CN118013823A - Steel structure temperature field real-time monitoring and early warning system based on heat conduction theory - Google Patents
Steel structure temperature field real-time monitoring and early warning system based on heat conduction theory Download PDFInfo
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Abstract
The invention discloses a steel structure temperature field real-time monitoring and early warning system based on a heat conduction theory. According to the invention, by acquiring a three-dimensional model of the steel structure, the necessary measuring points of the steel structure and the historical solar radiation data of the region where the steel structure is located, calculating the simulation value of the temperature stress change coefficient K of each measuring point of the steel structure at different temperatures by using a finite element model; based on the simulation value, carrying out region division on the steel structure measuring points by using a density clustering method and determining the heat conduction weight corresponding to each steel structure measuring point division region; and optimizing an arrangement scheme of the temperature sensor by using a particle swarm optimization method based on the heat conduction weight corresponding to each steel structure measuring point dividing region, and carrying out early warning on stress response change under the action of temperature by using an LSTM error early warning model according to temperature data obtained by monitoring the temperature sensor. The invention improves the rationality of the position of the temperature sensor and the accuracy of temperature stress prediction.
Description
Technical Field
The invention relates to the technical field of monitoring data processing of civil engineering structures, in particular to a steel structure temperature field real-time monitoring and early warning system based on a heat conduction theory.
Background
Spatial steel structures are known as large building structures, which are characterized by their large span, three-dimensional spatial morphology and complex stress characteristics. This structure belongs to a high-order hyperstatic structure, and its temperature effect is a factor that cannot be ignored. The temperature effect is closely related to the thermal expansion coefficient of the structural material, the number of structural hyperstatic times and the non-uniformity of the temperature field distribution. With the ever changing form and span of the spatial steel structure, the effect of temperature effects becomes more and more pronounced. Therefore, the premise of researching the temperature effect of the spatial steel structure is to acquire the temperature field of the structure and the response of the structure under the action of temperature, and the premise of analyzing the temperature effect of the spatial steel structure is to acquire the temperature field of the structure.
At present, the method for acquiring the temperature field of the space steel structure mainly comprises numerical simulation and local simulation and experiment. However, these methods have limitations. Numerical simulation of the overall structure often makes it difficult to fully consider various influencing factors and makes idealized assumptions on parameter values and heat conduction modes. And the analysis results of the partial members are difficult to be directly applied to the analysis of the whole structure.
In recent years, structural health monitoring systems have become a hotspot for research. Such a system can directly monitor the temperature and response data of the structure in a real environment. By arranging the sensors structurally, temperature data and response data can be acquired in real time. However, due to a limited number of sensors or improper placement, the obtained steel structure temperature field data may not be sufficiently comprehensive and accurate, which results in errors in subsequent spatial steel structure temperature effect analysis.
Disclosure of Invention
The invention aims to provide a steel structure temperature field real-time monitoring and early warning system based on a heat conduction theory so as to solve the problems in the background technology.
In order to solve the technical problems, the invention provides a steel structure temperature field real-time monitoring and early warning system based on a heat conduction theory, which comprises the following technical scheme:
Acquiring a three-dimensional model of a steel structure and a steel structure necessary measuring point; the steel structure essential measuring point is a steel structure measuring point with higher early warning degree which is counted by analyzing and counting the test data of the similar steel structure;
Acquiring historical solar radiation data of an area where the steel structure is located, and calculating the simulation value of the temperature stress change coefficient K of each steel structure measuring point at different temperatures by using a finite element model; establishing a finite element model of a steel structure by MIDASGen, and according to working conditions (1.2 times dead weight+1.4 times temperature load), assuming uniform change of a temperature field when setting environmental load, analyzing influence on structural stress change to obtain a simulation value of a temperature stress change coefficient K of a corresponding structural measuring point;
based on the simulation value of the temperature stress change coefficient K of each steel structure measuring point at different temperatures, carrying out region division on the steel structure measuring points by using a density clustering method and determining the heat conduction weight corresponding to each steel structure measuring point division region;
Optimizing an arrangement scheme of the temperature sensor by using a particle swarm optimization method based on the heat conduction weight corresponding to each steel structure measuring point dividing region, and setting the temperature sensor according to the optimized arrangement scheme;
and according to the temperature data obtained by monitoring the temperature sensor, carrying out early warning on stress response change under the action of temperature by utilizing an LSTM error early warning model.
According to the technical scheme, the historical solar radiation data of the region where the steel structure is located is preprocessed, the average number of the historical solar radiation data in the high temperature period in different time periods of the day is calculated by using a statistical analysis method, the average number of the historical solar radiation data in the high temperature period in different time periods of the day is used as a solar radiation simulation value in different time periods of the day, and the simulation value of the temperature stress change coefficient K of each steel structure measuring point in different time periods of the day is calculated by using a finite element model based on the solar radiation simulation value.
According to the invention, historical solar radiation data are preprocessed, and the average value of solar radiation in different time periods of a day is calculated, so that the solar radiation in different time periods of the day is simulated, and the accuracy of calculating the simulation value of the temperature stress change coefficient K is improved.
According to the technical scheme, based on the simulation value of the temperature stress change coefficient K of the steel structure measuring point in each time period, clustering is carried out by using the density clustering method, a steel structure measuring point division area diagram of each time period is obtained, the steel structure measuring point division area diagram of each time period is utilized for carrying out integration, abnormal points are removed, a partition boundary is smoothed, and a final steel structure measuring point division area diagram is obtained. The invention divides the steel structure measuring points by using a density clustering method to obtain a final steel structure measuring point division area diagram, and provides a basis for subsequent heat conduction weight distribution.
According to the technical scheme, the simulated heat conduction rate of each steel structure measuring point dividing region is calculated according to each heat conduction rate formula, the heat conduction rate alpha is taken as the standard heat conduction rate, and the ratio of the simulated heat conduction rate of each steel structure measuring point dividing region to the standard heat conduction rate is calculated as the heat conduction weight corresponding to each steel structure measuring point dividing region;
The heat transfer rate calculation formula V icon:
Wherein, kappa represents the thermal conductivity of the steel structure material, Representing the laplace operator, T ical represents the simulated average temperature corresponding to region i in the three-dimensional model of the steel structure.
According to the above technical solution, the step of optimizing the arrangement scheme of the temperature sensor includes:
S401, initializing particle swarm parameters and randomly initializing the position and the speed of each particle;
s402, judging a steel structure measuring point dividing region where each particle position is located, and updating the speed and the position of each particle;
S403, calculating the fitness of each particle, updating the optimal position of each particle, calculating the shortest distance between the optimal position of each particle and a steel structure essential point closest to the optimal position, judging whether particle variation occurs, determining the positions of the variant particles and the variant particles by using a variation formula if the next moment of particle variation occurs, judging whether iteration conditions are met, outputting an optimal solution if the iteration conditions are met, and jumping to the step S402 to continue execution if the iteration conditions are not met;
S404, if no particle variation occurs, judging whether the iteration condition is met, if so, outputting an optimal solution, and if not, jumping to the step S402 to continue execution.
According to the above technical solution, the velocity update formula v i (t+1):
vi(t+1)＝w·am·vi(t+1)+c1·r1(pi(t)-xi(t))+c2·r2(g(t)-xi(t));
the location update formula x i (t+1):
xi(t+1)＝vi(t+1)+xi(t)；
Wherein r 1 and r 2 are random numbers with the value range of [0,1], the inertia weight omega is more than or equal to 0, the capacity of particles in an algorithm to inherit the last iteration speed is reflected, a m represents the corresponding heat conduction weight of a division m of a steel structure measuring point, c 1 represents an algorithm individual learning factor, c 2 represents an algorithm group learning factor, p i (t) and g (t) are respectively a local optimal solution and a global optimal solution of an algorithm at a specific moment, x i (t) and v i (t) are respectively the position and the speed of a certain particle at any moment, and x i (t+1) and v i (t+1) are respectively the position and the speed of a certain particle at the next moment.
According to the technical scheme, calculating the shortest distance d between the optimal position of each particle and the steel structure essential point closest to the optimal position, when the shortest distance d is smaller than or equal to a threshold value beta, the particles are mutated, and when the shortest distance d is larger than the threshold value beta, the particles are proved not to be mutated;
The positional formula of the particle variation:
Wherein X i (t+1) represents the position of the variant particle where the variant particle is located at the next moment of the particle i, X i (t) represents the position of the particle i at the moment of t, X ri (t) represents the position of the steel structure essential point nearest to the particle i at the moment of t+1, Representing the variation factor. The mutation factor/>Wherein d represents the shortest distance between the optimal position and the point of the steel structure necessary measurement closest to the optimal position.
According to the technical scheme, the historical temperature measurement data of the sensor with the similar steel structure and the corresponding temperature stress are obtained, a training set and a testing set are constructed, and an LSTM temperature stress prediction model is trained by utilizing an LSTM network structure;
And inputting data measured by temperature data obtained by monitoring a temperature sensor into an LSTM temperature stress prediction model to calculate simulated temperature stress, calculating a difference value between the simulated temperature stress and the actual temperature stress, and comparing the difference value with a threshold delta to realize stress response early warning under the action of temperature.
Compared with the prior art, the invention has the following beneficial effects: according to the invention, firstly, the area division is carried out on each steel structure measuring point according to the radiation condition of the sun on the steel structure, then the simulated heat conduction rate of each steel structure measuring point division area is calculated according to each heat conduction rate formula, the heat conduction weight is distributed, the heat conduction weight is used as an influence factor of a particle swarm optimization algorithm, the position of the temperature sensor is optimized through the particle swarm optimization algorithm, so that the position of the temperature sensor is more reasonable, the accuracy of temperature stress prediction is improved, and meanwhile, the LSTM network is trained through historical temperature measurement data and temperature stress data, so that the prediction of the temperature stress is realized. The predicted result is compared with the actual temperature stress, so that stress response early warning under the action of temperature can be realized, and potential safety hazards can be found and prevented in time. The method improves the rationality of the position of the temperature sensor, thereby improving the accuracy of temperature stress prediction, being capable of more accurately predicting the thermal stress change of the steel structure, being beneficial to timely finding and preventing potential safety hazards, having higher accuracy and practicability and being beneficial to improving the safety and reliability of the steel structure.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
FIG. 1 is a flow chart of working steps of a steel structure temperature field real-time monitoring and early warning system based on a heat conduction theory;
FIG. 2 is a flow chart of steps of a sensor optimization method using a particle swarm optimization method according to an embodiment of the present invention;
FIG. 3 is a partial sensor layout position diagram of the steel structure roof of the present embodiment;
FIG. 4 is a graph showing the temperature profiles of points 4-6 and necessary point 2 according to the present embodiment;
FIG. 5 is a graph showing the temperature profiles of the measured and simulated values at points 4-6 and 2 of the present embodiment.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Taking a stadium as an example, optimizing the sensor layout position according to a spatial steel structure, acquiring a temperature field based on measured temperature data, and predicting the thermal stress change of the steel structure to explain the technical scheme: the process flow (figure 1) of the steel structure temperature field real-time monitoring and early warning system based on the heat conduction theory comprises the following steps:
S1, acquiring a three-dimensional model of a steel structure of a stadium and a steel structure necessary measuring point; the steel structure essential measuring points comprise a rod piece middle section, a ring truss position of a hinged support and the like;
S2, acquiring historical solar radiation data of an area where a steel structure is located, preprocessing the historical solar radiation data of the area where the steel structure is located, calculating the average number of the historical solar radiation data in a high temperature period in different time periods of a day by using a statistical analysis method, taking the average number of the historical solar radiation data in the high temperature period in different time periods of the day as solar radiation simulation values in different time periods of the day, establishing a steel structure finite element model based on the solar radiation simulation values by using MIDASGen, and assuming uniform change of a temperature field when setting environmental load according to working conditions (1.2×dead weight+1.4×temperature load) and obtaining a simulation value of a temperature stress change coefficient K of a corresponding structure measuring point by adopting the statistical analysis method;
S3, based on the simulation value of the temperature stress change coefficient K of each steel structure measuring point at different temperatures, carrying out region division on the steel structure measuring points by using a density clustering method and determining the heat conduction weight corresponding to each steel structure measuring point division region, wherein the method specifically comprises the following steps:
Firstly, clustering by using the density clustering method based on the analog value of the temperature stress change coefficient K of the steel structure measuring point in each time period to obtain a steel structure measuring point dividing region diagram of each time period, integrating the steel structure measuring point dividing region diagram of each time period by using Arcgis space, removing abnormal points, and smoothing partition boundaries to obtain a final steel structure measuring point dividing region diagram;
secondly, calculating the simulated heat conduction rate of each steel structure measuring point dividing region according to each heat conduction rate formula, taking the heat conduction rate alpha as a standard heat conduction rate, and calculating the ratio of the simulated heat conduction rate of each steel structure measuring point dividing region to the standard heat conduction rate as the heat conduction weight corresponding to each steel structure measuring point dividing region;
The heat transfer rate calculation formula V icon:
Wherein, kappa represents the thermal conductivity of the steel structure material, Representing the laplace operator, T ical represents the simulated average temperature corresponding to region i in the three-dimensional model of the steel structure.
S4, optimizing an arrangement scheme of the temperature sensor by using a particle swarm optimization method based on heat conduction weights corresponding to the divided areas of each steel structure measuring point, and setting the temperature sensor according to the optimized arrangement scheme, wherein the arrangement scheme specifically comprises the following steps: the arrangement optimization step (fig. 2) of the temperature sensor includes:
S401, initializing particle swarm parameters and randomly initializing the position and the speed of each particle;
s402, judging a steel structure measuring point dividing region where each particle position is located, and updating the speed and the position of each particle;
S403, calculating the fitness of each particle, updating the optimal position of each particle, calculating the shortest distance between the optimal position of each particle and a steel structure essential point closest to the optimal position, judging whether particle variation occurs, determining the positions of the variant particles and the variant particles by using a variation formula if the next moment of particle variation occurs, judging whether iteration conditions are met, outputting an optimal solution if the iteration conditions are met, and jumping to the step S402 to continue execution if the iteration conditions are not met;
S404, if no particle variation occurs, judging whether the iteration condition is met, if so, outputting an optimal solution, and if not, jumping to the step S402 to continue execution.
Wherein the velocity update formula v i (t+1):
vi(t+1)＝w·am·vi(t+1)+c1·r1(pi(t)-xi(t))+c2·r2(g(t)-xi(t));
the location update formula x i (t+1):
xi(t+1)＝vi(t+1)+xi(t)；
Wherein r 1 and r 2 are random numbers with the value range of [0,1], the inertia weight omega is more than or equal to 0, a m represents the corresponding heat conduction weight of a steel structure measuring point dividing region m, c 1 represents an algorithm individual learning factor, c 2 represents an algorithm group learning factor, p i (t) and g (t) are respectively a local optimal solution and a global optimal solution of an algorithm at a specific moment, x i (t) and v i (t) are respectively the position and the speed of a certain particle at any moment, and x i (t+1) and v i (t+1) are respectively the position and the speed of a certain particle at the next moment.
Calculating the shortest distance d between the optimal position of each particle and the steel structure necessary measuring point closest to the optimal position, when the shortest distance d is smaller than or equal to a threshold value beta, the particles are mutated, and when the shortest distance d is larger than the threshold value beta, the particles are proved not to be mutated;
The positional formula of the particle variation:
Wherein X i (t+1) represents the position of the variant particle where the variant particle is located at the next moment of the particle i, X i (t) represents the position of the particle i at the moment of t, X ri (t) represents the position of the steel structure essential point nearest to the particle i at the moment of t+1, Representing the variation factor.
The layout positions of partial sensors of the steel structure roof optimized by the method are shown in fig. 3.
S5, according to the temperature data obtained by monitoring the temperature sensor, carrying out early warning on stress response change under the action of temperature by utilizing an LSTM error early warning model, wherein the method specifically comprises the following steps: acquiring historical temperature measurement data of a sensor with a similar steel structure and corresponding temperature stress thereof, constructing a training set and a testing set, and training an LSTM temperature stress prediction model by utilizing an LSTM network structure;
And inputting data measured by temperature data obtained by monitoring a temperature sensor into an LSTM temperature stress prediction model to calculate simulated temperature stress, calculating a difference value between the simulated temperature stress and the actual temperature stress, and comparing the difference value with a threshold delta to realize stress response early warning under the action of temperature.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Finally, it should be noted that: the foregoing description is only a preferred embodiment of the present invention, and the present invention is not limited thereto, but it is to be understood that modifications and equivalents of some of the technical features described in the foregoing embodiments may be made by those skilled in the art, although the present invention has been described in detail with reference to the foregoing embodiments. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (8)
1. A steel structure temperature field real-time monitoring and early warning system based on a heat conduction theory is characterized by comprising:
Acquiring a three-dimensional model of a steel structure and a steel structure necessary measuring point;
Acquiring historical solar radiation data of an area where the steel structure is located, and calculating the simulation value of the temperature stress change coefficient K of each steel structure measuring point at different temperatures by using a finite element model;
based on the simulation value of the temperature stress change coefficient K of each steel structure measuring point at different temperatures, carrying out region division on the steel structure measuring points by using a density clustering method and determining the heat conduction weight corresponding to each steel structure measuring point division region;
Optimizing an arrangement scheme of the temperature sensor by using a particle swarm optimization method based on the heat conduction weight corresponding to each steel structure measuring point dividing region, and setting the temperature sensor according to the optimized arrangement scheme;
and according to the temperature data obtained by monitoring the temperature sensor, carrying out early warning on stress response change under the action of temperature by utilizing an LSTM error early warning model.
2. The system for monitoring and early warning the temperature field of the steel structure in real time based on the heat conduction theory according to claim 1, wherein the historical solar radiation data of the region where the steel structure is located is preprocessed, the average number of the historical solar radiation data of the high temperature period in different time periods of the day is calculated by utilizing a statistical analysis method, the average number of the historical solar radiation data of the high temperature period in different time periods of the day is used as a solar radiation simulation value of different time periods of the day, and the simulation value of the temperature stress change coefficient K of each steel structure measuring point in different time periods of the day is calculated by utilizing a finite element model based on the solar radiation simulation value.
3. The steel structure temperature field real-time monitoring and early warning system based on the heat conduction theory according to claim 1, wherein the simulation value of the temperature stress change coefficient K of the steel structure measuring points in each time period is clustered by using the density clustering method to obtain a steel structure measuring point division area diagram of each time period, the steel structure measuring point division area diagram of each time period is integrated by using Arcgis space, abnormal points are removed, and a partition boundary is smoothed to obtain a final steel structure measuring point division area diagram.
4. The steel structure temperature field real-time monitoring and early warning system based on the heat conduction theory according to claim 1, wherein,
Calculating the simulated heat conduction rate of each steel structure measuring point dividing region according to each heat conduction rate formula, taking the heat conduction rate alpha as a standard heat conduction rate, and calculating the ratio of the simulated heat conduction rate of each steel structure measuring point dividing region to the standard heat conduction rate as the heat conduction weight corresponding to each steel structure measuring point dividing region;
The heat transfer rate calculation formula V icon:
Wherein, kappa represents the thermal conductivity of the steel structure material, Representing the laplace operator, T ical represents the simulated average temperature corresponding to region i in the three-dimensional model of the steel structure.
5. The steel structure temperature field real-time monitoring and early warning system based on the heat conduction theory according to claim 1, wherein the arrangement scheme optimizing step of the temperature sensor comprises the following steps:
S401, initializing particle swarm parameters and randomly initializing the position and the speed of each particle;
s402, judging a steel structure measuring point dividing region where each particle position is located, and updating the speed and the position of each particle;
S403, calculating the fitness of each particle, updating the optimal position of each particle, calculating the shortest distance between the optimal position of each particle and a steel structure essential point closest to the optimal position, judging whether particle variation occurs, determining the positions of the variant particles and the variant particles by using a variation formula if the next moment of particle variation occurs, judging whether iteration conditions are met, outputting an optimal solution if the iteration conditions are met, and jumping to the step S402 to continue execution if the iteration conditions are not met;
S404, if no particle variation occurs, judging whether the iteration condition is met, if so, outputting an optimal solution, and if not, jumping to the step S402 to continue execution.
6. The system for monitoring and pre-warning the temperature field of the steel structure in real time based on the heat conduction theory according to claim 5, wherein the speed update formula v i (t+1):
vi(t+1)＝w·am·vi(t+1)+c1·r1(pi(t)-xi(t))+c2·r2(g(t)-xi(t));
the location update formula x i (t+1):
xi(t+1)＝vi(t+1)+xi(t)；
Wherein r 1 and r 2 are random numbers with the value range of [0,1], the inertia weight omega is more than or equal to 0, a m represents the corresponding heat conduction weight of a steel structure measuring point dividing region m, c 1 represents an algorithm individual learning factor, c 2 represents an algorithm group learning factor, p i (t) and g (t) are respectively a local optimal solution and a global optimal solution of an algorithm at a specific moment, x i (t) and v i (t) are respectively the position and the speed of a certain particle at any moment, and x i (t+1) and v i (t+1) are respectively the position and the speed of a certain particle at the next moment.
7. The system for monitoring and early warning the temperature field of the steel structure in real time based on the heat conduction theory according to claim 1, wherein the shortest distance d between the optimal position of each particle and the steel structure necessary measuring point closest to the optimal position is calculated, when the shortest distance d is smaller than or equal to a threshold value beta, the particles are mutated, and when the shortest distance d is larger than the threshold value beta, the particles are proved not to be mutated;
The positional formula of the particle variation:
Wherein X i (t+1) represents the position of the variant particle where the variant particle is located at the next moment of the particle i, X i (t) represents the position of the particle i at the moment of t, X ri (t) represents the position of the steel structure essential point nearest to the particle i at the moment of t+1, Representing the variation factor.
8. The steel structure temperature field real-time monitoring and early warning system based on the heat conduction theory according to claim 1, wherein the system is characterized in that the historical temperature measurement data of the sensors of the similar steel structure and the corresponding temperature stress are obtained, a training set and a testing set are constructed, and an LSTM temperature stress prediction model is trained by utilizing an LSTM network structure;
And inputting data measured by temperature data obtained by monitoring a temperature sensor into an LSTM temperature stress prediction model to calculate simulated temperature stress, calculating a difference value between the simulated temperature stress and the actual temperature stress, and comparing the difference value with a threshold delta to realize stress response early warning under the action of temperature.
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