Fire resistance real-time evaluation system and method based on steel structure

Abstract

The invention relates to the technical field of structural engineering safety monitoring, in particular to a fire resistance real-time evaluation system and method based on a steel structure, and the method comprises the steps: collecting the surface radiation energy of the steel structure through a distributed thermal infrared imager array, and constructing a millimeter-level precision three-dimensional temperature field distribution diagram in combination with an environment temperature compensation algorithm; inputting the temperature field into the phase change correction model, and dynamically calculating a yield strength attenuation coefficient and an elastic modulus reduction coefficient; constructing a thermal-mechanical coupling nonlinear finite element model based on the degradation parameters, iteratively solving the displacement and stress response of the structure under the fire load action by adopting an arc length method, and extracting a buckling critical load spectrum; and according to the ratio of the real-time fire load to the critical load, a fire-resistant safety level signal is generated, and a graded early warning instruction is triggered. According to the invention, thermal-force response whole-process monitoring and intelligent early warning of the steel structure in a fire disaster can be realized, and the system has high spatial resolution, high physical coupling and multi-level response capability.

Description

Technical Field

[0001] This invention relates to the field of structural engineering safety monitoring technology, and in particular to a real-time assessment system and method for the fire resistance performance of steel structures. Background Technology

[0002] With the large-scale application of steel structures in urban high-rise buildings, large-span factories, and transportation infrastructure, their excellent strength-to-mass ratio and structural flexibility have provided significant structural advantages for modern buildings. However, the thermophysical properties of steel deteriorate sharply under high-temperature fire conditions. In particular, when the temperature exceeds 600K, its yield strength and elastic modulus decrease exponentially, which can easily lead to structural instability or even overall collapse.

[0003] In existing technologies, point thermocouples or a small number of thermistor measuring points are often used to monitor the structural temperature, and static thermal performance charts are used to estimate the degradation of the mechanical properties of steel. Then, linear buckling analysis is used to predict the risk of instability. However, such schemes have three main technical problems: (1) the temperature sensing range is limited, and a continuous and complete three-dimensional temperature field cannot be constructed, resulting in insufficient spatial coverage; (2) the material degradation parameters rely on simplified models and do not consider the coupling between phase transformation evolution and local thermally affected zones; (3) the structural analysis is mostly based on linear or quasi-linear assumptions and fails to integrate thermal expansion boundaries with actual fire thermal history, resulting in delayed, coarse, and unresponsive buckling critical load prediction results. Summary of the Invention

[0004] This invention provides a real-time fire resistance performance evaluation system and method based on steel structures, and offers a technical solution for real-time fire resistance performance evaluation that integrates multi-source thermal imaging, high-precision physical modeling and structural response feedback, breaking through the accuracy and practicality bottlenecks of existing methods.

[0005] A real-time fire resistance performance assessment method for steel structures includes the following steps:

[0006] S1: Collect the surface radiation energy of the steel structure by a distributed infrared thermal imager array, and generate a three-dimensional temperature field distribution map with millimeter-level accuracy by combining it with an ambient temperature compensation algorithm;

[0007] S2: Input the three-dimensional temperature field distribution map into the phase transformation correction model to dynamically output the yield strength attenuation coefficient and elastic modulus reduction coefficient of steel at each temperature node;

[0008] S3: Based on the yield strength attenuation coefficient and elastic modulus reduction coefficient, and combined with the thermal expansion constraint boundary conditions of the component, perform iterative solution of the multi-degree-of-freedom buckling critical load spectrum;

[0009] S4: Based on the ratio range of the buckling critical load spectrum and the real-time fire load, generate a fire safety level signal and trigger a graded early warning command.

[0010] Optionally, the ambient temperature compensation algorithm includes a blackbody radiation reference, a smoke transmission compensation module, and a thermal convection correction module, wherein;

[0011] The blackbody radiation reference is used to perform real-time emissivity calibration on the distributed infrared thermal imager array and generate a radiation energy data matrix after emissivity calibration.

[0012] The smoke transmission compensation module is used to dynamically compensate for the radiation energy transmission loss caused by fire smoke through a convolutional neural network, and generate a radiation energy data matrix after smoke transmission compensation.

[0013] The thermal convection correction module is used to eliminate the interference of air thermal convection on radiation energy measurement based on the computational fluid dynamics model, and generate a radiation energy data matrix after thermal convection correction.

[0014] Optionally, S1 includes:

[0015] S11: Synchronously collect the surface radiation energy of the steel structure through a distributed infrared thermal imager array to generate the original radiation energy data matrix;

[0016] S12: Input the original radiation energy data matrix into the ambient temperature compensation algorithm, use a blackbody radiation reference to perform real-time emissivity calibration on the distributed infrared thermal imager array, and output the emissivity-calibrated radiation energy data matrix.

[0017] S13: Input the emissivity-calibrated radiation energy data matrix into the smoke transmission compensation module of the ambient temperature compensation algorithm, dynamically analyze the fire smoke concentration distribution through a convolutional neural network, and output the smoke transmission-compensated radiation energy data matrix.

[0018] S14: Input the radiation energy data matrix after flue gas transmission compensation into the thermal convection correction module of the ambient temperature compensation algorithm, reconstruct the thermal convection boundary layer on the steel structure surface based on the computational fluid dynamics model, and output the radiation energy data matrix after thermal convection correction.

[0019] S15: Convert the thermal convection corrected radiation energy data matrix into a three-dimensional temperature field distribution map with millimeter-level precision.

[0020] Optionally, the conversion of the thermal convection-corrected radiation energy data matrix into a three-dimensional temperature field distribution map with millimeter-level precision satisfies the following relationship:

[0021] ;

[0022] in, This refers to the surface temperature of the steel structure. Three-dimensional coordinate points in a three-dimensional temperature field distribution map with millimeter-level precision The corresponding surface temperature value of the steel structure, The center wavelength, It is the first Planck constant. This is the second Planck constant.

[0023] Optionally, S2 includes:

[0024] S21: Input the output three-dimensional temperature field distribution map with millimeter-level precision into the phase transformation correction model to calculate the real-time temperature change gradient of the steel at each point in three-dimensional space;

[0025] S22: Based on the real-time temperature change gradient, the phase transformation activation region of the steel is dynamically identified by the austenitic phase transformation critical criterion, and a spatial distribution map marking the phase transformation state is generated.

[0026] S23: Couple the phase transition state spatial distribution map with the three-dimensional temperature field distribution map, dynamically solve the steel yield strength attenuation coefficient according to the temperature segment calculation rules, and output the yield strength attenuation coefficient matrix of the entire spatial domain;

[0027] S24: Synchronously couple the phase transition state spatial distribution map and the three-dimensional temperature field distribution map, solve the steel elastic modulus reduction coefficient based on the phase transition-dependent nonlinear reduction rule, and output the elastic modulus reduction coefficient matrix in the entire spatial domain.

[0028] Optionally, S3 includes:

[0029] S31: Map the output yield strength attenuation coefficient matrix and elastic modulus reduction coefficient matrix to each element of the steel structure finite element model, and apply thermal expansion constraint boundary conditions to the components to construct a thermo-mechanical coupled nonlinear finite element model that integrates material performance degradation and thermal expansion effects.

[0030] S32: Based on the aforementioned thermo-coupled nonlinear finite element model, the arc length method is used to control the iteration process. The load increment step size is dynamically adjusted according to the time change rate of the three-dimensional temperature field distribution diagram. The displacement field vector and stress field vector of the steel structure under fire load are obtained by solving the solution.

[0031] S33: Calculate the geometric stiffness matrix and tangent stiffness matrix of the structure in its current state based on the displacement field vector and stress field vector.

[0032] Optionally, S3 further includes:

[0033] S34: Based on the geometric stiffness matrix and tangent stiffness matrix, solve the buckling stability determination equation to obtain the eigenvalues ​​corresponding to each buckling mode of the steel structure;

[0034] S35: When the iteration converges, integrate all buckling mode eigenvalues ​​to generate a buckling critical load spectrum characterizing the critical point of structural instability.

[0035] Optionally, S4 includes:

[0036] S41: Compare the output buckling critical load spectrum with the real-time monitored fire load point by point, and calculate the safety margin ratio of each region of the structure.

[0037] S42: Generate a corresponding fire safety level signal based on the preset threshold range where the safety margin ratio is located. The fire safety level signal includes three states: Level 1 safety, Level 2 safety, and Level 3 safety.

[0038] Optionally, S4 further includes:

[0039] S43: If the fire safety level signal is Level 1, trigger the Level 1 warning command and start the sprinkler cooling system;

[0040] S44: If the fire safety level signal is Level 2, trigger the Level 2 warning command and activate the local support device of the steel structure;

[0041] S45: If the fire safety level signal is Level 3, trigger the Level 3 warning instruction, open the emergency evacuation channel and issue the personnel evacuation instruction.

[0042] A real-time fire resistance performance assessment system based on steel structures is used to implement the aforementioned real-time fire resistance performance assessment method based on steel structures, and includes the following modules:

[0043] Distributed infrared thermal imager array acquisition module: used to synchronously acquire radiation energy data of steel structure surface and generate raw radiation energy data matrix;

[0044] The ambient temperature compensation algorithm processing module includes a blackbody radiation reference, a flue gas transmission compensation module, and a thermal convection correction module. It sequentially performs emissivity calibration, flue gas transmission compensation, and thermal convection correction on the original radiation energy data, outputs the radiation energy data matrix after thermal convection correction, and generates a three-dimensional temperature field distribution map with millimeter-level accuracy.

[0045] Phase transformation correction model calculation module: used to calculate the temperature gradient based on the three-dimensional temperature field distribution map, identify the phase transformation activation region of steel, and solve the yield strength attenuation coefficient matrix and elastic modulus reduction coefficient matrix based on the phase transformation state spatial distribution map and temperature information;

[0046] Thermo-coupled nonlinear finite element modeling module: used to map the yield strength attenuation coefficient matrix and elastic modulus reduction coefficient matrix to the finite element model of steel structure, while applying thermal expansion constraints, using the arc length method to iteratively solve the displacement field and stress field under fire load, generating the geometric stiffness matrix and tangent stiffness matrix, and solving the buckling stability judgment equation to obtain the buckling critical load spectrum.

[0047] Fire safety level assessment module: It is used to compare the buckling critical load spectrum with the real-time fire load, calculate the safety margin ratio, generate fire safety level signal, and trigger early warning response commands such as sprinkler cooling, local support, and emergency evacuation according to the safety level classification.

[0048] The beneficial effects of this invention are:

[0049] This invention acquires surface radiation energy of steel structures using a distributed infrared thermal imager array. It then combines this with a blackbody radiation reference, a smoke transmission compensation module, and a thermal convection correction module to construct an environmental temperature compensation algorithm. This algorithm effectively eliminates measurement noise introduced by infrared emissivity errors, smoke obstruction in the fire scene, and air convection disturbances, ultimately generating a three-dimensional temperature field distribution map with millimeter-level accuracy. This high-precision thermal field input provides a reliable data foundation for subsequent material degradation assessment and buckling criticality modeling, significantly improving the physical reliability and spatial resolution of temperature inversion.

[0050] This invention constructs a phase transformation identification model for steel based on real-time temperature gradient and phase transformation criteria. Combining a piecewise reduction rule across temperature ranges, it dynamically calculates the yield strength attenuation coefficient matrix and elastic modulus reduction coefficient matrix across the entire spatial domain and maps them to three-dimensional finite element model elements. By introducing material property degradation terms and thermal expansion strain boundary conditions into the thermo-coupled nonlinear finite element model, it achieves joint modeling and solving of geometric nonlinearity and material nonlinearity behavior under fire conditions. Compared with traditional calculation methods that only consider static extrapolation of thermal loads, this method can accurately capture abrupt structural displacement changes and buckling precursors during fire heating, improving the timeliness and completeness of buckling critical load prediction.

[0051] This invention proposes a safety margin ratio matrix based on the ratio of the buckling critical load spectrum to the fire load, and generates fire safety level signals according to a set interval, thereby triggering graded response early warning commands. These commands cover measures such as sprinkler cooling, local support, and emergency evacuation, forming a fire control system driven by a closed loop of monitoring, analysis, and response. In particular, through a buckling analysis mechanism that uses the arc length method and thermal history synchronous control, the system can identify structural instability trends in advance, achieving dynamic division of three safety levels and regional linkage control. This significantly reduces the risk of sudden structural collapse due to fire, improving the overall disaster resilience and intelligent response level of the building. Attached Figure Description

[0052] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only for this invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0053] Figure 1 This is a schematic diagram of the method flow according to an embodiment of the present invention;

[0054] Figure 2 This is a schematic diagram of the system flow according to an embodiment of the present invention. Detailed Implementation

[0055] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. It should also be noted that, to make the embodiments more comprehensive, the following embodiments are the best and preferred embodiments, and those skilled in the art can use other alternative methods to implement some well-known technologies; moreover, the accompanying drawings are only for more specific description of the embodiments and are not intended to specifically limit the present invention.

[0056] It should be noted that the use of terms such as "an embodiment," "an embodiment," "an exemplary embodiment," and "some embodiments" in the specification indicates that the described embodiment may include a specific feature, structure, or characteristic, but not every embodiment necessarily includes that specific feature, structure, or characteristic. Furthermore, when a specific feature, structure, or characteristic is described in connection with an embodiment, implementing such a feature, structure, or characteristic in conjunction with other embodiments (whether explicitly described or not) should be within the knowledge of those skilled in the art.

[0057] Generally, terms can be understood at least partly from their use in context. For example, depending at least partly on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in a singular sense, or a combination of features, structures, or characteristics in a plural sense. Additionally, the term "based on" can be understood not necessarily to convey an exclusive set of factors, but rather, alternatively, depending at least partly on the context, to allow for the presence of other factors that are not necessarily explicitly described.

[0058] like Figure 1 As shown, a real-time evaluation method for the fire resistance performance of steel structures is presented.

[0059] The ambient temperature compensation algorithm in S1 includes a blackbody radiation reference, a flue gas transmission compensation module, and a thermal convection correction module.

[0060] The blackbody radiation reference is used to perform real-time emissivity calibration on a distributed infrared thermal imager array and generate an emissivity-calibrated radiation energy data matrix.

[0061] The smoke transmission compensation module is used to dynamically compensate for the radiation energy transmission loss caused by fire smoke through a convolutional neural network, and generate a radiation energy data matrix after smoke transmission compensation.

[0062] The thermal convection correction module is used to eliminate the interference of air thermal convection on radiation energy measurement based on the computational fluid dynamics model, and generate a radiation energy data matrix after thermal convection correction.

[0063] S1 specifically includes:

[0064] S11, Acquisition of Raw Radiation Energy Data Matrix: A distributed infrared thermal imager array is deployed around the surface of the steel structure component to synchronously acquire the radiation energy of the steel structure surface in real time, generating a raw radiation energy data matrix covering the target area, denoted as: ;

[0065] in, This represents the position coordinates of the corresponding infrared pixel in three-dimensional space.

[0066] S12, Emissivity calibration process: [The process is missing here, likely due to a formatting error.] The blackbody radiation reference module in the input ambient temperature compensation algorithm sets the standard blackbody temperature. And record the infrared response value. The spatial emissivity matrix of the current infrared thermal imager array is calculated by ratio. :

[0067] ;

[0068] Then, the radiant energy data matrix after emissivity calibration is calculated in reverse. ,

[0069] ;

[0070] S13, Flue gas penetration compensation treatment: [The following text appears to be incomplete and requires further context: "will..."] The input module is a smoke transmission compensation module. This module incorporates a convolutional neural network model based on a U-Net architecture to perform image regression analysis on the smoke concentration distribution at the fire scene. The model takes emissivity calibration data and smoke obscuration images as input and outputs a smoke transmittance matrix. The result after compensation calculation is as follows:

[0071] ;

[0072] in, This represents the transmittance of flue gas to infrared radiation per unit path.

[0073] S14, Thermal convection correction processing: [The following text appears to be incomplete and requires further context:] Input the thermal convection correction module. This module reconstructs the thermal boundary layer distribution on the steel structure surface based on a three-dimensional computational fluid dynamics (CFD) model. During the simulation, it combines the fire source temperature rise rate, air disturbance velocity field, and thermal buoyancy parameters to decouple natural convection and forced convection components, correcting infrared radiation errors. The final thermal convection-corrected radiation energy data matrix is ​​obtained. :

[0074] ;

[0075] in, This is the compensation term for the estimated thermal convection disturbance.

[0076] S15, Generation of 3D Temperature Field Distribution Map: [The remaining text appears to be incomplete and requires further context.] Converted to a three-dimensional temperature field distribution map with millimeter-level precision, the temperature calculation satisfies the Planck inversion relation:

[0077] ;

[0078] in, Three-dimensional coordinate points in a three-dimensional temperature field distribution map with millimeter-level precision The corresponding surface temperature value of the steel structure, The center wavelength, It is the first Planck constant. This is the second Planck constant.

[0079] S2 includes:

[0080] S21, Calculate the three-dimensional temperature gradient of the steel: output the three-dimensional temperature field distribution map. Input the phase transition correction model and use the central difference algorithm to calculate the real-time temperature change gradient vector of each coordinate point on the steel structure surface in its local neighborhood:

[0081] ;

[0082] The temperature gradient reflects the direction and magnitude of the thermal conduction rate of the structure, and is measured in K / m. Regions with drastic temperature changes often correspond to phase transition sensitive zones, serving as the basis for subsequent identification of active phase transition regions.

[0083] S22, Identifying Phase Transformation Activation Regions: Constructing Austenite Phase Transformation Activation Criteria Based on the Physical Properties of Steel. The criteria for judgment are as follows:

[0084] ;

[0085] in, These are the initiation and completion temperatures of the austenitic phase transformation in steel (e.g., 723K and 873K). This is the temperature gradient threshold. The phase transition activation indicator function has a value of 1, which indicates that the point has entered the phase transition region.

[0086] according to A spatial distribution map of the phase transition state is generated, which serves as a spatial mask for subsequent degradation parameter calculations.

[0087] S23, Solve for the yield strength attenuation coefficient matrix: Within the identified phase transition activation region, and The coupling is used to construct the yield strength attenuation coefficient as a piecewise function. The specific calculation formula is as follows:

[0088] ;

[0089] in, , The piecewise reduction coefficients are used to ultimately output the yield strength attenuation coefficient matrix in the entire space domain. , Represents the three-dimensional coordinates of the yield strength attenuation coefficient matrix of steel in the full spatial domain. The corresponding yield strength attenuation coefficient.

[0090] S24, Solving for the elastic modulus reduction coefficient matrix: The change in elastic modulus has a significant impact on the overall stiffness of the structure. A nonlinear degradation model is used to construct the elastic modulus reduction coefficient, and the expression is as follows:

[0091] ;

[0092] in: As a nonlinear reduction control factor, the final output is the elastic modulus reduction coefficient matrix in the entire space domain. Used for subsequent finite element stiffness matrix construction. This represents the three-dimensional coordinates of the elastic modulus reduction coefficient matrix in the full-space domain. The corresponding elastic modulus reduction factor.

[0093] S3 includes:

[0094] S31, Construct a thermo-coupled nonlinear finite element model: Construct a three-dimensional finite element simulation model that reflects material degradation, thermal expansion, and geometric nonlinear behavior. The operation procedure is as follows:

[0095] (1) Material property mapping: The output full-space domain steel yield strength attenuation coefficient matrix is ​​mapped. Matrix of reduction coefficients for elastic modulus of steel in the whole space domain This is mapped to the spatial coordinates of each element in the finite element model, and the material properties of each element are updated accordingly.

[0096] ;

[0097] ;

[0098] in, These represent the initial yield strength and elastic modulus of the steel before it is affected by heat.

[0099] (2) Thermal expansion strain calculation: combined with the generated three-dimensional temperature field distribution map Calculate the thermal expansion strain at each element location:

[0100] ;

[0101] in, The coefficient of thermal expansion of steel, This serves as a structural reference at room temperature (e.g., 293K).

[0102] (3) Model boundary setting: While ensuring the application of thermal expansion degree of freedom, set the bottom constraints of the structure, node connection relationship, free boundary and other conditions to construct a complete boundary constraint system.

[0103] (4) Model assembly: The above data are summarized to form a thermo-mechanical coupled nonlinear finite element model. This model can characterize the nonlinear geometric deformation and potential buckling evolution caused by material degradation and thermal expansion during the heating process of the structure.

[0104] S32, using the arc-length method iteratively to solve the structural response: After establishing the model, a nonlinear finite element solution process controlled by the arc-length method is used to perform loading analysis on the structure and calculate its response evolution under fire load, including:

[0105] 1. Arc Length Control Principle: To overcome the non-convergence problem of traditional load control methods near the buckling point, the arc length method is introduced as the control variable. This variable adjusts the direction and magnitude of the total load increment, allowing the path to cross the limit point and fully capture the entire process response before and after structural instability.

[0106] 2. Introduction of time correlation: using three-dimensional temperature field distribution maps Rate of change over time:

[0107] ;

[0108] Dynamically adjust arc length path step size This achieves physical coordination between time, temperature, and load.

[0109] 3. Solving for Output: In each increment step, the system iteratively solves for the following two types of response vectors:

[0110] Displacement field vector The total deformation response of each node in three directions;

[0111] Stress field vector The stress state of each element contains six components, among which... This represents the total number of nodes in the structure, and the results are stored for stiffness matrix calculation.

[0112] S33, Constructing the structural stiffness matrix: Using the obtained displacement and stress results, calculate the two types of key stiffness matrices of the structure at each iteration time:

[0113] 1. Geometric stiffness matrix : Represents the structural flexibility stiffness caused by the initial stress state, and is the dominant term in structural stability problems.

[0114] 2. Tangent stiffness matrix It is the derivative of the internal force increment caused by the unit displacement change under the nonlinear state of the material, reflecting the adjustment of the overall response by local nonlinearity.

[0115] Both are in matrix form This forms the basis of the characteristic problems in buckling analysis.

[0116] S34, Solving the buckling stability eigenvalue problem: After determining the structural stiffness response matrix, establish the generalized eigenvalue problem: ;

[0117] in, For the first The eigenvalues ​​corresponding to the first buckling mode, The corresponding buckling mode shape vector reflects the spatial deformation mode of local or global buckling.

[0118] The above eigenvalue problem is solved by using the subspace iteration method or the Arnoldi method. Typically, the first 10 eigenvalues ​​are extracted to cover the instability trend of the main components.

[0119] S35, Integrating buckling modes to generate the buckling critical load spectrum: When the iterative calculation converges and the fire thermal history loading is completed, the system will integrate all the obtained buckling mode eigenvalues. Integrate the data to form the buckling critical load spectrum:

[0120] This spectrum clearly reflects: the instability critical points of different modes; the ranking of buckling sensitivities of each component; and the basic boundary data for subsequent fire safety level classification.

[0121] This spectrum will serve as one of the input variables for calculating the safety margin ratio and triggering early warning commands in S4, enabling refined and intelligent identification of structural health status.

[0122] S4 includes:

[0123] S41, Calculate the structural safety margin ratio: This involves analyzing the output buckling critical load spectrum. The structural disaster load spectrum collected in real time by the fire monitoring system Perform point-by-point comparison. Define the structure. Safety margin ratio of region or mode for:

[0124] ;

[0125] in, The initial design loads of the structure before the fire. For the first The scaling factor corresponding to the critical buckling load. For the first Current measured fire load values ​​for the area.

[0126] Traverse all regions and construct a spatially distributed safety margin ratio matrix. Used for grade determination.

[0127] S42, Fire safety level classification signal: based on each ratio. The area in question is divided into three safety levels, and a fire safety level signal matrix is ​​constructed. Each position corresponds to the following hierarchical rules:

[0128] Level 1 security: >1.5, response signal X1, sufficient safety margin, monitoring is recommended.

[0129] Level 2 security: 1.0 < ≤1.5, response signal X2, margin critical, local intervention recommended.

[0130] Level 3 security: ≤1.0, response signal X3, insufficient margin, urgent response required.

[0131] The system encodes the above results into fire safety level signals and generates spatial level maps for each region, which are then used by subsequent triggering mechanisms for identification.

[0132] S43, triggering the first-level warning instruction: When the safety level signal of a certain area is X1, it means that the disaster load in the area is far below the critical level, but has entered the risk monitoring zone.

[0133] The system response mechanism is as follows:

[0134] Automatic linkage spray cooling system;

[0135] Reduce the heat flux density on the structural surface and control the heating rate;

[0136] Simultaneously record the current temperature point and response time;

[0137] It does not affect the structural functionality and is only used for monitoring and response.

[0138] This type of response is called "non-intrusive physical cooling measures" and is suitable for early risk management in long-term fire environments.

[0139] S44, triggering the second-level warning instruction: When the safety level signal of a certain area is X2, it indicates that the current load is close to the buckling critical value and the structure has a risk of local instability.

[0140] The system then executes the following command:

[0141] Activate temporary structural support devices (such as adjustable steel braces).

[0142] Initiate a regional stress unloading strategy to reduce the concentration of thermal stress at nodes;

[0143] A notice was issued to suspend partial construction work.

[0144] The thermal deformation trend map is pushed to the monitoring terminal.

[0145] This type of response is a “structural intervention microcontrol” that prevents initial buckling from extending into overall instability.

[0146] S45: Trigger Level 3 Warning Instruction When the safety level signal of a certain area is X3, it indicates that the current fire load on the structure has reached or exceeded the buckling critical level, and there is a serious risk of instability.

[0147] The system will immediately enter emergency response mode and perform the following operations:

[0148] Trigger the emergency evacuation system:

[0149] Turn on the interlocked emergency lighting and evacuation guidance passages;

[0150] Voice and text prompts are sent to various monitoring terminals inside the building.

[0151] Structural mitigation measures activated:

[0152] Quickly detach from removable structural components (such as vulnerable links);

[0153] Activate the gas extinguishing system to quickly suppress the spread of the fire.

[0154] Data upload and backup:

[0155] Upload the current full-structure safety level diagram to the regulatory platform;

[0156] All stress-temperature-load data are automatically saved for accident analysis.

[0157] This level of response is a "critical control emergency order," designed to ensure personnel safety and maintain structural residual stability to the greatest extent possible.

[0158] like Figure 2 As shown, the real-time fire resistance performance evaluation system based on steel structures is used to implement the above-mentioned real-time fire resistance performance evaluation method based on steel structures, and includes the following modules:

[0159] Distributed infrared thermal imager array acquisition module: used to synchronously acquire radiation energy data of steel structure surface and generate raw radiation energy data matrix;

[0160] The ambient temperature compensation algorithm processing module includes a blackbody radiation reference, a flue gas transmission compensation module, and a thermal convection correction module. It sequentially performs emissivity calibration, flue gas transmission compensation, and thermal convection correction on the original radiation energy data, outputs the radiation energy data matrix after thermal convection correction, and generates a three-dimensional temperature field distribution map with millimeter-level accuracy.

[0161] Phase transformation correction model calculation module: used to calculate the temperature gradient based on the three-dimensional temperature field distribution map, identify the phase transformation activation region of steel, and solve the yield strength attenuation coefficient matrix and elastic modulus reduction coefficient matrix based on the phase transformation state spatial distribution map and temperature information;

[0162] Thermo-coupled nonlinear finite element modeling module: used to map the yield strength attenuation coefficient matrix and elastic modulus reduction coefficient matrix to the finite element model of steel structure, while applying thermal expansion constraints, using the arc length method to iteratively solve the displacement field and stress field under fire load, generating the geometric stiffness matrix and tangent stiffness matrix, and solving the buckling stability judgment equation to obtain the buckling critical load spectrum.

[0163] Fire safety level assessment module: It is used to compare the buckling critical load spectrum with the real-time fire load, calculate the safety margin ratio, generate fire safety level signal, and trigger early warning response commands such as sprinkler cooling, local support, and emergency evacuation according to the safety level classification.

[0164] This invention encompasses any substitutions, modifications, equivalent methods, and solutions made within the spirit and scope of this invention. To provide the public with a thorough understanding of this invention, specific details are described in detail in the following preferred embodiments; however, those skilled in the art will fully understand the invention even without these details. Furthermore, to avoid unnecessary misunderstanding of the essence of this invention, well-known methods, processes, procedures, components, and circuits are not described in detail.

[0165] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for real-time evaluation of the fire resistance performance of steel structures, characterized in that, Includes the following steps: S1: Collect the surface radiation energy of the steel structure by a distributed infrared thermal imager array, and generate a three-dimensional temperature field distribution map with millimeter-level accuracy by combining it with an ambient temperature compensation algorithm; S2: Input the three-dimensional temperature field distribution map into the phase transformation correction model to dynamically output the yield strength attenuation coefficient and elastic modulus reduction coefficient of steel at each temperature node; S3: Based on the yield strength attenuation coefficient and elastic modulus reduction coefficient, and combined with the thermal expansion constraint boundary conditions of the component, perform iterative solution of the multi-degree-of-freedom buckling critical load spectrum; S4: Based on the ratio range of the buckling critical load spectrum and the real-time fire load, generate a fire safety level signal and trigger a graded early warning command.

2. The method for real-time evaluation of fire resistance performance based on steel structures according to claim 1, characterized in that, The ambient temperature compensation algorithm includes a blackbody radiation reference, a flue gas transmission compensation module, and a thermal convection correction module, wherein; The blackbody radiation reference is used to perform real-time emissivity calibration on the distributed infrared thermal imager array and generate a radiation energy data matrix after emissivity calibration. The smoke transmission compensation module is used to dynamically compensate for the radiation energy transmission loss caused by fire smoke through a convolutional neural network, and generate a radiation energy data matrix after smoke transmission compensation. The thermal convection correction module is used to eliminate the interference of air thermal convection on radiation energy measurement based on the computational fluid dynamics model, and generate a radiation energy data matrix after thermal convection correction.

3. The real-time fire resistance performance evaluation method based on steel structures according to claim 2, characterized in that, S1 includes: S11: Synchronously collect the surface radiation energy of the steel structure through a distributed infrared thermal imager array to generate the original radiation energy data matrix; S12: Input the original radiation energy data matrix into the ambient temperature compensation algorithm, use a blackbody radiation reference to perform real-time emissivity calibration on the distributed infrared thermal imager array, and output the emissivity-calibrated radiation energy data matrix. S13: Input the emissivity-calibrated radiation energy data matrix into the smoke transmission compensation module of the ambient temperature compensation algorithm, dynamically analyze the fire smoke concentration distribution through a convolutional neural network, and output the radiation energy data matrix after smoke transmission compensation. S14: Input the radiation energy data matrix after flue gas transmission compensation into the thermal convection correction module of the ambient temperature compensation algorithm, reconstruct the thermal convection boundary layer on the steel structure surface based on the computational fluid dynamics model, and output the radiation energy data matrix after thermal convection correction. S15: Convert the thermal convection corrected radiation energy data matrix into a three-dimensional temperature field distribution map with millimeter-level precision.

4. The real-time fire resistance performance evaluation method based on steel structures according to claim 3, characterized in that, The process of converting the thermal convection-corrected radiation energy data matrix into a three-dimensional temperature field distribution map with millimeter-level precision satisfies the following relationship: ； in, This refers to the surface temperature of the steel structure. Three-dimensional coordinate points in a three-dimensional temperature field distribution map with millimeter-level precision The corresponding surface temperature value of the steel structure, The center wavelength, It is the first Planck constant. This is the second Planck constant.

5. The real-time fire resistance performance evaluation method based on steel structures according to claim 4, characterized in that, S2 includes: S21: Input the output three-dimensional temperature field distribution map with millimeter-level precision into the phase transformation correction model to calculate the real-time temperature change gradient of the steel at each point in three-dimensional space; S22: Based on the real-time temperature change gradient, the phase transformation activation region of the steel is dynamically identified by the austenitic phase transformation critical criterion, and a spatial distribution map marking the phase transformation state is generated. S23: Couple the phase transition state spatial distribution map with the three-dimensional temperature field distribution map, dynamically solve the steel yield strength attenuation coefficient according to the temperature segment calculation rules, and output the yield strength attenuation coefficient matrix of the entire spatial domain; S24: Synchronously couple the phase transition state spatial distribution map and the three-dimensional temperature field distribution map, solve the steel elastic modulus reduction coefficient based on the phase transition-dependent nonlinear reduction rule, and output the elastic modulus reduction coefficient matrix in the entire spatial domain.

6. The method for real-time evaluation of fire resistance performance based on steel structures according to claim 5, characterized in that, S3 includes: S31: Map the output yield strength attenuation coefficient matrix and elastic modulus reduction coefficient matrix to each element of the steel structure finite element model, and apply thermal expansion constraint boundary conditions to the components to construct a thermo-mechanical coupled nonlinear finite element model that integrates material performance degradation and thermal expansion effects. S32: Based on the aforementioned thermo-coupled nonlinear finite element model, the arc length method is used to control the iteration process. The load increment step size is dynamically adjusted according to the time change rate of the three-dimensional temperature field distribution diagram. The displacement field vector and stress field vector of the steel structure under fire load are obtained by solving the solution. S33: Calculate the geometric stiffness matrix and tangent stiffness matrix of the structure in its current state based on the displacement field vector and stress field vector.

7. The method for real-time evaluation of fire resistance performance based on steel structures according to claim 6, characterized in that, S3 further includes: S34: Based on the geometric stiffness matrix and tangent stiffness matrix, solve the buckling stability determination equation to obtain the eigenvalues ​​corresponding to each buckling mode of the steel structure; S35: When the iteration converges, integrate all buckling mode eigenvalues ​​to generate a buckling critical load spectrum characterizing the critical point of structural instability.

8. The method for real-time evaluation of fire resistance performance based on steel structures according to claim 7, characterized in that, S4 includes: S41: Compare the output buckling critical load spectrum with the real-time monitored fire load point by point, and calculate the safety margin ratio of each region of the structure. S42: Generate a corresponding fire safety level signal based on the preset threshold range where the safety margin ratio is located. The fire safety level signal includes three states: Level 1 safety, Level 2 safety, and Level 3 safety.

9. The method for real-time evaluation of fire resistance performance based on steel structures according to claim 8, characterized in that, S4 further includes: S43: If the fire safety level signal is Level 1, trigger the Level 1 warning command and start the sprinkler cooling system; S44: If the fire safety level signal is Level 2, trigger the Level 2 warning command and activate the local support device of the steel structure; S45: If the fire safety level signal is Level 3, trigger the Level 3 warning instruction, open the emergency evacuation channel and issue the personnel evacuation instruction.

10. A real-time fire resistance performance evaluation system based on steel structures, used to implement the real-time fire resistance performance evaluation method based on steel structures as described in any one of claims 1-9, characterized in that, Includes the following modules: Distributed infrared thermal imager array acquisition module: used to synchronously acquire radiation energy data of steel structure surface and generate raw radiation energy data matrix; The ambient temperature compensation algorithm processing module includes a blackbody radiation reference, a flue gas transmission compensation module, and a thermal convection correction module. It sequentially performs emissivity calibration, flue gas transmission compensation, and thermal convection correction on the original radiation energy data, outputs the radiation energy data matrix after thermal convection correction, and generates a three-dimensional temperature field distribution map with millimeter-level accuracy. Phase transformation correction model calculation module: used to calculate the temperature gradient based on the three-dimensional temperature field distribution map, identify the phase transformation activation region of steel, and solve the yield strength attenuation coefficient matrix and elastic modulus reduction coefficient matrix based on the phase transformation state spatial distribution map and temperature information; Thermo-coupled nonlinear finite element modeling module: used to map the yield strength attenuation coefficient matrix and elastic modulus reduction coefficient matrix to the finite element model of steel structure, while applying thermal expansion constraints, using the arc length method to iteratively solve the displacement field and stress field under fire load, generating the geometric stiffness matrix and tangent stiffness matrix, and solving the buckling stability judgment equation to obtain the buckling critical load spectrum. Fire safety level assessment module: It is used to compare the buckling critical load spectrum with the real-time fire load, calculate the safety margin ratio, generate fire safety level signal, and trigger early warning response commands such as sprinkler cooling, local support, and emergency evacuation according to the safety level classification.

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