CN115100176A - Neural network-based reinforced concrete column damage assessment method - Google Patents
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- 230000006378 damage Effects 0.000 title claims abstract description 106
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- 230000007547 defect Effects 0.000 abstract description 5
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- 239000004793 Polystyrene Substances 0.000 description 2
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- 238000010200 validation analysis Methods 0.000 description 2
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- 238000010146 3D printing Methods 0.000 description 1
- ORILYTVJVMAKLC-UHFFFAOYSA-N Adamantane Natural products C1C(C2)CC3CC1CC2C3 ORILYTVJVMAKLC-UHFFFAOYSA-N 0.000 description 1
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Abstract
The invention discloses a neural network-based reinforced concrete column damage assessment method, which comprises the following steps of: s1, establishing a damage data set of the reinforced concrete column model under the explosive load through a finite element method; s2, transmitting the damage data set into the constructed convolution neural network model for training, and storing the optimal training weight and the corresponding convolution neural network model; and S3, inputting the obtained target object picture into a stored convolutional neural network model for damage evaluation, and outputting a damage evaluation result, so that the defects that the manual evaluation method based on expert experience is time-consuming and labor-consuming, and has insufficient accuracy and safety are overcome.
Description
Technical Field
The invention relates to the technical field of neural networks, in particular to a reinforced concrete column damage assessment method based on a neural network.
Background
The reinforced concrete column is the most basic vertical bearing component in various engineering structures such as buildings, bridges, hydraulic engineering and the like, and the damage degree of the reinforced concrete column influences the local and overall safety of the structure. The traditional damage detection of the reinforced concrete column mainly depends on site investigation, and is highly dependent on engineering experience, time and labor are wasted, and the accuracy and the reliability are insufficient. Especially for the post-earthquake field damage assessment, the safety problem is more serious due to the possible follow-up aftershocks.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a reinforced concrete column damage assessment method based on a neural network, which can directly assess the damage result of the reinforced concrete column and overcome the defects of time and labor waste, insufficient accuracy and safety of a manual assessment method based on expert experience.
The invention provides a neural network-based reinforced concrete column damage assessment method, which comprises the following steps of:
s1, establishing a damage data set of the reinforced concrete column model under the explosive load through a finite element method;
s2, sending the damage data set into a built convolutional neural network model for training, and storing the optimal training weight and the corresponding convolutional neural network model;
and S3, inputting the acquired target object picture into a stored convolutional neural network model for damage evaluation, and outputting a damage evaluation result.
Preferably, the convolutional neural network model comprises:
the backbone network is used for extracting image characteristics and calculating the volume loss rate;
and the damage judgment network is used for simulating the functional relation between the volume loss rate and the damage and outputting the damage grade.
Preferably, the backbone network adopts MobilenetV 3-Small.
Preferably, the impairment determination network comprises two fully connected linear layers.
Preferably, the step S1 specifically includes:
constructing a reinforced concrete column and an explosive finite element model;
adjusting the explosive equivalent and the initiation point, and calculating a damaged reinforced concrete column model under the explosive load based on the reinforced concrete column and the explosive finite element model;
applying an axial pressure load to the reinforced concrete column model with the damage, and calculating the ultimate bearing capacity of the reinforced concrete column model with the damage;
calculating the ratio of the ultimate bearing capacity of the reinforced concrete column model with the damage to the vertical ultimate bearing capacity of the complete reinforced concrete column model to obtain a damage value;
selecting a typical angle, and acquiring image data of the reinforced concrete column model with the damage;
and classifying the acquired image data of the reinforced concrete column model with the damage according to the damage value to obtain a damage data set.
Preferably, the step S1 further includes:
and processing the image data of the classified reinforced concrete column model with the damage by using a preset edge detection algorithm.
Preferably, the step S1 further includes:
and dividing the damage data set according to a proportion to obtain a training set, a verification set and a test set.
Preferably, the step S2 specifically includes:
sending the training set and the verification set into a built convolutional neural network model for preliminary training;
fusing the training set and the verification set and sending the training set and the verification set into a convolution neural network model after primary training for further training and obtaining a predicted damage result;
and sending the test set into a further trained convolutional neural network model, selecting an optimal model parameter according to the accuracy and loss of the further trained convolutional neural network model on the test set, and storing an optimal training weight and a corresponding convolutional neural network model.
The invention has the beneficial effects that:
the method comprises the steps of establishing a damage data set through a finite element method, building a convolutional neural network model, training the model by using the damage data set, inputting a target object picture into a training model for damage assessment, directly predicting the damage degree of the reinforced concrete column, and overcoming the defects that a manual evaluation method based on expert experience is time-consuming and labor-consuming, and is insufficient in accuracy and safety. And the convolutional neural network model comprises a backbone network and a damage judgment network, and can calculate the damage of the reinforced concrete column according to the volume loss rate of the input image, so that the interpretability of the damage evaluation result is improved.
Drawings
In order to more clearly illustrate the detailed description of the invention or the technical solutions in the prior art, the drawings that are needed in the detailed description of the invention or the prior art will be briefly described below. Throughout the drawings, like elements or portions are generally identified by like reference numerals. In the drawings, elements or portions are not necessarily drawn to scale.
FIG. 1 is a schematic flow chart of a first embodiment of the present invention;
FIG. 2 is a schematic flow chart of a second embodiment of the present invention;
FIG. 3a is a picture before processing by the Canny edge detection algorithm;
FIG. 3b is a picture after processing by the Canny edge detection algorithm;
FIG. 4 is a schematic structural diagram of a convolutional neural network model according to a second embodiment of the present invention;
FIG. 5a is a picture of mild injury;
FIG. 5b is a picture of moderate lesions;
fig. 5c is a picture of severe injury.
Detailed Description
Embodiments of the present invention will be described in detail below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and therefore are only examples, and the protection scope of the present invention is not limited thereby.
It is to be noted that, unless otherwise specified, technical or scientific terms used herein shall have the ordinary meaning as understood by those skilled in the art to which the invention pertains.
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the specification of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be further understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
The first embodiment is as follows:
as shown in fig. 1, an embodiment of the present invention provides a method for evaluating damage to a reinforced concrete column based on a neural network, including the following steps:
s1, establishing a damage data set of the reinforced concrete column model under the explosive load through a finite element method;
s2, sending the damage data set into the constructed convolutional neural network model for training, and storing the optimal training weight and the corresponding convolutional neural network model;
and S3, inputting the acquired target object picture into a stored convolutional neural network model for damage evaluation, and outputting a damage evaluation result.
The second embodiment:
as shown in fig. 2, an embodiment of the present invention provides a method for evaluating damage to a reinforced concrete column based on a neural network, including:
the method comprises the following steps: modeling concrete, steel bars, an air domain and explosives by using Hypermesh software to obtain a reinforced concrete column and explosive finite element model;
step two: adjusting explosive equivalent and a detonation point, and introducing the finite element model into LS-DYNA software for calculation to obtain a damaged reinforced concrete column model;
step three: and reintroducing the obtained reinforced concrete column model with the damage into Hypermesh software, adding a rigid pressing plate model for applying axial pressure load, reintroducing LS-DYNA software, and calculating the calculation ratio of the ultimate bearing capacity of the reinforced concrete column model with the damage to the vertical ultimate bearing capacity of the complete reinforced concrete column model to obtain the damage value. The automatic adjustment of explosive equivalent and initiation point and the interface connection of Hypermesh and LS-DYNA are realized by utilizing Python program;
step four: importing a damaged reinforced concrete column model into Hypermesh software, selecting a typical angle, acquiring image data of the damaged reinforced concrete column model, compiling a Python program and calling a Hypermesh interface to perform batch operation;
step five: classifying the acquired image data of the damaged reinforced concrete column model according to the calculated damage value;
step six: and processing the image data of the damaged reinforced concrete column model by using a Canny algorithm to obtain a damage data set, and dividing the damage data set according to a proportion to obtain a training set, a verification set and a test set. Because the two-dimensional image obtained in the finite element model may not restore the imaging effect of the structure in the real environment, such as reflection, shadow and the like, the Canny algorithm is adopted to extract the edge information of the image data, the edge lines of the image are obtained, and then the subsequent operation is performed, so that the convolutional neural network model obtained by training has certain applicability in the actual situation, as shown in fig. 3a and fig. 3 b.
Step seven: adjusting the parameters of the reinforced concrete column model with the damage, sending the damage data set into a convolutional neural network for training, and obtaining a predicted damage result; specifically, the step of sending the damage data set into a convolutional neural network for training, and obtaining a predicted damage result comprises the following steps:
the training batch size is set to be 128, the training set and the verification set are sent to a built convolutional neural network model for preliminary training, the iteration times are set to be 30 times, and preliminary learning is carried out;
and fusing the training set and the verification set, sending the fused training set and the verification set into the preliminarily trained convolutional neural network model, further training, obtaining a predicted damage result, setting the iteration number to be 20 times, and further learning. Adam is used as an optimizer in the initial training stage, and the initial learning rate is 1 multiplied by 10 -3 The learning rate is reduced by half when the loss of neural network model on the validation set exceeds 3 times without decreasing, β 1 And beta 2 Set to 0.9 and 0.999,. epsilon.is set to 1X 10 -8 . The further training phase uses SGDM as optimizer, with an initial learning rate of 1 × 10 -4 The learning rate is reduced by half when the loss of the neural network model on the validation set exceeds 3 times without decreasing, and β is set to 0.9.
Step eight: sending the test set into a convolutional neural network model after further training, selecting an optimal model parameter according to the accuracy and loss of the convolutional neural network model after further training on the test set, and storing an optimal training weight and a corresponding convolutional neural network model;
step nine: and calling the stored convolutional neural network model, shooting a target object picture, removing the background of the target object picture through image processing tools such as PS (polystyrene), and sending the target object picture into the convolutional neural network model after further training for damage judgment and evaluation to obtain a damage evaluation result.
In the embodiment of the present invention, the convolutional neural network model includes a trunk network and a damage judgment network, as shown in fig. 4, the trunk network is used to extract image features to calculate a volume loss rate, and the damage judgment network is used to simulate a functional relationship between the volume loss rate and a damage, and output a damage level. The main network selects MobilenetV3-Small, the damage judgment network is composed of two fully-connected linear layers, the input dimensionality of the first layer is 1, the output dimensionality is 64, the input dimensionality of the second layer is 64, and the output dimensionality is 3. When Error calculation is carried out in a training stage, double errors are adopted while the volume loss rate Error and the damage level Error are considered, the average Absolute Error (MAE) is adopted as a calculation method of the volume loss rate Error, a calculation result is multiplied by a coefficient alpha, and the Cross Entropy (Cross Entropy) is adopted as a calculation method of the damage level Error, and the two errors are added to obtain a final Error. According to the method and the device, when the network is optimized, the volume loss error and the damage classification error are combined, and the calculation precision of the image volume loss rate and the damage classification precision are balanced, so that the whole network is balanced. Meanwhile, the network model is more in line with the process from the volume loss rate to the damage level, and the classification effect is improved.
By adopting the embodiment of the invention to carry out simulation experiments, the convolutional neural network model operates under a preset platform, and the specific parameters of the platform are as follows: intel (R) Xeon (R) Gold 6248@2.5Ghz x 80; RAM: 128 GB; GPU: GeForce RTX 3090; operating the system: ubuntu 18.04.5 LTS. The alpha parameter of the model is set to 2 in the first phase and 10 in the second phase. The convolutional neural network model stored in the embodiment of the invention can achieve 99.71% of accuracy on a test set, and the time for carrying out structural damage assessment based on the damage assessment model of the convolutional neural network model is far shorter than that of a traditional finite element method, so that the method is more suitable for rapidly judging structural damage in emergency.
To test the application of the trained convolutional neural network model in reality, a 3D printing technique is used to print one model each of mild, moderate, and severe injury models, and fig. 5a, 5b, and 5c show photographs of three samples, all of which are image-processed to remove the background. A total of 24 test photographs were taken by taking at different angles. The test picture is sent to the optimal model obtained by the training, the final accuracy rate is 70.83%, and the convolutional neural network model stored in the embodiment of the invention can be considered to have a certain application value in an actual sample.
According to the method for evaluating the damage of the reinforced concrete column based on the neural network, provided by the invention, the damage data set is established through a finite element method, the convolutional neural network model is built, the damage data set is used for training the model, and the target object picture is input into the model for damage evaluation, so that the damage degree of the reinforced concrete column can be directly predicted, and the defects of time and labor waste, and insufficient accuracy and safety of a manual evaluation method based on expert experience are overcome. In addition, in the embodiment of the invention, the convolutional neural network model comprises a backbone network and a damage judgment network, and the damage of the reinforced concrete column can be calculated according to the volume loss rate of the input image, so that the interpretability of the damage evaluation result is improved.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the present invention, and they should be construed as being included in the following claims and description.
Claims (8)
1. A damage assessment method for a reinforced concrete column based on a neural network is characterized by comprising the following steps:
s1, establishing a damage data set of the reinforced concrete column model under the explosive load through a finite element method;
s2, sending the damage data set into a built convolutional neural network model for training, and storing the optimal training weight and the corresponding convolutional neural network model;
and S3, inputting the acquired target object picture into a stored convolutional neural network model for damage evaluation, and outputting a damage evaluation result.
2. The neural network-based reinforced concrete column damage assessment method according to claim 1, wherein the convolutional neural network model comprises:
the backbone network is used for extracting image characteristics and calculating the volume loss rate;
and the damage judgment network is used for simulating the functional relation between the volume loss rate and the damage and outputting the damage grade.
3. The method for evaluating the damage of the reinforced concrete column based on the neural network as claimed in claim 2, wherein the trunk network adopts MobilenetV 3-Small.
4. The neural network-based reinforced concrete column damage assessment method according to claim 2, wherein the damage judgment network comprises two fully connected linear layers.
5. The method for evaluating damage to a reinforced concrete column based on a neural network as claimed in claim 1, wherein said step S1 specifically comprises:
constructing a reinforced concrete column and an explosive finite element model;
adjusting the explosive equivalent and the initiation point, and calculating a damaged reinforced concrete column model under the explosive load based on the reinforced concrete column and the explosive finite element model;
applying an axial pressure load to the reinforced concrete column model with the damage, and calculating the ultimate bearing capacity of the reinforced concrete column model with the damage;
calculating the ratio of the ultimate bearing capacity of the reinforced concrete column model with the damage to the vertical ultimate bearing capacity of the complete reinforced concrete column model to obtain a damage value;
selecting a typical angle, and acquiring image data of the reinforced concrete column model with the damage;
and classifying the acquired image data of the reinforced concrete column model with the damage according to the damage value to obtain a damage data set.
6. The neural-network-based reinforced concrete column damage assessment method as claimed in claim 5, wherein said step S1 further comprises:
and processing the image data of the classified reinforced concrete column model with the damage by using a preset edge detection algorithm.
7. The method for evaluating damage of a reinforced concrete column based on a neural network as claimed in claim 1,
the step S1 further includes:
and dividing the damage data set according to a proportion to obtain a training set, a verification set and a test set.
8. The method for evaluating damage to a reinforced concrete column based on a neural network as claimed in claim 7, wherein said step S2 specifically includes:
sending the training set and the verification set into a built convolutional neural network model for preliminary training;
fusing the training set and the verification set and sending the training set and the verification set into a convolution neural network model after primary training for further training and obtaining a predicted damage result;
and sending the test set into a further trained convolutional neural network model, selecting an optimal model parameter according to the accuracy and loss of the further trained convolutional neural network model on the test set, and storing an optimal training weight and a corresponding convolutional neural network model.
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