CN112784336A - Bridge static displacement prediction technology based on deep learning LSTM network - Google Patents

Bridge static displacement prediction technology based on deep learning LSTM network Download PDF

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CN112784336A
CN112784336A CN202011628545.9A CN202011628545A CN112784336A CN 112784336 A CN112784336 A CN 112784336A CN 202011628545 A CN202011628545 A CN 202011628545A CN 112784336 A CN112784336 A CN 112784336A
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deflection
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lstm
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康春光
包元锋
胡辉
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Hangzhou Ruhr Technology Co Ltd
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Abstract

The invention discloses a bridge static displacement prediction technology based on a deep learning LSTM network, which comprises the following steps: collecting static response monitoring data of bridge deflection; preprocessing the bridge deflection data; establishing an LSTM neural network for training; and predicting the bridge deflection by using the trained LSTM neural network model. The technical scheme is based on a deep learning artificial intelligence algorithm LSTM RNN and a long-short term memory cyclic neural network, sample data is obtained from deflection monitoring data of the bridge to conduct neural network model training, the deflection of the bridge is predicted through a neural network model trained through the past deflection information of the bridge, the purpose of predicting and early warning the deflection of the bridge is finally achieved, and the evaluation on the working performance of the bridge and the accurate judgment on the structure safety are achieved.

Description

Bridge static displacement prediction technology based on deep learning LSTM network
Technical Field
The invention relates to the technical field of bridge structure safety, in particular to a bridge static displacement prediction technology based on a deep learning LSTM network.
Background
The static displacement of the bridge is an important monitoring content for monitoring the safety of the bridge structure, and the change of the deflection of the bridge can most directly reflect the change of the integral vertical rigidity and the bearing capacity of the bridge. The prediction of the deformation trend of the bridge structure can be helpful for the working performance evaluation of the bridge and the early warning of the structure safety.
The data shows that the commonly used prediction models have defects in some aspects, cannot well meet the precision requirement, and inevitably cause large errors, so that the prediction result is not credible. At present, the monitoring data of the bridge deflection has a large influence on a prediction result due to a large number of abnormal values of environmental interference, and finally the prediction result is greatly different from an actual situation.
Chinese patent document CN102635059B discloses a "bridge reconnaissance method". The following steps are adopted: 1) acquiring three-dimensional ground and geological data to generate DGSM; 2) generating DGESM; 3) generating a DBSM; 4) forming a bridge global three-dimensional entity curved surface model; 5) carrying out solid mechanical analysis on the bridge structure on the bridge global three-dimensional solid curved surface model, and selecting a route linear scheme and a bridge scheme; 6) establishing a geological entity section prediction model, and optimizing a route and bridge scheme; 7) generating a three-dimensional steel bar graph of the bridge structure by the bridge global three-dimensional entity curved surface model; 8) generating a structural diagram of the upper bridge structure and the lower bridge structure; 9) generating a lower and upper structural body of the bridge in the construction stage; 10) and comparing the built three-dimensional bridge entity model with the global three-dimensional entity curved surface model of the bridge before building to obtain parameters such as displacement, settlement, deformation and the like, and carrying out bridge detection. The technical scheme only carries out reconnaissance on the current situation of the bridge, lacks the prediction on the bridge and is difficult to meet the expected judgment on the safety of the bridge.
Disclosure of Invention
The invention mainly solves the technical problems that the original technical scheme has more abnormal values due to environmental interference and the deviation of a prediction result is larger, provides a bridge static displacement prediction technology based on a deep learning LSTM network, obtains sample data from deflection monitoring data of a bridge to carry out neural network model training based on a deep learning artificial intelligence algorithm LSTM RNN and a long-short term memory cyclic neural network, predicts the deflection of the bridge from a neural network model trained by the past deflection information of the bridge, finally achieves the aim of predicting and early warning the deflection of the bridge, and realizes the evaluation of the working performance of the bridge and the accurate judgment of the structural safety.
The technical problem of the invention is mainly solved by the following technical scheme: the invention comprises the following steps:
(1) collecting static response monitoring data of bridge deflection;
(2) preprocessing the bridge deflection data;
(3) establishing an LSTM neural network for training;
(4) and predicting the bridge deflection by using the trained LSTM neural network model.
Preferably, the preprocessing of step 2 adopts a zero-mean normalization method, and the specific formula is as follows:
Figure BDA0002878018670000021
wherein, x'i(ii) the prediction data normalized for the ith time instant; x is the number ofiThe data is predicted at the ith time, sigma is a data standard deviation, and u is a data average value.
Preferably, the LSTM neural network in step 3 is a modified RNN network, and the weight parameter of the self-loop is changed by adding an input gate, an output gate, a forgetting gate and a cell state. Under the condition that the model parameters are fixed, the problem of gradient disappearance or explosion can be effectively avoided. Wherein the input gate and the output gate control the flow-in and flow-out of information flow, and the forgetting gate selects how much the unit state at the previous time is stored until the previous time
Preferably, the output f of the forgetting gatetFrom the hidden layer state h at the previous momentt-1And input x of the current timetJointly determining:
ft＝σ(Wfht-1+Ufxt+bf)
wherein, Wf、UfAnd bfTo forget the gate-related weights and bias matrices, σ is the sigmod function.
Preferably, the input gate controls the input of the current time, and comprises two parts:
it＝σ(Wiht-1+Uixt+bi)
at＝tanh(Waht-1+Uaxt+ba)
wherein, Wi、Wa、Ui、Ua、biAnd baThe gate dependent weights and bias matrices are input.
Preferably, the LSTM network propagates the state h of the hidden layer forward at time ttAnd cell state CtCell state CtThe updating of (1) consists of two results of an input gate and a forgetting gate:
Ct＝Ct-1⊙ft+it⊙at
wherein, u is the Hadamard product.
Preferably, the output of the input gate in the LSTM neural network is:
ot＝σ(Woht-1+Uoxt+bo)
the hidden layer ht is updated as follows:
ht＝ot⊙tanh(Ct)。
preferably, the step 4 inputs data to obtain predicted preprocessed data, and performs inverse normalization on the data to obtain predicted bridge deflection data, so as to predict the static displacement of the bridge.
The invention has the beneficial effects that: based on a deep learning artificial intelligence algorithm LSTM RNN and a long-short term memory cyclic neural network, sample data is obtained from deflection monitoring data of a bridge for neural network model training, the deflection of the bridge is predicted from a neural network model trained by the previous deflection information of the bridge, the purpose of predicting and early warning the deflection of the bridge is finally achieved, and the evaluation on the working performance of the bridge and the accurate judgment on the structural safety are realized.
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FIG. 1 is a flow chart of the present invention.
FIG. 2 is a diagram of an LSTM neural network architecture of the present invention.
Detailed Description
The technical scheme of the invention is further specifically described by the following embodiments and the accompanying drawings.
Example (b): the bridge static displacement prediction technology based on the deep learning LSTM network of the embodiment, as shown in fig. 1, includes the following steps:
(1) collecting static response monitoring data of bridge deflection;
(2) preprocessing the bridge deflection data;
(3) establishing an LSTM neural network for training;
(4) and predicting the bridge deflection by using the trained LSTM neural network model.
And (3) preprocessing the bridge deflection data in the step (2), namely preprocessing the bridge deflection data by a zero-mean normalization method. The specific formula is as follows.
Figure BDA0002878018670000041
In the formula: x'i(ii) the prediction data normalized for the ith time instant; x is the number ofiThe data is predicted at the ith time, sigma is a data standard deviation, and u is a data average value.
The LSTM neural network in step 3 is an improved RNN network, and the self-loop weight parameters are changed by adding an input gate, an output gate, a forgetting gate, and a cell state (cell state), so that the problem of gradient "disappearance" or "explosion" can be effectively avoided under the condition that the model parameters are fixed. The input gate and the output gate control the inflow and outflow of information flow, and the forgetting gate selects how much of the unit state at the previous time is stored at the time. The network structure of the LSTM is shown in fig. 2.
Which propagates throughCompared with the traditional RNN network, the LSTM network propagates the state h except the hidden layer at the moment ttAlso, cell state C is addedt. The forgetting gate is used for controlling the state of the hidden layer at the last moment by a certain probability matrix. Output f of forgetting gatetFrom the hidden layer state h at the previous momentt-1And input x of the current timetJointly determining:
ft＝σ(Wfht-1+Ufxt+bf)
wherein, Wf、UfAnd bfTo forget the gate-related weights and bias matrices, σ is the sigmod function. The input gate functions to control the input at the current time. The device consists of two parts:
it＝σ(Wiht-1+Uixt+bi)
at＝tanh(Waht-1+Uaxt+ba)
wherein, Wi、Wa、Ui、Ua、biAnd baThe gate dependent weights and bias matrices are input. Cell state C in LSTMtThe updating of (1) consists of two results of an input gate and a forgetting gate:
Ct＝Ct-1⊙ft+it⊙at
wherein, u is the Hadamard product.
Finally, the output of the input gate in the LSTM is:
ot＝σ(Woht-1+Uoxt+bo)
hidden layer htThe update of (1) is:
ht＝ot⊙tanh(Ct)
the specific embodiments described herein are merely illustrative of the spirit of the invention. Various modifications or additions may be made to the described embodiments or alternatives may be employed by those skilled in the art without departing from the spirit or ambit of the invention as defined in the appended claims.
Although the terms bridge deflection, LSTM neural network, etc. are used more herein, the possibility of using other terms is not excluded. These terms are used merely to more conveniently describe and explain the nature of the present invention; they are to be construed as being without limitation to any additional limitations that may be imposed by the spirit of the present invention.

Claims (8)

1. A bridge static displacement prediction technology based on a deep learning LSTM network is characterized by comprising the following steps:
(1) collecting static response monitoring data of bridge deflection;
(2) preprocessing the bridge deflection data;
(3) establishing an LSTM neural network for training;
(4) and predicting the bridge deflection by using the trained LSTM neural network model.
2. The bridge static displacement prediction technology based on the deep learning LSTM network as claimed in claim 1, wherein the preprocessing of step 2 adopts a zero mean normalization method, and the concrete formula is as follows:
Figure FDA0002878018660000011
wherein, x'i(ii) the prediction data normalized for the ith time instant; x is the number ofiThe data is predicted at the ith time, sigma is a data standard deviation, and u is a data average value.
3. The technology for predicting static displacement of a bridge based on a deep learning LSTM network as claimed in claim 1, wherein the LSTM neural network in step 3 is a modified RNN network, and the weight parameters of the self-loop are changed by adding an input gate, an output gate, a forgetting gate and a cell state.
4. The technology of claim 3, wherein the output f of the forgetting gate is obtained by predicting the static displacement of the bridge based on the deep learning LSTM networktFrom the hidden layer state h at the previous momentt-1And input x of the current timetJointly determining:
ft＝σ(Wfht-1+Ufxt+bf)
wherein, Wf、UfAnd bfTo forget the gate-related weights and bias matrices, σ is the sigmod function.
5. The bridge static displacement prediction technology based on the deep learning LSTM network as claimed in claim 3, wherein the input gate controls the input at the current time and consists of two parts:
it＝σ(Wiht-1+Uixt+bi)
at＝tanh(Waht-1+Uaxt+ba)
wherein, Wi、Wa、Ui、Ua、biAnd baThe gate dependent weights and bias matrices are input.
6. The technology of claim 3, wherein at time t the LSTM network propagates the state h of the hidden layer forwardtAnd cell state CtCell state CtThe updating of (1) consists of two results of an input gate and a forgetting gate:
Ct＝Ct-1⊙ft+it⊙at
wherein, u is the Hadamard product.
7. The deep learning LSTM network-based bridge static displacement prediction technique of claim 6, wherein the output of the input gate in the LSTM neural network is:
ot＝σ(Woht-1+Uoxt+bo)
the hidden layer ht is updated as follows:
ht＝ot⊙tanh(Ct)。
8. the technology for predicting the static displacement of the bridge based on the deep learning LSTM network as claimed in claim 2, wherein the step 4 is to input data to obtain predicted preprocessed data, and perform inverse normalization on the data to obtain predicted bridge deflection data, so as to predict the static displacement of the bridge.
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CN113836783A (en) * 2021-07-16 2021-12-24 东南大学 Digital regression model modeling method for main beam temperature-induced deflection monitoring reference value of cable-stayed bridge
CN114548375A (en) * 2022-02-23 2022-05-27 合肥工业大学 Cable-stayed bridge main beam dynamic deflection monitoring method based on bidirectional long-short term memory neural network
CN114741976A (en) * 2022-06-13 2022-07-12 西南交通大学 Displacement prediction method, device and equipment and readable storage medium
CN115114842A (en) * 2022-04-27 2022-09-27 中国水利水电科学研究院 Rainstorm waterlogging event prediction method based on small sample transfer learning algorithm
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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113656859A (en) * 2021-07-05 2021-11-16 哈尔滨工业大学 Different-appearance bridge self-excited vibration prediction modeling method based on deep learning
CN113836783A (en) * 2021-07-16 2021-12-24 东南大学 Digital regression model modeling method for main beam temperature-induced deflection monitoring reference value of cable-stayed bridge
CN113836783B (en) * 2021-07-16 2022-11-18 东南大学 Digital regression model modeling method for main beam temperature-induced deflection monitoring reference value of cable-stayed bridge
CN114548375A (en) * 2022-02-23 2022-05-27 合肥工业大学 Cable-stayed bridge main beam dynamic deflection monitoring method based on bidirectional long-short term memory neural network
CN114548375B (en) * 2022-02-23 2024-02-13 合肥工业大学 Cable-stayed bridge girder dynamic deflection monitoring method based on two-way long-short-term memory neural network
CN115114842A (en) * 2022-04-27 2022-09-27 中国水利水电科学研究院 Rainstorm waterlogging event prediction method based on small sample transfer learning algorithm
CN114741976A (en) * 2022-06-13 2022-07-12 西南交通大学 Displacement prediction method, device and equipment and readable storage medium
CN114741976B (en) * 2022-06-13 2022-09-23 西南交通大学 Displacement prediction method, device and equipment and readable storage medium
CN115392142A (en) * 2022-10-31 2022-11-25 深圳市城市交通规划设计研究中心股份有限公司 Coastal environment simply supported beam elastic modulus prediction method, electronic equipment and storage medium
CN116011125A (en) * 2023-03-23 2023-04-25 成都理工大学 Response prediction method for uncertain axle coupling system
CN116011125B (en) * 2023-03-23 2023-06-06 成都理工大学 Response prediction method for uncertain axle coupling system
CN116046303A (en) * 2023-03-30 2023-05-02 辽宁省交通规划设计院有限责任公司 Deflection intelligent detection system, method and device

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