CN112749511A - Intelligent analysis method for building structure reliability diagnosis - Google Patents

Intelligent analysis method for building structure reliability diagnosis Download PDF

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CN112749511A
CN112749511A CN202110057577.6A CN202110057577A CN112749511A CN 112749511 A CN112749511 A CN 112749511A CN 202110057577 A CN202110057577 A CN 202110057577A CN 112749511 A CN112749511 A CN 112749511A
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曾滨
邵彦超
许庆
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Central Research Institute of Building and Construction Co Ltd MCC Group
China Jingye Engineering Corp Ltd
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Abstract

The intelligent analysis method for building structure reliability diagnosis comprises the following steps: setting M weak classifiers and a set of known building samples T { (x)1,y1),(x2,y2),…(xn,yn) In a known building sample set T, x1、x2…xnFor known n input samples, each input sample contains a input parameters, y1、y2…ynThe known output parameters corresponding to each sample are 1 output parameter in the b parameters, and the method comprises the following steps: respectively using M weak classifiers to self-learn the known building sample set T to obtainTo an intelligent analysis network for building structure reliability diagnosis; inputting an input parameter of an unknown building structure into the network of intelligent analysis of building structure reliability diagnosis to obtain a reliability rating of the unknown building structure.

Description

Intelligent analysis method for building structure reliability diagnosis
Technical Field
The invention relates to an intelligent analysis method, in particular to an intelligent analysis method for building structure reliability diagnosis.
Background
In the existing building structure reliability diagnosis process, raw data is a set of normal samples (first, second, and third-level samples) and abnormal samples (fourth-level samples). It makes sense to use a data-driven approach to model historical samples and classify new samples.
In order to improve the precision, an ensemble learning method is adopted, and the accuracy of the classification method is improved.
In the supervised learning algorithm of machine learning, the goal is to learn a stable model which is better in all aspects, but the actual situation is often not ideal, and sometimes, only a plurality of preferred models can be obtained: the definition input is a weakly supervised model, which performs better in some respects. The integrated learning is to combine a plurality of weak supervision models to obtain a better and more comprehensive strong supervision model, and the potential idea of the integrated learning is that even if a weak classifier obtains wrong prediction, other weak classifiers can correct the errors.
AdaBoost: defining the input as Adaptive boosting; the algorithm is as follows: each training example is endowed with equal weight when training is started, then the algorithm is used for training t rounds on a training set, and after each training, the training examples which fail in training are endowed with larger weight, namely, the learning algorithm is enabled to pay more attention to wrongly learned samples after each learning, so that a plurality of prediction functions are obtained. And gradually reducing the residual error in a residual error fitting mode, and superposing the models generated in each step to obtain a final model.
Disclosure of Invention
The invention aims to overcome the defects caused by a traditional stepwise calculation method based on an empirical formula and provides an intelligent analysis method for building structure reliability diagnosis.
In order to achieve the purpose of the invention, the following technical scheme is adopted in the application:
the invention relates to an intelligent analysis method for building structure reliability diagnosis, which comprises the following steps: setting M weak classifiers and a set of known building samples T { (x)1,y1),(x2,y2),…(xn,yn) In a known building sample set T, x1、x2…xnFor known n input samples, each input sample contains a input parameters, y1、y2…ynThe output parameters are respectively corresponding to the known input samples, and the output parameters are 1 output parameter in the b parameters, wherein: the method comprises the following steps:
(I) respectively using M weak classifiers to carry out self-learning on the known building sample set T to obtain an intelligent analysis network for building structure reliability diagnosis
(a) Initializing the weight D of the known building sample set T1
Firstly, setting m as 1, D1＝(ω11,ω12,…ω1n) Wherein
Figure BDA0002901331420000021
(b) D, mixingmInputting T into m weak classifier for one iteration
Will Dm·T＝{ωm1x1,ωm2x2…ωmnxn) Inputting the data into the m weak classifiers respectively as an iteration and recording the iteration times q, wherein each sample obtains an output result: psim(xi):{1,2,3,4,…,b}，ψm(x) A set of n output parameters for the known building sample set T output by the m-th weak classifier in that iteration;
(c) calculating psi of step (b)m(x) Classification error Rate em
Figure BDA0002901331420000022
In-situ typeI(ψm(xi)≠yi) When the output parameter y corresponding to each sample is knowniObtaining corresponding output parameter psi with the m-th weak classifierm(xi) Equal, I (psi)m(xi)≠yi) The value is taken to be 0; when the output parameter y corresponding to each sample is knowniObtaining corresponding output parameter psi with the m-th weak classifierm(xi) When they are not equal, I (psi)m(xi)≠yi) The value is taken to be 1;
when e ismIf the iteration number q of the m-th weak classifier is more than 0.05 or less than 100, repeating the step (b) and the step (c); otherwise, finishing the training of the mth weak classifier, and entering the step (d);
(d) calculating the importance coefficient alpha of the mth weak classifier in the final resultm
Figure BDA0002901331420000023
Wherein, R is b, b is the output number of the sample, and the m-th weak classifier G which is trained is obtainedm(ii) a And obtaining the importance coefficient alpha corresponding to the weak classifierm；
(e) Updating the weight D of the known building sample set T in the step (a)m+1
Dm+1＝(ωm+1,1,ωm+1,2,…ωm+1,n)
Figure BDA0002901331420000024
Wherein ZmIs a normalization factor, Gm(xi) When the ith sample passes through the m-th weak classifier, y is the output resultiThe output parameter corresponding to the known ith sample;
Figure BDA0002901331420000031
when M is less than the set weak classifier number M, the weak classifier number M is set
Figure BDA0002901331420000033
In place of Dm＝(ωm1,ωm2,…ωmn) M +1 instead of m, repeating steps (b) to (d); when M is equal to the set weak classifier number M, entering the step (f);
(f) the intelligent analysis network for building structure reliability diagnosis comprises M trained weak classifiers GmAnd each weak classifier GmCorresponding importance coefficient alpham；
(II) inputting an input parameter of an unknown building structure into the network of intelligent analysis of the building structure reliability diagnosis to obtain a reliability rating of the unknown building structure
Inputting the input parameters of the unknown building structure into M trained weak classifiers of the intelligent analysis network for building structure reliability diagnosis, and giving out a corresponding output result G by each weak classifieri(x) Using the importance coefficient alpha of the corresponding weak classifieriAnd the above output result Gi(x) Calculating to obtain an output parameter G (x) of the unknown building structure;
Figure BDA0002901331420000032
wherein i is 1,2,3, …, M
Wherein G isi(x) Respectively inputting the input parameters of the unknown building structure into 1-M weak classifiers to obtain the output results, alpha, of the corresponding weak classifiersiThe importance coefficients corresponding to the 1-M weak classifiers obtained in the step (d); and the output parameter G (x) is rounded to obtain the reliability rating of the unknown building structure.
The invention relates to an intelligent analysis method for building structure reliability diagnosis, which comprises the following steps: the weak classifier is a classical BP neural network, the number of input nodes of the weak classifier is a term input parameter of each input sample, the number of output nodes is 1, and the number of hidden nodes is 5-7.
The invention relates to an intelligent analysis method for building structure reliability diagnosis, which comprises the following steps: the number of the M weak classifiers is set to be an integer from 3 to 100.
The invention relates to an intelligent analysis method for building structure reliability diagnosis, which comprises the following steps: the a-term input parameters of each input sample are 57, which are respectively:
(1) planning and designing purposes: civil: defining the input as 1; industrial use: defining the input as 2; public buildings: defining the input as 3; o others: defining the input as 4;
(2) layer number: defining input n, wherein n is a specific layer number and is an integer;
(3) the structure type is as follows: the concrete structure: defining the input as 1; steel structure: defining the input as 2; the masonry structure: defining the input as 3; mixed structure: defining the input as 4; o-bent structure: defining the input as 5; the gantry steel frame: defining the input as 6; framework construction: defining the input as 7; o others: defining the input as 8;
(4) planar form: o type I: defining the input as 1; l-type: defining the input as 2; concave: defining the input as 3; convex type: defining the input as 4; other: defining the input as 5;
(5) foundation form: natural foundation: defining the input as 1; pile foundation: defining the input as 2; artificial foundation treatment: defining the input as 3; other: defining the input as 4;
(6) basic form: o strip foundation: defining the input as 1; independent basis: defining the input as 2; o, raft foundation: defining the input as 3; other: defining the input as 4;
(7) the floor form is as follows: the assembled type: defining the input as 1; o cast-in-place: defining the input as 2; steel and concrete composite structure: defining the input as 3; other: defining the input as 4;
(8) the roof form is as follows: the assembled type: defining the input as 1; o cast-in-place: defining the input as 2; steel roof truss: defining the input as 3; steel net frame: defining the input as 4; other: defining the input as 5;
(9) history reconstruction: comprises the following steps: defining the input as 1; none: defining the input as 2;
(10) history reinforcement: comprises the following steps: defining the input as 1; none: defining the input as 2;
(11) and (3) grading the safety of the foundation: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(12) and (3) usability rating of foundation: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(13) grading the reliability of foundation: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(14) and (3) safety identification and rating of foundation: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(15) and (3) usability identification and rating of foundation: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(16) column system safety rating: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(17) pillar system usability rating: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(18) column system reliability rating: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(19) crane beam system safety rating: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(20) crane beam system usability rating: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(21) and (3) reliability rating of the crane beam system: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(22) roof system security rating: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(23) roof system usability rating: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(24) and (3) reliability rating of the roof system: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(25) upper load bearing structure/column system structural integrity: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(26) upper load bearing structure/column system load bearing function: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(27) upper load-bearing structure/column system safety certification rating: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(28) upper load bearing structure/crane beam system structural integrity: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(29) upper load bearing structure/crane beam system load bearing function: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(30) upper load-bearing structure/crane beam system security qualification grade: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(31) upper load bearing structure/roof system structural integrity: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(32) upper load bearing structure/roof system load bearing function: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(33) upper load-bearing structure/roof system security qualification grade: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(34) upper load bearing structure/column system use case: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(35) horizontal displacement of upper load bearing structure/column system: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(36) upper load bearing structure/column system usability rating: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(37) upper load bearing structure/crane beam system usage: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(38) horizontal displacement of upper load bearing structure/crane beam system: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(39) upper load-bearing structure/crane beam system usability qualification grade: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(40) upper load bearing structure/roof system usage: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(41) horizontal displacement of upper load-bearing structure/roof system: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(42) upper load-bearing structure/roof system usability qualification grade: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(43) and (3) carrying out safety rating on the building envelope: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(44) and (3) rating the usability of the enclosure: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(45) and (3) evaluating the reliability of the enclosure structure: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(46) the using condition of the building envelope system is as follows: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(47) the enclosure uses the functional concrete structure roofing system: defining the input as optional; first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(48) the building envelope uses the functional metal building envelope roofing system: defining the input as optional; first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(49) the enclosure structure has a functional wall body: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(50) the enclosure uses functional doors and windows: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(51) the using function of the enclosure structure is underground waterproof: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(52) the enclosure uses other safeguard of function: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(53) and (3) the usability appraisal grade of the building envelope system: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(54) the bearing function of the enclosure structure system is as follows: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(55) the building construction of the building envelope system is connected: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(56) and (3) safety identification grade of the building envelope: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(57) safety level: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: the definition input is 4.
The invention relates to an intelligent analysis method for building structure reliability diagnosis, which comprises the following steps: the output parameter b is 4, which represents the reliability level, i.e. one level: defining the output as 1; and (2) second stage: defining the output to be 2; third-stage: define the output as 3; and (4) fourth stage: the definition output is 4.
The invention relates to an intelligent analysis method for building structure reliability diagnosis, which comprises the following steps: the number n of said known input samples is at least 30.
The intelligent analysis method for building structure reliability diagnosis is different from the traditional stepwise calculation method based on empirical formulas, is based on data driving, and intelligently diagnoses the reliability information of the building structure, thereby avoiding the influence of subjective factors on reliability conclusions in the traditional method and remarkably improving the reliability.
Drawings
FIG. 1 is a schematic diagram of an intelligent analysis network for building structure reliability diagnosis constructed by the method of the present invention;
FIG. 2 is a schematic diagram of a reliability rating for diagnosing an unknown building structure using the intelligent analysis network for building structure reliability diagnosis shown in FIG. 1.
Detailed Description
The intelligent analysis method for building structure reliability diagnosis comprises the following steps: set 20 weak classifiers and a set of known building samples T { (x)1,y1),(x2,y2),…(xn,yn) In a known building sample set T, x1、x2…xnFor the known 430 input samples, 57 input parameters, y, are included in each of the input samples1、y2…ynThe output parameters are respectively corresponding to the known input samples, and the output parameters are 1 output parameter in 4 parameters, and the method comprises the following specific steps:
(I) As shown in FIG. 1, respectively using M weak classifiers to self-learn the known building sample set T to obtain an intelligent analysis network for building structure reliability diagnosis
(a) Initializing the weight D of the known building sample set T1
Firstly, setting m as 1, D1＝(ω11,ω12,…ω1n) Wherein
Figure BDA0002901331420000081
(b) D, mixingmInputting T into m weak classifier for one iteration
Will Dm·T＝{ωm1x1,ωm2x2…ωmnxn) Inputting the data into the m weak classifiers respectively as an iteration and recording the iteration times q, wherein each sample obtains an output result: psim(xi):{1,2,3,4,…,b}，ψm(x) A set of 430 output parameters for the known building sample set T output by the mth weak classifier in that iteration;
(c) calculating psi of step (b)m(x) Classification error Rate em
Figure BDA0002901331420000082
In the formula I (psi)m(xi)≠yi) When the output parameter y corresponding to each sample is knowniObtaining corresponding output parameter psi with the m-th weak classifierm(xi) Equal, I (psi)m(xi)≠yi) The value is taken to be 0; when the output parameter y corresponding to each sample is knowniObtaining corresponding output parameter psi with the m-th weak classifierm(xi) When they are not equal, I (psi)m(xi)≠yi) The value is taken to be 1;
when e ismIf the iteration number q of the m-th weak classifier is more than 0.05 or less than 100, repeating the step (b) and the step (c); otherwise, finishing the training of the mth weak classifier, and entering the step (d);
(d) calculating the importance coefficient alpha of the mth weak classifier in the final resultm
Figure BDA0002901331420000091
Wherein, R is b, b is the output number of the sample, and the m-th weak classifier G which is trained is obtainedm(ii) a And obtaining the importance coefficient alpha corresponding to the weak classifierm；
(e) Updating the weight D of the known building sample set T in the step (a)m+1
Dm+1＝(ωm+1,1,ωm+1,2,…ωm+1,n)
Figure BDA0002901331420000092
Wherein ZmIs a normalization factor, Gm(xi) When the ith sample passes through the m-th weak classifier, y is the output resultiThe output parameter corresponding to the known ith sample;
Figure BDA0002901331420000093
when M is less than the set weak classifier number M equal to 20, the weak classifier will be selected
Figure BDA0002901331420000094
In place of Dm＝(ωm1,ωm2,…ωmn) M +1 instead of m, repeating steps (b) to (d); when M is equal to the set weak classifier number M equal to 20, entering the step (f);
(f) the intelligent analysis network for building structure reliability diagnosis comprises M trained weak classifiers GmAnd each weak classifier GmCorresponding importance coefficient alpham；
(II) as shown in FIG. 2, inputting an input parameter of an unknown building structure into the network of intelligent analysis of building structure reliability diagnosis to obtain a reliability rating of the unknown building structure
Inputting the input parameters of the unknown building structure into M trained weak classifiers of the intelligent analysis network for building structure reliability diagnosis, and giving out a corresponding output result G by each weak classifieri(x) Using the importance coefficient alpha of the corresponding weak classifieriAnd the above output result Gi(x) Calculating to obtain an output parameter G (x) of the unknown building structure;
Figure BDA0002901331420000101
wherein i is 1,2,3, …, M
Wherein G isi(x) Respectively inputting the input parameters of the unknown building structure into 1-20 weak classifiers to obtain the output results, alpha, of the corresponding weak classifiersiThe importance coefficients corresponding to the 1-20 weak classifiers obtained in the step (d); and the output parameter G (x) is rounded to obtain the reliability rating of the unknown building structure.
The weak classifier is a known classical BP neural network, the number of input nodes of the weak classifier is 57 input parameters of each input sample, the number of output nodes is 1, and the number of hidden nodes is 5-7.
The 57 input parameters for each input sample are:
(1) planning and designing purposes: civil: defining the input as 1; industrial use: defining the input as 2; public buildings: defining the input as 3; o others: defining the input as 4;
(2) layer number: defining input n, wherein n is a specific layer number and is an integer;
(3) the structure type is as follows: the concrete structure: defining the input as 1; steel structure: defining the input as 2; the masonry structure: defining the input as 3; mixed structure: defining the input as 4; o-bent structure: defining the input as 5; the gantry steel frame: defining the input as 6; framework construction: defining the input as 7; o others: defining the input as 8;
(4) planar form: o type I: defining the input as 1; l-type: defining the input as 2; concave: defining the input as 3; convex type: defining the input as 4; other: defining the input as 5;
(5) foundation form: natural foundation: defining the input as 1; pile foundation: defining the input as 2; artificial foundation treatment: defining the input as 3; other: defining the input as 4;
(6) basic form: o strip foundation: defining the input as 1; independent basis: defining the input as 2; o, raft foundation: defining the input as 3; other: defining the input as 4;
(7) the floor form is as follows: the assembled type: defining the input as 1; o cast-in-place: defining the input as 2; steel and concrete composite structure: defining the input as 3; other: defining the input as 4;
(8) the roof form is as follows: the assembled type: defining the input as 1; o cast-in-place: defining the input as 2; steel roof truss: defining the input as 3; steel net frame: defining the input as 4; other: defining the input as 5;
(9) history reconstruction: comprises the following steps: defining the input as 1; none: defining the input as 2;
(10) history reinforcement: comprises the following steps: defining the input as 1; none: defining the input as 2;
(11) and (3) grading the safety of the foundation: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(12) and (3) usability rating of foundation: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(13) grading the reliability of foundation: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(14) and (3) safety identification and rating of foundation: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(15) and (3) usability identification and rating of foundation: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(16) column system safety rating: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(17) pillar system usability rating: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(18) column system reliability rating: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(19) crane beam system safety rating: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(20) crane beam system usability rating: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(21) and (3) reliability rating of the crane beam system: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(22) roof system security rating: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(23) roof system usability rating: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(24) and (3) reliability rating of the roof system: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(25) upper load bearing structure/column system structural integrity: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(26) upper load bearing structure/column system load bearing function: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(27) upper load-bearing structure/column system safety certification rating: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(28) upper load bearing structure/crane beam system structural integrity: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(29) upper load bearing structure/crane beam system load bearing function: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(30) upper load-bearing structure/crane beam system security qualification grade: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(31) upper load bearing structure/roof system structural integrity: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(32) upper load bearing structure/roof system load bearing function: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(33) upper load-bearing structure/roof system security qualification grade: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(34) upper load bearing structure/column system use case: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(35) horizontal displacement of upper load bearing structure/column system: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(36) upper load bearing structure/column system usability rating: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(37) upper load bearing structure/crane beam system usage: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(38) horizontal displacement of upper load bearing structure/crane beam system: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(39) upper load-bearing structure/crane beam system usability qualification grade: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(40) upper load bearing structure/roof system usage: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(41) horizontal displacement of upper load-bearing structure/roof system: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(42) upper load-bearing structure/roof system usability qualification grade: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(43) and (3) carrying out safety rating on the building envelope: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(44) and (3) rating the usability of the enclosure: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(45) and (3) evaluating the reliability of the enclosure structure: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(46) the using condition of the building envelope system is as follows: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(47) the enclosure uses the functional concrete structure roofing system: defining the input as optional; first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(48) the building envelope uses the functional metal building envelope roofing system: defining the input as optional; first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(49) the enclosure structure has a functional wall body: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(50) the enclosure uses functional doors and windows: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(51) the using function of the enclosure structure is underground waterproof: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(52) the enclosure uses other safeguard of function: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(53) and (3) the usability appraisal grade of the building envelope system: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(54) the bearing function of the enclosure structure system is as follows: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(55) the building construction of the building envelope system is connected: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(56) and (3) safety identification grade of the building envelope: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(57) safety level: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: the definition input is 4.
The output parameter b is 4, which represents the reliability level, i.e. one level: defining the output as 1; and (2) second stage: defining the output to be 2; third-stage: define the output as 3; and (4) fourth stage: the definition output is 4.
The above-mentioned embodiments are merely illustrative of the preferred embodiments of the present invention, and do not limit the scope of the present invention, and various modifications and improvements of the technical solution of the present invention by those skilled in the art should fall within the protection scope defined by the claims of the present invention without departing from the spirit of the present invention.

Claims (6)

1. An intelligent analysis method for building structure reliability diagnosis, the method comprising: setting M weak classifiers and a set of known building samples T { (x)1,y1),(x2,y2),…(xn,yn) In a known building sample set T, x1、x2…xnFor known n input samples, each input sample contains a input parameters, y1、y2…ynThe output parameters are respectively corresponding to the known input samples, and the output parameters are 1 output parameter in the b parameters, and the method is characterized in that: the method comprises the following steps:
(I) respectively using M weak classifiers to carry out self-learning on the known building sample set T to obtain an intelligent analysis network for building structure reliability diagnosis
(a) Initializing the weight D of the known building sample set T1
Firstly, setting m as 1, D1＝(ω11,ω12,…ω1n) Wherein
Figure FDA0002901331410000011
(b) D, mixingmInputting T into m weak classifier for one iteration
Will Dm·T＝{ωm1x1,ωm2x2…ωmnxn) Are inputted into m weak classifiers as one respectivelyAnd (5) iterating and recording the iteration times q, wherein each sample obtains an output result: psim(xi):{1,2,3,4,…,b}，ψm(x) A set of n output parameters for the known building sample set T output by the m-th weak classifier in that iteration;
(c) calculating psi of step (b)m(x) Classification error Rate em
Figure FDA0002901331410000012
In the formula I (psi)m(xi)≠yi) When the output parameter y corresponding to each sample is knowniObtaining corresponding output parameter psi with the m-th weak classifierm(xi) Equal, I (psi)m(xi)≠yi) The value is taken to be 0; when the output parameter y corresponding to each sample is knowniObtaining corresponding output parameter psi with the m-th weak classifierm(xi) When they are not equal, I (psi)m(xi)≠yi) The value is taken to be 1;
when e ismIf the iteration number q of the m-th weak classifier is more than 0.05 or less than 100, repeating the step (b) and the step (c); otherwise, finishing the training of the mth weak classifier, and entering the step (d);
(d) calculating the importance coefficient alpha of the mth weak classifier in the final resultm
Figure FDA0002901331410000021
Wherein, R is b, b is the output number of the sample, and the m-th weak classifier G which is trained is obtainedmAnd obtaining the importance coefficient alpha corresponding to the weak classifierm；
(e) Updating the weight D of the known building sample set T in the step (a)m+1
Dm+1＝(ωm+1,1,ωm+1,2,…ωm+1,n)
Figure FDA0002901331410000022
Wherein ZmIs a normalization factor, Gm(xi) When the ith sample passes through the m-th weak classifier, y is the output resultiThe output parameter corresponding to the known ith sample;
Figure FDA0002901331410000023
when M is less than the set weak classifier number M, D is setm+1＝(ωm+1,1,ωm+1,2,…ωm+1,n) In place of Dm＝(ωm1,ωm2,…ωmn) M +1 instead of m, repeating steps (b) to (d); when M is equal to the set weak classifier number M, entering the step (f);
(f) the intelligent analysis network for building structure reliability diagnosis comprises M trained weak classifiers GmAnd each weak classifier GmCorresponding importance coefficient alpham；
(II) inputting an input parameter of an unknown building structure into the network of intelligent analysis of the building structure reliability diagnosis to obtain a reliability rating of the unknown building structure
Inputting the input parameters of the unknown building structure into M trained weak classifiers of the intelligent analysis network for building structure reliability diagnosis, and giving out a corresponding output result G by each weak classifieri(x) Using the importance coefficient alpha of the corresponding weak classifieriAnd the above output result Gi(x) Calculating to obtain an output parameter G (x) of the unknown building structure;
Figure FDA0002901331410000031
wherein i is 1,2,3, …, M
Wherein G isi(x) Respectively inputting the input parameters of the unknown building structure into 1-M weak classifiers to obtain the output results, alpha, of the corresponding weak classifiersiThe importance coefficients corresponding to the 1-M weak classifiers obtained in the step (d); and the output parameter G (x) is rounded to obtain the reliability rating of the unknown building structure.
2. The intelligent analysis method for building structure reliability diagnosis according to claim 1, characterized in that: the weak classifier is a classical BP neural network, the number of input nodes of the weak classifier is a term input parameter of each input sample, the number of output nodes is 1, and the number of hidden nodes is 5-7.
3. The intelligent analysis method for building structure reliability diagnosis according to claim 2, characterized in that: the number of the M weak classifiers is set to be an integer from 3 to 100.
4. The intelligent analysis method for building structure reliability diagnosis according to claim 3, characterized in that: the a-term input parameters of each input sample are 57, which are respectively:
(1) planning and designing purposes: civil: defining the input as 1; industrial use: defining the input as 2; public buildings: defining the input as 3; o others: defining the input as 4;
(2) layer number: defining input n, wherein n is a specific layer number and is an integer;
(3) the structure type is as follows: the concrete structure: defining the input as 1; steel structure: defining the input as 2; the masonry structure: defining the input as 3; mixed structure: defining the input as 4; o-bent structure: defining the input as 5; the gantry steel frame: defining the input as 6; framework construction: defining the input as 7; o others: defining the input as 8;
(4) planar form: o type I: defining the input as 1; l-type: defining the input as 2; concave: defining the input as 3; convex type: defining the input as 4; other: defining the input as 5;
(5) foundation form: natural foundation: defining the input as 1; pile foundation: defining the input as 2; artificial foundation treatment: defining the input as 3; other: defining the input as 4;
(6) basic form: o strip foundation: defining the input as 1; independent basis: defining the input as 2; o, raft foundation: defining the input as 3; other: defining the input as 4;
(7) the floor form is as follows: the assembled type: defining the input as 1; o cast-in-place: defining the input as 2; steel and concrete composite structure: defining the input as 3; other: defining the input as 4;
(8) the roof form is as follows: the assembled type: defining the input as 1; o cast-in-place: defining the input as 2; steel roof truss: defining the input as 3; steel net frame: defining the input as 4; other: defining the input as 5;
(9) history reconstruction: comprises the following steps: defining the input as 1; none: defining the input as 2;
(10) history reinforcement: comprises the following steps: defining the input as 1; none: defining the input as 2;
(11) and (3) grading the safety of the foundation: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(12) and (3) usability rating of foundation: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(13) grading the reliability of foundation: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(14) and (3) safety identification and rating of foundation: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(15) and (3) usability identification and rating of foundation: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(16) column system safety rating: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(17) pillar system usability rating: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(18) column system reliability rating: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(19) crane beam system safety rating: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(20) crane beam system usability rating: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(21) and (3) reliability rating of the crane beam system: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(22) roof system security rating: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(23) roof system usability rating: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(24) and (3) reliability rating of the roof system: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(25) upper load bearing structure/column system structural integrity: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(26) upper load bearing structure/column system load bearing function: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(27) upper load-bearing structure/column system safety certification rating: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(28) upper load bearing structure/crane beam system structural integrity: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(29) upper load bearing structure/crane beam system load bearing function: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(30) upper load-bearing structure/crane beam system security qualification grade: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(31) upper load bearing structure/roof system structural integrity: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(32) upper load bearing structure/roof system load bearing function: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(33) upper load-bearing structure/roof system security qualification grade: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(34) upper load bearing structure/column system use case: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(35) horizontal displacement of upper load bearing structure/column system: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(36) upper load bearing structure/column system usability rating: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(37) upper load bearing structure/crane beam system usage: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(38) horizontal displacement of upper load bearing structure/crane beam system: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(39) upper load-bearing structure/crane beam system usability qualification grade: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(40) upper load bearing structure/roof system usage: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(41) horizontal displacement of upper load-bearing structure/roof system: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(42) upper load-bearing structure/roof system usability qualification grade: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(43) and (3) carrying out safety rating on the building envelope: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(44) and (3) rating the usability of the enclosure: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(45) and (3) evaluating the reliability of the enclosure structure: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(46) the using condition of the building envelope system is as follows: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(47) the enclosure uses the functional concrete structure roofing system: defining the input as optional; first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(48) the building envelope uses the functional metal building envelope roofing system: defining the input as optional; first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(49) the enclosure structure has a functional wall body: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(50) the enclosure uses functional doors and windows: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(51) the using function of the enclosure structure is underground waterproof: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(52) the enclosure uses other safeguard of function: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(53) and (3) the usability appraisal grade of the building envelope system: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(54) the bearing function of the enclosure structure system is as follows: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(55) the building construction of the building envelope system is connected: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(56) and (3) safety identification grade of the building envelope: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: defining the input as 4;
(57) safety level: first order: defining the input as 1; second order: defining the input as 2; three stages: defining the input as 3; four stages: the definition input is 4.
5. The intelligent analysis method for building structure reliability diagnosis according to claim 4, characterized in that: the output parameter b is 4, which represents the reliability level, i.e. one level: defining the output as 1; and (2) second stage: defining the output to be 2; third-stage: define the output as 3; and (4) fourth stage: the definition output is 4.
6. The intelligent analysis method for building structure reliability diagnosis according to claim 5, characterized in that: the number n of said known input samples is at least 30.
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113706390A (en) * 2021-10-29 2021-11-26 苏州浪潮智能科技有限公司 Image conversion model training method, image conversion method, device and medium

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103136539A (en) * 2013-03-08 2013-06-05 西安科技大学 Grounding grid corrosion rate level prediction method
CN111400811A (en) * 2020-04-01 2020-07-10 安徽建工集团股份有限公司 Reliability analysis method for mixed uncertainty structure

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103136539A (en) * 2013-03-08 2013-06-05 西安科技大学 Grounding grid corrosion rate level prediction method
CN111400811A (en) * 2020-04-01 2020-07-10 安徽建工集团股份有限公司 Reliability analysis method for mixed uncertainty structure

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
张才等: "基于噪声自检测的分段非线性组合Adaboost改进算法", 《计算机工程》 *
魏庆晨: "基于人工神经网络的既有建筑结构可靠性鉴定方法", 《广东土木与建筑》 *

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113706390A (en) * 2021-10-29 2021-11-26 苏州浪潮智能科技有限公司 Image conversion model training method, image conversion method, device and medium

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