CN116756825A - Group structural performance prediction system for middle-small span bridge - Google Patents
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Abstract
The application provides a group structure performance prediction system for a middle-small span bridge, wherein a data crawling unit is used for acquiring an calendar year detection report of the bridge to be detected through a crawler technology; the database construction unit is used for sequentially carrying out data extraction, data cleaning and data processing on the annual detection report to obtain a processing database; the clustering unit is used for clustering the data in the processing database to obtain a plurality of clustering centers; the training unit is used for inputting the clustering center into a preset neural network for training to obtain a group structure performance prediction model; the prediction unit is used for inputting a real-time detection report of the bridge to be detected into the group structure performance prediction model so as to predict the performance change trend of the whole structure and the local components of the bridge group. According to the application, through data cleaning and clustering of the sample data, the convergence of the neural network can be accelerated, and the prediction accuracy of the group structure performance of the middle-small span bridge can be greatly improved.
Description
Technical Field
The application relates to the technical field of bridge safety, in particular to a group structure performance prediction system for a middle-small span bridge.
Background
The bridge is a part of the traffic system of China, which is particularly outstanding in some mountain regions of China.
Bridge is an important component in traffic lines, and particularly in the construction of modern high-grade highways, bridge construction is often a key for the early traffic of left and right lines. In the design of the expressway, the bridge with the middle and small span has a large proportion of structures along the line, but for a long time, due to the relatively simple structure and mature technology, the original design drawing is always used in the design process, no concrete analysis is performed in the concrete condition, so that problems in the construction stage are frequently caused, and the problems are small, but the problems are large in quantity, large in processing engineering quantity and large in influence on the whole engineering cost and construction period. Therefore, the structural design significance of the manufactured middle-small span bridge is great.
Structural performance assessment of small and medium span bridge clusters has been plagued with a number of problems in engineering practice. For example, building an evaluation model of a small and medium span bridge group requires a huge amount of data as support. Therefore, the existing historical bridge detection data and the traffic flow observation records of all road sections are required to be subjected to data mining, interesting and valuable information is extracted, and a relational database is obtained through data integration, data cleaning and data conversion. At the same time, how to simulate the complex nonlinear and logical relations between the degradation trend of the bridge performance and the basic parameters based on the database is a difficult task.
The machine learning method based on the neural network is provided in the prior art, so that the extracted data is converted into valuable knowledge in the field of bridge management and maintenance, and the bridge structure performance evaluation prediction and management and maintenance guidance of the small and medium span bridge groups are realized. But its data authenticity is not high, resulting in inaccurate model predictions.
Disclosure of Invention
In order to overcome the defects of the prior art, the application aims to provide a group structure performance prediction system for a middle-small span bridge.
In order to achieve the above object, the present application provides the following solutions:
a group structural performance prediction system for small and medium span bridges, comprising:
the data crawling unit is used for acquiring an calendar year detection report of the bridge to be detected through a crawler technology;
the database construction unit is used for sequentially carrying out data extraction, data cleaning and data processing on the annual detection report to obtain a processing database;
the clustering unit is used for clustering the data in the processing database to obtain a plurality of clustering centers;
the training unit is used for inputting the clustering center into a preset neural network for training to obtain a group structure performance prediction model;
the prediction unit is used for inputting the real-time detection report of the bridge to be detected into the group structure performance prediction model so as to predict the performance change trend of the whole structure and the local components of the bridge group.
Preferably, the database construction unit includes:
the extraction module is used for extracting the original evaluation data of each bridge to be detected from the annual detection report;
the cleaning rule storage module is used for storing data cleaning rules for cleaning the original evaluation data;
the data cleaning module is used for cleaning the original evaluation data according to the data cleaning rule to obtain predicted evaluation data;
the normalization module is used for carrying out normalization processing on the prediction evaluation data to obtain training sample data;
the data processing unit is used for processing the training sample data and constructing a processing database which is based on the prediction evaluation data and is an attribute set; the attribute set comprises maintenance behavior attributes, structure type attributes, bridge age attributes, traffic volume attributes and technical condition scoring attributes of the bridge to be tested.
Preferably, the data cleansing module includes:
the arrangement sub-module is used for arranging the original evaluation data of each bridge to be tested in a mode that the technical condition scoring attribute of the bridge to be tested is sequentially increased to obtain a bridge parameter sequence;
the computing sub-module is used for sequentially computing the correlation coefficients of the current bridge parameter sequence and the previous group of bridge parameter sequences;
the judging submodule is used for judging whether the value of the correlation coefficient is in a preset range or not; if the value of the correlation coefficient is not in the preset range, removing the corresponding bridge parameter sequence; and if the value of the correlation coefficient is in the preset range, reserving the corresponding bridge parameter sequence until all bridge parameter sequences are traversed, and obtaining the prediction evaluation data.
Preferably, the correlation coefficient calculation formula is:
wherein p is X,Y As correlation coefficients cov (X, Y) represents the covariance between the current earth bridge Liang Canshu sequence X and the previous set of bridge parameter sequences Y, σ X Representing the variance, sigma, of the current bridge parameter sequence X Y Representing the variance of the previous set of bridge parameter sequences Y.
Preferably, the clustering unit includes:
the function construction module is used for constructing a clustering function according to the distance from the data point to the clustering center;
the solving module is used for carrying out iterative solving on the clustering function to obtain an updated function;
and the clustering module is used for clustering the data in the processing database according to the updating function to obtain a plurality of clustering centers.
Preferably, the function construction module includes:
the function determination submodule is used for constructing a clustering function by utilizing the vector distance from the data point to the clustering center; wherein, the clustering function is:
wherein v is i Represents the ith cluster center, m represents the blur threshold,representing data point x j Membership degree of the ith cluster center,/-membership degree of the ith cluster center>Representing data point x j Vector distance from the i-th cluster center.
Preferably, the solving module includes:
the updating function submodule is used for carrying out iterative solution on the clustering function by utilizing a Lagrangian multiplier method to obtain an updating function; wherein the update function is:
wherein d kj Representing data point x j Vector distance to the kth cluster center.
Preferably, the normalization module comprises:
normalization submodule for adopting formulaNormalizing the prediction evaluation data to obtain training sample data; wherein x' pi Normalized data for the ith variable of the p-th sample, x pi For the original data of the ith variable of the p-th sample, min { X } is the minimum value in the predictive evaluation data, and max { X } is the maximum value in the predictive evaluation data.
According to the specific embodiment provided by the application, the application discloses the following technical effects:
the application provides a group structure performance prediction system of a middle-small span bridge, which comprises the following components: the data crawling unit is used for acquiring an calendar year detection report of the bridge to be detected through a crawler technology; the database construction unit is used for sequentially carrying out data extraction, data cleaning and data processing on the annual detection report to obtain a processing database; the clustering unit is used for clustering the data in the processing database to obtain a plurality of clustering centers; the training unit is used for inputting the clustering center into a preset neural network for training to obtain a group structure performance prediction model; the prediction unit is used for inputting the real-time detection report of the bridge to be detected into the group structure performance prediction model so as to predict the performance change trend of the whole structure and the local components of the bridge group. According to the application, the data mining technology is integrated, mass detection data accumulated in the long-term bridge inspection work are effectively and fully utilized, and the convergence of the neural network is quickened and the prediction precision of the group structure performance of the medium-small span bridge is greatly improved by carrying out data cleaning and clustering on the sample data.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions of the prior art, the drawings that are needed in the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a method according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.
The terms "first," "second," "third," and "fourth" and the like in the description and in the claims and drawings are used for distinguishing between different objects and not necessarily for describing a particular sequential or chronological order. Furthermore, the terms "comprise" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion. For example, inclusion of a list of steps, processes, methods, etc. is not limited to the listed steps but may alternatively include steps not listed or may alternatively include other steps inherent to such processes, methods, products, or apparatus.
The application aims to provide a group structural performance prediction system for a middle-small span bridge, which integrates a data mining technology, effectively and fully utilizes mass detection data accumulated in the inspection work of the bridge for a long time, and can not only accelerate the convergence of a neural network, but also greatly improve the prediction precision of the group structural performance of the middle-small span bridge by carrying out data cleaning and clustering on sample data.
In order that the above-recited objects, features and advantages of the present application will become more readily apparent, a more particular description of the application will be rendered by reference to the appended drawings and appended detailed description.
Fig. 1 is a flowchart of a method provided by an embodiment of the present application, and as shown in fig. 1, the present application provides a group structure performance prediction system for a middle-small span bridge, including:
the data crawling unit is used for acquiring an calendar year detection report of the bridge to be detected through a crawler technology;
the database construction unit is used for sequentially carrying out data extraction, data cleaning and data processing on the annual detection report to obtain a processing database;
the clustering unit is used for clustering the data in the processing database to obtain a plurality of clustering centers;
the training unit is used for inputting the clustering center into a preset neural network for training to obtain a group structure performance prediction model;
the prediction unit is used for inputting the real-time detection report of the bridge to be detected into the group structure performance prediction model so as to predict the performance change trend of the whole structure and the local components of the bridge group.
Preferably, the database construction unit includes:
the extraction module is used for extracting the original evaluation data of each bridge to be detected from the annual detection report;
the cleaning rule storage module is used for storing data cleaning rules for cleaning the original evaluation data;
the data cleaning module is used for cleaning the original evaluation data according to the data cleaning rule to obtain predicted evaluation data;
the normalization module is used for carrying out normalization processing on the prediction evaluation data to obtain training sample data;
the data processing unit is used for processing the training sample data and constructing a processing database which is based on the prediction evaluation data and is an attribute set; the attribute set comprises maintenance behavior attributes, structure type attributes, bridge age attributes, traffic volume attributes and technical condition scoring attributes of the bridge to be tested.
Specifically, the raw evaluation data in this embodiment includes the technical condition score, bridge age, structure type, traffic volume, and maintenance behavior of each bridge. And selecting the characteristics, screening bridge age, structure type, average annual traffic volume, maintenance behavior and annual technical condition scoring fields of the bridge as an attribute set of a relational database, and extracting and integrating data of each report.
Preferably, the data cleansing module includes:
the arrangement sub-module is used for arranging the original evaluation data of each bridge to be tested in a mode that the technical condition scoring attribute of the bridge to be tested is sequentially increased to obtain a bridge parameter sequence;
the computing sub-module is used for sequentially computing the correlation coefficients of the current bridge parameter sequence and the previous group of bridge parameter sequences;
the judging submodule is used for judging whether the value of the correlation coefficient is in a preset range or not; if the value of the correlation coefficient is not in the preset range, removing the corresponding bridge parameter sequence; and if the value of the correlation coefficient is in the preset range, reserving the corresponding bridge parameter sequence until all bridge parameter sequences are traversed, and obtaining the prediction evaluation data.
Preferably, the correlation coefficient calculation formula is:
wherein p is X,Y As correlation coefficients cov (X, Y) represents the covariance between the current earth bridge Liang Canshu sequence X and the previous set of bridge parameter sequences Y, σ X Representing the variance, sigma, of the current bridge parameter sequence X Y Representing the variance of the previous set of bridge parameter sequences Y.
Optionally, in this embodiment, a correlation coefficient calculation formula is constructed by covariance, and then the unsatisfactory land parameter sequence is removed based on the calculation formula, so that the authenticity of the data can be ensured. In the training process of the network, because the data attribute difference is large, the large input is easy to suppress the small input, so that the training speed of the network is reduced, and even the network cannot be converged. In order to avoid the problems, the stability of the model is maintained, and the model achieves better effect, therefore, the model needs to be normalized and clustered before training by using the sample, so as to accelerate convergence.
Furthermore, the embodiment further cleans the original evaluation data according to the data cleaning rule by the data cleaning module so as to obtain the predicted evaluation data. Namely, deleting the data with the missing or error phenomenon. Specifically, according to the data cleaning rule, if any attribute value of a record in the database has the phenomena of deletion and error, the record is deleted, so that the validity and usability of the data are ensured. For example, if the bridge age attribute value of a record is-1 or blank, then the entire record is deleted.
Further, the data processing unit comprises a maintenance behavior processing module, a structure type processing module, a bridge age processing module, a traffic volume processing module and a technical condition scoring processing module. The maintenance behavior processing module is used for carrying out binary conversion on the maintenance behavior attribute, if the bridge is maintained, the value of the corresponding maintenance behavior attribute is set to be 1, and otherwise, the value of the corresponding maintenance behavior attribute is set to be 0. The structure type processing module is used for carrying out vectorization processing on the structure type attribute, and dividing all bridges in the relational database into three types, namely a slab bridge, a box bridge and others, and converting the three types into (1, 0), (0, 1, 0), (0, 1) respectively. The bridge age processing module is used for carrying out normalization transformation on the bridge age attribute. The traffic processing module is used for carrying out normalization transformation on the traffic attributes. The technical condition scoring processing module is used for carrying out normalization transformation on the technical condition scoring attribute.
Preferably, the clustering unit includes:
the function construction module is used for constructing a clustering function according to the distance from the data point to the clustering center;
the solving module is used for carrying out iterative solving on the clustering function to obtain an updated function;
and the clustering module is used for clustering the data in the processing database according to the updating function to obtain a plurality of clustering centers.
Preferably, the function construction module includes:
the function determination submodule is used for constructing a clustering function by utilizing the vector distance from the data point to the clustering center; wherein, the clustering function is:
wherein v is i Represents the ith cluster center, m represents the blur threshold,representing data point x j Membership degree of the ith cluster center,/-membership degree of the ith cluster center>Representing data point x j Vector distance from the i-th cluster center.
Preferably, the solving module includes:
the updating function submodule is used for carrying out iterative solution on the clustering function by utilizing a Lagrangian multiplier method to obtain an updating function; wherein the update function is:
wherein d kj Representing data point x j Vector distance to the kth cluster center.
Since the conventional clustering function is constructed based on euclidean distance, the difference between samples can be measured only by euclidean distance between different samples. The clustering function is constructed through the vector distance between the data point and the clustering center, so that the difference between each clustering sample can be enlarged from two aspects, and the clustering result is more reliable.
Preferably, the normalization module comprises:
normalization submodule for adopting formulaNormalizing the prediction evaluation data to obtain training sample data; wherein x' pi Normalized data for the ith variable of the p-th sample, x pi For the original data of the ith variable of the p-th sample, min { X } is the minimum value in the predictive evaluation data, and max { X } is the predictive evaluation dataIs the maximum value of (a).
The beneficial effects of the application are as follows:
according to the application, the data mining technology is integrated, mass detection data accumulated in the long-term bridge inspection work are effectively and fully utilized, and the convergence of the neural network is quickened and the prediction precision of the group structure performance of the medium-small span bridge is greatly improved by carrying out data cleaning and clustering on the sample data.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other.
The principles and embodiments of the present application have been described herein with reference to specific examples, the description of which is intended only to assist in understanding the methods of the present application and the core ideas thereof; also, it is within the scope of the present application to be modified by those of ordinary skill in the art in light of the present teachings. In view of the foregoing, this description should not be construed as limiting the application.
Claims (8)
1. A group structural performance prediction system for a medium and small span bridge, comprising:
the data crawling unit is used for acquiring an calendar year detection report of the bridge to be detected through a crawler technology;
the database construction unit is used for sequentially carrying out data extraction, data cleaning and data processing on the annual detection report to obtain a processing database;
the clustering unit is used for clustering the data in the processing database to obtain a plurality of clustering centers;
the training unit is used for inputting the clustering center into a preset neural network for training to obtain a group structure performance prediction model;
the prediction unit is used for inputting the real-time detection report of the bridge to be detected into the group structure performance prediction model so as to predict the performance change trend of the whole structure and the local components of the bridge group.
2. The group structural performance prediction system of a medium-small span bridge according to claim 1, wherein the database construction unit comprises:
the extraction module is used for extracting the original evaluation data of each bridge to be detected from the annual detection report;
the cleaning rule storage module is used for storing data cleaning rules for cleaning the original evaluation data;
the data cleaning module is used for cleaning the original evaluation data according to the data cleaning rule to obtain predicted evaluation data;
the normalization module is used for carrying out normalization processing on the prediction evaluation data to obtain training sample data;
the data processing unit is used for processing the training sample data and constructing a processing database which is based on the prediction evaluation data and is an attribute set; the attribute set comprises maintenance behavior attributes, structure type attributes, bridge age attributes, traffic volume attributes and technical condition scoring attributes of the bridge to be tested.
3. The group structural performance prediction system of a medium and small span bridge according to claim 2, wherein the data cleaning module comprises:
the arrangement sub-module is used for arranging the original evaluation data of each bridge to be tested in a mode that the technical condition scoring attribute of the bridge to be tested is sequentially increased to obtain a bridge parameter sequence;
the computing sub-module is used for sequentially computing the correlation coefficients of the current bridge parameter sequence and the previous group of bridge parameter sequences;
the judging submodule is used for judging whether the value of the correlation coefficient is in a preset range or not; if the value of the correlation coefficient is not in the preset range, removing the corresponding bridge parameter sequence; and if the value of the correlation coefficient is in the preset range, reserving the corresponding bridge parameter sequence until all bridge parameter sequences are traversed, and obtaining the prediction evaluation data.
4. The group structural performance prediction system of a medium-small span bridge according to claim 3, wherein the correlation coefficient calculation formula is:
wherein p is X,Y As correlation coefficients cov (X, Y) represents the covariance between the current earth bridge Liang Canshu sequence X and the previous set of bridge parameter sequences Y, σ X Representing the variance, sigma, of the current bridge parameter sequence X Y Representing the variance of the previous set of bridge parameter sequences Y.
5. A group structural performance prediction system for small and medium span bridges according to claim 3, wherein the clustering unit comprises:
the function construction module is used for constructing a clustering function according to the distance from the data point to the clustering center;
the solving module is used for carrying out iterative solving on the clustering function to obtain an updated function;
and the clustering module is used for clustering the data in the processing database according to the updating function to obtain a plurality of clustering centers.
6. The group structural performance prediction system of a medium-small span bridge according to claim 5, wherein the function construction module comprises:
the function determination submodule is used for constructing a clustering function by utilizing the vector distance from the data point to the clustering center; wherein, the clustering function is:
wherein v is i Represents the ith cluster center, m represents ambiguityThe threshold value is set to be a threshold value,representing data point x j Membership degree of the ith cluster center,/-membership degree of the ith cluster center>Representing data point x j Vector distance from the i-th cluster center.
7. The group structural performance prediction system of a medium and small span bridge of claim 6, wherein the solution module comprises:
the updating function submodule is used for carrying out iterative solution on the clustering function by utilizing a Lagrangian multiplier method to obtain an updating function; wherein the update function is:
wherein d kj Representing data point x j Vector distance to the kth cluster center.
8. The group structural performance prediction system of a medium and small span bridge according to claim 2, wherein the normalization module comprises:
normalization submodule for adopting formulaNormalizing the prediction evaluation data to obtain training sample data; wherein x' pi Normalized data for the ith variable of the p-th sample, x pi For the original data of the ith variable of the p-th sample, min { X } is the minimum value in the predictive evaluation data, and max { X } is the maximum value in the predictive evaluation data.
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| CN117669394A (en) * | 2024-02-02 | 2024-03-08 | 贵州交通建设集团有限公司 | Mountain canyon bridge long-term performance comprehensive evaluation method and system |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117669394A (en) * | 2024-02-02 | 2024-03-08 | 贵州交通建设集团有限公司 | Mountain canyon bridge long-term performance comprehensive evaluation method and system |
| CN117669394B (en) * | 2024-02-02 | 2024-04-12 | 贵州交通建设集团有限公司 | Mountain canyon bridge long-term performance comprehensive evaluation method and system |
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