if the experimental data curve is longer, the cut-off operation is executed, the data exceeding the set limit value is discarded, and after the data pretreatment, all axial load-strain curve data have uniform axial strain length;
s3: data constitution;
each set of design parameters and corresponding axle load-strain curve is used as a data sample; dividing experimental data of the axial pressure load-strain curve of the steel tube concrete column subjected to data pretreatment into m points according to the strain value, wherein each point comprises an axial pressure load value N and an axial strain value epsilon; the input data of the long-term and short-term memory unit neural network is built by using the axial strain value and the design parameter; building output data of the long-term memory unit neural network by using the axle load value;
s4: training an offline model;
after all samples are formed by input and output data in the step S3, performing offline model training on all samples by adopting a long-short-period memory unit neural network to obtain a long-short-period memory unit neural network model conforming to convergence;
s5: predicting an axle load-strain curve;
arranging design parameters of a concrete filled steel tube column of which the axial compression load-strain curve needs to be predicted; constructing an axial strain data sequence, and taking a vector with the maximum value of 25000 mu epsilon and containing m data values distributed evenly as the axial strain data sequence; the steel pipe concrete sample data which needs to be subjected to axle load-strain curve prediction is converted into input data constitution which can be used for a long-period and short-period memory network by utilizing the input data constitution construction method of S3; inputting the input data number into the offline model obtained in the step S4, and obtaining corresponding axle load output; taking the average value of all axial strains in each row of the input data and the axial pressure load value in the row corresponding to the output data as one point of an axial pressure load-strain plane, sequentially calculating all axial pressure load-strain points so as to restore a complete axial pressure load-strain curve, and completing the axial pressure load-strain curve prediction.
As a preferred embodiment of the present invention:
in the step S3 of the method,
s31: the input data structure includes: taking k axial strains as a group, and forming a first row in a data sample by the k axial strains and five design parameters of the sample; then taking another group of k axial strains by adopting a sliding window method, and forming another row in one data sample by the group of k axial strains and the five design parameters; and so on until all axial strains are taken, at which time the input data for each data sample comprises m+1-k rows and 5+k columns;
s32: the output data structure includes: taking a group of k axle load values to obtain each corresponding axle load value, and averaging the group of axle load values to obtain the axle load corresponding to the line of input dataThe output data of each data sample is taken as the output data of the neural network, and the output data of each data sample comprises m+1-k rows and 1 column.
As a preferred embodiment of the present invention:
in the step S4 of the process,
s41: the long-term and short-term memory unit neural network sequentially comprises: an input layer, a long-short-period memory unit layer, a full connection layer, a random discarding layer, a full connection layer and a data regression layer; the long-period memory unit layer comprises G long-period memory units, namely the input time sequence length is G, and the input data feature quantity is C; wherein the long-short-period memory unit comprises:
at the current time of t-1, the output at the time of t needs to be calculated through a long-short-period memory unit, and the input data is x t ，h t And c t Respectively representing the output and the cell state at time t; the long-short-period memory unit uses the current state h t-1 And c t-1 To calculate the output h t And updated cell state c t The method comprises the steps of carrying out a first treatment on the surface of the Using input gate i t Forgetting door f t Candidate unit g t And an output gate o t To control the update of the long-short-period memory unit, the calculation formula is as follows:
i t ＝σ(W i x t +R i h t-1 +b i )
f t ＝σ(W f x t +R f h t-1 +b f )
g t ＝σ(W g x t +R g h t-1 +b g )
c t ＝f t ⊙c t-1 +i t ⊙g t
o t ＝σ(W o x t +R o h t-1 +b o )
h t ＝o t ⊙tanh(c t )
wherein W is i ,W f ,W g ,W o Is an input weight matrix, R i ,R f ,R g ,R o Is a cyclic weight matrix, b i ,b f ,b g ,b o Is an offset matrix, and the matrix is adjusted through model training; sigma is a sigmoid activation function, and by; directly output state h t As the output of the long-short-period memory unit;
s42: the offline model training in S4 specifically includes:
5+k data in each line of the long-period memory cell neural network input data in the step S3 are input into each long-period memory cell as the input data features in the step S41, namely the feature quantity C= 5+k; and (3) respectively inputting the m+1-k rows in the S3 into G long-short-period memory units of the long-short-period memory unit layer in the S41, namely, the number G=m+1-k of the long-short-period memory units, and training a network by adopting a backward error propagation algorithm.
As a preferred embodiment of the present invention:
in the step S5 of the method,
the construction method of the input data composition of S31 is utilized to convert steel pipe concrete sample data which needs to be subjected to axle load-strain curve prediction into input data composition which can be used for long-period and short-period memory networks.
Compared with the prior art, the invention has the beneficial effects that:
according to the invention, through design parameters, axial pressure load and axial strain data, a data preprocessing and data forming method is designed, and the method is used for training an off-line model of a long-period memory network; constructing an axial strain data sequence, inputting the axial strain data sequence and design parameters into a trained offline model, and obtaining axial pressure load data output by the model; and restoring the axle pressure load-strain curve by using the axial strain data and the axle pressure load data, so as to realize the axle pressure load-strain curve prediction of the system.
Drawings
Fig. 1 is a flow of the curve prediction principle.
Fig. 2 is an input/output data configuration.
FIG. 3 shows a long and short term memory cell layer.
FIG. 4 is a long and short term memory cell.
Detailed Description
The invention is described in further detail below with reference to the attached drawings and detailed description:
the invention provides a method for predicting a steel tube concrete column axial compressive load-strain curve based on a long-term and short-term memory network. For design parameters, axial pressure load and axial strain data, a design data preprocessing and data forming method is used for training an off-line model of a long-period memory network; constructing an axial strain data sequence, inputting the axial strain data sequence and design parameters into a trained offline model, and obtaining axial pressure load data output by the model; and restoring the axle pressure load-strain curve by using the axial strain data and the axle pressure load data, so as to realize the axle pressure load-strain curve prediction of the system.
The curve prediction principle flow of the invention is shown in figure 1.
The invention is realized by the following technical scheme:
s1: and (5) collecting experimental data.
The method comprises the steps of collecting experimental data of axial compression load-strain curves of steel tube concrete columns with different materials and sizes, and establishing a load-strain curve and five design parameters, wherein the experimental data are respectively as follows: diameter D of steel pipe and wall thickness t of steel pipe s Column height H, steel strength f s And concrete strength f c And constructing a load-strain curve prediction training sample according to the corresponding system database.
S2: and (5) preprocessing data.
For all the experimental data of the axial compression load-strain curve of the concrete filled steel tube column, 25000 mu epsilon is set as the limit value for collecting the axial strain, so that all the load-strain curves can capture the peak load point and the post-peak curve characteristic. If the experimental data curve is short and does not reach the 25000 mu epsilon axial strain limit, a compensation operation is performed to extend the axial compressive load-strain curve to the set axial strain limit according to the final slope of the curve. If the experimental data curve is longer, a truncation operation is performed to discard the data exceeding the set limit value, and after the data preprocessing, all the axial load-strain curve data have uniform axial strain length.
S3: data formation.
Each set of design parameters and corresponding axle pressure load-strain curve is taken as a data sample. And (3) dividing the experimental data of the axial pressure load-strain curve of the steel tube concrete column subjected to data pretreatment into m points according to strain values, wherein each point comprises an axial pressure load value N and an axial strain value epsilon. And using the axial strain value and design parameters to build the input data of the long-term and short-term memory unit neural network. And building output data of the long-term memory unit neural network by using the axle load value. The input-output data structure of the present invention is shown in fig. 2, where symbol i represents the ith sample in the database.
S31: the input data structure includes: taking k axial strains as a group, and forming a first row in a data sample by the k axial strains and five design parameters of the sample; then taking another group of k axial strains by adopting a sliding window method, and forming another row in one data sample by the group of k axial strains and the five design parameters; and so on until all axial strain is taken, at which time the input data for each data sample consists of m+1-k rows and 5+k columns.
S32: the output data structure includes: taking a group of k axle load values to obtain each corresponding axle load value, and averaging the group of axle load values to obtain the axle load corresponding to the line of input dataThe output data of each data sample is taken as the output data of the neural network, and the output data of each data sample comprises m+1-k rows and 1 column.
S4: and (5) training an offline model.
And after all samples are formed by input and output data in the step S3, performing offline model training on all samples by adopting a long-short-period memory unit neural network to obtain a long-short-period memory unit neural network model conforming to convergence.
S41: the long-term and short-term memory unit neural network sequentially comprises: an input layer, a long-short-term memory unit layer, a full connection layer, a random discard (drop) layer, a full connection layer and a data regression layer. The long-short-period memory unit layer is shown in fig. 3, and comprises G long-short-period memory units, namely, the input time sequence length is G, and the input data feature quantity is C. Wherein the long-short-period memory unit is as shown in fig. 4, and comprises:
at the current time of t-1, the output at the time of t needs to be calculated through a long-short-period memory unit, and the input data is x t ，h t And c t The output at time t (also referred to as hidden state) and the cell state are shown, respectively. The long-short-period memory unit uses the current state (h t-1 And c t-1 ) To calculate the output h t And updated cell state c t . Using input gate i t Forgetting door f t Candidate unit g t And an output gate o t To control the refresh of the long-short-period memory unit. The calculation formula is as follows:
i t ＝σ(W i x t +R i h t-1 +b i )
f t ＝σ(W f x t +R f h t-1 +b f )
g t ＝σ(W g x t +R g h t-1 +b g )
c t ＝f t ⊙c t-1 +i t ⊙g t
o t ＝σ(W o x t +R o h t-1 +b o )
h t ＝o t ⊙tanh(c t )
wherein W is i ,W f ,W g ,W o Is an input weight matrix, R i ,R f ,R g ,R o Is a cyclic weight matrix, b i ,b f ,b g ,b o Is the bias matrix, which is adjusted by model training. Sigma is the sigmoid activation function, and by the dot product operation. Directly hide state h t As the output of the long and short term memory unit.
S42: the offline model training in S4 specifically includes:
5+k data in each line of the long-period memory cell neural network input data in the step S3 are input into each long-period memory cell as the input data features in the step S41, namely the feature quantity C= 5+k; and (3) respectively inputting the m+1-k rows in the S3 into G long-period memory cells of the long-period memory cell layer in the S41, namely, the number G=m+1-k of the long-period memory cells. The network is trained by adopting a backward error propagation algorithm, so that the axle load predicted by the long-term memory network and the experimental value have minimum root mean square error.
S5: and predicting an axle load-strain curve.
And (3) finishing design parameters of the concrete filled steel tube column of which the axle load-strain curve needs to be predicted. An axial strain data sequence was constructed taking a vector containing m evenly distributed data values with a maximum of 25000 mu epsilon as the axial strain data sequence. And (3) converting steel pipe concrete sample data which needs to be subjected to axle load-strain curve prediction into input data composition which can be used for a long-period and short-period memory network by using the input data composition construction method of S31. And (3) inputting the input data number into the offline model obtained in the step (S4) to obtain corresponding axle load output. Taking the average value of all axial strains in each row of the input data and the axial pressure load value in the row corresponding to the output data as one point of an axial pressure load-strain plane, sequentially calculating all axial pressure load-strain points so as to restore a complete axial pressure load-strain curve, and completing the axial pressure load-strain curve prediction.
According to the invention, through design parameters, axial pressure load and axial strain data, a data preprocessing and data forming method is designed, and the method is used for training an off-line model of a long-period memory network; constructing an axial strain data sequence, inputting the axial strain data sequence and design parameters into a trained offline model, and obtaining axial pressure load data output by the model; and restoring the axle pressure load-strain curve by using the axial strain data and the axle pressure load data, so as to realize the axle pressure load-strain curve prediction of the system.
The above description is only of the preferred embodiment of the present invention, and is not intended to limit the present invention in any other way, but is intended to cover any modifications or equivalent variations according to the technical spirit of the present invention, which fall within the scope of the present invention as defined by the appended claims.
Claims (4)
1. The method for predicting the axial compressive load-strain curve of the concrete filled steel tube column based on the long-short-term memory network is characterized by comprising the following steps of: the method comprises the following steps:
s1: collecting experimental data;
collecting experimental data of axial compression load-strain curves of steel tube concrete columns with different materials and sizes, and establishing a load-strain curve and five design parameters, wherein the five design parameters are respectively as follows: diameter D of steel pipe and wall thickness t of steel pipe s Column height H, steel strength f s And concrete strength f c Constructing a load-strain curve prediction training sample through design parameters according to the corresponding system database;
s2: preprocessing data;
setting 25000 mu epsilon as a limit value for collecting axial strain for all axial pressure load-strain curve experimental data of the concrete filled steel tube column, if the experimental data curve is shorter and does not reach the axial strain limit value of 25000 mu epsilon, executing compensation operation, and extending the axial pressure load-strain curve to the set axial strain limit value according to the final slope of the load-strain curve; if the experimental data curve is longer, the cut-off operation is executed, the data exceeding the set limit value is discarded, and after the data pretreatment, all axial load-strain curve data have uniform axial strain length;
s3: data constitution;
each set of design parameters and corresponding axle load-strain curve is used as a data sample; dividing experimental data of the axial pressure load-strain curve of the steel tube concrete column subjected to data pretreatment into m points according to the strain value, wherein each point comprises an axial pressure load value N and an axial strain value epsilon; the input data of the long-term and short-term memory unit neural network is built by using the axial strain value and the design parameter; building output data of the long-term memory unit neural network by using the axle load value;
s4: training an offline model;
after all samples are formed by input and output data in the step S3, performing offline model training on all samples by adopting a long-short-period memory unit neural network to obtain a long-short-period memory unit neural network model conforming to convergence;
s5: predicting an axle load-strain curve;
arranging design parameters of a concrete filled steel tube column of which the axial compression load-strain curve needs to be predicted; constructing an axial strain data sequence, and taking a vector with the maximum value of 25000 mu epsilon and containing m data values distributed evenly as the axial strain data sequence; the steel pipe concrete sample data which needs to be subjected to axle load-strain curve prediction is converted into input data constitution which can be used for a long-period and short-period memory network by utilizing the input data constitution construction method of S3; inputting the input data number into the offline model obtained in the step S4, and obtaining corresponding axle load output; taking the average value of all axial strains in each row of the input data and the axial pressure load value in the row corresponding to the output data as one point of an axial pressure load-strain plane, sequentially calculating all axial pressure load-strain points so as to restore a complete axial pressure load-strain curve, and completing the axial pressure load-strain curve prediction.
2. The method for predicting the axial compressive load-strain curve of the concrete filled steel tubular column based on the long-term memory network according to claim 1, which is characterized by comprising the following steps of:
in the step S3 of the method,
s31: the input data structure includes: taking k axial strains as a group, and forming a first row in a data sample by the k axial strains and five design parameters of the sample; then taking another group of k axial strains by adopting a sliding window method, and forming another row in one data sample by the group of k axial strains and the five design parameters; and so on until all axial strains are taken, at which time the input data for each data sample comprises m+1-k rows and 5+k columns;
s32: the output data structure includes: taking a group of k axle load values to obtain each corresponding axle load value, and averaging the group of axle load values to obtain the axle load corresponding to the line of input dataThe output data of each data sample is taken as the output data of the neural network, and the output data of each data sample comprises m+1-k rows and 1 column.
3. The method for predicting the axial compressive load-strain curve of the concrete filled steel tubular column based on the long-term memory network according to claim 1, which is characterized by comprising the following steps of:
in the step S4 of the process,
s41: the long-term and short-term memory unit neural network sequentially comprises: an input layer, a long-short-period memory unit layer, a full connection layer, a random discarding layer, a full connection layer and a data regression layer; the long-period memory unit layer comprises G long-period memory units, namely the input time sequence length is G, and the input data feature quantity is C; wherein the long-short-period memory unit comprises: at the current time of t-1, the output at the time of t needs to be calculated through a long-short-period memory unit, and the input data is x t ，h t And c t Respectively representing the output and the cell state at time t; the long-short-period memory unit uses the current state h t-1 And c t-1 To calculate the output h t And updated cell state c t The method comprises the steps of carrying out a first treatment on the surface of the Using input gate i t Forgetting door f t Candidate unit g t And an output gate o t To control the update of the long-short-period memory unit, the calculation formula is as follows:
i t ＝σ(W i x t +R i h t-1 +b i )
f t ＝σ(W f x t +R f h t-1 +b f )
g t ＝σ(W g x t +R g h t-1 +b g )
c t ＝f t ⊙c t-1 +i t ⊙g t
o t ＝σ(W o x t +R o h t-1 +b o )
h t ＝o t ⊙tanh(c t )
wherein W is i ,W f ,W g ,W o Is an input weight matrix, R i ,R f ,R g ,R o Is a cyclic weight matrix, b i ,b f ,b g ,b o Is an offset matrix, and the matrix is adjusted through model training; sigma is a sigmoid activation function, and by; directly output state h t As the output of the long-short-period memory unit;
s42: the offline model training in S4 specifically includes:
5+k data in each line of the long-period memory cell neural network input data in the step S3 are input into each long-period memory cell as the input data features in the step S41, namely the feature quantity C= 5+k; and (3) respectively inputting the m+1-k rows in the S3 into G long-short-period memory units of the long-short-period memory unit layer in the S41, namely, the number G=m+1-k of the long-short-period memory units, and training a network by adopting a backward error propagation algorithm.
4. The method for predicting the axial compressive load-strain curve of the concrete filled steel tubular column based on the long-term memory network according to claim 2, which is characterized by comprising the following steps of:
in the step S5 of the method,
the construction method of the input data composition of S31 is utilized to convert steel pipe concrete sample data which needs to be subjected to axle load-strain curve prediction into input data composition which can be used for long-period and short-period memory networks.
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