CN114894619B - Prediction method of axial compression load-strain curve of concrete-filled steel tube columns based on long short-term memory network - Google Patents
Prediction method of axial compression load-strain curve of concrete-filled steel tube columns based on long short-term memory network Download PDFInfo
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Abstract
本发明提出了基于长短期记忆网络的钢管混凝土柱轴压荷载‑应变曲线预测方法。对于设计参数、轴压荷载和轴向应变数据,设计数据预处理和数据构成方法,用于长短期记忆网络离线模型训练;构造轴向应变数据序列,并将其与设计参数输入到训练好的离线模型中,得到模型输出的轴压荷载数据;用轴向应变数据和轴压荷载数据还原轴压荷载‑应变曲线,实现系统轴压荷载‑应变曲线预测。
The present invention proposes a method for predicting the axial compression load-strain curve of a steel tube concrete column based on a long short-term memory network. For design parameters, axial compression load and axial strain data, design data preprocessing and data composition methods are used for long short-term memory network offline model training; construct an axial strain data sequence, and input it and the design parameters into the trained offline model to obtain the axial compression load data output by the model; use the axial strain data and axial compression load data to restore the axial compression load-strain curve to achieve system axial compression load-strain curve prediction.
Description
技术领域Technical Field
本发明涉及建筑与土木工程领域,具体是基于长短期记忆网络的钢管混凝土柱轴压荷载-应变曲线预测方法。The present invention relates to the fields of architecture and civil engineering, and in particular to a method for predicting axial compression load-strain curve of a steel tube concrete column based on a long short-term memory network.
背景技术Background technique
钢管混凝土柱作为承受压缩和弯曲的结构构件,被公认为有希望成为高层建筑和城市高架梁桥施工中传统钢筋混凝土和钢柱的替代品。与传统结构柱相比,钢管混凝土柱具有更高的弯曲、轴向承载力和抗震能力。此外,钢管和填充混凝土的相互保护使钢管混凝土柱具有更好的耐火性、抗冲击性和耐腐蚀性。As a structural member that bears compression and bending, steel tube concrete columns are recognized as promising substitutes for traditional reinforced concrete and steel columns in the construction of high-rise buildings and urban viaduct bridges. Compared with traditional structural columns, steel tube concrete columns have higher bending, axial bearing capacity and seismic resistance. In addition, the mutual protection of steel tubes and filling concrete makes steel tube concrete columns have better fire resistance, impact resistance and corrosion resistance.
轴压荷载-应变曲线是结构柱安全设计中最基本、最重要的特征之一。一旦知道荷载-应变曲线,就可以很容易地获得关键的设计参数,包括柱的弹性模量、极限承载力和延性。因此在过去三十年中,轴压荷载-应变曲线一直是钢管混凝土柱的基本研究目标。利用经验参数进行理论推导和有限元模型分析是当前计算钢管混凝土柱荷载-应变曲线的两种主要方法。在基于理论方法的研究中,通常先假设外钢管和内填混凝土的应力-应变关系,然后根据平衡条件和预定的相互作用关系逐步推导出柱的荷载-应变曲线。然而随着材料的更新,这种方法的适用性逐渐降低,而且基于经验参数的理论方法不可避免地局限于当时的材料强度与设计理念。The axial compressive load-strain curve is one of the most basic and important features in the safety design of structural columns. Once the load-strain curve is known, key design parameters, including the elastic modulus, ultimate bearing capacity and ductility of the column, can be easily obtained. Therefore, the axial compressive load-strain curve has been the basic research target of steel tube concrete columns in the past three decades. Theoretical derivation using empirical parameters and finite element model analysis are the two main methods for calculating the load-strain curve of steel tube concrete columns. In the research based on the theoretical method, the stress-strain relationship between the outer steel tube and the inner concrete is usually assumed first, and then the load-strain curve of the column is gradually derived according to the equilibrium condition and the predetermined interaction relationship. However, with the update of materials, the applicability of this method has gradually decreased, and the theoretical method based on empirical parameters is inevitably limited to the material strength and design concept at that time.
有限元模型分析是获得钢管混凝土柱轴向荷载-应变曲线的另一种方法。在有限元分析中,混凝土和钢管应采用适当的单元类型进行模拟,通常为混凝土实体单元和钢管壳单元,同时需要使用接触模型或弹簧模型来定义混凝土和钢表面之间的相互作用。通过合理采用本构关系和适当的网格,可以得到钢管混凝土柱的轴压荷载-应变曲线。Finite element model analysis is another method to obtain the axial load-strain curve of steel tube concrete columns. In finite element analysis, concrete and steel tubes should be simulated using appropriate unit types, usually concrete solid units and steel tube shell units, and contact models or spring models are needed to define the interaction between concrete and steel surfaces. By reasonably adopting constitutive relations and appropriate meshes, the axial compression load-strain curve of steel tube concrete columns can be obtained.
近年来,软计算方法和智能技术在土木工程中得到了显著的发展和广泛的应用。然而机器学习算法在钢管混凝土柱研究中的应用仍处于起步和探索阶段。大多数现有研究都集中在基于收集的实验数据库的性能指标(如轴向承载力和刚度)预测上。目前缺少关于使用机器学习方法计算钢管混凝土构件的完整轴压荷载-应变曲线的研究。In recent years, soft computing methods and intelligent technologies have been significantly developed and widely used in civil engineering. However, the application of machine learning algorithms in the study of concrete-filled steel tube columns is still in its infancy and exploration stage. Most existing studies focus on the prediction of performance indicators (such as axial bearing capacity and stiffness) based on collected experimental databases. There is a lack of research on the use of machine learning methods to calculate the complete axial compressive load-strain curve of concrete-filled steel tube components.
长短期记忆网络是循环神经网络的一种,常用来处理时间序列。长短期记忆网络的特点是它考虑数据的长短期依赖关系。为解决上述存在的问题,有必要提出一种能够将轴压荷载-应变曲线中的点视作具有长短期依赖关系的时间序列,并通过设计数据构成方法用长短期记忆网络来实现曲线预测的技术方案。Long short-term memory network is a type of recurrent neural network, which is often used to process time series. The characteristic of long short-term memory network is that it considers the long-term and short-term dependencies of data. In order to solve the above problems, it is necessary to propose a technical solution that can regard the points in the axial compression load-strain curve as a time series with long-term and short-term dependencies, and use the long short-term memory network to realize curve prediction by designing a data composition method.
发明内容Summary of the invention
针对上述提到的钢管混凝土柱轴压荷载-应变曲线预测问题,本发明提出了基于长短期记忆网络的钢管混凝土柱轴压荷载-应变曲线预测方法,能够将轴压荷载-应变曲线中的点视作具有长短期依赖关系的时间序列,并通过设计数据构成方法用长短期记忆网络来实现曲线预测。In response to the above-mentioned problem of axial compressive load-strain curve prediction for steel tube concrete columns, the present invention proposes an axial compressive load-strain curve prediction method for steel tube concrete columns based on long short-term memory network, which can regard the points in the axial compressive load-strain curve as a time series with long-term and short-term dependencies, and realize curve prediction using long short-term memory network through design data construction method.
为实现上述目的,本发明采取的技术方案是:To achieve the above object, the technical solution adopted by the present invention is:
基于长短期记忆网络的钢管混凝土柱轴压荷载-应变曲线预测方法,其特征在于:包括有如下步骤:The method for predicting axial compressive load-strain curve of steel tube concrete column based on long short-term memory network is characterized by comprising the following steps:
S1:实验数据采集;S1: Experimental data collection;
采集不同材料、尺寸的钢管混凝土柱的轴压荷载-应变曲线实验数据,建立荷载-应变曲线与五个设计参数,五个设计参数分别为:钢管直径D、钢管壁厚ts、柱高度H、钢材强度fs,以及混凝土强度fc对应的系统数据库,通过设计参数构建荷载-应变曲线预测训练样本;The experimental data of axial compression load-strain curve of steel tube concrete columns of different materials and sizes are collected to establish the load-strain curve and five design parameters. The five design parameters are: steel tube diameter D, steel tube wall thickness ts , column height H, steel strength fs , and concrete strength fc. The system database corresponding to the design parameters is used to construct the load-strain curve prediction training samples;
S2:数据预处理;S2: data preprocessing;
对于所有钢管混凝土柱轴压荷载-应变曲线实验数据,设定25000με为采集轴向应变的限值,若实验数据曲线较短,未达到25000με的轴向应变限值,将执行补偿操作,根据荷载-应变曲线最终斜率将轴压荷载-应变曲线延伸至设定轴向应变限值;若实验数据曲线较长,将执行截断操作,丢弃超过设定限值的数据,再经过上述数据预处理后,所有的轴向载荷-应变曲线数据具有统一的轴向应变长度;For all CFST column axial compression load-strain curve experimental data, 25000με is set as the limit value for collecting axial strain. If the experimental data curve is short and does not reach the axial strain limit of 25000με, a compensation operation will be performed to extend the axial compression load-strain curve to the set axial strain limit according to the final slope of the load-strain curve. If the experimental data curve is long, a truncation operation will be performed to discard data exceeding the set limit. After the above data preprocessing, all axial load-strain curve data have a uniform axial strain length.
S3:数据构成;S3: Data composition;
每一组设计参数和对应的轴压荷载-应变曲线作为一个数据样本;将经过数据预处理的钢管混凝土柱轴压荷载-应变曲线的实验数据按应变值平均分成m个点,每个点包含轴压荷载值N和轴向应变值ε;用轴向应变值和设计参数搭建长短期记忆单元神经网络的输入数据构成;再用轴压荷载值搭建长短期记忆单元神经网络的输出数据构成;Each set of design parameters and the corresponding axial compression load-strain curve is taken as a data sample; the experimental data of the axial compression load-strain curve of the steel tube concrete column after data preprocessing is evenly divided into m points according to the strain value, and each point contains the axial compression load value N and the axial strain value ε; the input data composition of the long short-term memory unit neural network is constructed using the axial strain value and the design parameters; and the output data composition of the long short-term memory unit neural network is constructed using the axial compression load value;
S4:离线模型训练;S4: offline model training;
所有样本经过S3中输入和输出数据构成后,采用长短期记忆单元神经网络对所有样本进行离线模型训练,得到符合收敛性的长短期记忆单元神经网络模型;After all samples are constructed through input and output data in S3, the long short-term memory unit neural network is used to perform offline model training on all samples to obtain a long short-term memory unit neural network model that meets the convergence requirements;
S5:轴压荷载-应变曲线预测;S5: prediction of axial compression load-strain curve;
整理需要预测轴压荷载-应变曲线的钢管混凝土柱的设计参数;构造轴向应变数据序列,取最大值为25000με包含m个平均分布的数据值的向量作为轴向应变数据序列;利用S3所述输入数据构成搭建方法将需要进行轴压荷载-应变曲线预测的钢管混凝土样本数据转变成可以用于长短期记忆网络的输入数据构成;将输入数据数输入至S4所得到的离线模型中,得到对应的轴压荷载输出;取输入数据每一个行中所有轴向应变的平均值与输出数据对应行中的轴压荷载值作为轴压荷载-应变平面的一个点,依次计算所有轴压荷载-应变点从而还原完整的轴压荷载-应变曲线,完成轴压荷载-应变曲线预测。Arrange the design parameters of the steel tube concrete column for which the axial compressive load-strain curve needs to be predicted; construct an axial strain data sequence, and take a vector with a maximum value of 25000με and m evenly distributed data values as the axial strain data sequence; use the input data composition construction method described in S3 to convert the steel tube concrete sample data for axial compressive load-strain curve prediction into an input data composition that can be used for a long short-term memory network; input the input data number into the offline model obtained in S4 to obtain the corresponding axial compressive load output; take the average value of all axial strains in each row of the input data and the axial compressive load value in the corresponding row of the output data as a point on the axial compressive load-strain plane, calculate all axial compressive load-strain points in turn to restore the complete axial compressive load-strain curve, and complete the axial compressive load-strain curve prediction.
作为本发明的优选方案:As a preferred embodiment of the present invention:
在S3中,In S3,
S31:所述输入数据构成包括:取k个轴向应变作为一组,将其与该样本的五个设计参数构成一个数据样本中的第一行;之后采用滑动窗口方法取另外一组k个轴向应变,将其与五个设计参数构成一个数据样本中的另一行;以此类推直到取完所有的轴向应变,此时每一个数据样本的输入数据构成包含m+1-k行和5+k列;S31: The input data composition includes: taking k axial strains as a group, and forming the first row of a data sample with the five design parameters of the sample; then taking another group of k axial strains using a sliding window method, and forming another row of a data sample with the five design parameters; and so on until all axial strains are taken, at which time the input data composition of each data sample includes m+1-k rows and 5+k columns;
S32:所述输出数据构成包括:取一组k个轴压荷载值得到每个对应的轴压荷载值,将这组压荷载值取平均得到输入数据这一行对应的轴压荷载将其作为神经网络的输出数据,此时每一个数据样本的输出数据构成包含m+1-k行和1列。S32: The output data composition includes: taking a group of k axial compressive load values to obtain each corresponding axial compressive load value, averaging the group of compressive load values to obtain the axial compressive load corresponding to this row of input data It is used as the output data of the neural network. At this time, the output data structure of each data sample contains m+1-k rows and 1 column.
作为本发明的优选方案:As a preferred embodiment of the present invention:
在S4中,In S4,
S41:所述长短期记忆单元神经网络依次包括:输入层、长短期记忆单元层、全连接层、随机丢弃层、全连接层以及数据回归层;其中所述长短期记忆单元层包括G个长短期记忆单元,即输入的时间序列长度为G,输入数据特征数量为C;其中所述长短期记忆单元包括:S41: The LSTM neural network includes, in sequence: an input layer, a LSTM layer, a fully connected layer, a random drop layer, a fully connected layer, and a data regression layer; wherein the LSTM layer includes G LSTMs, i.e., the input time series length is G, and the number of input data features is C; wherein the LSTM includes:
当前为t-1时刻,需要通过长短期记忆单元计算t时刻的输出,输入数据为xt,ht和ct分别表示在t时刻的输出和单元状态;该长短期记忆单元使用当前状态ht-1和ct-1来计算输出ht和更新后的单元状态ct;使用输入门it、遗忘门ft、候选单元gt和输出门ot来控制长短期记忆单元的更新,计算公式如下:The current moment is t-1, and the output at time t needs to be calculated through the long short-term memory unit. The input data is x t , h t and c t represent the output and unit state at time t respectively; the long short-term memory unit uses the current states h t-1 and c t-1 to calculate the output h t and the updated unit state c t ; the input gate i t , forget gate f t , candidate unit g t and output gate o t are used to control the update of the long short-term memory unit. The calculation formula is as follows:
it=σ(Wixt+Riht-1+bi) it =σ( Wixt + Rit -1 + bi )
ft=σ(Wfxt+Rfht-1+bf) ft = σ(Wfxt + Rfht - 1 + bf )
gt=σ(Wgxt+Rght-1+bg)g t =σ(W g x t +R g h t-1 +b g )
ct=ft⊙ct-1+it⊙gt c t = f t ⊙ c t-1 + i t ⊙ g t
ot=σ(Woxt+Roht-1+bo)o t =σ(W o x t +R o h t-1 +b o )
ht=ot⊙tanh(ct)h t = o t ⊙ tanh(c t )
其中Wi,Wf,Wg,Wo是输入权重矩阵,Ri,Rf,Rg,Ro是循环权重矩阵,bi,bf,bg,bo是偏置矩阵,通过模型训练调整上述矩阵;σ是sigmoid激活函数,⊙是点乘运算;直接将输出状态ht作为长短期记忆单元的输出;Where Wi , Wf , Wg , Wo are input weight matrices, Ri , Rf , Rg , Ro are cyclic weight matrices, bi , bf , bg , bo are bias matrices, and the above matrices are adjusted through model training; σ is the sigmoid activation function, ⊙ is the dot multiplication operation; the output state ht is directly used as the output of the long short-term memory unit;
S42:在S4中所述离线模型训练具体包括:S42: The offline model training described in S4 specifically includes:
将S3所述长短期记忆单元神经网络输入数据每一行中的5+k个数据作为S41中所述的输入数据特征输入到每一个长短期记忆单元中,即特征数量C=5+k;将S3中所述的m+1-k行分别输入S41中所述长短期记忆单元层的G个长短期记忆单元中,即长短期记忆单元数量G=m+1-k,采用后向误差传播算法来训练网络。The 5+k data in each row of the LSTM neural network input data described in S3 are input into each LSTM unit as the input data features described in S41, that is, the number of features C=5+k; the m+1-k rows described in S3 are respectively input into the G LSTM units of the LSTM unit layer described in S41, that is, the number of LSTM units G=m+1-k, and the backward error propagation algorithm is used to train the network.
作为本发明的优选方案:As a preferred embodiment of the present invention:
在S5中,In S5,
利用S31的输入数据构成搭建方法将需要进行轴压荷载-应变曲线预测的钢管混凝土样本数据转变成可以用于长短期记忆网络的输入数据构成。The input data composition construction method of S31 is used to transform the steel tube concrete sample data that needs to be predicted for axial compression load-strain curve into an input data composition that can be used for long short-term memory network.
与现有技术相比,本发明的有益效果为:Compared with the prior art, the present invention has the following beneficial effects:
本发明通过设计参数、轴压荷载和轴向应变数据,设计数据预处理和数据构成方法,用于长短期记忆网络离线模型训练;构造轴向应变数据序列,并将其与设计参数输入到训练好的离线模型中,得到模型输出的轴压荷载数据;用轴向应变数据和轴压荷载数据还原轴压荷载-应变曲线,实现系统轴压荷载-应变曲线预测。The present invention uses design parameters, axial compression load and axial strain data, and designs data preprocessing and data construction methods for long short-term memory network offline model training; constructs an axial strain data sequence, and inputs it and the design parameters into the trained offline model to obtain axial compression load data output by the model; uses the axial strain data and axial compression load data to restore the axial compression load-strain curve, and realizes the prediction of the system axial compression load-strain curve.
附图说明BRIEF DESCRIPTION OF THE DRAWINGS
图1是曲线预测原理流程。Figure 1 is the curve prediction principle flow.
图2是输入输出数据构成。Figure 2 shows the input and output data structure.
图3是长短期记忆单元层。Figure 3 is a long short-term memory unit layer.
图4是长短期记忆单元。Figure 4 is a long short-term memory unit.
具体实施方式Detailed ways
下面结合附图与具体实施方式对本发明作进一步详细描述:The present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments:
本发明提出了基于长短期记忆网络的钢管混凝土柱轴压荷载-应变曲线预测方法。对于设计参数、轴压荷载和轴向应变数据,设计数据预处理和数据构成方法,用于长短期记忆网络离线模型训练;构造轴向应变数据序列,并将其与设计参数输入到训练好的离线模型中,得到模型输出的轴压荷载数据;用轴向应变数据和轴压荷载数据还原轴压荷载-应变曲线,实现系统轴压荷载-应变曲线预测。The present invention proposes a method for predicting the axial compression load-strain curve of a steel tube concrete column based on a long short-term memory network. For design parameters, axial compression load and axial strain data, design data preprocessing and data composition methods are used for long short-term memory network offline model training; construct an axial strain data sequence, and input it and the design parameters into the trained offline model to obtain the axial compression load data output by the model; use the axial strain data and axial compression load data to restore the axial compression load-strain curve to achieve system axial compression load-strain curve prediction.
本发明的曲线预测原理流程如图1所示。The curve prediction principle flow of the present invention is shown in FIG1 .
本发明通过以下技术方案实现:The present invention is achieved through the following technical solutions:
S1:实验数据采集。S1: Experimental data collection.
采集不同材料、尺寸的钢管混凝土柱的轴压荷载-应变曲线实验数据,建立荷载-应变曲线与五个设计参数,分别为:钢管直径D、钢管壁厚ts、柱高度H、钢材强度fs,以及混凝土强度fc对应的系统数据库,构建荷载-应变曲线预测训练样本。The experimental data of axial compression load-strain curves of steel tube concrete columns of different materials and sizes are collected, and a system database corresponding to the load-strain curve and five design parameters, namely, steel tube diameter D, steel tube wall thickness ts , column height H, steel strength fs , and concrete strength fc , is established to construct a load-strain curve prediction training sample.
S2:数据预处理。S2: Data preprocessing.
对于所有钢管混凝土柱轴压荷载-应变曲线实验数据,设定25000με为采集轴向应变的限值,以确保所有的荷载-应变曲线捕捉到峰值荷载点和峰后曲线特征。如果实验数据曲线较短,没有达到25000με的轴向应变限值,将执行补偿操作,根据曲线最终斜率将轴压荷载-应变曲线延伸至设定轴向应变限值。如果实验数据曲线较长,将执行截断操作,以丢弃超过设定限值的数据,经过上述数据预处理后,所有的轴向载荷-应变曲线数据具有统一的轴向应变长度。For all CFST column axial compression load-strain curve experimental data, 25000με is set as the limit for collecting axial strain to ensure that all load-strain curves capture the peak load point and post-peak curve characteristics. If the experimental data curve is short and does not reach the axial strain limit of 25000με, a compensation operation will be performed to extend the axial compression load-strain curve to the set axial strain limit according to the final slope of the curve. If the experimental data curve is long, a truncation operation will be performed to discard data that exceeds the set limit. After the above data preprocessing, all axial load-strain curve data have a uniform axial strain length.
S3:数据构成。S3: Data composition.
每一组设计参数和对应的轴压荷载-应变曲线作为一个数据样本。将经过数据预处理的钢管混凝土柱轴压荷载-应变曲线实验数据按应变值平均分成m个点,每个点包含轴压荷载值N和轴向应变值ε。用轴向应变值和设计参数搭建长短期记忆单元神经网络的输入数据构成。再用轴压荷载值搭建长短期记忆单元神经网络的输出数据构成。本发明的输入输出数据构成如图2所示,其中符号i表示数据库中的第i个样本。Each set of design parameters and the corresponding axial compression load-strain curve is taken as a data sample. The experimental data of the axial compression load-strain curve of the steel tube concrete column that has undergone data preprocessing is evenly divided into m points according to the strain value, and each point contains an axial compression load value N and an axial strain value ε. The input data composition of the long short-term memory unit neural network is constructed using the axial strain value and the design parameters. The output data composition of the long short-term memory unit neural network is then constructed using the axial compression load value. The input and output data composition of the present invention is shown in Figure 2, where the symbol i represents the i-th sample in the database.
S31:所述输入数据构成包括:取k个轴向应变作为一组,将其与该样本的五个设计参数构成一个数据样本中的第一行;之后采用滑动窗口方法取另外一组k个轴向应变,将其与五个设计参数构成一个数据样本中的另一行;以此类推直到取完所有的轴向应变,此时每一个数据样本的输入数据构成包含m+1-k行和5+k列。S31: The input data composition includes: taking k axial strains as a group, and forming the first row of a data sample with the five design parameters of the sample; then taking another group of k axial strains using the sliding window method, and forming another row of a data sample with the five design parameters; and so on until all axial strains are taken, at which time the input data composition of each data sample includes m+1-k rows and 5+k columns.
S32:所述输出数据构成包括:取一组k个轴压荷载值得到每个对应的轴压荷载值,将这组压荷载值取平均得到输入数据这一行对应的轴压荷载将其作为神经网络的输出数据,此时每一个数据样本的输出数据构成包含m+1-k行和1列。S32: The output data composition includes: taking a group of k axial compressive load values to obtain each corresponding axial compressive load value, averaging the group of compressive load values to obtain the axial compressive load corresponding to this row of input data It is used as the output data of the neural network. At this time, the output data structure of each data sample contains m+1-k rows and 1 column.
S4:离线模型训练。S4: Offline model training.
所有样本经过S3中输入和输出数据构成后,采用长短期记忆单元神经网络对所有样本进行离线模型训练,得到符合收敛性的长短期记忆单元神经网络模型。After all samples are constructed through input and output data in S3, the long short-term memory unit neural network is used to perform offline model training on all samples to obtain a long short-term memory unit neural network model that meets the convergence requirements.
S41:所述长短期记忆单元神经网络依次包括:输入层,长短期记忆单元层,全连接层,随机丢弃(dropout)层,全连接层,数据回归层。其中所述长短期记忆单元层如图3所示,它包括G个长短期记忆单元,即输入的时间序列长度为G,输入数据特征数量为C。其中所述长短期记忆单元如图4所示,它包括:S41: The LSTM neural network includes in sequence: input layer, LSTM layer, fully connected layer, random dropout layer, fully connected layer, data regression layer. The LSTM layer is shown in FIG3 , which includes G LSTMs, that is, the input time series length is G, and the number of input data features is C. The LSTM is shown in FIG4 , which includes:
当前为t-1时刻,需要通过长短期记忆单元计算t时刻的输出,输入数据为xt,ht和ct分别表示在t时刻的输出(也称为隐藏状态)和单元状态。该长短期记忆单元使用当前状态(ht-1和ct-1)来计算输出ht和更新后的单元状态ct。使用输入门it、遗忘门ft、候选单元gt和输出门ot来控制长短期记忆单元的更新。计算公式如下:The current moment is t-1, and the output at time t needs to be calculated through the long short-term memory unit. The input data is x t , and h t and c t represent the output (also called hidden state) and unit state at time t, respectively. The long short-term memory unit uses the current state (h t-1 and c t-1 ) to calculate the output h t and the updated unit state c t . The input gate i t , forget gate f t , candidate unit g t and output gate o t are used to control the update of the long short-term memory unit. The calculation formula is as follows:
it=σ(Wixt+Riht-1+bi) it =σ( Wixt + Rit -1 + bi )
ft=σ(Wfxt+Rfht-1+bf) ft = σ(Wfxt + Rfht - 1 + bf )
gt=σ(Wgxt+Rght-1+bg)g t =σ(W g x t +R g h t-1 +b g )
ct=ft⊙ct-1+it⊙gt c t = f t ⊙ c t-1 + i t ⊙ g t
ot=σ(Woxt+Roht-1+bo)o t =σ(W o x t +R o h t-1 +b o )
ht=ot⊙tanh(ct)h t = o t ⊙ tanh(c t )
其中Wi,Wf,Wg,Wo是输入权重矩阵,Ri,Rf,Rg,Ro是循环权重矩阵,bi,bf,bg,bo是偏置矩阵,通过模型训练调整这些矩阵。σ是sigmoid激活函数,⊙是点乘运算。直接将隐藏状态ht作为长短期记忆单元的输出。Where Wi , Wf , Wg , Wo are input weight matrices, Ri , Rf , Rg , Ro are recurrent weight matrices, and bi , bf , bg , bo are bias matrices that are adjusted through model training. σ is the sigmoid activation function, and ⊙ is the dot multiplication operation. The hidden state ht is directly used as the output of the long short-term memory unit.
S42:S4中所述离线模型训练具体包括:S42: The offline model training described in S4 specifically includes:
将S3所述长短期记忆单元神经网络输入数据每一行中的5+k个数据作为S41中所述的输入数据特征输入到每一个长短期记忆单元中,即特征数量C=5+k;将S3中所述的m+1-k行分别输入S41中所述长短期记忆单元层的G个长短期记忆单元中,即长短期记忆单元数量G=m+1-k。采用后向误差传播算法来训练网络,使长短期记忆网络预测的轴压荷载与实验值具有最小的均方根误差。The 5+k data in each row of the LSTM neural network input data described in S3 are input into each LSTM as the input data features described in S41, that is, the number of features C=5+k; the m+1-k rows described in S3 are respectively input into the G LSTM units of the LSTM layer described in S41, that is, the number of LSTM units G=m+1-k. The network is trained using a backward error propagation algorithm so that the axial compressive load predicted by the LSTM network has the smallest root mean square error with the experimental value.
S5:轴压荷载-应变曲线预测。S5: Prediction of axial compression load-strain curve.
整理需要预测轴压荷载-应变曲线的钢管混凝土柱的设计参数。构造轴向应变数据序列,取最大值为25000με包含m个平均分布的数据值的向量作为轴向应变数据序列。利用S31所述输入数据构成搭建方法将需要进行轴压荷载-应变曲线预测的钢管混凝土样本数据转变成可以用于长短期记忆网络的输入数据构成。将输入数据数输入至S4所得到的离线模型中,得到对应的轴压荷载输出。取输入数据每一个行中所有轴向应变的平均值与输出数据对应行中的轴压荷载值作为轴压荷载-应变平面的一个点,依次计算所有轴压荷载-应变点从而还原完整的轴压荷载-应变曲线,完成轴压荷载-应变曲线预测。Arrange the design parameters of the steel tube concrete column that needs to predict the axial compression load-strain curve. Construct the axial strain data sequence, and take the vector with a maximum value of 25000με and m evenly distributed data values as the axial strain data sequence. Use the input data composition construction method described in S31 to transform the steel tube concrete sample data that needs to predict the axial compression load-strain curve into an input data composition that can be used for the long short-term memory network. Input the input data number into the offline model obtained in S4 to obtain the corresponding axial compression load output. Take the average value of all axial strains in each row of the input data and the axial compression load value in the corresponding row of the output data as a point on the axial compression load-strain plane, calculate all axial compression load-strain points in turn to restore the complete axial compression load-strain curve, and complete the axial compression load-strain curve prediction.
本发明通过设计参数、轴压荷载和轴向应变数据,设计数据预处理和数据构成方法,用于长短期记忆网络离线模型训练;构造轴向应变数据序列,并将其与设计参数输入到训练好的离线模型中,得到模型输出的轴压荷载数据;用轴向应变数据和轴压荷载数据还原轴压荷载-应变曲线,实现系统轴压荷载-应变曲线预测。The present invention uses design parameters, axial compression load and axial strain data, and designs data preprocessing and data construction methods for long short-term memory network offline model training; constructs an axial strain data sequence, and inputs it and the design parameters into the trained offline model to obtain axial compression load data output by the model; uses the axial strain data and axial compression load data to restore the axial compression load-strain curve, and realizes the prediction of the system axial compression load-strain curve.
以上所述,仅是本发明的较佳实施例而已,并非是对本发明作任何其他形式的限制,而依据本发明的技术实质所作的任何修改或等同变化,仍属于本发明所要求保护的范围。The above description is only a preferred embodiment of the present invention and does not constitute any other form of limitation to the present invention. Any modification or equivalent change made based on the technical essence of the present invention still falls within the scope of protection required by the present invention.
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