CN112668078B - Method for identifying damage of rusted reinforced concrete beam after fire disaster - Google Patents
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Abstract
A method for identifying damage of a rusted reinforced concrete beam after fire relates to the technical field of safety risk assessment, and the identification method is used for identifying damage indexes according to a particle swarm optimization support vector machine method and a residual correction combined algorithm and comprises the following steps: step 1, building a damage model of a corroded reinforced concrete beam after a fire disaster by using ABAQUS finite element analysis software; step 2, calculating a construction combination parameter A according to the frequency and the vibration mode, and taking the construction combination parameter A as an input parameter of a combination algorithm; step 3, selecting the indirect damage index and the direct damage index as output parameters of a combined algorithm; step 4, selecting a radial basis kernel function as a kernel function, and mapping original data of a low-dimensional space into a high-dimensional space; step 5, selecting the mean square error and the goodness of fit to measure the prediction precision; and 6, calculating according to a combined algorithm. The method has obvious advantages on the algorithm of damage prediction, and is more accurate in calculation, shorter in time consumption and higher in result reliability.
Description
Technical Field
The invention relates to the technical field of safety risk assessment, in particular to a method for identifying damage of a rusted reinforced concrete beam after a fire.
Background
The fire is one of the causes of structural damage, collapse and casualties, and is the main cause of direct and indirect economic loss. The structure damage identification and health monitoring technology can detect the existence and the position of the structure damage, predict the residual life of the structure and provide a theoretical basis for the damage assessment and the reinforcement repair of the structure after the fire. Therefore, establishing a damage identification algorithm of the structure to quickly and accurately identify the existence, position and degree of the damage has great engineering significance. The vibration characteristic test of the structure after the fire is less performed at present under the limitation of various conditions such as complex fire process, expensive test instruments, damage effect of high temperature to equipment and the like.
Conventional damage detection methods often rely too heavily on the experience of the technician, or on the formation of different levels of damage to the structure during the detection process. The damage identification method based on the vibration test mostly takes single frequency as an input index, and ignores a vibration mode index which is more sensitive to damage. The damage identification method based on the Support Vector Machine (SVM) has certain difficulty in selecting the penalty parameter c and the kernel function parameter g, the convergence effect is not obvious, and the dimension disaster easily occurs. Based on a damage identification algorithm of a particle swarm optimization support vector machine (PSO-SVM), in the parameter optimization process of the particle swarm optimization algorithm, when an example finds a current optimal position, other examples are rapidly closed, the local optimal condition is easily caused, and a global optimal solution cannot be found.
At present, particularly, fire damage identification research aiming at rusted reinforced concrete beams is few, and damage identification at normal temperature is mostly concentrated, wherein most damage identification methods based on SVM for vibration measurement do not well solve the problems of selection of punishment parameters c and kernel function parameters g, so that the defects of large errors, time consumption for optimization and the like in parameter selection depending on experience are caused, and in the process of damage identification application of traditional algorithms (SVM, BP neural network and the like), data errors caused by model errors, measurement errors and environmental factors cause the defects of prediction accuracy and stability, and fault tolerance and robustness are not ideal.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a method for identifying the damage of the rusted reinforced concrete beam after the fire disaster, the method is characterized in that a combined algorithm for residual correction is superposed on the basis of a PSO-SVM algorithm, the problems can be effectively solved, the algorithm has obvious advantages, the calculation is more accurate, the consumed time is shorter, and the result reliability is higher.
In order to solve the problems, the technical scheme of the invention is as follows:
a method for identifying damage of a rusted reinforced concrete beam after fire disaster is characterized in that damage indexes are identified according to a particle swarm optimization support vector machine method and a residual correction combined algorithm, and the method comprises the following steps: step 1, establishing a damage model of a corroded reinforced concrete beam after fire by using ABAQUS finite element analysis software, selecting a Frequency analysis step, and performing modal analysis by using a Lanczos eigenvalue solver to further obtain modal parameters after fire damage; meanwhile, a static force general analysis step is selected to obtain the residual bearing capacity of the corroded reinforced concrete beam after fire disaster, and the residual rigidity is calculated; step 2, calculating a construction combination parameter A according to the frequency and the vibration mode calculated in the step 1 as combination parameters, and using the construction combination parameter A as an input parameter of a combination algorithm; step 3, selecting the indirect damage index and the direct damage index as output parameters of a combined algorithm; step 4, selecting a radial basis kernel function as a kernel function in the SVM of the combined algorithm, and mapping the original data of the low-dimensional space to the high-dimensional space; step 5, in the combined algorithm, selecting the mean square error and the goodness of fit as precision evaluation indexes to measure the prediction precision of the damage index; and 6, calculating the damage index of the rusted reinforced concrete after the fire disaster according to a particle swarm optimization support vector machine method and a combined algorithm of residual correction.
Preferably, in the step 1, on the basis of modeling, based on a temperature field numerical simulation result, literature values of mass density, elastic modulus and poisson ratio of the concrete and the rusted steel bar after high temperature are selected as parameters, and a rusted reinforced concrete beam damage model after fire is constructed.
Preferably, in the step 1, SPRING2 SPRING units are used for bonding between the steel bar units and the concrete units, and the SPRING unit stiffness of the steel bar unit nodes and the concrete unit nodes in the horizontal direction is determined by an energy equivalence method.
Preferably, in step 2, the calculation formula for constructing the combination parameter a is:
A＝{FR1,FR2,···,FRm；MO1,MO2,···,MOn}
in the formula:
m: order of frequency
n: order of mode shape
FRi: i order frequency of structure
When constructing the combination parameter a, the following principle is followed:
m≤4，n≤4。
preferably, in step 3, the calculation formulas of the indirect damage index and the direct damage index are respectively:
B＝{T,ηs}
C＝{α,β}
wherein B is an indirect damage index, C is a direct damage index, T is an ignition time, etasFor corrosion rate, α is the reduction coefficient of bearing capacity and β is the reduction coefficient of stiffness.
Preferably, in step 5, the calculation formulas of the mean square error and the goodness of fit are respectively:
where MSE is the mean square error, R2Is goodness of fit, yiIs the actual value of the,
is a predicted value, and n is the number of test samples.
Preferably, the step 6 includes the following specific algorithm steps:
(a) establishing a corrosion beam structure model, taking the former three-order frequency vibration mode as an input parameter, taking a direct damage index and an indirect damage index as output parameters, and constructing a training sample;
(b) initializing parameters of a particle swarm algorithm: the population scale, the iteration times, the inertia weight, the learning factor, the position and the speed of each particle and the like are randomly valued;
(c) calculating the fitness value of each particle, wherein the fitness function takes the mean square error;
(d) updating individual extreme value Pbest and group extreme value Gbest of the particles according to the fitness value of the initial particles;
(e) according to the formula
And
updating the speed and the position of the particle, wherein omega is an inertia weight; d is the dimension of the search space, D1, 2. 1,2, …, n; k is the current generation times; vid is the velocity of the particles; c1 and c2 are non-negative constants called acceleration factors; r1 and r2 are distributed in [0,1 ]]A random number in between;
(f) updating individual extreme values and population extreme values according to the particle fitness values in the new population;
(g) judging whether the maximum iteration times or the minimum error precision is met, if so, stopping iteration and outputting an optimal parameter; if not, turning to the step (c);
(h) applying the obtained optimal parameters to the SVM, and training the SVM by using a training set;
(i) obtaining a predicted value of a training set through training, and subtracting the predicted value from an actual value to obtain a corresponding residual error;
(j) then training the same input and residual in the training set to obtain a residual predicted value;
(k) summing the residual prediction value and the prediction value of the initial algorithm to obtain a combined prediction value, and constructing a combined prediction algorithm by using the corresponding input and combined prediction values in the training set to construct a combined prediction model;
(l) Constructing a test sample by using the actual measurement modal parameters, and substituting the test sample into the combined prediction model for verification;
(m) determining whether the obtained output prediction value satisfies MSE of 0.05 or less and R2And (4) being more than or equal to 0.95, if the result is satisfied, outputting the result, and if the result is not satisfied, re-perfecting the model and performing iterative optimization again.
The method for identifying the damage of the rusted reinforced concrete beam after the fire disaster has the following beneficial effects:
1. the invention takes the frequency and the vibration mode sensitive to the structural damage as the combined input parameters for identifying the damage after the fire, thereby being beneficial to improving the prediction precision.
2. Considering damage assessment and reinforcement repair after fire, two sets of damage indexes are selected as output parameters, indirect damage indexes are fire receiving time and corrosion rate, and direct damage indexes are bearing capacity reduction coefficient and rigidity reduction coefficient, wherein the indirect damage indexes correspond to the damage assessment after the fire, and the direct indexes correspond to the reinforcement repair after the fire.
And 3, the damage recognition algorithm of the PSO-SVM and residual error correction solves the problem that the efficiency of manually adjusting parameters is too low in the using process of the SVM, and the problem that local optimal parameters are easy to find in the parameter optimizing process of the PSO-SVM is solved.
4, the PSO-SVM and residual error correction damage identification algorithm has stronger robustness and fault tolerance, improves the convergence rate, greatly reduces the risk of adjusting parameters by experience, and has higher prediction precision and stability.
Drawings
FIG. 1 is a schematic diagram of the stiffness of a spring unit in one embodiment of the present invention;
FIG. 2 is a flow chart diagram of a particle swarm optimization support vector machine method and a residual error correction combined algorithm of the invention;
FIG. 3 is a diagram showing arrangement of beam reinforcing bars and thermocouples in an experimental example;
Detailed Description
In the following, embodiments of the present invention are described in detail in a stepwise manner, which is merely a preferred embodiment of the present invention and is not intended to limit the scope of the present invention, and any modifications, equivalents, improvements, etc. made within the spirit and principle of the present invention should be included in the scope of the present invention.
In the description of the present invention, it should be noted that the terms "upper", "lower", "left", "right", "top", "bottom", "inner", "outer", and the like indicate orientations and positional relationships based on the orientations and positional relationships shown in the drawings, and are only used for describing the present invention and simplifying the description, but do not indicate or imply that the referred device or element must have a specific orientation and a specific orientation configuration and operation, and thus, the present invention is not to be construed as being limited thereto.
A method for identifying damage of a rusted reinforced concrete beam after fire disaster is characterized in that damage indexes are identified according to a particle swarm optimization support vector machine method and a residual correction combined algorithm, and the method comprises the following steps: step 1, establishing a damage model of a corroded reinforced concrete beam after fire by using ABAQUS finite element analysis software, selecting a Frequency analysis step, and performing modal analysis by using a Lanczos eigenvalue solver to further obtain modal parameters after fire damage; meanwhile, a static force general analysis step is selected to obtain the residual bearing capacity of the corroded reinforced concrete beam after fire disaster, and the residual rigidity is calculated; step 2, calculating a construction combination parameter A according to the frequency and the vibration mode calculated in the step 1 as combination parameters, and using the construction combination parameter A as an input parameter of a combination algorithm; step 3, selecting the indirect damage index and the direct damage index as output parameters of a combined algorithm; step 4, selecting a radial basis kernel function as a kernel function in the SVM of the combined algorithm, and mapping the original data of the low-dimensional space to the high-dimensional space; step 5, in the combined algorithm, selecting the mean square error and the goodness of fit as precision evaluation indexes to measure the prediction precision of the damage index; step 6, calculating damage indexes of the rusted reinforced concrete after the fire disaster according to a particle swarm optimization support vector machine method and a combined algorithm of residual correction;
in the step 1, on the basis of modeling, based on a temperature field numerical simulation result, selecting literature numerical values of mass density, elastic modulus and Poisson ratio of the concrete and the rusted steel bar after high temperature as parameters, and constructing a damage model of the rusted reinforced concrete beam after fire; as shown in FIG. 1, the constitutive relation of bonding slip of the rusted steel bar and the concrete after fire (the curve in FIG. 1, i.e. the line of ABCGF) obtained by the previous experiments of the inventor is that S isOABCD＝SDEFGThe residual section F point slip value is the slip value of the intersection point H of the extension line of the descending section CG and the sliding amount coordinate axis, the slope corresponding to the line segment ODE is the rigidity of the spring unit obtained by an energy equivalence method, and the rigidity of the spring unit of the reinforcing steel bar unit node and the concrete unit node in the vertical direction is the maximum value;
in the step 1, SPRING2 SPRING units are adopted for bonding between the steel bar units and the concrete units, and the rigidity of the SPRING units of the steel bar unit nodes and the concrete unit nodes in the horizontal direction is determined by an energy equivalence method;
in the step 2, the calculation formula for constructing the combination parameter a is as follows:
A＝{FR1,FR2,···,FRm；MO1,MO2,···,MOn}
in the formula:
m: order of frequency
n: order of mode shape
FRi: i order frequency of structure
When the combination parameter A is constructed, in consideration of actual engineering, the structural dynamic response test data has the characteristics of strong randomness, small amplitude and easy noise pollution, and the following principle needs to be followed:
m is less than or equal to 4, n is less than or equal to 4; within the numerical range, the test result can be ensured to be effective;
in step 3, the calculation formulas of the indirect damage index and the direct damage index are respectively as follows:
B＝{T,ηs}
C＝{α,β}
wherein B is an indirect damage index, C is a direct damage index, T is an ignition time, etasThe corrosion rate is adopted, alpha is a bearing capacity reduction coefficient, and beta is a rigidity reduction coefficient; considering that the purpose of fire damage identification is to provide theoretical support for damage assessment and reinforcement repair of a structure after fire, the indirect damage index and the direct damage index are selected as output parameters in an algorithm;
in step 5, the calculation formulas of the mean square error and the goodness of fit are respectively as follows:
where MSE is the mean square error, R2Is goodness of fit, yiIs the actual value of the,
is a predicted value, and n is the number of test samples; wherein the MSE approaches 0 and R2A closer to 1 represents a higher prediction accuracy of the algorithm.
In the step 6, the following specific algorithm steps are included, which are shown in detail in fig. 2:
(a) and (3) establishing a corrosion beam structure model, taking the former three-order frequency mode as an input parameter, taking the direct damage index and the indirect damage index as output parameters, and constructing a training sample.
(b) Initializing parameters of a particle swarm algorithm: the population scale, the iteration times, the inertia weight, the learning factor, the position and the speed of each particle and the like are randomly valued;
(c) calculating the fitness value of each particle, wherein the fitness function takes the mean square error;
(d) updating individual extreme value Pbest and group extreme value Gbest of the particles according to the fitness value of the initial particles;
(e) according to the formula
And
updating the speed and the position of the particle, wherein omega is an inertia weight; d is the dimension of the search space, D1, 2. 1,2, …, n; k is the current generation times; vid is the velocity of the particles; c1 and c2 are non-negative constants called acceleration factors; r1 and r2 are distributed in [0,1 ]]A random number in between;
(f) updating individual extreme values and population extreme values according to the particle fitness values in the new population;
(g) judging whether the maximum iteration times or the minimum error precision is met, if so, stopping iteration and outputting an optimal parameter; if not, turning to the step (c);
(h) applying the obtained optimal parameters to the SVM, and training the SVM by using a training set;
(i) obtaining a predicted value of a training set through training, and subtracting the predicted value from an actual value to obtain a corresponding residual error;
(j) then training the same input and residual in the training set to obtain a residual predicted value;
(k) summing the residual prediction value and the prediction value of the initial algorithm to obtain a combined prediction value, and constructing a combined prediction algorithm by using the corresponding input and combined prediction values in the training set to construct a combined prediction model;
(l) Constructing a test sample by using the actual measurement modal parameters, and substituting the test sample into the combined prediction model for verification;
(m) determining whether the obtained output prediction value satisfies MSE of 0.05 or less and R2And (4) being more than or equal to 0.95, if the result is satisfied, outputting the result, and if the result is not satisfied, re-perfecting the model and performing iterative optimization again.
Test example:
a steel reinforced concrete beam test piece of a reduced scale is designed and manufactured, wherein the length of a concrete beam is 3m, the effective length is 2.4m, the section width is 150mm, the height is 300mm, the thickness of a concrete protective layer is 20mm, a C30 commercial concrete pouring test piece is adopted, a tension steel bar of the beam is a HRB400 steel bar with the diameter of 16mm, and a lead is welded and led out at one end of the tension steel bar in the manufacturing process for an electrified accelerated corrosion test. The stand bar adopts the HRB400 reinforcing bar that the diameter is 12mm, and the stirrup adopts the HPB300 reinforcing bar that the diameter is 8mm, and the interval is 100mm, and detailed experimental design parameter is shown in Table 1, wherein B1 roof beam is the comparison roof beam, and roof beam arrangement and thermocouple arrangement are shown in figure 3.
TABLE 1 test design parameters
And carrying out dynamic test on the corroded reinforced concrete beam before and after the fire, selecting modal measuring points as sextant points between the supports, carrying out static loading on the corroded reinforced concrete beam after the fire, and using loading points as trisect points between the supports.
The results of damage identification after fire are shown in tables 2 and 3.
TABLE 2 post-fire Indirect Damage index identification results
Declaring that: t is the equivalent detonation time, TpAnd (5) predicting the equivalent fire explosion time. The equivalent explosion time is the time corresponding to a standard temperature rise curve with the same area under the actual temperature rise curve. EtasIs the corrosion rate, etas,pThe predicted value of the corrosion rate is.
yiIs the actual value of the,
is a predicted value.
TABLE 3 direct damage index identification after fire
Claims (6)
1. A method for identifying damage of a rusted reinforced concrete beam after a fire disaster is characterized by comprising the following steps: the identification method is used for identifying the damage index according to a particle swarm optimization support vector machine method and a residual correction combined algorithm, and comprises the following steps: step 1, establishing a damage model of a corroded reinforced concrete beam after fire by using ABAQUS finite element analysis software, selecting a Frequency analysis step, and performing modal analysis by using a Lanczos eigenvalue solver to further obtain modal parameters after fire damage; meanwhile, a static force general analysis step is selected to obtain the residual bearing capacity of the corroded reinforced concrete beam after fire disaster, and the residual rigidity is calculated; step 2, calculating a construction combination parameter A according to the frequency and the vibration mode calculated in the step 1 as combination parameters, and using the construction combination parameter A as an input parameter of a combination algorithm; step 3, selecting the indirect damage index and the direct damage index as output parameters of a combined algorithm; step 4, selecting a radial basis kernel function as a kernel function in the SVM of the combined algorithm, and mapping the original data of the low-dimensional space to the high-dimensional space; step 5, in the combined algorithm, selecting the mean square error and the goodness of fit as precision evaluation indexes to measure the prediction precision of the damage index; step 6, calculating damage indexes of the rusted reinforced concrete after the fire disaster according to a particle swarm optimization support vector machine method and a combined algorithm of residual correction;
the step 6 comprises the following specific algorithm steps:
(a) establishing a corrosion beam structure model, taking the former three-order frequency vibration mode as an input parameter, taking a direct damage index and an indirect damage index as output parameters, and constructing a training sample;
(b) initializing parameters of a particle swarm algorithm: the population scale, the iteration times, the inertia weight, the learning factor, the position and the speed of each particle and the like are randomly valued;
(c) calculating the fitness value of each particle, and taking the mean square error as a fitness function;
(d) updating individual extreme value Pbest and group extreme value Gbest of the particles according to the fitness value of the initial particles;
(e) according to the formula
And
updating the speed and the position of the particle, wherein omega is an inertia weight; d is the dimension of the search space, D1, 2. 1,2, …, n; k is the current generation times; vid is the velocity of the particles; c1 and c2 are non-negative constants called acceleration factors; r1 and r2 are distributed in [0,1 ]]A random number in between;
(f) updating individual extreme values and population extreme values according to the particle fitness values in the new population;
(g) judging whether the maximum iteration times or the minimum error precision is met, if so, stopping iteration and outputting an optimal parameter; if not, turning to the step (c);
(h) applying the obtained optimal parameters to the SVM, and training the SVM by using a training set;
(i) obtaining a predicted value of a training set through training, and subtracting the predicted value from an actual value to obtain a corresponding residual error;
(j) then training the same input and residual in the training set to obtain a residual predicted value;
(k) summing the residual prediction value and the prediction value of the initial algorithm to obtain a combined prediction value, and constructing a combined prediction algorithm by using the corresponding input and combined prediction values in the training set to construct a combined prediction model;
(l) Constructing a test sample by using the actual measurement modal parameters, and substituting the test sample into the combined prediction model for verification;
(m) judging whether the obtained output prediction value meets MSE (mean Square error) of less than or equal to 0.05 and R2And (4) being more than or equal to 0.95, if the result is satisfied, outputting the result, and if the result is not satisfied, re-perfecting the model and performing iterative optimization again.
2. The method for identifying the damage of the rusted reinforced concrete beam after the fire disaster as claimed in claim 1, is characterized in that: in the step 1, on the basis of modeling, literature values of mass density, elastic modulus and Poisson's ratio of the concrete and the corroded steel bar after high temperature are selected as parameters based on a temperature field numerical simulation result, and a corroded reinforced concrete beam damage model after fire is constructed.
3. The method for identifying the damage of the rusted reinforced concrete beam after the fire disaster as claimed in claim 1, is characterized in that: in the step 1, the steel bar units and the concrete units are bonded by SPRING units SPRING2, and the SPRING unit stiffness of the steel bar unit nodes and the concrete unit nodes in the horizontal direction is determined by an energy equivalence method.
4. The method for identifying the damage of the rusted reinforced concrete beam after the fire disaster as claimed in claim 1, is characterized in that: in the step 2, the calculation formula for constructing the combination parameter a is as follows:
A＝{FR1,FR2,…,FRm；MO1,MO2,…,MOn}
in the formula:
m: order of frequency
n: order of mode shape
F, FRi: i order frequency of structure
When constructing the combination parameter a, the following principle is followed:
m≤4，n≤4。
5. the method for identifying the damage of the rusted reinforced concrete beam after the fire disaster as claimed in claim 1, is characterized in that: in step 3, the calculation formulas of the indirect damage index and the direct damage index are respectively as follows:
B＝{T,ηs}
C＝{α,β}
wherein B is an indirect damage index, C is a direct damage index, T is an ignition time, etasFor corrosion rate, α is the reduction coefficient of bearing capacity and β is the reduction coefficient of stiffness.
6. The method for identifying the damage of the rusted reinforced concrete beam after the fire disaster as claimed in claim 1, is characterized in that: in step 5, the calculation formulas of the mean square error and the goodness of fit are respectively as follows:
where MSE is the mean square error, R2Is goodness of fit, yiIs the actual value of the,
is a predicted value, and n is the number of test samples.
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