CN115964913A - Real-time prediction method for damage of fired steel structure based on digital twinning - Google Patents
Real-time prediction method for damage of fired steel structure based on digital twinning Download PDFInfo
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- CN115964913A CN115964913A CN202211473195.2A CN202211473195A CN115964913A CN 115964913 A CN115964913 A CN 115964913A CN 202211473195 A CN202211473195 A CN 202211473195A CN 115964913 A CN115964913 A CN 115964913A
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Abstract
The invention discloses a real-time prediction method for damage of a fired steel structure based on digital twins, which solves the technical problem that in the prior art, firemen mainly rely on experience to evaluate whether buildings collapse or not in a fire so as to have potential safety hazards. The invention comprises the following steps: step 1, constructing a quantitative damage assessment model of a fired steel structure, and training the quantitative damage assessment model of the fired steel structure by adopting a deep learning algorithm; step 2, constructing a steel structure building digital twin body to obtain the temperature and deformation data output of a steel structure fire scene; and 3, inputting the acquired temperature and deformation data into the trained quantitative evaluation model of the damage of the fired steel structure so as to output the current damage index of the steel structure on the fire scene in real time. The invention has scientific and reasonable design, carries out virtual-real mapping on the virtual digital twin body and the physical entity of the building, realizes dynamic real-time interaction to improve the information perception capability of the rescue site, and provides reliable decision support for the fire rescue to grasp the opportunity of attack and evacuation.
Description
Technical Field
The invention belongs to the technical field of fire prediction, and particularly relates to a real-time prediction method for damage of a fired steel structure based on digital twinning.
Background
After the building conflagration takes place, the fire fighter needs in time to rescue, including rescue building inside stranded personnel and attack the fire extinguishing in and alleviate loss of property, if the building structure collapses suddenly at the rescue in-process, will seriously threaten fire fighter and building inside personnel's life safety. Relevant statistical data indicate that the collapse of a burning building has always been one of the most major factors responsible for the casualties of firefighters.
Firefighters currently rely primarily on field observation and experience to assess whether a building in a fire will collapse. However, the empirical judgment may cause sudden collapse to cause a great amount of casualties due to failure of timely giving early warning of collapse, and may also cause delay of internal attack and delay of optimal fire-fighting and rescue fighters due to over-conservative judgment. Because the mechanical property of the material is seriously attenuated at high temperature, the steel structure is easy to collapse under the fire. Therefore, a method capable of performing real-time early warning on the fire response of the steel structure building is urgently needed to be developed, and a basis is provided for fire extinguishing and rescue command decision of a fire scene.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the method for predicting the damage of the fired steel structure in real time based on the digital twinning is provided to at least solve part of technical problems.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
a real-time prediction method for damage of a fired steel structure based on digital twinning comprises the following steps:
step 1, constructing a quantitative damage assessment model of a fired steel structure, and calculating damage indexes of the steel structure in a fire scene by adopting finite element software based on a residual bearing capacity method; taking temperature and deformation data corresponding to each damage index as input, taking the damage index as output, and training a fired steel structure damage quantitative evaluation model by adopting a deep learning algorithm;
step 2, constructing a steel structure building digital twin body to obtain temperature and deformation data of a steel structure fire scene;
and 3, inputting the temperature and deformation data acquired in the step 2 into the trained quantitative evaluation model of the damage of the fired steel structure in the step 1 so as to output the damage index of the current steel structure fire scene in real time.
Preferably, in the step 2, the digital twin body constructed by the steel structure building directly receives the temperature and deformation data monitored by the steel structure fire scene.
Preferably, in the step 2, after the digital twin body of the steel structure building is constructed to receive the temperature data monitored by the steel structure fire scene, the deformation data is calculated according to the temperature data so as to output the temperature and the deformation data.
Further, the steel structure building digital twin body at least comprises a high-efficiency numerical analysis model for establishing steel structure damage on the finite element numerical simulation platform, and steel structure deformation data in a fire scene is obtained after temperature data of steel structure fire scene monitoring is input into the high-efficiency numerical analysis model, and the specific process is as follows: the method comprises the steps of constructing a multi-layer virtual steel structure building, setting typical fire scenes, analyzing temperature field distribution results of different components of the steel structure building through a fire dynamics simulation tool, inputting the temperature field distribution results as loads of steel structure fire response analysis, establishing a high-efficiency analysis model of steel structure damage in LS-DYNA software by adopting a fiber beam unit and a layered shell unit, calculating steel structure damage response processes under different typical fire scenes, and obtaining steel structure damage indexes according to residual bearing capacity under the condition that the steel structure is fired.
Further, recording that all steel beams of the virtual steel structure building are B1, B2, … … and Bk, steel columns are C1, C2, … … and Cm, and floors are S1, S2, … … and Sn, wherein k, m and n are the number of the steel beams, the steel columns and the floors in the virtual steel structure building respectively, setting j typical fire scenes, and recording the equivalent temperature of all the steel beams, the steel columns and the floors in each typical fire scene as Te (k + m + n).
Further, in the step 1, the method for acquiring the temperature and deformation data corresponding to the damage index in the fire scene by using finite element software comprises the following steps:
step 21, constructing a multi-layer steel structure building test entity, wherein a plurality of groups of first sensor groups at least comprising a first thermocouple, a first displacement meter and a first accelerometer are installed on each steel beam in the steel structure building test entity, a plurality of groups of second sensor groups at least comprising a second thermocouple, a second displacement meter and a second accelerometer are installed on each steel column, and a plurality of groups of third sensor groups at least comprising a third thermocouple, a third displacement meter and a third accelerometer are installed on each floor; a plurality of non-contact displacement monitors are arranged outside the test entity;
and 22, transmitting the test data of each thermocouple, each displacement meter, each accelerometer and each displacement monitor to the high-efficiency numerical analysis model in real time for numerical calculation to obtain a temperature field, a displacement field, an acceleration field and a stress field of the test entity.
Further, the non-contact displacement monitor includes, but is not limited to, radar or unmanned aerial vehicle.
Further, the displacement monitor is at least arranged outside a test entity with more combustible substances, high risk or bearing function.
Furthermore, the test data of each thermocouple, displacement meter, accelerometer and displacement monitor are all transmitted to the steel structure building digital twin through 4G.
Furthermore, fourth thermocouples which are in one-to-one correspondence are installed on each steel beam in the steel structure building prediction entity, fifth thermocouples which are in one-to-one correspondence are installed on each steel column, sixth thermocouples which are in one-to-one correspondence are installed on each floor slab, temperature data monitored by the thermocouples are collected and input into a digital twin of the steel structure building, steel structure deformation data under a fire scene are calculated, and then steel structure damage indexes are predicted through a fired steel structure damage quantitative evaluation model according to the temperature and the deformation data.
Compared with the prior art, the invention has the following beneficial effects:
the method is scientific and reasonable in design, comprises the steps of constructing a steel structure building digital twin body, establishing a high-efficiency numerical analysis model of steel structure damage on a finite element numerical simulation platform, and realizing rapid calculation of steel structure response in a fire scene; constructing a steel structure building entity sensing network, arranging a thermocouple on each beam, column and plate member, and transmitting temperature data to a remote platform in real time by adopting a wireless acquisition module; the method comprises the steps of constructing a quantitative damage assessment model of the fired steel structure based on deep learning, inputting the temperature and deformation of a component as a model, outputting the damage index based on the residual bearing capacity of the structure as the model, and training the quantitative damage assessment model of the fired steel structure by adopting a deep learning algorithm. When a steel structure building is in a fire, temperature data monitored by a field thermocouple are transmitted back to a digital twin body in real time, a finite element numerical simulation platform is called to carry out real-time calculation of structure fire response, and the temperature and deformation of a component are used as the input of a deep learning damage assessment model, so that the damage index of the structure is predicted. The virtual-real mapping method carries out virtual-real mapping on the virtual digital twin body and the physical entity of the building, realizes dynamic real-time interaction to improve the perception capability of rescue site information, and provides reliable decision support for fire rescue to grasp attack and evacuation opportunities.
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FIG. 1 is a flow chart of the method of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to the accompanying drawings. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of description and simplicity of description, but do not indicate or imply that the device or element being referred to must have a particular orientation or be constructed and operated in a particular orientation, and thus, it should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In the description of the present invention, it should be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; of course, the connection may be mechanical or electrical; alternatively, they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
At present, the early warning research of structure collapse under fire is still in the starting stage, and the early warning requirement of actual fire field collapse is difficult to meet. Firstly, research results are mostly on the structural member level, and collapse early warning research aiming at a structural system is very rare; secondly, the change rule of key easily-measured parameters in the structural collapse process is not systematically researched, and the correlation corresponding relation between the key easily-measured parameters and the collapse is not clear; and, a real-time early warning judgment method for structural collapse under fire is lacked.
Therefore, aiming at the problems that the steel structure building is damaged and damaged, partially or integrally collapsed under the action of fire, the emergency rescue process is influenced and the life safety of rescuers is threatened even, the invention provides a digital twin-based real-time damage prediction method for a fire-stricken steel structure.
As shown in FIG. 1, the method for predicting the damage of the fired steel structure in real time based on the digital twin comprises the following steps:
step 1, constructing a quantitative damage evaluation model of a fired steel structure, and calculating damage indexes of the steel structure in a fire scene by adopting finite element software based on a residual bearing capacity method; taking temperature and deformation data corresponding to each damage index as input, taking the damage index as output, and training a fired steel structure damage quantitative evaluation model by adopting a deep learning algorithm;
step 2, constructing a steel structure building digital twin body to obtain temperature and deformation data of a steel structure fire scene;
and 3, inputting the temperature and deformation data acquired in the step 2 into the trained quantitative evaluation model of the damage of the fired steel structure in the step 1 so as to output the damage index of the current steel structure fire scene in real time.
In the step 2, the digital twin body of the steel structure building is constructed to directly receive the temperature and deformation data monitored by the steel structure fire scene. After the digital twin body of the steel structure building is constructed to receive the temperature data monitored by the steel structure fire scene, the deformation data is calculated according to the temperature data so as to output the temperature and the deformation data.
The invention discloses a steel structure building digital twin body, which at least comprises a finite element numerical simulation platform, wherein a high-efficiency numerical analysis model of steel structure damage is established on the finite element numerical simulation platform, and steel structure deformation data in a fire scene is obtained after temperature data monitored in a steel structure fire scene is input into the high-efficiency numerical analysis model, and the specific process comprises the following steps: the method comprises the steps of constructing a multi-layer virtual steel structure building, setting typical fire scenes, analyzing temperature field distribution results of different components of the steel structure building through a fire dynamics simulation tool, inputting the temperature field distribution results as loads of steel structure fire response analysis, establishing a high-efficiency analysis model of steel structure damage in LS-DYNA software by adopting a fiber beam unit and a layered shell unit, calculating steel structure damage response processes under different typical fire scenes, and obtaining steel structure damage indexes according to residual bearing capacity under the condition that the steel structure is fired.
The method records that all steel beams of the virtual steel structure building are { B1, B2, … … and Bk }, steel columns are { C1, C2, … … and Cm }, floor slabs are { S1, S2, … … and Sn }, wherein k, m and n are the number of the steel beams, the steel columns and the floor slabs in the virtual steel structure building respectively, j typical fire scenes are set, and the equivalent temperatures of all the steel beams, the steel columns and the floor slabs in each typical fire scene are recorded as Te (k + m + n). And (4) taking the equivalent temperature as the input of a fire dynamics simulation tool, and analyzing the temperature field distribution results of different components of the steel structure building.
In the step 1, the method for acquiring the temperature and deformation data corresponding to the damage index in the fire scene by adopting finite element software comprises the following steps:
step 21, constructing a multi-layer steel structure building test entity, wherein a plurality of groups of first sensor groups at least comprising a first thermocouple, a first displacement meter and a first accelerometer are installed on each steel beam in the steel structure building test entity, a plurality of groups of second sensor groups at least comprising a second thermocouple, a second displacement meter and a second accelerometer are installed on each steel column, and a plurality of groups of third sensor groups at least comprising a third thermocouple, a third displacement meter and a third accelerometer are installed on each floor slab; a plurality of non-contact displacement monitors are arranged outside the test entity;
and step 22, transmitting the test data of each thermocouple, displacement meter, accelerometer and displacement monitor to the high-efficiency numerical analysis model in real time for numerical calculation to obtain a temperature field, a displacement field, an acceleration field and a stress field of the test entity.
The method simultaneously utilizes an entity as a Benchmark model, is used for verifying the damage relation virtually constructed on one hand, and is combined with a virtual body to accumulate damage data required by prediction on the other hand, and establishes the relation among temperature, displacement, acceleration, a wavelet time-frequency graph and a damage index. Under the high temperature environment of fire, except temperature sensor, other sensors all easily because of the high temperature damage, especially the data of displacement sensor can be because of the high temperature inaccurate, for this reason when carrying out the fire building collapse prediction, the displacement volume of building just participates as a compensation data. However, the non-contact displacement monitor is adopted, so that the defects of traditional internal installation can be effectively avoided, the non-contact displacement monitor comprises a radar or unmanned aerial vehicle monitoring method, the non-contact displacement monitor is far away from a high-temperature environment, the monitored data is accurate, and the reference value is high. The displacement monitor is at least arranged outside a test entity with more combustible substances, high risk or bearing function. In order to improve the transmission quality and efficiency of internal data, the test data of each thermocouple, displacement meter, accelerometer and displacement monitor are transmitted to the steel structure building digital twin through 4G. The entity test data of the invention can also refer to the data of the historical fire scene, thus greatly enriching the historical accumulated data and improving the accuracy of subsequent prediction.
When the method adopts a deep learning algorithm to train a quantitative evaluation model of the damage of the fired steel structure, the damage index D under the finite element secondary fire scene is calculated by adopting the following formula, D =1-P r /P 0 . Wherein D represents a steel structure damage index (between 0 and 1, 0 represents no damage, 1 represents damage), P r Indicates the overall bearing capacity, P, of the steel structure after being fired 0 Showing the overall bearing capacity of the steel structure before firing. P r And P 0 The device can be quickly detected according to the existing equipment during the physical test of the multi-layer steel structure building.
According to the method, fourth thermocouples which are in one-to-one correspondence are arranged on each steel beam in a steel structure building prediction entity, fifth thermocouples which are in one-to-one correspondence are arranged on each steel column, sixth thermocouples which are in one-to-one correspondence are arranged on each floor slab, temperature data monitored by the thermocouples are collected and input into a steel structure building digital twin body to calculate steel structure deformation data in a fire scene, and then steel structure damage indexes are predicted through a fire steel structure damage quantitative evaluation model according to the temperature and the deformation data.
The method is scientific and reasonable in design, comprises the steps of constructing a steel structure building digital twin body, establishing a high-efficiency numerical analysis model of steel structure damage on a finite element numerical simulation platform, and realizing rapid calculation of steel structure response in a fire scene; constructing a steel structure building entity sensing network, arranging a thermocouple on each beam, column and plate member, and transmitting temperature data to a remote platform in real time by adopting a wireless acquisition module; the method comprises the steps of constructing a quantitative damage assessment model of the fired steel structure based on deep learning, inputting the temperature and deformation of a component as a model, outputting the damage index based on the residual bearing capacity of the structure as the model, and training the quantitative damage assessment model of the fired steel structure by adopting a deep learning algorithm. When a fire disaster occurs in the steel structure building, temperature data monitored by a field thermocouple are transmitted back to the digital twin body in real time, a finite element numerical simulation platform is called to calculate the structural fire response in real time, the component temperature and deformation are used as the input of a deep learning damage assessment model, and then the damage index of the structure is predicted. The virtual-real mapping method carries out virtual-real mapping on the virtual digital twin body and the physical entity of the building, realizes dynamic real-time interaction to improve the perception capability of rescue site information, and provides reliable decision support for fire rescue to grasp attack and evacuation opportunities.
Finally, it should be noted that: the above embodiments are only preferred embodiments of the present invention to illustrate the technical solutions of the present invention, but not to limit the technical solutions, and certainly not to limit the patent scope of the present invention; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; the modifications or the substitutions do not cause the essence of the corresponding technical solutions to depart from the scope of the technical solutions of the embodiments of the present invention; that is, the technical problems to be solved by the present invention are still consistent with the present invention, and all the modifications or changes made without substantial meaning in the spirit and scope of the present invention should be included in the protection scope of the present invention; in addition, the technical scheme of the invention is directly or indirectly applied to other related technical fields, and the technical scheme of the invention is included in the patent protection scope of the invention.
Claims (10)
1. A real-time prediction method for damage of a heated steel structure based on digital twinning is characterized by comprising the following steps:
step 1, constructing a quantitative damage evaluation model of a fired steel structure, and calculating damage indexes of the steel structure in a fire scene by adopting finite element software based on a residual bearing capacity method; taking temperature and deformation data corresponding to each damage index as input, taking the damage index as output, and training a quantitative damage evaluation model of the fired steel structure by adopting a deep learning algorithm;
step 2, constructing a steel structure building digital twin body to obtain temperature and deformation data of a steel structure fire scene;
and 3, inputting the temperature and deformation data acquired in the step 2 into the trained quantitative evaluation model of the damage of the fired steel structure in the step 1 so as to output the damage index of the current steel structure fire scene in real time.
2. The method for predicting the damage of the fired steel structure based on the digital twin in real time as claimed in claim 1, wherein in the step 2, the constructed steel structure building digital twin directly receives the temperature and deformation data monitored on the steel structure fire scene.
3. The method for predicting the damage of the fired steel structure based on the digital twin in real time as claimed in claim 1, wherein in the step 2, after the digital twin of the steel structure building is constructed to receive the temperature data monitored in the fire scene of the steel structure, the deformation data is calculated according to the temperature data so as to output the temperature and the deformation data.
4. The method for predicting the damage of the fired steel structure in real time based on the digital twin as claimed in claim 3, wherein the digital twin of the steel structure building at least comprises the steps of establishing a high-efficiency numerical analysis model of the damage of the steel structure on a finite element numerical simulation platform, inputting temperature data monitored on a steel structure fire scene into the high-efficiency numerical analysis model to obtain steel structure deformation data under a fire scene, and the specific process is as follows: the method comprises the steps of constructing a multi-layer virtual steel structure building, setting typical fire scenes, analyzing temperature field distribution results of different components of the steel structure building through a fire dynamics simulation tool, inputting the temperature field distribution results as loads of steel structure fire response analysis, establishing a high-efficiency analysis model of steel structure damage in LS-DYNA software by adopting a fiber beam unit and a layered shell unit, calculating steel structure damage response processes under different typical fire scenes, and obtaining steel structure damage indexes according to residual bearing capacity under the condition that the steel structure is fired.
5. The method for predicting damage of the fired steel structure in real time based on the digital twinning as claimed in claim 4, wherein all steel beams of the virtual steel structure building are recorded as { B1, B2, … …, bk }, all steel columns are recorded as { C1, C2, … …, cm }, and floors are recorded as { S1, S2, … …, sn }, wherein k, m, and n are the number of the steel beams, the steel columns, and the floors in the virtual steel structure building respectively, j typical fire scenes are set, and the equivalent temperatures of all the steel beams, the steel columns, and the floors in each typical fire scene are recorded as Te (k + m + n).
6. The method for predicting the damage of the fired steel structure based on the digital twinning in real time as claimed in claim 5, wherein in the step 1, the method for acquiring the temperature and deformation data corresponding to the damage index in the fire scene by adopting finite element software comprises the following steps:
step 21, constructing a multi-layer steel structure building test entity, wherein a plurality of groups of first sensor groups at least comprising a first thermocouple, a first displacement meter and a first accelerometer are installed on each steel beam in the steel structure building test entity, a plurality of groups of second sensor groups at least comprising a second thermocouple, a second displacement meter and a second accelerometer are installed on each steel column, and a plurality of groups of third sensor groups at least comprising a third thermocouple, a third displacement meter and a third accelerometer are installed on each floor; a plurality of non-contact displacement monitors are arranged outside the test entity;
and step 22, transmitting the test data of each thermocouple, displacement meter, accelerometer and displacement monitor to the high-efficiency numerical analysis model in real time for numerical calculation to obtain a temperature field, a displacement field, an acceleration field and a stress field of the test entity.
7. The method for predicting the damage of the fired steel structure based on the digital twin in real time as claimed in claim 6, wherein the non-contact displacement monitor includes but is not limited to radar or unmanned aerial vehicle.
8. The method for predicting the damage of the fired steel structure based on the digital twin in real time as claimed in claim 6, wherein the displacement monitor is at least arranged outside a test entity with high combustibles, high risk or bearing effect.
9. The method for predicting the damage of the digital twin-based fired steel structure in real time as claimed in claim 6, characterized in that the test data of each thermocouple, displacement meter, accelerometer and displacement monitor are transmitted to the digital twin of the steel structure building through 4G.
10. The method for predicting the damage of the fired steel structure based on the digital twin in real time as claimed in claim 3, wherein a fourth thermocouple is installed on each steel beam in the steel structure building prediction entity in a one-to-one correspondence manner, a fifth thermocouple is installed on each steel column in a one-to-one correspondence manner, a sixth thermocouple is installed on each floor in a one-to-one correspondence manner, temperature data monitored by each thermocouple is collected and input into the steel structure building digital twin to calculate steel structure deformation data in a fire scene, and then steel structure damage indexes are predicted through a fired steel structure damage quantitative evaluation model according to the temperature and the deformation data.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116343455A (en) * | 2023-05-30 | 2023-06-27 | 广东广宇科技发展有限公司 | Digital twinning technology-based fire scene collapse risk early warning method |
| CN117131712A (en) * | 2023-10-26 | 2023-11-28 | 南开大学 | Virtual-real combined emergency rescue simulation system and method |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116343455A (en) * | 2023-05-30 | 2023-06-27 | 广东广宇科技发展有限公司 | Digital twinning technology-based fire scene collapse risk early warning method |
| CN116343455B (en) * | 2023-05-30 | 2023-09-26 | 广东广宇科技发展有限公司 | Digital twinning technology-based fire scene collapse risk early warning method |
| CN117131712A (en) * | 2023-10-26 | 2023-11-28 | 南开大学 | Virtual-real combined emergency rescue simulation system and method |
| CN117131712B (en) * | 2023-10-26 | 2024-01-16 | 南开大学 | Virtual-real combined emergency rescue simulation system and method |
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