CN113886952B - Self-matching arrangement method for loading vehicle in load test of simply supported beam bridge - Google Patents
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Abstract
The invention relates to the technical field of bridge load tests, in particular to a self-matching arrangement method of loading vehicles of a simple-supported girder bridge load test, which comprises the steps of establishing a loading vehicle arrangement database of the simple-supported girder bridge load test, adopting a fuzzy C-means clustering algorithm, verifying a fuzzy clustering objective function by iteration maximum error parameter epsilon and performing iterative calculation on the results of two times before and after the fuzzy clustering objective function, realizing optimal classification of all samples in a bridge parameter sample database, and obtaining class centers of samples of each class and corresponding theoretical loading vehicle quantity; when the load test is carried out, the test bridge is measured according to the parameters related to the bridge parameter sample database, the weighted calculation is carried out according to the membership value of the new bridge parameter sample relative to each class of clustering centers, the number of loading vehicles required by the new bridge load test is determined, the automatic load distribution of the simple bridge load test is realized, the load test scheme is added into the sample database to update the database, and the theoretical number of loading vehicles corresponding to the clustering centers and the classes is recalculated.
Description
Technical Field
The invention relates to the technical field of bridge load tests, in particular to a self-matching arrangement method of a load vehicle for a simple supported girder bridge load test.
Background
The bridge load test is used as a main method for evaluating the bearing capacity and the operation state of the bridge, the effect of the designed load and the vehicle load is calculated by establishing a bridge finite element model, and the arrangement scheme of the loading vehicles is further determined according to the loading efficiency, wherein the selection of the number of the loading vehicles becomes a core index of the load test load distribution.
In the implementation, according to the experience of a load test, a standard triaxial car becomes a loading car type mainly applied, and when the maximum bending moment loading in the midspan, the maximum bending moment loading in the 1/4 span and the maximum shearing force loading of the supporting point are carried out, the rear axle of the loading car is positioned on the loading control section, the loading car is generally arranged in the same direction, and only the number and the distance of the loading car need to be adjusted. At present, the load test is needed to depend on experience of technicians on one hand, and on the other hand, trial calculation is needed for many times, so that the load test loading scheme is obviously differentiated, and meanwhile, invalid calculation is needed for many times, so that the workload is high and the efficiency is low. Along with the continuous accumulation of the load test cases, a great deal of prior experience is obtained, but a database which can be used for automatic arrangement of the load test loading vehicle is not formed, and the self-matching of the loading scheme is realized.
In view of the above problems, the present inventors have actively studied and innovated based on the rich practical experience and expertise of such product engineering applications for years, so as to create a method for self-matching arrangement of load-bearing vehicles for simple girder bridge load test, which makes the method more practical.
Disclosure of Invention
The invention aims to provide a self-matching arrangement method for a load vehicle of a simply supported beam bridge load test, aiming at the defects in the prior art.
In order to achieve the above purpose, the technical scheme adopted by the invention is as follows: a load test loading vehicle self-matching arrangement method for a simply supported beam bridge comprises the following steps:
s1: setting unified standards of the loading vehicle in the load test;
S2: performing a large number of load tests by using a standard vehicle and collecting test parameters to construct a sample database;
S3: performing optimal classification on all samples, and calculating a clustering center o k of each class;
s4: counting the number of loading vehicles of all samples in each category, and analyzing the theoretical number of loading vehicles of each category;
s5: collecting new parameters of the bridge for load test, calculating membership values of the new parameters and each class of clustering centers, and further determining the number of loaded vehicles;
s6: and adding the new load test data into a sample database, and repeating the steps S3-S5.
Further, the step S3 of optimally classifying all samples includes the following steps:
S31: setting an initial class number k, setting a fuzzy weighting index m and an iteration maximum error parameter epsilon, and initializing J m 0 =0 and T=1;
S32: calculating a membership value u ij at the time of the T iteration;
S33: substituting the calculated membership value and the clustering center into a clustering objective function J m (U, O) respectively, solving the difference and comparing the difference with an iteration maximum error parameter epsilon;
S34: if the condition is satisfied: if J m T-Jm (T-1) is less than epsilon, the calculation is terminated and the step S37 is carried out, otherwise, the step S35 is carried out;
S35: calculating a cluster center o i of each category according to the membership value u ij;
s36: the iteration number is increased, namely T=T+1, and the steps S32 to S34 are repeated;
S37: and classifying the samples according to the membership value.
Further, the sample database x= { X 1,x2,x3,…,xn }, and the cluster center corresponding to the k categories is o= { O 1,o2,o3,…,ok } (1 < k < n), and the membership value u ij is calculated according to the following formula in the step S32:
Wherein d ij is the Euclidean distance from the sample to the cluster center, and is calculated according to the following formula:
dij＝||xj-oi||；
Wherein u ij represents the membership value of the jth sample to the ith class, i is not less than 1 and not more than k, j is not less than 1 and not more than n, d rj represents the distance between the jth sample and the cluster center of 1-k classes, and the value range of the fuzzy weighting index m is not less than 1.
Further, in the step S33, the clustering objective function J m is set as follows:
In the above step S35, the clustering center o i is calculated according to the following formula:
Further, in the step S2, the test parameters collected by the load test include the bridge span L, the load level P, the number of lanes M, and the number of loaded vehicles V, and the samples X n＝[Ln,Pn,Mn in the sample database X correspond to one loaded vehicle number V k for each sample X n.
Further, in step S3, the sample database is divided into k categories, and the cluster centers corresponding to all the categories are o= { O 1,o2,o3,…,ok }, and the theoretical loading vehicle number v= { V 1,v2,v3,…,vk }, which is obtained by analysis in step S4, is obtained, so that each cluster center is O k and corresponds to one theoretical loading vehicle number V k.
Further, when calculating the theoretical loading vehicle number, v k represents the theoretical loading vehicle number corresponding to the kth class clustering center, and after the loading vehicle numbers of all samples in the kth class clustering center are sequentially arranged according to the size, the median of the number sequence is selected as the theoretical loading vehicle number of the kth class clustering center.
Further, in the step S5, the new parameter is set to the j+1st sample, and the membership value u i(j+1) of the j+1st sample with respect to all the cluster centers o= { O 1,o2,o3,…,ok } is sequentially calculated;
u i(j+1) represents the membership value of the j+1th sample to the i-th class and satisfies the following formula:
The theoretical loaded vehicle number N Management device (j+1) for sample j+1 is calculated by the following formula:
And rounding the calculation result of the theoretical loading vehicle number calculation formula to obtain the theoretical loading vehicle number of the j+1th sample.
Further, in the step S1, the unified criteria for loading the vehicle in the load test set include a loading vehicle type, a loading control section, an axle spacing, a distance between two vehicles, and a loading vehicle orientation.
The beneficial effects of the invention are as follows:
The invention establishes a simply supported girder bridge load test loading vehicle arrangement database, wherein the loading vehicle arrangement database comprises a bridge parameter sample database and a loading vehicle information database corresponding to the samples, a fuzzy C-means clustering algorithm is adopted, and the optimal classification of all samples in the bridge parameter sample database is realized by verifying the iterative calculation results of the fuzzy clustering objective function twice before and after the iterative maximum error parameter epsilon, and the theoretical loading vehicle number of all samples of each class is obtained;
And when the load test is carried out, the test bridge is measured only according to the parameters related in the bridge parameter sample database, and the weighted calculation is carried out according to the membership value of the new bridge parameter sample relative to each class of clustering center, so that the number of loading vehicles required by the new bridge load test is determined, and the automatic load distribution of the simple bridge load test is realized.
The method is simple and practical, replaces model calculation and debugging by database matching, avoids repeated work, reduces workload, can quickly obtain the number of loaded vehicles, forms a standardized load distribution scheme, and is more objective and effective in bearing capacity assessment. Along with the update of the load test cases, the database is continuously iterated and perfected, and the self-matching reliability of the loading scheme is improved.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments described in the present invention, and other drawings may be obtained according to the drawings without inventive effort to those skilled in the art.
FIG. 1 is a schematic illustration of a standard loading vehicle as defined in an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention 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 invention, but not all embodiments.
It will be understood that when an element is referred to as being "fixed to" another element, it can be directly on the other element or intervening elements may also be present. When an element is referred to as being "connected" to another element, it can be directly connected to the other element or intervening elements may also be present. The terms "vertical," "horizontal," "left," "right," and the like are used herein for illustrative purposes only and are not meant to be the only embodiment.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and/or" as used herein includes any and all combinations of one or more of the associated listed items.
At present, a bridge load test is used as a main method for evaluating the bearing capacity and the operation state of a bridge, the load test is needed to rely on experience of technicians on one hand, on the other hand, trial calculation is needed for many times, the effect of the designed load and the vehicle load is calculated by establishing a bridge finite element model, the arrangement scheme of the loading vehicle is further determined according to the loading efficiency, the calculated amount is large, the efficiency is low, the difference of the loading scheme of the load test is obvious, and the self-matching of the loading scheme of the loading vehicle of the load test cannot be realized even though a large amount of experience is accumulated when a large amount of load tests are carried out.
The invention discloses a self-matching arrangement method of a load vehicle for a load test of a simply supported girder bridge, which can carry out self-matching load distribution on each test working condition respectively and specifically comprises the following steps: s1: setting unified standards of the loading vehicle in the load test; s2: performing a large number of load tests by using a standard vehicle and collecting test parameters to construct a sample database; s3: performing optimal classification on all samples, and calculating a clustering center o k of each class; s4: counting the number of loading vehicles of all samples in each category, and analyzing the theoretical number of loading vehicles of each category; s5: collecting new parameters of the bridge for load test, calculating membership values of the new parameters and each class of clustering centers, and further determining the number of loaded vehicles; s6: and adding the new load test data into a sample database, and repeating the steps S3-S5.
In the self-matching method disclosed by the invention, firstly, standardized regulations are required to be carried out on a loading vehicle and a vehicle arrangement; the unified standard of loading vehicles in the load test set in the step S1 comprises loading vehicle types, loading control sections, axle spacing, two-vehicle spacing and loading vehicle orientations.
Specifically, the control section positions corresponding to the rear axle are determined, the rear axle of the loading vehicle is located on the loading control section when the midspan maximum bending moment loading, the 1/4 midspan maximum bending moment loading and the fulcrum maximum shearing force loading are carried out, the loading vehicle type is set to be a standard triaxial vehicle, in the embodiment, the front axle weight is 70kN, the rear axle weight is 140kN, the front and rear axle distance is 350cm, the rear axle distance is 140cm, the front and rear axle distances of the two vehicles are 500cm, and the loading vehicle is arranged in the same direction as shown in fig. 1. Wherein, the ratio of the vehicle load effect to the design load effect is generally 0.85-1.05.
The method comprises the steps of carrying out a simple girder bridge load test by adopting a standardized loading vehicle and a vehicle layout mode specified in the application and building a load test loading vehicle arrangement database by accumulating parameter samples in the test, wherein the load test loading vehicle arrangement database comprises a bridge parameter sample database and a loading vehicle information database corresponding to the samples; and on the basis of the membership value, when a load test is carried out, firstly testing parameters of the simply supported bridge are measured and used as new samples, then the membership value of the new samples compared with the clustering centers of the categories is calculated, and the number of loading vehicles required by the new sample test is determined according to the membership value, so that the automatic load distribution of the test is realized.
Further, the step S3 of optimally classifying all samples includes the following steps: s31: setting an initial class number k, setting a fuzzy weighting index m and an iteration maximum error parameter epsilon, and initializing J m 0 =0 and T=1; s32: calculating a membership value u ij at the time of the T iteration; s33: substituting the calculated membership value and the clustering center into a clustering objective function J m (U, O) respectively, solving the difference and comparing the difference with an iteration maximum error parameter epsilon; s34: if the condition is satisfied: if J m T-Jm (T-1) is less than epsilon, the calculation is terminated and the step S37 is carried out, otherwise, the step S35 is carried out; s35: calculating a cluster center o i of each category according to the membership value u ij; s36: the iteration number is increased, namely T=T+1, and the steps S32 to S34 are repeated; s37: and classifying the samples according to the membership value.
In the step S2, the test parameters collected by the load test include the bridge span L, the load level P, the number of lanes M, and the number of loaded vehicles V, and the samples X n＝[Ln,Pn,Mn in the sample database X correspond to one loaded vehicle number V k for each sample X n. The sample database X= { X 1,x2,x3,…,xn}∈RS to be classified contains n samples, and s is the feature space dimension.
In step S3, the sample database is divided into k categories (1 < k < n), and the corresponding cluster centers of all the categories are o= { O 1,o2,o3,…,ok }, the classification matrix u= [ U ij]k*n, and the theoretical loading vehicle number v= { V 1,v2,v3,…,vk }, which is obtained by analysis in step S4, is obtained, so that each cluster center O k is set corresponding to one theoretical loading vehicle number V k.
Wherein u ij represents the membership value of the jth sample to the ith class and satisfies the following constraint:
uij≥0,1≤j≤k,1≤j≤n。
After the establishment of the sample database is completed, setting the initial number of categories and the cluster centers of each category, and knowing that the sample database x= { X 1,x2,x3,…,xn }, the cluster centers corresponding to the k categories are o= { O 1,o2,o3,…,ok } (1 < k < n), and calculating the membership value u ij according to the following formula in the step S32:
Wherein d ij is the Euclidean distance from the sample to the cluster center, and is calculated according to the following formula:
dij＝||xj-oi||：
in the above step S33, the clustering center o i is calculated according to the following formula:
Wherein u ij represents the membership value of the jth sample to the ith class, i is not less than 1 and not more than k, j is not less than 1 and not more than n, d rj represents the distance between the jth sample and the cluster center of 1-k classes, and the value range of the fuzzy weighting index m is not less than 1.
Setting a fuzzy clustering objective function J m:
In the iterative calculation process, firstly, the Euclidean distance d ij from a sample to a clustering center is calculated, the degree membership value U ij in the current iteration is calculated, the difference is made between the previous iteration results after the clustering objective function J m (U, O) is solved, and if the condition is met: if J m T-Jm (T-1) is less than epsilon, terminating calculation and classifying the samples based on the maximum membership rule according to the membership value; otherwise, iteratively calculating a clustering center, repeating the steps, and finally realizing the optimal classification of all samples in the bridge parameter sample database.
In step S4, when calculating the number of theoretical loading vehicles, v k represents the number of theoretical loading vehicles corresponding to the kth class clustering center, and after the number of loading vehicles of all samples in the kth class clustering center is sequentially arranged according to the size, the median of the number sequence is selected as the number of theoretical loading vehicles in the kth class clustering center. If the median value is not unique, calculating the average value of the median value and the loading number as the theoretical loading number of the clustering center.
After the automatic arrangement database of the load test is obtained, when the load test of the simply supported bridge is carried out, setting the new parameter as a j+1st sample in the step S5, and sequentially calculating the membership value u i(j+1) of the j+1st sample relative to all clustering centers O= { O 1,o2,o3,…,ok };
u i(j+1) represents the membership value of the j+1th sample to the i-th class and satisfies the following formula:
The theoretical loaded vehicle number for sample j+1 is calculated by the following formula:
And rounding the calculation result of the theoretical loading vehicle number calculation formula to obtain the theoretical loading vehicle number of the j+1th sample.
In the specific experimental process, after the membership value of the new sample to each class of clustering centers is obtained through calculation, the number of loading vehicles required by the new bridge load test can be determined through weighted calculation, and the theoretical loading vehicle number of the class can be directly selected after the class to which the new sample belongs is determined based on the maximum membership principle. And finally, adding the load test scheme into a sample database to update the database, and calculating to obtain a new clustering center and the theoretical number of loading vehicles corresponding to the category.
It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, and that the above embodiments and descriptions are merely illustrative of the principles of the present invention, and various changes and modifications may be made without departing from the spirit and scope of the invention, which is defined in the appended claims. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (6)
1. A load test loading vehicle self-matching arrangement method for a simply supported girder bridge is characterized by comprising the following steps:
s1: setting unified standards of the loading vehicle in the load test;
S2: performing a large number of load tests by using a standard vehicle and collecting test parameters to construct a sample database;
S3: performing optimal classification on all samples, and calculating a clustering center of each category ；
S4: counting the number of loading vehicles of all samples in each category, and analyzing the theoretical number of loading vehicles of each category;
s5: collecting new parameters of the bridge for load test, calculating membership values of the new parameters and each class of clustering centers, and further determining the number of loaded vehicles;
s6: adding the new load test data into a sample database, and repeating the steps S3-S5;
In step S3, the sample database is divided into k categories, and the clustering center corresponding to all the categories is obtained as The theoretical number of loaded vehicles obtained by analysis in step S4Let each cluster center be/>Are all equal to the theoretical loading vehicle quantity/>Is correspondingly arranged;
In calculating the number of theoretical loaded vehicles, Representing the theoretical loading vehicle number corresponding to the k-th type clustering center, and selecting the median of the series as the theoretical loading vehicle number of the k-th type clustering center after sequentially arranging the loading vehicle numbers of all samples of the k-th type clustering center according to the size;
In the above step S5, the new parameter is set to be the (j+1) th sample, and the (j+1) th sample is calculated with respect to all cluster centers Membership value/>；
Representing the membership value of the j+1th sample to the ith class, and satisfying the following formula:
；
The theoretical loaded vehicle number for sample j+1 is calculated by the following formula:
，
And rounding the calculation result of the theoretical loading vehicle number calculation formula to obtain the theoretical loading vehicle number of the j+1th sample.
2. The method for self-matching placement of load-tested vehicles on a simply supported girder bridge according to claim 1, wherein the step S3 of optimally classifying all samples comprises the steps of:
S31: setting an initial class number k, setting a fuzzy weighting index m and an iteration maximum error parameter epsilon, and initializing ，T=1；
S32: calculating membership value at the time of the T-th iteration；
S33: substituting the calculated membership value and the clustering center into the clustering objective function respectivelyThe difference is calculated and compared with the iteration maximum error parameter epsilon;
S34: if the condition is satisfied: The calculation is terminated and the step S37 is entered, otherwise the step S35 is entered;
S35: calculating a cluster center o i of each category according to the membership value u ij;
s36: increasing the iteration number, namely T=T+1, and repeating the steps S32-S34;
S37: and classifying the samples according to the membership value.
3. The method for self-matching placement of simply supported girder bridge load test loading vehicles according to claim 2, wherein the sample database is characterized by the followingThe clustering centers corresponding to the k categories areIn the step S32, the membership value/>, is calculated according to the following formula：
；
Wherein,For the Euclidean distance of the sample to the cluster center, the calculation is performed according to the following formula:
；
Wherein, Representing the membership value of the jth sample to the ith class, wherein i is more than or equal to 1 and less than or equal to k, j is more than or equal to 1 and less than or equal to n,/>And the distance between the jth sample and the 1~k class clustering centers is represented, and the value range of the fuzzy weighting index m is more than or equal to 1.
4. The method for self-matching layout of load-tested vehicles on a simply supported girder bridge according to claim 2, wherein the objective function is clustered in step S33The method comprises the following steps:
；
In the above step S35, the clustering center is calculated according to the following formula ：
。
5. The method for self-matching placement of load-tested loaded vehicles on a simply supported girder bridge according to claim 1, wherein in the step S2, the test parameters collected by the load test include bridge span L, load level P, number of lanes M and number of loaded vehicles V, and the samples in the sample database XAnd each sample/>Corresponds to one loaded vehicle quantity/>。
6. The method for self-matching arrangement of load-tested vehicles for a simply supported beam bridge according to claim 1, wherein the step S1 is set to a unified standard of load-tested vehicles including a load-tested vehicle type, a load-controlled section, an axle spacing, a two-vehicle spacing, and a load-tested vehicle orientation.
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