CN118329342B - Bridge fatigue heavy vehicle dynamic threshold value determining method, system and storage medium - Google Patents
Bridge fatigue heavy vehicle dynamic threshold value determining method, system and storage medium Download PDFInfo
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- G01M5/0008—Investigating the elasticity of structures, e.g. deflection of bridges or air-craft wings of bridges
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- G—PHYSICS
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- G01G—WEIGHING
- G01G19/00—Weighing apparatus or methods adapted for special purposes not provided for in the preceding groups
- G01G19/02—Weighing apparatus or methods adapted for special purposes not provided for in the preceding groups for weighing wheeled or rolling bodies, e.g. vehicles
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Abstract
The invention discloses a method, a system and a storage medium for determining a dynamic threshold value of a fatigue heavy truck of a bridge, which comprise the following contents: collecting fatigue parameter data of bridge members, and establishing a bridge fatigue information database; intercepting the vehicle load and the selected component/structure strain time course data in the updating period, eliminating trend items by a moving average method, extracting the vehicle load strain, calculating strain amplitude, and further calculating the cumulative probability distribution function of the strain amplitude and the vehicle load; and finally, taking the cumulative probability distribution function, the infinite life strain amplitude limit value and the reliability factor in the bridge fatigue information database as inputs to generate a bridge fatigue and heavy truck threshold value which can be changed according to the period. According to the method, the influence relation between the vehicle load and the structural damage is directly considered, the dynamic updating strategy of the fatigue heavy vehicle threshold value of the bridge is provided, the recognition speed and accuracy of the heavy vehicle are improved, the limitation and risk caused by the fixed threshold value are avoided, and the support is provided for the development of the bridge health monitoring data analysis system and the structural performance evaluation.
Description
Technical Field
The invention relates to bridge performance evaluation and structure early warning technology, in particular to a method, a system and a storage medium for determining a dynamic threshold value of a fatigue heavy vehicle of a bridge.
Technical Field
The load of the vehicle is one of the main loads of the bridge in the operation period, and under the repeated action of the load of the vehicle, the member is easy to generate brittle fatigue damage lower than the yield strength of the material, so that the heavy vehicle causes fatal damage to the road and the bridge. The existing dynamic stress monitoring data and stress spectrum are mostly used for predicting the fatigue life, and the component/structure is alarmed according to the fatigue life. The existing heavy vehicle threshold values adopt a certain value in a unified way, the threshold values do not have the function of updating in real time, and the accurate connection between the vehicle quality and the damage condition of the vehicle to the bridge is difficult; or according to the vehicle mass and fixed overload rate in the specification, the method is convenient for managing the overloaded vehicle, but because the fatigue effect is mainly related to the vehicle load, the vehicle which generates fatigue damage to the bridge is difficult to determine accordingly. Therefore, there are limitations in both stress data utilization and vehicle warning methods.
Disclosure of Invention
The invention aims to: aiming at the defects existing in the prior art, the invention provides a method, a system and a storage medium for determining the dynamic threshold value of the fatigue heavy vehicle of the bridge, which can accurately and rapidly grasp the passing vehicle with fatigue damage to a certain component in the bridge and obviously save the operation and maintenance cost of the bridge.
The technical scheme is as follows: a method for determining dynamic threshold value of fatigue and heavy vehicle of bridge comprises the following steps:
(1) Collecting bridge member/structure fatigue parameter data, and establishing a bridge fatigue information database; the bridge fatigue information database comprises fatigue information of different components/structures, wherein the fatigue information comprises type names, grades, materials of the components/structures, whether redundancy exists, an infinite life strain amplitude limit value and a reliability factor; the infinite life strain amplitude value and the reliability factor are determined by the type name, grade, material, whether or not there is redundancy of the component/structure;
(2) Recording strain time course data of a vehicle load and a member/structure in real time for the bridge member/structure provided with the resistance strain gauge;
(3) Selecting bridge components/structures, presetting an updating period, intercepting vehicle load and strain time course data of the selected components/structures in the current updating period, adopting a moving average method to eliminate trend items, extracting vehicle load strain, and calculating strain amplitude according to the vehicle load strain;
(4) Calculating the vehicle load of the selected component/configuration during the current update period Amplitude of strainIs a cumulative probability density distribution function of (2)、；
(5) Calculating the bridge fatigue and weight threshold of the component/structure in the next updating period：
；
Wherein,、And respectively representing the infinite life strain amplitude value and the reliability factor corresponding to the selected component/structure in the bridge fatigue information database in the current updating period.
In one embodiment, the step (1) obtains fatigue information of the bridge fatigue information database through a web crawler method.
Further, for the selected bridge member/structure, it is determined whether or not there is fatigue information corresponding to the member/structure in the bridge fatigue information database, if so, the piece of fatigue information is indexed in step (5), and if not, the missing fatigue information is predicted.
In one embodiment, if the type name, grade, material, and/or redundancy of the component/structure is complete, the infinite life strain amplitude value and/or reliability factor of the component/structure is missing, the missing infinite life strain amplitude value and/or reliability factor is predicted based on the type name, grade, material, and/or redundancy of the component/structure.
In one embodiment, if the infinite life strain amplitude value and the reliability factor of the component/structure are complete, at least one of the type name, the grade, the material, and whether the component/structure has redundancy is missing, the missing component/structure type name and/or grade and/or material and/or whether the component/structure has redundancy is first predicted, and then the infinite life strain amplitude value and the reliability factor are predicted according to the complete predicted component/structure name, grade, material, and whether the component/structure has redundancy.
In one embodiment, if at least one of the type name, grade, material, and redundancy of the component/structure is missing, and at the same time, the infinite life strain amplitude value and/or the reliability factor is missing, firstly, performing preliminary prediction on each piece of fatigue information missing, and then performing secondary prediction on the infinite life strain amplitude value and the reliability factor according to the complete component/structure name, grade, material, and redundancy after prediction.
Preferably, the missing fatigue information is predicted by a least squares support vector machine, which specifically includes the following contents:
The following linear system of equations is defined:
；
Wherein, The weight vector is represented by a weight vector,A non-linear function is represented and,The term of the bias is indicated,The inner product operation is represented by the equation,Representing the lagrangian multiplier and,Representing radial basis functions, whereinIs a 6-dimensional partial missing input vector,Is the input vector of the 6-dimensional basic database,AndFatigue information for each corresponding component/structure; Is a corresponding output data vector, namely the missing fatigue information to be predicted; N is the total number of training data, i.e. the number of component/construction types;
constructing a Lagrange function:
；
Representing regularization parameters for determining a tradeoff between model complexity and accuracy, Regression errors representing the predicted values;
By combining 、、、The derivative is set to zero, and the output data vector corresponding to the optimal solution of the problem is obtained；
，；
，；
，；
，。
In one embodiment, the predicted fatigue information is not added to the bridge fatigue information database and is used only for the current calculation of step (5).
A system for performing the bridge fatigue heavy vehicle dynamic threshold determination method, comprising:
The beam fatigue information database unit is used for collecting the fatigue parameter data of the bridge components/structures and establishing a bridge fatigue information database; the bridge fatigue information database comprises fatigue information of different components/structures, wherein the fatigue information comprises type names, grades, materials of the components/structures, whether redundancy exists, an infinite life strain amplitude limit value and a reliability factor;
the vehicle load and strain time interval acquisition unit is used for recording the strain time interval data of the vehicle load and the member/structure in real time aiming at the member/structure provided with the resistance strain gauge;
The strain time course processing unit is used for intercepting the vehicle load and the strain time course data of the selected component/structure in the current updating period, eliminating trend items by adopting a moving average method, extracting the vehicle load strain and calculating the strain amplitude;
A vehicle load and strain amplitude analysis unit for calculating a cumulative probability density distribution function of the vehicle load, strain amplitude over a current update period for the selected component/structure;
and the dynamic threshold value construction unit is used for calculating the bridge fatigue and heavy truck threshold value of the component/structure in the next updating period.
A computer readable storage medium storing at least one executable instruction that, when executed on an electronic device, causes the electronic device to perform the bridge fatigue heavy vehicle dynamic threshold determination method.
Compared with the prior art, the invention has the following remarkable progress:
1. the invention establishes a bridge fatigue information database by collecting the bridge member fatigue parameter data through the in-situ computer system, predicts the missing data through a prediction algorithm such as a least squares support vector machine (Least Squares Support Vector Machine, LS-SVM) and the like, perfects the bridge member fatigue parameter data and improves the accuracy of key fatigue parameters;
2. According to the method, the influence relation between the vehicle load and the structural damage is directly considered, a dynamic update strategy of the bridge fatigue heavy vehicle threshold is provided, dynamic vehicle load strain is extracted by intercepting the vehicle load and the component strain time course data in the update period, and the bridge fatigue heavy vehicle dynamic threshold is generated by combining the key fatigue parameters, so that the brittle fatigue failure condition of the component lower than the material yield strength is easier to control, and the fatigue life of the component/structure is prolonged;
3. For the bridge with the dynamic weighing system, the total weight concentrated load of the vehicle in each updating period can be automatically read, and the weighing system is used for alarming the vehicle with the total weight exceeding the load threshold value by combining the calculated bridge fatigue heavy vehicle threshold value.
Drawings
FIG. 1 is a flow chart of a method for determining dynamic threshold of fatigue and heavy truck in a bridge according to an embodiment of the invention;
FIG. 2 is a vehicle load G distribution of embodiment (1) of the present invention;
FIG. 3 is the strain time course data of strain gauge number 01 in embodiment (1) during a certain update period;
FIG. 4 is a strain trend corresponding to FIG. 3;
FIG. 5 is a vehicle load strain corresponding to FIG. 3;
FIG. 6 is a strain amplitude distribution corresponding to FIG. 5;
fig. 7 is a vehicle load G distribution of embodiment (2) of the present invention;
FIG. 8 is the strain schedule data for strain gauge number 02 of example (2) during a certain update period;
FIG. 9 is a strain trend term corresponding to FIG. 8;
FIG. 10 is a corresponding vehicle load strain of FIG. 8;
fig. 11 shows the strain amplitude distribution corresponding to fig. 10.
Detailed Description
In order to make the above objects, features and advantages of the present application more comprehensible, the following detailed description of the technical solutions of the present application refers to the accompanying drawings.
In a first aspect, the present invention provides a method for determining a dynamic threshold value of fatigue and heavy vehicle of a bridge, referring to fig. 1, including the following contents:
step one: and establishing and perfecting a bridge member fatigue information database, and extracting key fatigue information.
The specific contents are as follows:
And collecting bridge member/structure fatigue parameter data, and establishing a bridge fatigue information database. As shown in table 1, the fatigue information database of the bridge stores the fatigue information of different components/structures, including but not limited to 6 items of fatigue information including type name, grade, material, whether there is redundancy, infinite life strain amplitude limit, and reliability factor of the components/structures, wherein the infinite life strain amplitude limit and the reliability factor are determined by the type name, grade, material, whether there is redundancy of the components/structures.
Table 1 infinite life strain amplitude values and reliability factors for different types, grades, materials, redundancies
When the component/construction type, grade, material, redundancy are all determined, the infinite life strain amplitude value and the reliability factor are fixed values.
Alternatively, the fatigue parameter data may be obtained according to a related specification, or obtained by a web crawler method.
If a certain bridge member/structure has the accurate corresponding fatigue information in the database, the fatigue information of the missing bridge member is obtained by directly taking the fatigue information through the data index, and if the fatigue information of the missing bridge member is missing, the prediction algorithm is adopted, and the machine learning is carried out on the predicted bridge fatigue information so as to improve the accuracy of the key fatigue information. The predicted data is not suitable to be added into a bridge fatigue information database and is only used for the current calculation. The method is concretely divided into the following three cases:
Case 1: if the type name, grade, material, whether the redundancy exists or not and the like of the component/structure are complete, and the missing fatigue information is an infinite life strain amplitude value and/or reliability factor of the component/structure, predicting the missing infinite life strain amplitude value and/or reliability factor according to the type name, grade, material and whether the redundancy exists or not of the component/structure.
Case 2: if the infinite life strain amplitude value and the reliability factor of the component/structure are complete, the missing fatigue information is at least one of the type name, the grade, the material and whether the component/structure has redundancy, and the infinite life strain amplitude value and the reliability factor in the piece of data are low in accuracy, the missing component/structure type name and/or grade and/or material and/or whether the component/structure has redundancy are predicted firstly, and then the infinite life strain amplitude value and the reliability factor are predicted according to the complete component/structure name, grade, material and whether the component/structure has redundancy after prediction.
Case 3: if the missing fatigue information is at least one of the type name, grade, material and redundancy of the component/structure, and meanwhile, the infinite life strain amplitude limit value and/or the reliability factor are also missing, firstly predicting each piece of the missing fatigue information once, and secondly predicting the infinite life strain amplitude limit value and the reliability factor according to the complete component/structure name, grade, material and redundancy after prediction.
Preferably, the fatigue information of the missing bridge member is rapidly obtained by a least square support vector machine method, and the method specifically comprises the following steps:
The following linear system of equations is defined:
；
Wherein, The weight vector is represented by a weight vector,A non-linear function is represented and,The term of the bias is indicated,The inner product operation is represented by the equation,Representing the lagrangian multiplier and,Representing radial basis functions, whereinIs a 6-dimensional partial missing input vector,Is the input vector of the 6-dimensional basic database,AndFatigue information for each corresponding component/structure; Is a corresponding output data vector, namely the missing fatigue information to be predicted; N is the total number of training data, i.e. the number of component/construction types;
constructing a Lagrange function:
；
Representing regularization parameters for determining a tradeoff between model complexity and accuracy, Regression errors representing the predicted values;
By combining 、、、The derivative is set to zero, and the output data vector corresponding to the optimal solution of the problem is obtained；
，；
，；
，；
，。
Step two: strain time course data for bridge vehicle loads and components/structures is monitored.
The specific contents are as follows:
Recording strain time course data of a vehicle load and a member/structure in real time for the bridge member/structure provided with the resistance strain gauge; in general, a welded resistance strain gauge may be selected.
Selecting bridge components/structures, presetting an updating period, intercepting vehicle load and strain time course data of the selected components/structures in the current updating period, adopting a moving average method to eliminate trend items, extracting vehicle load strain, and calculating strain amplitude according to the vehicle load strain, namely the amplitude of all stress cycles in the period; preferably, the strain amplitude is calculated by a rain flow count method.
Preferably, the bridge dynamic weighing system can be utilized to automatically read the firstThe total weight of the vehicle recorded during each update period concentrates the load. Aiming at the parts with short stress paths, such as a steel box girder top plate, a suspension bridge sling and the like, the data are generally directly influenced by the load of the vehicle, the correlation between the strain time course and the load time course of the vehicle is strong, and the data recorded by a health monitoring system are suitable to be used as the strain time course data.
Calculate the selected component/configuration at() Strain amplitude in a single update periodCumulative probability density distribution of (2); Calculate the selected component/configuration at() Cumulative probability density distribution of vehicle load G over a single update period. Preferably, the updating period is set to be 1 hour-1 month, and the updating period is not too long or too short; the period is too short, the sampling data is limited, so that the threshold value fluctuation is large, and the practicability is reduced; too long a period makes it difficult to embody the dynamic characteristics of the threshold.
Step three: according to the firstFatigue information, monitored vehicle load and strain data of bridge members/structures in the updating period are calculated, and a bridge fatigue heavy truck threshold value in the (i+1) th updating period is calculated to form a bridge fatigue heavy truck dynamic threshold valueAnd so on. The specific contents are as follows:
According to the current (th Personal) strain amplitude during update periodCumulative probability density distribution of vehicle load G、Infinite life strain amplitude limitAnd reliability factorCalculate the next (thAnd a plurality of) the bridge fatigue and heavy truck thresholds in the updating period are as follows:
；
In the method, in the process of the invention, Indicating that the selected member/configuration is atAn infinite life strain amplitude value within a single update period; Indicating that the selected member/configuration is at Reliability factors over an update period; Indicating that the selected member/configuration is at Cumulative probability distribution function of strain amplitude in each update period; Is shown in the first A cumulative probability distribution function of the vehicle load over the update period. Wherein the method comprises the steps ofIs the strain amplitude thresholdThis value varies with the fatigue information of the bridge component/structure, i.e. the ratio of the infinite life strain amplitude value to the reliability factor (strain amplitude threshold value) when the component/structure type, grade, material, redundancy are determined) To a fixed value, take it into functionBy a function ofLocating strain amplitude thresholds。
Step four: in the (i+1) th update period, the total weight exceedsThe vehicle is used for reminding fatigue damage. Preferably, the total weight exceeds that of the bridge dynamic weighing systemIs directly alarmed.
Through the first step to the fourth step, the determination of the threshold value of the fatigue and the heavy vehicle of the bridge and the dynamic update of the threshold value are realized.
In a second aspect, the present invention provides a system for performing the above method for determining a dynamic threshold value of fatigue heavy vehicles of a bridge, comprising:
The beam fatigue information database unit is used for collecting the fatigue parameter data of the bridge components/structures and establishing a bridge fatigue information database; the bridge fatigue information database comprises fatigue information of different components/structures, wherein the fatigue information comprises type names, grades, materials of the components/structures, whether redundancy exists, an infinite life strain amplitude limit value and a reliability factor;
A vehicle load and strain time course acquisition unit configured to record strain time course data of a member/structure on which the resistance strain gauge is arranged in real time;
The strain time course processing unit is used for intercepting the vehicle load and the strain time course data of the selected component/structure in the current updating period, eliminating trend items by adopting a moving average method, extracting the vehicle load strain and calculating the strain amplitude;
A vehicle load and strain amplitude analysis unit for calculating a cumulative probability density distribution function of the vehicle load, strain amplitude over a current update period for the selected component/structure;
and the dynamic threshold value construction unit is used for calculating the bridge fatigue and heavy truck threshold value of the component/structure in the next updating period.
Further, the system also comprises an early warning module, and the early warning module is used for warning the vehicles with the weight being above the threshold value of the fatigue heavy vehicles of the bridge.
In a third aspect, the present invention provides a computer readable storage medium storing at least one executable instruction that, when executed on an electronic device, causes the electronic device to perform operations of a bridge fatigue heavy vehicle dynamic threshold determination method.
Those skilled in the art will appreciate that the present invention may be implemented as a system, method, or computer program product. Accordingly, the present disclosure may be embodied in the following forms, namely: either entirely hardware, entirely software (including firmware, resident software, micro-code, etc.), or entirely software, or a combination of hardware and software, referred to herein generally as a "circuit," module "or" system. Furthermore, in some embodiments, the invention may also be embodied in the form of a computer program product in one or more computer-readable media, which contain computer-readable program code.
The dynamic threshold calculation process of the present invention will be described in detail below with reference to fig. 2 to 11 by taking a bridge of south Beijing as an example, and the feasibility and beneficial effects of the present invention are verified.
1. Bridge profile and data source:
the weighing system is arranged at the north side of the bridge at 500m from the north side toll station of the bridge.
The dynamic strain monitoring measuring points of the orthotropic steel bridge deck are selected at the corresponding positions of wheel traces of heavy lanes or traffic lanes, preferably at fatigue hot spots such as top plates, U ribs, transverse partition plates, slings and the like, and flexible vehicle management can be carried out according to the real-time characteristics of strain change.
TABLE 2 measurement station position information Table
2. Example verification:
Example (1), 2023, 11/2 through 3564 vehicles, the vehicle load and the strain time course data of the components/structures were recorded in real time for the second road steel box girder inner roof U rib side at the south tower. The corresponding vehicle load for this example is shown in FIG. 2 The distribution of the vehicle load in a certain updating period is obtained by calculating the probability density of the vehicle weight dataIs a cumulative probability density distribution function of (2)。
Fig. 3 shows strain time-course data of the strain gauge 01 in the update period, and the strain trend term shown in fig. 4 is extracted by means of a sliding average. Subtracting the strain trend term of FIG. 4 from the strain time course data of FIG. 3 to obtain the vehicle load strain shown in FIG. 5Calculating the strain amplitudeAs shown in fig. 6.
From the strain amplitudeCalculating the probability density to obtain。
Indexing bridge fatigue information database or LS-SVM prediction to obtain infinite life strain amplitude limit valueAnd reliability factor。
Threshold value of strain amplitude。
Defining bridge fatigue and heavy truck threshold value in next updating periodThe method comprises the following steps:
。
Vehicle load threshold of this example Vehicles with gross weights exceeding the load threshold are the subject of early warning.
Example (2), 10-24-2023 passing 19613 vehicles, real-time recording of vehicle load and strain time course data of components/structures for the second road steel box girder inner roof at the south tower. The corresponding vehicle load for this example is shown in FIG. 7The distribution of the vehicle load in a certain updating period is obtained by calculating the probability density of the vehicle weight dataIs a cumulative probability density distribution function of (2)。
FIG. 8 shows the strain time history data of strain gauge 02 during the update period, again by extracting the strain trend term from the moving average, the strain trend term is eliminated from the vehicle load strainCalculating the strain amplitudeThe corresponding data are shown in fig. 8-10.
From the strain amplitudeCalculating the probability density to obtain。
Indexing bridge fatigue information database or LS-SVM prediction to obtain infinite life strain amplitude limit valueAnd reliability factor。
Threshold value of strain amplitude。
Bridge fatigue and heavy truck threshold value in next updating periodA bridge weighing system may be used to alert vehicles that have a gross weight exceeding the load threshold.
The foregoing embodiments are merely illustrative of the technical concept and features of the present invention, and are intended to enable those skilled in the art to understand the present invention and to implement the same according to the present invention, not to limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications and adaptations can be made without departing from the principles of the present invention, and such modifications and adaptations are intended to be comprehended within the scope of the invention.
Claims (10)
1. A method for determining a dynamic threshold value of a fatigue heavy truck of a bridge is characterized by comprising the following steps:
(1) Collecting bridge member/structure fatigue parameter data, and establishing a bridge fatigue information database; the bridge fatigue information database comprises fatigue information of different components/structures, wherein the fatigue information comprises type names, grades, materials of the components/structures, whether redundancy exists, an infinite life strain amplitude limit value and a reliability factor; the infinite life strain amplitude value and the reliability factor are determined by the type name, grade, material, whether or not there is redundancy of the component/structure;
(2) Recording strain time course data of a vehicle load and a member/structure in real time for the bridge member/structure provided with the resistance strain gauge;
(3) Selecting bridge components/structures, presetting an updating period, intercepting vehicle load and strain time course data of the selected components/structures in the current updating period, adopting a moving average method to eliminate trend items, extracting vehicle load strain, and calculating strain amplitude according to the vehicle load strain;
(4) Calculating the vehicle load of the selected component/configuration during the current update period Amplitude of strainIs a cumulative probability density distribution function of (2)、；
(5) Calculating the bridge fatigue and weight threshold of the component/structure in the next updating period：
；
Wherein,、And respectively representing the infinite life strain amplitude value and the reliability factor corresponding to the selected component/structure in the bridge fatigue information database in the current updating period.
2. The method for determining the dynamic threshold value of the fatigue and heavy vehicle of the bridge according to claim 1, wherein in the step (1), the fatigue information of the bridge fatigue information database is obtained by a web crawler method.
3. The method according to claim 1, wherein for the selected bridge member/structure, it is determined whether or not there is fatigue information corresponding to the member/structure in the bridge fatigue information database, if so, the piece of fatigue information is indexed in step (5), and if not, the missing fatigue information is predicted.
4. The method for determining the dynamic threshold value of the fatigue and the heavy vehicle of the bridge according to claim 3, wherein:
If the type name, grade, material, and whether the redundancy of the component/structure is complete, the infinite life strain amplitude value and/or the reliability factor of the component/structure are missing, the missing infinite life strain amplitude value and/or reliability factor is predicted according to the type name, grade, material, and whether the redundancy of the component/structure is present.
5. The method for determining the dynamic threshold value of the fatigue and the heavy vehicle of the bridge according to claim 3, wherein:
If the infinite life strain amplitude value and the reliability factor of the component/structure are complete, at least one of the type name, grade, material and redundancy of the component/structure is missing, predicting the type name and/or grade and/or material of the missing component/structure and/or redundancy of the component/structure is first performed, and then predicting the infinite life strain amplitude value and the reliability factor according to the complete predicted component/structure name, grade, material and redundancy of the component/structure.
6. The method for determining the dynamic threshold value of the fatigue and the heavy vehicle of the bridge according to claim 3, wherein:
If at least one of the type name, grade, material and redundancy of the component/structure is lost, and meanwhile, the infinite life strain amplitude value and/or the reliability factor is also lost, firstly, primarily predicting each piece of fatigue information which is lost, and secondly, predicting the infinite life strain amplitude value and the reliability factor according to the completely predicted component/structure name, grade, material and redundancy.
7. The method for determining the dynamic threshold value of the fatigue and heavy vehicle of the bridge according to any one of claims 4 to 6, wherein the missing fatigue information is predicted by a least square support vector machine, specifically comprising the following contents:
The following linear system of equations is defined:
；
Wherein, The weight vector is represented by a weight vector,A non-linear function is represented and,The term of the bias is indicated,The inner product operation is represented by the equation,Representing the lagrangian multiplier and,Representing radial basis functions, whereinIs a 6-dimensional partial missing input vector,Is the input vector of the 6-dimensional basic database,AndFatigue information for each corresponding component/structure; Is a corresponding output data vector, namely the missing fatigue information to be predicted; N is the total number of training data, i.e. the number of component/construction types;
constructing a Lagrange function:
；
Representing regularization parameters for determining a tradeoff between model complexity and accuracy, Regression errors representing the predicted values;
By combining 、、、The derivative is set to zero, and the output data vector corresponding to the optimal solution of the problem is obtained；
，；
，；
，；
，。
8. The method for determining dynamic threshold value of fatigue and heavy vehicle of bridge according to claim 3, wherein the predicted fatigue information is not added into the bridge fatigue information database and is used only for the current calculation of step (5).
9. A system for performing the method of determining a dynamic threshold value for fatigue and heavy vehicle of a bridge as claimed in any of claims 1-6, comprising:
The beam fatigue information database unit is used for collecting the fatigue parameter data of the bridge components/structures and establishing a bridge fatigue information database; the bridge fatigue information database comprises fatigue information of different components/structures, wherein the fatigue information comprises type names, grades, materials of the components/structures, whether redundancy exists, an infinite life strain amplitude limit value and a reliability factor;
the vehicle load and strain time interval acquisition unit is used for recording the strain time interval data of the vehicle load and the member/structure in real time aiming at the member/structure provided with the resistance strain gauge;
The strain time course processing unit is used for intercepting the vehicle load and the strain time course data of the selected component/structure in the current updating period, eliminating trend items by adopting a moving average method, extracting the vehicle load strain and calculating the strain amplitude;
A vehicle load and strain amplitude analysis unit for calculating a cumulative probability density distribution function of the vehicle load, strain amplitude over a current update period for the selected component/structure;
and the dynamic threshold value construction unit is used for calculating the bridge fatigue and heavy truck threshold value of the component/structure in the next updating period.
10. A computer readable storage medium, wherein at least one executable instruction is stored in the storage medium, which when executed on an electronic device, causes the electronic device to perform the operations of the bridge fatigue heavy truck dynamic threshold determination method according to any one of claims 1 to 6.
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| US10571360B2 (en) * | 2013-03-11 | 2020-02-25 | Board Of Trustees Of Michigan State University | Methods for estimating remaining life of a monitored structure |
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