Concrete structure damage evaluation method based on multi-source detection data

Abstract

The invention discloses a concrete structure damage evaluation method based on multi-source detection data, and the method comprises the following steps: S1, based on multi-sequence data samples of stress, temperature and environment obtained in concrete structure detection, employing a data regularization method of damage degree equivalent constraint, the multi-sequence data samples are converted into unified data sequences which do not contain physical significance and are graded according to degrees, and therefore the unified data sequences are fused into a unified data sample space; s2, on the basis of the unified data sample space, constructing a damage state basis function and a comprehensive damage state function with universality; and S3, based on the probability integral of the unified data sample space and the damage state primary function, establishing an evaluation method for the damage degree and the evolution trend of the concrete structure. The method has the advantages that the method is suitable for quantitative evaluation of the comprehensive damage state of the concrete structure fusing multi-physical-quantity mass detection data under the multi-field coupling condition, and the defects of related methods at home and abroad are overcome.

Description

Technical Field

[0001] The invention belongs to the technical field of concrete structure damage assessment, and in particular relates to a concrete structure damage assessment method based on multi-source detection data. Background Art

[0002] Concrete structures are usually in a state of internal damage evolution coupled with multiple fields such as stress, temperature and environmental corrosion, and their service safety and durability are at risk. Therefore, regular or irregular inspections (monitoring) are required during the service life of concrete structures to obtain corresponding basic data to evaluate and predict the damage state and risk level of engineering structures.

[0003] The damage state of concrete structures is very complicated because it is affected by multiple factors such as stress, temperature, and environmental corrosives, and the multiple factors are coupled with each other. So far, the detection and evaluation methods are mainly based on the detection (monitoring) data of a single physical field quantity and the evaluation of technical indicators. Due to the different physical meanings and indicator systems of the data, it is impossible to conduct a comprehensive and systematic evaluation between the sequences, and it is even more impossible to consider the multi-field coupling effect. It is difficult to scientifically evaluate the comprehensive damage degree of concrete structures and their risk evolution trend under the multi-field coupling conditions.

[0004] In summary, there is an urgent need to develop a comprehensive evaluation method for the damage state of multi-field coupled concrete structures that can integrate massive data from multi-field and multi-sequence detection (monitoring) into a unified sample space, conform to industry technical specifications, and have rigorous mathematical and statistical theory. Summary of the invention

[0005] The purpose of the present invention is to provide a concrete structure damage assessment method based on multi-source detection data in light of the deficiencies in the above-mentioned prior art. The method is based on multi-field and multi-sequence data samples of stress, temperature, environmental corrosion, etc. obtained in concrete structure monitoring (inspection), and a data regularization method with equivalent constraints on damage degree is used to condense the multi-sequence data into a data sample space without physical meaning; a concrete structure damage state basis function is constructed and a comprehensive state function of structural damage risk is defined, thereby establishing a comprehensive risk assessment method for multi-field coupled damage during the service life of concrete structures that conforms to the characteristics of engineering structures and is simple and easy to implement according to a probability analysis method of the detection data sample interval.

[0006] The purpose of the present invention is achieved by the following technical solutions:

[0007] A concrete structure damage assessment method based on multi-source detection data, the method comprising the following steps:

[0008] S1: Based on the multi-sequence data samples of stress, temperature and environment obtained in the concrete structure inspection, the data regularization method with equivalent constraint of damage degree is adopted to transform the multi-sequence data samples into a unified data sequence without physical meaning and graded by degree, so as to merge them into a unified data sample space;

[0009] S2: Based on the unified data sample space, a universal damage state basis function and a comprehensive damage state function are constructed;

[0010] S3: Based on the probability integral of the unified data sample space and the damage state basis function, an evaluation method for the damage degree and evolution trend of the concrete structure is established.

[0011] Assume that the multi-sequence data samples of stress, temperature and environment obtained in the concrete structure inspection are σ=(σ 1 ,σ 2 ,...,σ i ,...,σ n )、T=(T 1 ,T 2 ,...,T i ,...,T n ) and η=(η 1 ,η 2 ,...,η i ,...,η n ), after data regularization processing, the unified data sequence ξ=(ξ 1 ,ξ 2 ,…,ξ i ,…,ξ n ), the corresponding subset occurrence probability is P(ξ i ); Among the n detection data, the probability of corresponding data in any interval is P(Δξ i )=P(ξ i+1 -ξ i );

[0012] The probability integral interval of the unified data sequence is divided into m equal parts at equal intervals, and the probability integral of the unified data sequence is:

[0013]

[0014] Where, f(ξ) is the probability density function of the field detection data sequence;

[0015] Change the integral interval of probability density in equation (1) [ξ i ,ξ j ], and obtain the damage degree characterization areas {I,II,III,IV,V}.

[0016] The damage state basis function is:

[0017] Ψ(ξ)=A(e ξ -e -ξ );

[0018] The unified data sample space {ξ 1 ,ξ 2 ,…,ξ m} is divided into n equally spaced subspaces [ξ i ,ξ i+1 ], the probability integral of the corresponding subinterval is:

[0019]

[0020] The result obtained is the risk weight of the detected physical quantity corresponding to the corresponding interval;

[0021] The weighted risk value is calculated by partitioning the damage state basis function according to the probability distribution weight, and the calculation formula is:

[0022]

[0023] Where: i is the probability distribution weight, λ i =P(ξ i ≤ξ≤ξ i+1 );

[0024] Define vector λ = {λ 1 ,λ 2 ,…,λ i ,…,λ n}, the weighted modified risk function is:

[0025] F(ξ)=λΨ(ξ) (4);

[0026] The integral of formula (4) on the interval [-1, 0] and [0, 1] is defined as the weighted cumulative risk;

[0027] Construct the comprehensive risk state function:

[0028] β risk =100(S m -S r );

[0029] Where: S m To accumulate risk, S r For risk redundancy accumulation,

[0030] The evaluation method for the damage degree and evolution trend of concrete structure is as follows:

[0031] The probability integral of the corresponding sample space when all detection data sequences in the single sample space are greater than the safety warning threshold is defined as a significant risk state value; according to the probability integral property of the unified data sample space, its value is 100%, that is, the risk state interval of the comprehensive risk state function is defined as (-∞, 100);

[0032] When the risk level is I, the comprehensive risk status parameter is ≤0%, and the risk level is no risk; when the risk level is II, the comprehensive risk status parameter is 0-40%, and the risk level is slight risk; when the risk level is III, the comprehensive risk status parameter is 40-60%, and the risk level is medium risk; when the risk level is IV, the comprehensive risk status parameter is 60-80%, and the risk level is greater risk; when the risk level is V, the comprehensive risk status parameter is 80-100%, and the risk level is significant risk.

[0033] The advantages of the present invention are: it is suitable for quantitative evaluation of the comprehensive damage state of concrete structures under multi-field coupling conditions and integrates massive detection data of multiple physical quantities, thus filling the gaps of related methods at home and abroad. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 It is a schematic diagram of the probability density of the unified data sequence of the present invention;

[0035] Figure 2 This is a conceptual diagram of concrete structure damage prediction and evaluation based on damage state basis functions of the present invention;

[0036] Figure 3 This is a table diagram of risk status evaluation indicators for single detection data of the present invention. DETAILED DESCRIPTION

[0037] The features of the present invention and other related features are further described in detail below through embodiments in conjunction with the accompanying drawings to facilitate understanding by those skilled in the art:

[0038] Example: Figure 1-3 As shown, this embodiment specifically relates to a concrete structure damage assessment method based on multi-source detection data, and the method mainly includes the following steps:

[0039] S1: Based on the multi-sequence data samples of stress (stress field), temperature (physical field) and environment (chemical field) obtained in the concrete structure inspection, the data regularization method with equivalent constraint of damage degree is adopted to transform the multi-sequence data samples into a unified data sequence without physical meaning and graded by degree, and then merge them into a unified data sample space.

[0040] Among them, Figure 1As shown in the figure, it is assumed that the multi-series data samples of stress, temperature and environment obtained in the concrete structure inspection are σ=(σ 1 ,σ 2 ,...,σ i ,...,σ n )、T=(T 1 ,T 2 ,...,T i ,...,T n ) and η=(η 1 ,η 2 ,...,η i ,...,η n ), after data regularization, we get a unified data sequence ξ=(ξ 1 ,ξ 2 ,…,ξ i ,…,ξ n ), the corresponding subset occurrence probability is P(ξ i ). Among the n detection data, the probability of corresponding data in any interval is P(Δξ i )=P(ξ i+1 -ξ i ).

[0041] Divide the probability integral interval of the uniform data sequence into m equal parts, then the probability integral of the uniform data sequence is:

[0042]

[0043] Where, f(ξ) is the probability density function of the field detection data sequence;

[0044] Change the integral interval of probability density in equation (1) [ξ i ,ξ j ], and obtain the damage degree characterization areas {I,II,III,IV,V}.

[0045] S2: Based on the unified data sample space, construct universal damage state basis functions and comprehensive damage state functions.

[0046] The damage state basis function is:

[0047] Ψ(ξ)=A(e ξ -e -ξ );

[0048] According to the characteristics of this function, combined with the probability density and distribution relationship of the test data, the physical meaning of the test results is ignored and the data sequence with emergency thresholds is directly classified as risk limit, and a comprehensive damage quantitative evaluation system is established without emergency conditions. Figure 2As shown, the damage state basis function Ψ(ξ) = A(e ξ -e -ξ ) are as follows: i) passing through the origin of the coordinate system, the direction of risk change is zero and the risk level is zero; ii) the positive horizontal direction indicates an increase in risk, and the negative horizontal direction indicates an increase in the structural redundancy against risk; iii) the positive horizontal direction indicates an increase in the sensitivity of the risk level, and the negative horizontal direction indicates an increase in the sensitivity of the redundancy against risk.

[0049] The unified data sample space {ξ 1 ,ξ 2 ,…,ξ m} is divided into n equally spaced subspaces [ξ i ,ξ i+1 ], the probability integral of the corresponding subinterval is:

[0050]

[0051] The result obtained is the risk weight of the detected physical quantity corresponding to the corresponding interval;

[0052] The weighted risk value is calculated by partitioning the damage state basis function according to the probability distribution weight. The calculation formula is:

[0053] F(ξ i )=λ i Ψ(ξ i ) (3);

[0054] Where: i is the probability distribution weight, λ i =P(ξ i ≤ξ≤ξ i+1 );

[0055] Define vector λ = {λ 1 ,λ 2 ,…,λ i ,…,λ n}, the weighted modified risk function is:

[0056] F(ξ)=λΨ(ξ) (4);

[0057] The integral of formula (4) on the interval [-1, 0] and [0, 1] is defined as the weighted cumulative risk.

[0058] Construct a comprehensive risk state function:

[0059] β risk =100(S m -S r );

[0060] Where: S m To accumulate risk, S r For risk redundancy accumulation,

[0061] S3: Based on the probability integral and damage state basis function of the unified data sample space, an evaluation method for the damage degree and evolution trend of concrete structures is established.

[0062] Among them, Figure 3 As shown in Figure 2, the evaluation method for the damage degree and evolution trend of concrete structures is as follows:

[0063] The probability integral of the corresponding sample space when all the detection data sequences in the single sample space are greater than the safety warning threshold is defined as the significant risk state value; according to the probability integral property of the unified data sample space, its value is 100%, that is, the risk state interval of the comprehensive risk state function is defined as (-∞, 100);

[0064] When the risk level is I, the comprehensive risk status parameter is ≤0%, and the risk level is no risk; when the risk level is II, the comprehensive risk status parameter is 0-40%, and the risk level is slight risk; when the risk level is III, the comprehensive risk status parameter is 40-60%, and the risk level is medium risk; when the risk level is IV, the comprehensive risk status parameter is 60-80%, and the risk level is greater risk; when the risk level is V, the comprehensive risk status parameter is 80-100%, and the risk level is significant risk.

[0065] The beneficial effect of this embodiment is that it is applicable to the quantitative evaluation of the comprehensive damage state of concrete structures under multi-field coupling conditions and integrates massive detection data of multiple physical quantities, filling the gaps of related methods at home and abroad.

Claims

1. A concrete structure damage assessment method based on multi-source detection data, characterized in that The method comprises the following steps: S1: Based on the multi-sequence data samples of stress, temperature and environment obtained in the concrete structure inspection, the data regularization method with equivalent constraint of damage degree is adopted to transform the multi-sequence data samples into a unified data sequence without physical meaning and graded by degree, so as to merge them into a unified data sample space; S2: Based on the unified data sample space, a universal damage state basis function and a comprehensive damage state function are constructed; S3: Based on the probability integral of the unified data sample space and the damage state basis function, an evaluation method for the damage degree and evolution trend of the concrete structure is established.

2. A concrete structure damage assessment method based on multi-source detection data according to claim 1, characterized in that Assume that the multi-sequence data samples of stress, temperature and environment obtained in the concrete structure inspection are σ=(σ1,σ2,...,σ i ,...,σ n )、T=(T1,T2,...,T i ,...,T n ) and η=(η1,η2,...,η i ,...,η n ), after data regularization processing, the unified data sequence ξ=(ξ1,ξ2,…,ξ i ,…,ξ n ), the corresponding subset occurrence probability is P(ξ i ); Among the n detection data, the probability of corresponding data in any interval is P(Δξ i )=P(ξ i+1 -ξ i ); The probability integral interval of the unified data sequence is divided into m equal parts at equal intervals, and the probability integral of the unified data sequence is: Where, f(ξ) is the probability density function of the field detection data sequence; Change the integral interval of probability density in equation (1) [ξ i ,ξ j ], and obtain the damage degree characterization areas {I,II,III,IV,V}.

3. A concrete structure damage assessment method based on multi-source detection data according to claim 2, characterized in that The damage state basis function is: Ψ(ξ)=A(e ξ -e -ξ ); The unified data sample space {ξ1, ξ2, ..., ξ m } is divided into n equally spaced subspaces [ξ i ,ξ i+1 ], the probability integral of the corresponding subinterval is: The result obtained is the risk weight of the detected physical quantity in the corresponding interval; The weighted risk value is calculated by partitioning the damage state basis function according to the probability distribution weight, and the calculation formula is: F(ξ i )=λ i Ψ(ξ i ) (3); Where: i is the probability distribution weight, λ i =P(ξ i ≤ξ≤ξ i+1 ); Define vector λ = {λ1,λ2,…,λ i ,…,λ n }, the weighted modified risk function is: F(ξ)=λΨ(ξ) (4); The integral of formula (4) on the interval [-1, 0] and [0, 1] is defined as the weighted cumulative risk; Construct the comprehensive risk state function: b risk =100(S m -S r ); Where: S m To accumulate risk, S r For risk redundancy accumulation, 4. A concrete structure damage assessment method based on multi-source detection data according to claim 3, characterized in that The evaluation method for the damage degree and evolution trend of concrete structure is as follows: The probability integral of the corresponding sample space when all detection data sequences in the single sample space are greater than the safety warning threshold is defined as a significant risk state value; according to the probability integral property of the unified data sample space, its value is 100%, that is, the risk state interval of the comprehensive risk state function is defined as (-∞, 100); When the risk level is I, the comprehensive risk status parameter is ≤0%, and the risk level is no risk; when the risk level is II, the comprehensive risk status parameter is 0-40%, and the risk level is slight risk; When the risk level is III, the comprehensive risk status parameter is 40-60%, and the risk level is medium risk; When the risk level is IV, the comprehensive risk status parameter is 60-80%, and the risk level is relatively high; When the risk level is V, the comprehensive risk status parameter is 80-100%, and the risk level is significant risk.

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