CN117933730A - Comprehensive bridge safety assessment method based on random forest - Google Patents
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Abstract
The invention discloses a comprehensive bridge safety assessment method based on random forests, which comprises the following steps: s1, collecting bridge technical state data and geological environment data, and respectively preprocessing the data; s2, performing geological risk assessment according to the preprocessed geological environment data and the historical geological events to obtain a geological risk assessment result; s3, carrying out structural technical state evaluation according to the preprocessed bridge technical state data to obtain a structural technical state evaluation result; s4, training a random forest model according to the geological risk evaluation result and the structural technical state evaluation result to obtain a trained random forest model; s5, constructing and training an interaction model according to the preprocessed geological environment data and the preprocessed bridge technical state data; s6, comprehensively evaluating the bridge safety to be evaluated by using the trained random forest model and the trained interaction model, and improving the evaluation accuracy and reliability of the bridge safety.
Description
Technical Field
The patent relates to the field of bridge safety evaluation, in particular to a comprehensive bridge safety evaluation method based on random forests.
Background
Bridges play a critical role in modern traffic infrastructure, and therefore their safety is critical. The safety of the bridge is not only related to the technical state of the structure of the bridge, but also closely related to geological environment factors. Traditional bridge safety evaluation methods generally consider structural technical states and geological risk factors respectively, but do not consider the association between the structural technical states and the geological risk factors, and lack comprehensiveness and comprehensiveness.
Disclosure of Invention
Aiming at the defects in the prior art, the comprehensive bridge safety evaluation method based on the random forest provided by the invention solves the problems that the traditional bridge safety evaluation method respectively considers the structural technical state and the geological risk factor, but does not consider the association between the structural technical state and the geological risk factor, and lacks comprehensiveness and comprehensiveness.
In order to achieve the aim of the invention, the invention adopts the following technical scheme: a comprehensive bridge safety evaluation method based on random forests comprises the following steps:
S1, collecting bridge technical state data and geological environment data, and respectively preprocessing the data;
S2, performing geological risk assessment according to the preprocessed geological environment data and the historical geological events to obtain a geological risk assessment result;
s3, carrying out structural technical state evaluation according to the preprocessed bridge technical state data to obtain a structural technical state evaluation result;
s4, training a random forest model according to the geological risk evaluation result and the structural technical state evaluation result to obtain a trained random forest model;
s5, constructing and training an interaction model according to the preprocessed geological environment data and the preprocessed bridge technical state data;
and S6, performing comprehensive bridge safety evaluation on the bridge to be evaluated by using the trained random forest model and the trained interaction model.
Further: in the step S1, the bridge technical state data includes upper structural component data, lower structural component data and bridge deck system component data, specifically including material quality, crack condition, corrosion degree and deformation condition of each structural component;
The geological environment data includes geological structure, geological topography and groundwater conditions.
Further: the step S2 comprises the following sub-steps:
S21, determining the possibility of different geological disaster factors according to the preprocessed geological environment data and the historical geological events;
S22, according to the possibility of the geological disaster factors, calculating a risk index GRI of each geological disaster factor by using a probability model to obtain a geological risk assessment result, wherein the formula of the risk index GRI is as follows:
where P represents the likelihood of a geological disaster factor and S represents the severity of the geological disaster factor.
Further: the step S3 comprises the following sub-steps:
s31, determining technical state evaluation indexes of all structural components according to the preprocessed bridge technical state data and evaluation standards;
s32, distributing corresponding weights for each technical state evaluation index to obtain a technical state evaluation formula;
S33, calculating the value of a technical state evaluation index of each structural component according to a technical state evaluation formula;
s34, calculating a comprehensive technical state evaluation value by using weighted summation according to the technical state evaluation index value of each structural component;
And S35, taking the value of the technical state evaluation index of each structural component and the comprehensive technical state evaluation value together as a structural technical state evaluation result.
Further: the step S4 includes the following sub-steps:
s41, taking a geological risk assessment result and a structural technical state assessment result as a data set, and decomposing the data set into a training set and a plurality of random subsets;
s42, establishing a decision tree according to the random subset;
S43, carrying out feature selection and classification on the data set through each decision tree to complete construction of a random forest model;
s44, training the constructed random forest model by using a training set, and adjusting the super parameters of the random forest model by using a cross verification technology to obtain a trained random forest model.
Further: the step S5 includes the following sub-steps:
s51, extracting and constructing feature variables of the preprocessed geological environment data and the preprocessed bridge technical state data by using feature engineering;
S52, representing interaction between the geological risk factors and the technical state factors according to the characteristic variables to obtain an interaction model;
and S53, training the interaction model by using the preprocessed geological environment data and the feature variables of the preprocessed bridge technical state data to obtain a trained interaction model.
Further: in the step S6, the method for performing comprehensive bridge safety assessment includes the following steps:
S61, performing geological risk assessment and structural technology state assessment on the bridge to be assessed, and performing data fusion on the assessment result to obtain fused data;
S62, inputting the fused data into a trained random forest model, and evaluating the bridge safety risk;
S63, inputting the fused data into a trained interaction model, and evaluating interactions between different geological disaster factors and technical states of each structural component of the bridge;
s64, obtaining a bridge comprehensive risk index according to the bridge safety risk and the interaction between different geological disaster factors and the technical states of each structural component of the bridge;
s65, determining the bridge safety risk level according to the bridge comprehensive risk index, and completing comprehensive bridge safety assessment.
The beneficial effects of the invention are as follows:
1. The geological environment factors and the bridge technical state data are combined, and the correlation between the geological environment factors and the bridge technical state data is considered, so that the safety of the bridge is more comprehensively evaluated;
2. Through the random forest model, the random forest can process a large amount of data and complex characteristics, and can capture nonlinear relations and interaction effects in the data. By training a plurality of decision trees and combining the results, the random forest reduces the risk of overfitting, improves the generalization capability of the model, and has more credible bridge safety evaluation results;
3. And geological risk factors and technical state evaluation are combined, so that potential safety risks can be identified in time. When a geological event or the technical state of the bridge component changes, the comprehensive evaluation model can rapidly update the evaluation result to provide information about whether maintenance or repair measures need to be taken.
Drawings
Fig. 1 is a flowchart of a comprehensive bridge safety evaluation method based on random forests.
Fig. 2 is a schematic diagram of a geological risk assessment result.
FIG. 3 is a schematic diagram of a structural state-of-the-art evaluation result.
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and all the inventions which make use of the inventive concept are protected by the spirit and scope of the present invention as defined and defined in the appended claims to those skilled in the art.
As shown in fig. 1, in one embodiment of the present invention, there is provided a comprehensive bridge safety evaluation method based on random forest, including the steps of:
S1, collecting bridge technical state data and geological environment data, and respectively preprocessing the data;
S2, performing geological risk assessment according to the preprocessed geological environment data and the historical geological events to obtain a geological risk assessment result;
s3, carrying out structural technical state evaluation according to the preprocessed bridge technical state data to obtain a structural technical state evaluation result;
s4, training a random forest model according to the geological risk evaluation result and the structural technical state evaluation result to obtain a trained random forest model;
s5, constructing and training an interaction model according to the preprocessed geological environment data and the preprocessed bridge technical state data;
and S6, performing comprehensive bridge safety evaluation on the bridge to be evaluated by using the trained random forest model and the trained interaction model.
In one embodiment of the present invention, in the step S1, the bridge technology status data includes upper structural component data, lower structural component data, and bridge deck system component data, specifically including material quality, crack condition, corrosion degree, and deformation condition of each structural component;
the geological environment data comprise geological structures, geological landforms and groundwater conditions;
data preprocessing includes data cleansing and data normalization to ensure the quality and consistency of the data.
In one embodiment of the invention, the step S2 comprises the following sub-steps:
S21, determining the possibility of different geological disaster factors according to the preprocessed geological environment data and the historical geological events;
the probability of occurrence of each geological disaster factor is reflected by the probability of different geological disaster factors;
S22, according to the possibility of the geological disaster factors, calculating a risk index GRI of each geological disaster factor by using a probability model to obtain a geological risk assessment result, wherein the formula of the risk index GRI is as follows:
wherein P represents the possibility of a geological disaster factor, and S represents the severity of the geological disaster factor;
In this embodiment, the severity evaluation indexes defined in the "DB 51/T3088-2023 operating mountain highway geological disaster and side slope engineering risk evaluation procedure" are adopted, and these indexes include a first-level evaluation index and a second-level evaluation index, where the first-level evaluation index is a general index representing typical characteristics of the disaster, and the second-level evaluation index is a sub-item index of the first-level evaluation index, and by scoring these indexes, the severity of the geological disaster can be comprehensively evaluated.
In this embodiment, the geological risk assessment result obtained in step S2 is shown in fig. 2.
In one embodiment of the present invention, the step S3 includes the following sub-steps:
s31, determining technical state evaluation indexes of all structural components according to the preprocessed bridge technical state data and evaluation standards;
In the embodiment, the evaluation standard adopts JTG/TH21-2011 highway bridge technical condition evaluation standard, and structural health monitoring data and visual inspection results are used for quantifying technical condition evaluation;
s32, distributing corresponding weights for each technical state evaluation index to obtain a technical state evaluation formula;
the expression of the technical state evaluation formula relates to weighted summation of the technical state scores of all the components of the bridge;
S33, calculating the value of a technical state evaluation index of each structural component according to a technical state evaluation formula;
s34, calculating a comprehensive technical state evaluation value by using weighted summation according to the technical state evaluation index value of each structural component;
And S35, taking the value of the technical state evaluation index of each structural component and the comprehensive technical state evaluation value together as a structural technical state evaluation result.
In the present embodiment, the structural technology state evaluation result obtained in step S3 is shown in fig. 3.
In one embodiment of the present invention, the step S4 includes the following sub-steps:
s41, taking a geological risk assessment result and a structural technical state assessment result as a data set, and decomposing the data set into a training set and a plurality of random subsets;
s42, establishing a decision tree according to the random subset;
S43, carrying out feature selection and classification on the data set through each decision tree to complete construction of a random forest model;
when the features are selected, selecting the most distinguishing features according to the features in the data set;
s44, training the constructed random forest model by using a training set, and adjusting the super parameters of the random forest model by using a cross verification technology to obtain a trained random forest model.
In one embodiment of the present invention, the step S5 includes the following sub-steps:
s51, extracting and constructing feature variables of the preprocessed geological environment data and the preprocessed bridge technical state data by using feature engineering;
S52, representing interaction between the geological risk factors and the technical state factors according to the characteristic variables to obtain an interaction model;
and S53, training the interaction model by using the preprocessed geological environment data and the feature variables of the preprocessed bridge technical state data to obtain a trained interaction model.
In one embodiment of the present invention, in the step S6, the method for performing comprehensive bridge safety assessment includes the following steps:
S61, performing geological risk assessment and structural technology state assessment on the bridge to be assessed, and performing data fusion on the assessment result to obtain fused data;
S62, inputting the fused data into a trained random forest model, and evaluating the bridge safety risk;
S63, inputting the fused data into a trained interaction model, and evaluating interactions between different geological disaster factors and technical states of each structural component of the bridge;
s64, obtaining a bridge comprehensive risk index according to the bridge safety risk and the interaction between different geological disaster factors and the technical states of each structural component of the bridge;
When calculating the bridge comprehensive risk index, adding the scores of the geological risk factors and the technical state factors, and weighting to consider the relative importance of the geological risk factors and the technical state factors;
s65, determining the bridge safety risk level according to the bridge comprehensive risk index, and completing comprehensive bridge safety assessment.
In this embodiment, the security risk is classified into different levels, such as low risk, medium risk, high risk, by setting a threshold;
according to the security risk level, coping measures are formulated, including maintenance, repair, reinforcement and decision support tools are provided, so that bridge managers are helped to formulate priority and budget allocation;
Low risk: periodic monitoring and routine maintenance;
risk of (1): increasing the monitoring frequency, periodically checking and maintaining, and possibly performing small-scale repair work;
high risk: emergency maintenance and reinforcement may require limited use of the bridge to ensure safety.
The above embodiments are only for illustrating the technical solution of the present application, and are not limiting; although the application has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application.
Claims (7)
1. The comprehensive bridge safety evaluation method based on the random forest is characterized by comprising the following steps of:
S1, collecting bridge technical state data and geological environment data, and respectively preprocessing the data;
S2, performing geological risk assessment according to the preprocessed geological environment data and the historical geological events to obtain a geological risk assessment result;
s3, carrying out structural technical state evaluation according to the preprocessed bridge technical state data to obtain a structural technical state evaluation result;
s4, training a random forest model according to the geological risk evaluation result and the structural technical state evaluation result to obtain a trained random forest model;
s5, constructing and training an interaction model according to the preprocessed geological environment data and the preprocessed bridge technical state data;
and S6, performing comprehensive bridge safety evaluation on the bridge to be evaluated by using the trained random forest model and the trained interaction model.
2. The method for evaluating the safety of the comprehensive bridge based on the random forest according to claim 1, wherein in the step S1, the bridge technical state data comprise upper structural part data, lower structural part data and bridge deck system part data, and specifically comprise material quality, crack condition, corrosion degree and deformation condition of each structural part;
The geological environment data includes geological structure, geological topography and groundwater conditions.
3. The method for evaluating the safety of the comprehensive bridge based on the random forest according to claim 1, wherein the step S2 comprises the following sub-steps:
S21, determining the possibility of different geological disaster factors according to the preprocessed geological environment data and the historical geological events;
S22, according to the possibility of the geological disaster factors, calculating a risk index GRI of each geological disaster factor by using a probability model to obtain a geological risk assessment result, wherein the formula of the risk index GRI is as follows:
where P represents the likelihood of a geological disaster factor and S represents the severity of the geological disaster factor.
4. The method for evaluating the safety of the comprehensive bridge based on the random forest according to claim 1, wherein the step S3 comprises the following sub-steps:
s31, determining technical state evaluation indexes of all structural components according to the preprocessed bridge technical state data and evaluation standards;
s32, distributing corresponding weights for each technical state evaluation index to obtain a technical state evaluation formula;
S33, calculating the value of a technical state evaluation index of each structural component according to a technical state evaluation formula;
s34, calculating a comprehensive technical state evaluation value by using weighted summation according to the technical state evaluation index value of each structural component;
And S35, taking the value of the technical state evaluation index of each structural component and the comprehensive technical state evaluation value together as a structural technical state evaluation result.
5. The method for evaluating the safety of the comprehensive bridge based on the random forest according to claim 1, wherein the step S4 comprises the following sub-steps:
s41, taking a geological risk assessment result and a structural technical state assessment result as a data set, and decomposing the data set into a training set and a plurality of random subsets;
s42, establishing a decision tree according to the random subset;
S43, carrying out feature selection and classification on the data set through each decision tree to complete construction of a random forest model;
s44, training the constructed random forest model by using a training set, and adjusting the super parameters of the random forest model by using a cross verification technology to obtain a trained random forest model.
6. The method for evaluating the safety of the comprehensive bridge based on the random forest according to claim 1, wherein the step S5 comprises the following sub-steps:
s51, extracting and constructing feature variables of the preprocessed geological environment data and the preprocessed bridge technical state data by using feature engineering;
S52, representing interaction between the geological risk factors and the technical state factors according to the characteristic variables to obtain an interaction model;
and S53, training the interaction model by using the preprocessed geological environment data and the feature variables of the preprocessed bridge technical state data to obtain a trained interaction model.
7. The method for evaluating the safety of the comprehensive bridge based on the random forest according to claim 1, wherein in the step S6, the method for evaluating the safety of the comprehensive bridge comprises the following steps:
S61, performing geological risk assessment and structural technology state assessment on the bridge to be assessed, and performing data fusion on the assessment result to obtain fused data;
S62, inputting the fused data into a trained random forest model, and evaluating the bridge safety risk;
S63, inputting the fused data into a trained interaction model, and evaluating interactions between different geological disaster factors and technical states of each structural component of the bridge;
s64, obtaining a bridge comprehensive risk index according to the bridge safety risk and the interaction between different geological disaster factors and the technical states of each structural component of the bridge;
s65, determining the bridge safety risk level according to the bridge comprehensive risk index, and completing comprehensive bridge safety assessment.
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