A tunnel fire concrete lining damage evolution prediction method and system

Abstract

This invention provides a method and system for predicting the damage evolution of concrete lining in tunnel fires, relating to the field of computer technology. The method includes: acquiring geometric feature data of the tunnel, fire image data, and high-temperature performance data of the lining concrete; constructing a thermal flow field based on the geometric feature data and fire image data to obtain a three-dimensional transient temperature field; calculating the internal forces of each section based on the three-dimensional transient temperature field and the high-temperature performance data of the material to obtain the real-time internal force state of each section of the lining; determining damage initiation based on the real-time internal force state of each section of the lining to obtain the spatiotemporal distribution of the initial damage zone; performing damage-internal force coupled evolution based on the spatiotemporal distribution of the initial damage zone to obtain the damage evolution path; and generating a safety situation map based on the damage evolution path to obtain a spatiotemporal evolution map of structural risk. This invention significantly improves the accuracy and reliability of predicting the damage evolution process and final safety state of tunnel concrete lining under fire conditions.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and more specifically, to a method and system for predicting the damage evolution of concrete lining in tunnel fires. Background Technology

[0002] With the increasing scale and complexity of tunnel engineering, fire safety assessment has become one of the core challenges in tunnel operation and management. Tunnel fires are characterized by intense combustion, rapid smoke spread, and heat accumulation that is difficult to dissipate. The damage to the concrete lining structure is a complex process involving strong coupling of multiple physical fields, including high-temperature fluid dynamics, material property degradation, structural mechanical response, and damage accumulation evolution. To address the aforementioned problems, Chinese patent CN116522457A discloses a method for predicting the extent of burst damage in tunnel linings after a fire. This method combines model tests and finite element simulations to predict the burst damage range of the lining. However, it focuses on specific burst damage modes and fails to adequately characterize the dynamic coupling process between damage initiation, propagation, and the overall mechanical response of the structure. Another example is Chinese patent CN114547870A, which discloses a method for assessing tunnel damage under fire-explosion conditions based on the material point method. This method uses numerical techniques such as the material point method to assess damage under a fire-explosion cascade disaster. However, it treats the high-temperature effect of the fire as a static reduction of material parameters, failing to accurately simulate the spatiotemporal evolution of the temperature field during the fire and the resulting continuous thermal stress and iterative development of damage. Existing technologies fail to fully consider the constraints of the narrow, confined space of tunnels on fire development and thermal flow fields, and lack characterization of the thermo-mechanical response of the lining structure under both circumferential and longitudinal constraints. This restricts the timeliness and scientific rigor of emergency decision-making during disasters and post-disaster safety assessments.

[0003] Based on the shortcomings of the existing technologies, there is an urgent need for a method and system for predicting the damage evolution of concrete lining in tunnel fires. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for predicting the damage evolution of concrete lining in tunnel fires, thereby improving the aforementioned problems. To achieve the above objective, the technical solution adopted by this invention is as follows:

[0005] In a first aspect, this application provides a method for predicting the damage evolution of concrete lining in tunnel fires, including:

[0006] Acquire geometric feature data of the tunnel, fire image data, and high-temperature performance data of the lining concrete;

[0007] Based on the geometric feature data and the fire image data, a thermal flow field is constructed. By simulating the movement of the fire buoyancy plume in the tunnel space and dynamically calibrating the heat source, a three-dimensional transient temperature field acting on the inner surface of the lining is obtained.

[0008] Based on the three-dimensional transient temperature field and the high-temperature performance data of the material, the internal force of the cross section is calculated. By mapping the instantaneous temperature value of each point in space to the corresponding material performance parameters, the internal force of the cross section caused by the constraint of non-uniform thermal expansion is solved in the structural system considering the circumferential constraint and longitudinal continuity of the lining, so as to obtain the real-time internal force state of each cross section of the lining.

[0009] Damage initiation is determined based on the real-time internal force state of each section of the lining. The damage initiation is determined by comparing the principal stress with the concrete strength threshold at the corresponding temperature, and the spatiotemporal distribution of the initial damage zone is obtained.

[0010] Based on the spatiotemporal distribution of the initial damage zone, damage-internal force coupling evolution is performed. By equating the damage zone with stiffness reduction and iteratively solving for new internal forces and damage propagation, the damage evolution path is obtained.

[0011] A safety situation map is generated based on the damage evolution path. By extracting the spatial distribution of the remaining bearing capacity of the lining section at different times and identifying the area range and time node that drops below the safety threshold, a spatiotemporal evolution map of structural risk is obtained.

[0012] Secondly, this application also provides a tunnel fire concrete lining damage evolution prediction system, comprising:

[0013] The acquisition module is used to acquire geometric feature data of the tunnel, fire image data, and high-temperature performance data of the lining concrete.

[0014] The construction module is used to construct the thermal flow field based on the geometric feature data and the fire image data. By simulating the movement of the fire buoyancy plume in the tunnel space and dynamically calibrating the heat source, a three-dimensional transient temperature field acting on the inner surface of the lining is obtained.

[0015] The calculation module is used to calculate the internal forces of the cross section based on the three-dimensional transient temperature field and the high-temperature performance data of the material. By mapping the instantaneous temperature values ​​of each point in space to the corresponding material performance parameters, the internal forces of the cross section generated by the constraint of non-uniform thermal expansion are solved in the structural system considering the circumferential constraint and longitudinal continuity of the lining, so as to obtain the real-time internal force state of each cross section of the lining.

[0016] The determination module is used to determine the damage initiation based on the real-time internal force state of each section of the lining. The damage initiation is determined by comparing the principal stress with the concrete strength threshold at the corresponding temperature, and the spatiotemporal distribution of the initial damage zone is obtained.

[0017] The evolution module is used to perform damage-internal force coupled evolution based on the spatiotemporal distribution of the initial damage zone. By treating the damage zone as equivalent to stiffness reduction and iteratively solving for new internal forces and damage expansion, the damage evolution path is obtained.

[0018] The generation module is used to generate a safety situation map based on the damage evolution path. By extracting the spatial distribution of the remaining bearing capacity of the lining section at different times and identifying the area range and time node that has dropped below the safety threshold, a spatiotemporal evolution map of structural risk is obtained.

[0019] The beneficial effects of this invention are as follows:

[0020] This invention integrates geometric, fire, and material data to construct a dynamically calibrated three-dimensional transient temperature field. Based on this field, it performs thermo-mechanical coupling analysis in a mechanical system considering the constraints of the lining structure. This enables a full-chain, dynamically coupled prediction from damage initiation determination and damage-internal force iterative evolution to the generation of the final safety profile, significantly improving the accuracy and reliability of predicting the damage evolution process and final safety status of tunnel concrete lining under fire. Attached Figure Description

[0021] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a flowchart illustrating a method for predicting the damage evolution of concrete lining in a tunnel fire, as described in an embodiment of the present invention.

[0023] Figure 2 This is a schematic diagram of the structure of a tunnel fire concrete lining damage evolution prediction system according to an embodiment of the present invention;

[0024] Figure 3 This is a schematic diagram of the structure of a tunnel fire concrete lining damage evolution prediction device as described in an embodiment of the present invention.

[0025] Figure 4 This is the elastic modulus decay curve;

[0026] Figure 5 The compressive strength decay curve;

[0027] Figure 6 This is the tensile strength decay curve.

[0028] The diagram is labeled as follows: 800, a device for predicting the evolution of damage to concrete lining in tunnel fires; 801, processor; 802, memory; 803, multimedia component; 804, I / O interface; 805, communication component; 901, acquisition module; 902, construction module; 903, calculation module; 904, judgment module; 905, evolution module; 906, generation module. Detailed Implementation

[0029] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0030] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this invention, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0031] In real-world post-accident assessments of tunnel fires, such as vehicle fires within tunnels, the high-temperature smoke lingers and circulates for extended periods in the confined space, subjecting the inner surface of the concrete lining to prolonged and non-uniform thermal shock. Due to the strong circumferential constraints of the surrounding rock and the longitudinal continuity of the lining structure, this non-uniform thermal expansion triggers a highly complex redistribution of internal forces. Simultaneously, the mechanical properties of concrete deteriorate significantly at high temperatures, and once damage occurs, it alters local stiffness, further redistributing internal forces, resulting in a complex evolutionary process of continuous coupling and mutual feedback among heat, force, and damage. Post-disaster, to accurately assess structural safety and provide a basis for repair and reinforcement decisions, it is crucial to precisely understand the spatial distribution and evolution path of the damage caused by the fire, as well as the final state of remaining load-bearing capacity. However, existing assessment methods often rely on empirical damage observation and simplified static calculations, making it difficult to reconstruct and quantify the aforementioned dynamically coupled physical processes. This leads to significant uncertainties in assessing the extent of damage, the propagation mechanism, and the overall structural safety reserve, potentially resulting in substantial economic waste due to conservative repair plans or long-term safety hazards caused by insufficient repairs. The method described in this invention is precisely designed to address this critical technical need for post-disaster assessment and decision support.

[0032] Example 1:

[0033] This embodiment provides a method for predicting the damage evolution of concrete lining in tunnel fires.

[0034] See Figure 1 The figure shows that the method includes steps S100 to S600.

[0035] Step S100: Obtain the geometric feature data of the tunnel, fire image data, and high-temperature performance data of the lining concrete.

[0036] It should be noted that geometric feature data includes the cross-sectional profile dimensions and longitudinal profile parameters defined in the tunnel design drawings. These data collectively constitute the spatial domain of fluid flow and structural stress. Fire image data comes from visible light and thermal imaging videos recorded by the existing monitoring system within the tunnel during the accident, containing raw visual information on flame morphology, location, and the range and speed of smoke diffusion. High-temperature performance data of the lining concrete needs to be obtained through high-temperature tests on core samples taken from the site in the laboratory. The core task is to determine the curves of compressive strength, elastic modulus, and other mechanical parameters of the concrete as a function of temperature at different constant temperature levels. These data define the physical space of the problem, external excitations, and material constitutive properties, respectively, and form the material basis for subsequent numerical analysis. Figures 4-6The figures show the degradation curves of concrete's elastic modulus, compressive strength, and tensile strength as a function of temperature (20~1000℃), obtained through laboratory high-temperature tests in this embodiment. The solid line in the figure represents the fitting model based on the Eurocode 2 standard, and the discrete points represent the experimentally measured data. As can be seen from the figure, when the temperature exceeds 400℃, all three mechanical properties exhibit accelerated degradation. Tensile strength is the most sensitive to high temperatures, retaining only about 15% of its room-temperature value at 800℃. This high-temperature performance data of the material forms the basis for the strength threshold used in subsequent damage assessment.

[0037] Step S200: Construct a thermal flow field based on geometric feature data and fire image data. By simulating the movement of the fire buoyancy plume in the tunnel space and dynamically calibrating the heat source, obtain a three-dimensional transient temperature field acting on the inner surface of the lining.

[0038] Understandably, the narrow and confined geometric space of a tunnel profoundly alters the movement pattern of a fire's buoyant plume, causing its spread to differ from that in open spaces. This step first establishes a set of physical governing equations describing turbulent flow and convective heat transfer within this confined space based on geometric characteristic data, forming a parameterized basic model. Subsequently, using the dynamics of the fire source and the movement information of the smoke front recorded in fire imagery data, key unknown parameters in the model (such as the heat release rate time history) are reverse-identified and dynamically calibrated. This integrates general physical laws with the specific combustion process of this accident, resulting in a three-dimensional transient temperature field that accurately reflects the intensity and history of thermal shock at various points on the inner surface of the lining.

[0039] Step S300: Calculate the internal forces of the cross section based on the three-dimensional transient temperature field and the high-temperature performance data of the material. By mapping the instantaneous temperature values ​​of each point in space to the corresponding material performance parameters, solve the internal forces of the cross section caused by the constraint of non-uniform thermal expansion on the structural system considering the circumferential constraint and longitudinal continuity of the lining, and obtain the real-time internal force state of each cross section of the lining.

[0040] It should be noted that the material properties of concrete, especially its elastic modulus and coefficient of thermal expansion, change significantly with increasing temperature at its location. This step first maps the temperature field to the material parameter field, meaning the material properties at each calculation point are updated in real time based on its transient temperature. Then, considering the inherent circumferential closed constraints of the tunnel lining as a shell structure and the longitudinal continuous constraints between it and adjacent segments, the structural mechanics governing equations are established. Using the non-uniform temperature field and the correspondingly changing material parameter field as input loads, solving these equations calculates the internal forces within the sections caused by the uneven thermal expansion and constrained deformation of the concrete, thus obtaining the true state of axial force, bending moment, and shear force at each section of the lining throughout the fire process.

[0041] Step S400: Determine the damage initiation based on the real-time internal force state of each section of the lining. The damage initiation is determined by comparing the principal stress with the concrete strength threshold at the corresponding temperature, and the spatiotemporal distribution of the initial damage zone is obtained.

[0042] Understandably, concrete failure is typically triggered by tensile or shear stress exceeding its strength at that specific temperature. This step first extracts the principal stress components that best characterize the material's failure risk from the complex real-time internal force state. Simultaneously, a three-dimensional transient temperature field is used to determine the current temperature of each point in space, and the dynamic strength threshold of the concrete at that temperature is mapped based on the material's high-temperature performance data. By comparing the principal stress value at each calculated point with the dynamic strength threshold at the same location and moment, the spatiotemporal location where stress first exceeds strength can be accurately identified. These locations are determined as the damage initiation points, thus obtaining the temporal and spatial distribution of the initial damage zone.

[0043] Step S500: Based on the spatiotemporal distribution of the initial damage zone, the damage-internal force coupling evolution is performed. By equating the damage zone with stiffness reduction and iteratively solving for new internal forces and damage propagation, the damage evolution path is obtained.

[0044] It should be noted that the appearance of the initial damage zone signifies a reduction in material stiffness at that location. This leads to a redistribution of internal forces within the structure, altering the original force flow paths and potentially causing stress concentration in new areas, inducing further damage. This step quantifies the damage zone as a reduction in the material's elastic modulus and updates the material parameter field in the structural mechanics model accordingly. Recalculating the internal force field based on the updated model yields a new internal force state altered by local stiffness degradation. Submitting this new internal force state back into the damage initiation assessment process identifies the expanding damage region. Through iterative processing of the coupled feedback loop—"damage leading to stiffness reduction—stiffness change altering internal force distribution—new internal forces triggering damage expansion"—the system reaches stability or instability, thus fully reproducing the complete evolution from the onset of local damage to the final damage state of the structure.

[0045] Step S600: Generate a safety situation map based on the damage evolution path. By extracting the spatial distribution of the remaining bearing capacity of the lining section at different times and identifying the area range and time node that has dropped below the safety threshold, a spatiotemporal evolution map of structural risk is obtained.

[0046] Finally, the goal of step S600 is to transform the complex physical process of damage evolution into intuitive decision-making information that can be used for engineering safety assessment. The damage evolution path itself is the spatiotemporal distribution of damage variables, which needs to be further transformed into bearing capacity indicators that are of greater concern in engineering. This step first calculates the spatial and temporal distribution field of the remaining bearing capacity of the lining based on the complete damage evolution path and the quantitative relationship between damage, stiffness, and bearing capacity. Subsequently, the calculated remaining bearing capacity is compared with the safe bearing capacity threshold determined in advance based on the design load, identifying the areas where the bearing capacity is lower than the safe threshold and the time point of its first occurrence. This information is integrated into a spatiotemporal evolution map of structural risk, which can clearly show the order of bearing capacity loss in different sections of the tunnel during the fire process, the expansion of the risk area, and the overall evolution of the safety situation, providing a direct basis for post-disaster structural safety assessment and repair and reinforcement decisions.

[0047] Further, step S200 includes steps S210 to S230.

[0048] Step S210: Construct physical control equations based on geometric feature data. By establishing a set of control equations describing the flow and heat transfer of buoyancy-driven fluid constrained by the wall in the narrow space of the tunnel, a basic dynamic model without fire source information is obtained.

[0049] Step S220: Based on the basic dynamic model and fire image data, perform model calibration. By analyzing the spatiotemporal evolution information of flame and smoke fronts in the fire image data, calculate the heat release rate time history parameters that conform to the development law of plume in the tunnel, and substitute the heat release rate time history parameters as dynamic source terms into the basic dynamic model to obtain the tunnel fire model.

[0050] Step S230: Perform numerical analysis of the temperature field based on the tunnel fire model. By calculating the spatiotemporal distribution of gas temperature in the tunnel space, obtain the three-dimensional transient temperature field acting on the inner surface of the lining.

[0051] Specifically, step S210 first addresses the narrow and confined geometric characteristics of the tunnel by establishing a set of governing equations based on the fundamental principles of fluid mechanics and heat transfer. These equations describe the movement of high-temperature flue gas driven by buoyancy and its convection and radiation heat transfer with the tunnel walls. This set of equations defines the general laws governing flow and energy transfer. Preferably, in this embodiment, the governing equations consist of equations for the conservation of mass, momentum, and energy, and the Boussinesq approximation is used to handle the buoyancy effect.

[0052] The mass conservation equation describes the conservation of fluid mass during motion. Under the assumption that fire smoke is a low-speed, variable-density flow, its differential form is expressed as:

[0053] ;

[0054] In the formula, This refers to the local density of the flue gas. For time; As the flue gas velocity vector, its longitudinal component dominates the propagation process of high-temperature flue gas in the tunnel; For divergence operators; This is the symbol for partial differentials.

[0055] The momentum conservation equation describes the balance between the momentum change and the external forces (pressure gradient, viscous force, buoyancy) acting on the flue gas during its movement. Considering the effect of buoyancy, its form is:

[0056] ;

[0057] In the formula, This refers to the local density of the flue gas. For time; This is the flue gas velocity vector; For divergence operators; The symbol is for partial differentials; Fluid pressure, including static pressure and dynamic pressure; The dynamic viscosity of flue gas characterizes fluid viscosity and affects the uniformity of velocity distribution. For reference density, the density of ambient air at room temperature, away from a fire source, is used in this embodiment, such as 1.2 kg / m³. 3 , used to calculate density difference; This is the gravitational acceleration vector.

[0058] The energy conservation equation describes the changes in the thermal energy of flue gas, including convection, conduction, radiation, and heat generation from chemical reactions. When considering a simplified model of radiative heat transfer, its form is:

[0059] ;

[0060] In the formula, This refers to the local density of the flue gas. For time; This is the flue gas velocity vector; For divergence operators; The symbol is for partial differentials; The specific heat capacity of flue gas at constant pressure; Flue gas temperature is a direct physical quantity for assessing the thermal shock to the lining. The thermal conductivity of the flue gas; is the radiative heat flux vector, representing the transfer of radiative energy caused by temperature difference; As a volumetric heat source, it characterizes the rate at which heat is released in a combustion chemical reaction.

[0061] Step S220 then uses fire image data to correct the model. By analyzing the dynamic changes of the flame area and the spread trajectory of the smoke front in the video sequence, the law of heat release rate changing with time in this specific fire is deduced in reverse. This process quantitatively converts visual observation information into input parameters for the physical model, and injects these dynamic heat source parameters as boundary conditions into the aforementioned basic dynamic model, thereby obtaining a high-fidelity fire model that accurately describes the actual combustion and smoke spread process in the tunnel. In a preferred embodiment, the visual detection algorithm process described in step S220 is as follows:

[0062] First, spatiotemporal features are extracted synchronously from the input fire image sequence. Addressing the common characteristics of tunnel monitoring videos, such as fixed viewing angles, uneven lighting, and smoke interference, the algorithm processes two information streams in parallel: In the color-frequency domain stream, pixels are clustered in the HSV color space, and potential flame areas are initially segmented based on the high-intensity emission characteristics of flames in the red and yellow light bands and their unique flicker frequency (typically 1-10 Hz); In the motion-texture stream, pixel motion vector fields are calculated using multi-frame difference and optical flow methods, and combined with the unique non-rigid, diffuse texture features of smoke areas (such as characterization through local binary mode variance), smoke-permeable areas are initially identified.

[0063] Next, dynamic information fusion and trajectory tracking based on the geometric constraints of the tunnel scene are performed. Since the tunnel is a long and narrow structure, the algorithm uses the camera's calibration parameters and the tunnel's approximate orientation to back-project the initially segmented two-dimensional region onto a simplified three-dimensional model of the tunnel (such as a longitudinally extending rectangular pipe) for verification. For example, the flame must adhere to the height range of the "road surface" or "vehicle," while the smoke front should spread longitudinally along the arch area. Under this physical space constraint, for the verified flame region in each frame, its total area, centroid height, and equivalent diameter are calculated; for the smoke region, its contour at the farthest point along the tunnel's longitudinal direction (i.e., the smoke front position) and its lateral width occupying the arch are extracted. Subsequently, in the simplified three-dimensional model, these feature points (such as the flame centroid and the smoke front center point) are correlated across frames and their trajectories are tracked, thereby outputting time-series data of physical quantities such as flame area time history, flame center height time history, smoke front position time history, and smoke layer height time history.

[0064] Finally, inverse physics deduction is performed to calculate the heat release rate time history. This process uses the time-series data obtained in the previous step as observations and substitutes them into an inverse model that incorporates the basic physical laws of tunnel plumes. The core of this inverse model is to establish the following optimization problem: to find an optimal heat release rate time history function such that when this function is used as input to drive a simplified, parameterized tunnel plume physical model (e.g., a region-based model or an improved "two-zone model" whose governing equations have been embedded in the key simplified form of the basic dynamic model obtained in step S210), the overall error between the key indicators such as the average flame height and the smoke front spread velocity predicted by the simplified model and the corresponding physical quantity time-series data tracked from the image is minimized. By solving this optimization problem, the final output is a time-varying sequence of heat release rate parameters that matches this specific fire process, completing the quantitative conversion from visual information to key driving parameters of the physical model.

[0065] Step S230, based on the corrected fire model, numerically solves the fluid motion and energy equations covered by the model, which already include specific dynamic heat source conditions, to calculate the three-dimensional dynamic distribution of gas temperature throughout the tunnel space from the occurrence to development of the fire. Finally, it outputs the heat load acting on the inner surface of the lining as it changes over time, providing a real temperature input for subsequent thermo-mechanical coupling analysis.

[0066] Further, step S300 includes steps S310 to S330.

[0067] Step S310: Based on the three-dimensional transient temperature field and the high-temperature performance data of the material, perform temperature-material parameter field mapping. By querying and interpolating the instantaneous temperature values ​​of each point in space in the high-temperature performance data of the material, the corresponding elastic modulus and thermal expansion coefficient are obtained, and the time-varying field of material parameters at discrete points in space is obtained.

[0068] Step S320: Construct a constraint structure system based on the time-varying field of material parameters. Based on the shell geometry and connection characteristics of the tunnel lining, establish the structural mechanics control equations that reflect the circumferential closed constraint and longitudinal continuous constraint of the tunnel to obtain the parameterized mechanical model of the tunnel lining.

[0069] Step S330: Solve the thermally induced internal force field according to the parameterized mechanical model of the tunnel lining. By taking the time-varying field of material parameters and the three-dimensional transient temperature field as input, numerically solve the structural mechanics control equations, calculate the structural internal forces caused by non-uniform thermal expansion under constraint conditions, and obtain the real-time internal force state of each section of the lining.

[0070] Specifically, step S310 first performs temperature-material parameter field mapping, which associates the temperature value of each spatial location in the three-dimensional transient temperature field at each moment with the material performance database obtained through laboratory high-temperature tests. Through querying and interpolation, the elastic modulus and thermal expansion coefficient corresponding to the instantaneous temperature are dynamically assigned to each calculation point, thereby transforming the continuously changing temperature field into a material property field that also evolves in space and time, accurately characterizing the spatial non-uniformity and time dependence of concrete material performance under high temperature in a fire.

[0071] Based on this, step S320 constructs the constrained structural system. According to the shell geometry of the tunnel lining, circumferential closed constraints and longitudinal continuous constraints are explicitly introduced into the mechanical model to reflect the actual stress state of the tunnel lining under the action of surrounding rock, including its limited circumferential deformation and its coordinated deformation with adjacent segments. The resulting structural mechanics governing equations constitute the computational framework for analyzing the thermo-mechanical coupled response. Specifically, the tunnel lining is considered as a cylindrical thin-shell structure, and the structural mechanics governing equations include the shell equilibrium equations, circumferential closed constraints, and longitudinal continuous constraints.

[0072] The shell equilibrium equations describe the equilibrium relationship of a shell element under normal force, bending moment, and external loads, and are in the form of:

[0073] ;

[0074] In the formula, The bending moment per unit width in the longitudinal direction of the lining shell; The bending moment per unit width in the circumferential direction of the lining shell; Torque for the lining shell; The axial force (membrane force) per unit width in the circumferential direction of the lining shell. The radius of the mid-curvature surface of the tunnel lining; For the normal distributed load acting on the outer surface of the lining shell; The longitudinal coordinate of the shell; The circumferential angular coordinates of the shell; This is the symbol for partial differentials.

[0075] The circumferential closure constraint requires that the lining be a continuous closed loop in the circumferential direction, in the form of:

[0076] ;

[0077] ;

[0078] In the formula, The displacement vector of a point on the curved surface in the shell contains three components: radial, circumferential, and longitudinal. The longitudinal coordinate of the shell; The circumferential angular coordinates of the shell; The symbol is for partial differentials; Pi is the mathematical constant of a circle.

[0079] Longitudinal continuity constraints are used to simulate the interaction between the lining and adjacent segments or surrounding rock in the longitudinal direction. In this embodiment, a spring model is used to represent the constraint effect of the joint or surrounding rock, in the form of:

[0080] ;

[0081] In the formula, The longitudinal connection constraint stiffness matrix is ​​a diagonal matrix, whose diagonal elements represent the constraint stiffness of the connection for axial displacement, shear displacement and rotation angle, respectively, and the values ​​depend on the joint type or the surrounding rock properties. This is the relative displacement and rotation vector connecting the two sides of the interface; The vectors of internal forces and bending moments transmitted through the connection interface.

[0082] Step S330 finally solves the thermally induced internal force field. The time-varying field of material parameters and the three-dimensional transient temperature field obtained in the previous steps are simultaneously used as input loads and imported into the constructed parameterized mechanical model of the tunnel lining. By numerically solving the governing equations, the complex internal forces generated due to the different thermal expansion of the material at each point and the strong constraint of this expansion deformation are calculated. Thus, the real-time internal force state of the lining structure at each section during the entire fire process is output, such as axial force, bending moment and shear force, providing a direct mechanical state input for subsequent damage initiation determination.

[0083] Further, step S400 includes steps S410 to S430.

[0084] Step S410: Extract mechanical indices based on the real-time internal force state of each section of the lining. By calculating the magnitude and direction of the principal stress of each material element in three-dimensional space, obtain the principal stress field characterizing the degree of stress danger at the material point.

[0085] Step S420: Based on the principal stress field and the three-dimensional transient temperature field, construct a temperature-dependent strength threshold field. By mapping the instantaneous temperature value of each spatial point in the three-dimensional transient temperature field to the compressive strength of concrete at that temperature based on the material's high-temperature performance data, a dynamic strength threshold field corresponding to the spatiotemporal temperature field is obtained.

[0086] Step S430: Determine the spatiotemporal initiation of damage based on the principal stress field and the dynamic intensity threshold field. By comparing the principal stress value with the dynamic intensity threshold at the same location and time at each spatiotemporal point, the unit where the principal stress first exceeds the intensity threshold is marked as the damage initiation, and the spatiotemporal distribution of the initial damage zone is obtained.

[0087] Specifically, step S410 first extracts key mechanical indicators from the real-time internal force state of each section of the lining. That is, by calculating the magnitude and direction of the principal stresses of each material element in three-dimensional space through tensor transformation, the internal forces (axial force, bending moment, shear force) of the section are converted into a set of principal stress fields that can directly characterize the tensile or shear critical state of the material points. This provides a bridge from the macroscopic response of the structure to the judgment of local material failure. On this basis, step S420 constructs a temperature-dependent strength threshold field. The temperature value of each spatial point at each moment in the three-dimensional transient temperature field is used as a query condition to input into the high-temperature performance database of the material to obtain the compressive and tensile strengths of concrete at that specific temperature. This generates a dynamic strength threshold field that is synchronized with the temperature field in both spatial distribution and temporal evolution, accurately reflecting the characteristic that the strength of concrete material degrades significantly with increasing temperature. Step S430 finally performs damage initiation spatiotemporal determination. At each same spatial location and time node, the principal stress values ​​in the principal stress field are compared with the threshold values ​​in the dynamic intensity threshold field point by point and time by time. Based on the maximum principal stress theory or the Mohr-Coulomb criterion, the element whose principal stress value first exceeds the dynamic intensity threshold at that point and time is determined as the damage initiation point. In this way, the earliest location and time of damage occurrence are marked in spatiotemporal space, and the spatiotemporal distribution of the initial damage area is output, providing clear starting conditions for subsequent damage evolution analysis.

[0088] Further, step S500 includes steps S510 to S530.

[0089] Step S510: Reconstruct the damage-induced stiffness field based on the spatiotemporal distribution of the initial damage zone. By mapping the spatial coordinates of the initial damage zone to the mechanical model of the lining structure, and quantifying the damage state into the reduction coefficient of the elastic modulus of the material at the corresponding location based on the principle of continuous damage mechanics, the updated material parameter field characterizing the local stiffness degradation of the structure is obtained.

[0090] Step S520: Solve the internal force field based on the updated material parameter field. By calculating the redistribution of the internal forces in the lining section under the same temperature load, obtain the new internal force state caused by the change in the internal force path due to local stiffness degradation.

[0091] Step S530: Based on the new internal force state, damage expansion and evolution iteration are performed. By determining the damage initiation of the new internal force state, the expanding damage area is identified, and the stiffness field reconstruction and internal force field solution process is repeated until the damage area no longer expands or the structural mechanical response reaches the critical instability condition, thus obtaining the damage evolution path from damage initiation to final failure.

[0092] Specifically, step S510 reconstructs the damage-induced stiffness field based on the spatiotemporal distribution of the initial damage zone. The spatial coordinates of the identified damage zone are mapped to specific elements in the lining structure's mechanical model. Based on the fundamental principles of continuous damage mechanics, the abstract damage state is quantified into a damage variable between 0 and 1. This variable is then converted into a reduction coefficient of the material's elastic modulus at the corresponding location, resulting in an updated material parameter field. This material parameter field characterizes the local stiffness degradation caused by damage. Step S520 performs a new round of internal force field calculation based on the updated material parameter field. This process involves substituting the parameter field, which includes the locally reduced material properties, into the structural mechanics control equations while maintaining the original three-dimensional transient temperature field as the thermal load. The new internal force state is calculated after the change in the internal force path of the entire structure due to the reduction in stiffness in the local area. This reflects the mechanical feedback that "damage changes stiffness, and stiffness changes guide the redistribution of internal forces." Step S530 finally performs damage expansion and evolution iteration, taking the obtained new internal force state as input, and performs damage initiation judgment again, thereby identifying the newly emerging damage area under the changed internal force state, that is, the expanded part of the damage. Then, the expanded damage area information is returned to step S510 to start a new round of stiffness field reconstruction and internal force field solution. This process is repeated in a loop to simulate the dynamic process of continuous interaction and alternating development of damage and internal force, until no new damage is generated within a certain time step or the overall mechanical response of the structure (such as displacement and internal force) reaches the critical condition for predicting instability. Thus, a complete evolution path from the initial damage initiation to the final damage state of the structure is finally obtained.

[0093] Further, step S600 includes steps S610 to S630.

[0094] Step S610: Extract the bearing capacity field according to the damage evolution path. By quantifying the damage state at each spatiotemporal point in the damage evolution path into a reduction factor for the axial and bending stiffness of the lining section, and calculating the remaining bearing capacity of the section after damage, the spatiotemporal distribution field of the remaining bearing capacity of the lining is obtained.

[0095] Step S620: Identify risk areas based on the spatiotemporal distribution field of the remaining bearing capacity of the lining. By comparing the value of each point in the spatiotemporal distribution field of the remaining bearing capacity with the preset safe bearing capacity threshold based on the tunnel design load, the spatiotemporal points where the bearing capacity is first lower than the threshold are marked as risk points, and the connected risk points are aggregated to form risk areas, thereby obtaining the spatiotemporal boundary and risk level of the risk areas.

[0096] Step S630: Based on the spatiotemporal boundaries and risk levels of the risk areas, a comprehensive risk map is synthesized. By integrating and encoding risk areas at different times and spatial locations according to risk levels and occurrence sequence, a spatiotemporal evolution map of structural risk that comprehensively reflects the process of load-bearing capacity loss, key weak points, and failure sequence of the tunnel lining throughout the fire is generated.

[0097] Specifically, step S610 extracts the bearing capacity field based on the obtained complete damage evolution path. According to the quantitative relationship between damage variables and material stiffness in continuous damage mechanics, the damage state value at each spatiotemporal coordinate point in the path is mapped to specific reduction coefficients for the axial stiffness and bending stiffness of the lining section. Then, based on structural mechanics principles, the maximum load that the section can still withstand under this damage state is calculated, thus obtaining a distribution field reflecting the dynamic decay of the lining's remaining bearing capacity in space and time. Step S620 identifies risk areas based on this. The process involves comparing the bearing capacity value at each point in the calculated spatiotemporal distribution field of remaining bearing capacity with a pre-determined safe bearing capacity threshold based on tunnel design specifications and operating loads. When the remaining bearing capacity at a point first falls below the threshold, that spatiotemporal point is marked as a risk point. A spatial clustering algorithm is then used to aggregate adjacent risk points with continuous risk states, forming a risk region with a clear spatial range and temporal starting point. Simultaneously, the risk level can be divided according to the extent to which the bearing capacity falls below the threshold, thus obtaining the spatiotemporal boundary of the risk region and the corresponding risk level information. Step S630 concludes with the synthesis of a comprehensive risk map. This process involves color-coding or using legends to encode the risk areas identified in the preceding steps according to their risk levels, and then temporally associating and overlaying them based on their chronological order of appearance. The result is a comprehensive and intuitive structural risk spatiotemporal evolution map that shows the sequence of load-bearing capacity loss, spatial evolution patterns, key weak points, and the entire process of overall safety degradation of the tunnel lining during a fire. This provides quantitative and visual decision-making support for post-disaster assessment.

[0098] Further, step S610 includes steps S611 to S613.

[0099] Step S611: Analyze the damage state parameters according to the damage evolution path. By extracting the values ​​of continuous damage variables that characterize the degradation of material properties in the spatial and temporal dimensions from the damage evolution path, the spatiotemporal evolution matrix of the damage degree at each point is obtained.

[0100] Step S612: Map the damage-stiffness reduction relationship based on the spatiotemporal evolution matrix. By establishing a quantitative mapping function between damage variables and material elastic modulus, each damage value in the spatiotemporal evolution matrix is ​​converted into the corresponding axial stiffness and bending stiffness reduction coefficients, and the spatiotemporal distribution field of the reduction coefficients is obtained.

[0101] Step S613: Calculate the bearing capacity based on the spatiotemporal distribution field of the reduction factor. Solve for the maximum resistance that the section can provide when bearing the design load combination based on the principle of structural mechanics, and obtain the spatiotemporal distribution field of the remaining bearing capacity of the lining.

[0102] Specifically, step S611 first performs damage state parameter analysis on the determined damage evolution path, extracting values ​​recorded as continuous damage variables from the path. These values ​​are organized according to their corresponding spatial coordinates and time nodes to form a three-dimensional spatiotemporal evolution matrix. This matrix completely stores the damage degree information of each point inside the lining structure at different fire times. Step S612 performs damage-stiffness reduction relationship mapping based on this spatiotemporal evolution matrix. Applying the quantitative mathematical relationship established in continuous damage mechanics and verified by experiments, each damage variable value in the matrix is ​​converted into a specific reduction coefficient for the axial stiffness and bending stiffness of the lining section. This conversion process reflects the physical essence of the influence of the accumulation of microscopic defects in the material on the macroscopic structural stiffness, thereby outputting a stiffness reduction coefficient distribution field with the same spatiotemporal dimension. Step S613 finally calculates the bearing capacity based on the spatiotemporal distribution field of the reduction coefficient. The coefficients in the distribution field are applied to the constitutive relation of the lining section. Under the condition of considering the specific stress mode and boundary constraints of the tunnel lining, the maximum resistance that the section can provide when bearing the combination of permanent load and variable load specified in the code is solved through structural mechanics analysis. Finally, the spatiotemporal distribution field of the remaining bearing capacity, which reflects the dynamic decay process of the actual bearing capacity of the lining after the fire, is calculated, and the quantitative mapping from physical damage state to engineering performance index is completed.

[0103] Example 2:

[0104] like Figure 2 As shown, this embodiment provides a tunnel fire concrete lining damage evolution prediction system, the system including:

[0105] Acquisition module 901 is used to acquire geometric feature data of the tunnel, fire image data, and high-temperature performance data of the lining concrete.

[0106] The construction module 902 is used to construct the thermal flow field based on geometric feature data and fire image data. By simulating the movement of the fire buoyancy plume in the tunnel space and dynamically calibrating the heat source, a three-dimensional transient temperature field acting on the inner surface of the lining is obtained.

[0107] The calculation module 903 is used to calculate the internal forces of the cross section based on the three-dimensional transient temperature field and the high-temperature performance data of the material. By mapping the instantaneous temperature values ​​of each point in space to the corresponding material performance parameters, the internal forces of the cross section generated by the constraint of non-uniform thermal expansion are solved in the structural system considering the circumferential constraint and longitudinal continuity of the lining, so as to obtain the real-time internal force state of each cross section of the lining.

[0108] The determination module 904 is used to determine the damage initiation based on the real-time internal force state of each section of the lining. The damage initiation is determined by comparing the principal stress with the concrete strength threshold at the corresponding temperature, and the spatiotemporal distribution of the initial damage zone is obtained.

[0109] Evolution module 905 is used to perform damage-internal force coupled evolution based on the spatiotemporal distribution of the initial damage zone. By treating the damage zone as equivalent to stiffness reduction and iteratively solving for new internal forces and damage expansion, the damage evolution path is obtained.

[0110] The generation module 906 is used to generate a safety situation map based on the damage evolution path. By extracting the spatial distribution of the remaining bearing capacity of the lining section at different times and identifying the area range and time node that has dropped below the safety threshold, a spatiotemporal evolution map of structural risk is obtained.

[0111] In one specific embodiment of this application, the construction module 902 includes:

[0112] The first building unit is used to construct physical control equations based on geometric feature data. By establishing a set of control equations describing the flow and heat transfer of buoyancy-driven fluid constrained by the wall in the narrow space of the tunnel, a basic dynamic model without fire source information is obtained.

[0113] The second building unit is used to perform model correction based on the basic dynamic model and fire image data. By analyzing the spatiotemporal evolution information of flame and smoke fronts in the fire image data, the heat release rate time history parameters that conform to the development law of plume in the tunnel are calculated, and the heat release rate time history parameters are substituted into the basic dynamic model as dynamic source terms to obtain the tunnel fire model.

[0114] The third building block is used to perform numerical analysis of the temperature field based on the tunnel fire model. By calculating the spatiotemporal distribution of gas temperature in the tunnel space, the three-dimensional transient temperature field acting on the inner surface of the lining is obtained.

[0115] In one specific embodiment of this application, the computing module 903 includes:

[0116] The first calculation unit is used to perform temperature-material parameter field mapping based on the three-dimensional transient temperature field and material high-temperature performance data. By querying and interpolating the instantaneous temperature values ​​of each point in space in the material high-temperature performance data, the corresponding elastic modulus and thermal expansion coefficient are obtained, and the time-varying field of material parameters at discrete points in space is obtained.

[0117] The second calculation unit is used to construct the constraint structure system based on the time-varying field of material parameters. Based on the shell geometry and connection characteristics of the tunnel lining, it establishes the structural mechanics control equations that reflect the circumferential closed constraint and longitudinal continuous constraint of the tunnel, and obtains the parameterized mechanical model of the tunnel lining.

[0118] The third calculation unit is used to solve the thermally induced internal force field based on the parameterized mechanical model of the tunnel lining. By taking the time-varying field of material parameters and the three-dimensional transient temperature field as input, it numerically solves the structural mechanics control equations, calculates the structural internal forces caused by non-uniform thermal expansion under constraint conditions, and obtains the real-time internal force state of each section of the lining.

[0119] Example 3:

[0120] Corresponding to the above method embodiments, this embodiment also provides a tunnel fire concrete lining damage evolution prediction device. The tunnel fire concrete lining damage evolution prediction device described below and the tunnel fire concrete lining damage evolution prediction method described above can be referred to in correspondence.

[0121] Figure 3 This is a block diagram illustrating a tunnel fire concrete lining damage evolution prediction device 800 according to an exemplary embodiment. Figure 3 As shown, the tunnel fire concrete lining damage evolution prediction device 800 may include: a processor 801 and a memory 802. The tunnel fire concrete lining damage evolution prediction device 800 may also include one or more of a multimedia component 803, an I / O interface 804, and a communication component 805.

[0122] The processor 801 controls the overall operation of the tunnel fire concrete lining damage evolution prediction device 800 to complete all or part of the steps in the tunnel fire concrete lining damage evolution prediction method described above. The memory 802 stores various types of data to support the operation of the tunnel fire concrete lining damage evolution prediction device 800. This data may include, for example, instructions for any application or method operating on the tunnel fire concrete lining damage evolution prediction device 800, as well as application-related data such as contact data, sent and received messages, images, audio, video, etc. The memory 802 can be implemented using any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The multimedia component 803 may include a screen and an audio component. The screen may be, for example, a touchscreen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signals may be further stored in the memory 802 or transmitted via the communication component 805. The audio component also includes at least one speaker for outputting audio signals. I / O interface 804 provides an interface between processor 801 and other interface modules, such as a keyboard, mouse, and buttons. These buttons can be virtual or physical. Communication component 805 is used for wired or wireless communication between the tunnel fire concrete lining damage evolution prediction device 800 and other devices. Wireless communication includes Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, or 4G, or a combination thereof. Therefore, the corresponding communication component 805 may include a Wi-Fi module, a Bluetooth module, and an NFC module.

[0123] In an exemplary embodiment, a tunnel fire concrete lining damage evolution prediction device 800 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the aforementioned tunnel fire concrete lining damage evolution prediction method.

[0124] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, which, when executed by a processor, implement the steps of the tunnel fire concrete lining damage evolution prediction method described above. For example, the computer-readable storage medium may be the memory 802 including program instructions, which may be executed by a processor 801 of a tunnel fire concrete lining damage evolution prediction device 800 to complete the tunnel fire concrete lining damage evolution prediction method described above.

[0125] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for predicting the damage evolution of concrete lining in tunnel fires, characterized in that, include: Acquire geometric feature data of the tunnel, fire image data, and high-temperature performance data of the lining concrete; Based on the geometric feature data and the fire image data, a thermal flow field is constructed. By simulating the movement of the fire buoyancy plume in the tunnel space and dynamically calibrating the heat source, a three-dimensional transient temperature field acting on the inner surface of the lining is obtained. Based on the three-dimensional transient temperature field and the high-temperature performance data of the material, the internal force of the cross section is calculated. By mapping the instantaneous temperature value of each point in space to the corresponding material performance parameters, the internal force of the cross section caused by the constraint of non-uniform thermal expansion is solved in the structural system considering the circumferential constraint and longitudinal continuity of the lining, so as to obtain the real-time internal force state of each cross section of the lining. Damage initiation is determined based on the real-time internal force state of each section of the lining. The damage initiation is determined by comparing the principal stress with the concrete strength threshold at the corresponding temperature, and the spatiotemporal distribution of the initial damage zone is obtained. Based on the spatiotemporal distribution of the initial damage zone, damage-internal force coupling evolution is performed. By equating the damage zone with stiffness reduction and iteratively solving for new internal forces and damage propagation, the damage evolution path is obtained. A safety situation map is generated based on the damage evolution path. By extracting the spatial distribution of the remaining bearing capacity of the lining section at different times and identifying the area range and time node that drops below the safety threshold, a spatiotemporal evolution map of structural risk is obtained.

2. The method for predicting the damage evolution of concrete lining in tunnel fires according to claim 1, characterized in that, Based on the geometric feature data and the fire image data, a thermal flow field is constructed. By simulating the movement of the fire buoyancy plume in the tunnel space and dynamically calibrating the heat source, a three-dimensional transient temperature field acting on the inner surface of the lining is obtained, including: Based on the geometric feature data, physical control equations are constructed. By establishing a set of control equations describing the flow and heat transfer of buoyancy-driven fluid constrained by the wall in the narrow space of the tunnel, a basic dynamic model without fire source information is obtained. Based on the basic dynamic model and the fire image data, the model is calibrated. By analyzing the spatiotemporal evolution information of the flame and smoke front in the fire image data, the heat release rate time history parameter that conforms to the development law of the plume in the tunnel is calculated. The heat release rate time history parameter is then substituted into the basic dynamic model as a dynamic source term to obtain the tunnel fire model. Numerical analysis of the temperature field was performed based on the tunnel fire model. By calculating the spatiotemporal distribution of gas temperature in the tunnel space, the three-dimensional transient temperature field acting on the inner surface of the lining was obtained.

3. The method for predicting the evolution of concrete lining damage in tunnel fires according to claim 1, characterized in that, Based on the three-dimensional transient temperature field and the high-temperature performance data of the material, the internal forces of the cross sections are calculated. By mapping the instantaneous temperature values ​​at various points in space to the corresponding material performance parameters, the internal forces of the cross sections caused by the constraint of non-uniform thermal expansion are solved in a structural system considering the circumferential constraint and longitudinal continuity of the lining. The real-time internal force state of each cross section of the lining is obtained, including: Based on the three-dimensional transient temperature field and the material high-temperature performance data, a temperature-material parameter field mapping is performed. By querying and interpolating the instantaneous temperature values ​​at each point in space into the material high-temperature performance data, the corresponding elastic modulus and thermal expansion coefficient are obtained, thus obtaining the time-varying field of material parameters at discrete points in space. The constraint structure system is constructed based on the time-varying field of the material parameters. Based on the shell geometry and connection characteristics of the tunnel lining, the structural mechanics control equations reflecting the circumferential closed constraint and longitudinal continuous constraint of the tunnel are established, and the parameterized mechanical model of the tunnel lining is obtained. The thermally induced internal force field is solved based on the parameterized mechanical model of the tunnel lining. By taking the time-varying field of the material parameters and the three-dimensional transient temperature field as inputs, the structural mechanical control equations are numerically solved to calculate the structural internal forces caused by non-uniform thermal expansion under constraints, and the real-time internal force state of each section of the lining is obtained.

4. The method for predicting the damage evolution of concrete lining in tunnel fires according to claim 1, characterized in that, Damage initiation is determined based on the real-time internal force state of each section of the lining. Damage initiation is determined by comparing the principal stress with the concrete strength threshold at the corresponding temperature, resulting in the spatiotemporal distribution of the initial damage zone, including: Mechanical indices are extracted based on the real-time internal force state of each section of the lining. By calculating the magnitude and direction of the principal stress of each material element in three-dimensional space, the principal stress field characterizing the degree of stress danger at the material point is obtained. Based on the principal stress field and the three-dimensional transient temperature field, a temperature-dependent strength threshold field is constructed. By mapping the instantaneous temperature value of each spatial point in the three-dimensional transient temperature field to the compressive strength of concrete at that temperature based on the material's high-temperature performance data, a dynamic strength threshold field corresponding to the spatiotemporal temperature field is obtained. Damage initiation spatiotemporal determination is performed based on the principal stress field and the dynamic intensity threshold field. By comparing the principal stress value with the dynamic intensity threshold at the same location and time at each spatiotemporal point, the unit where the principal stress first exceeds the intensity threshold is marked as the damage initiation, thus obtaining the spatiotemporal distribution of the initial damage zone.

5. The method for predicting the evolution of concrete lining damage in tunnel fires according to claim 1, characterized in that, Based on the spatiotemporal distribution of the initial damage zone, a damage-internal force coupled evolution is performed. By representing the damage zone as an equivalent stiffness reduction and iteratively solving for the new internal forces and damage propagation, the damage evolution path is obtained, including: Based on the spatiotemporal distribution of the initial damage zone, the damage-induced stiffness field is reconstructed. By mapping the spatial coordinates of the initial damage zone to the mechanical model of the lining structure, and based on the principle of continuous damage mechanics, the damage state is quantified into the reduction coefficient of the elastic modulus of the material at the corresponding location, thus obtaining the updated material parameter field characterizing the local stiffness degradation of the structure. Based on the updated material parameter field, the internal force field is solved, and by calculating the redistribution of the internal forces in the lining section under the same temperature load, a new internal force state is obtained due to the change in the internal force path caused by local stiffness degradation. Damage propagation and evolution iteration are performed based on the new internal force state. By determining the damage initiation of the new internal force state, the expanding damage area is identified, and the stiffness field reconstruction and internal force field solution process is repeated until the damage area no longer expands or the structural mechanical response reaches the critical instability condition, thus obtaining the damage evolution path from damage initiation to final failure.

6. The method for predicting the damage evolution of concrete lining in tunnel fires according to claim 1, characterized in that, A safety situation map is generated based on the damage evolution path. By extracting the spatial distribution of the remaining bearing capacity of the lining section at different times and identifying the area and time node where the bearing capacity drops below the safety threshold, a spatiotemporal evolution map of structural risk is obtained, including: Based on the damage evolution path, the bearing capacity field is extracted. By quantifying the damage state at each spatiotemporal point in the damage evolution path into a reduction factor for the axial and bending stiffness of the lining section, and calculating the remaining bearing capacity of the section after damage, the spatiotemporal distribution field of the remaining bearing capacity of the lining is obtained. Risk zones are identified based on the spatiotemporal distribution field of the remaining bearing capacity of the lining. By comparing the value of each point in the spatiotemporal distribution field of the remaining bearing capacity with the preset safe bearing capacity threshold based on the tunnel design load, the spatiotemporal points where the bearing capacity first falls below the threshold are marked as risk points, and connected risk points are aggregated to form risk areas, thus obtaining the spatiotemporal boundaries and risk levels of the risk areas. Based on the spatiotemporal boundaries and risk levels of the risk areas, a comprehensive risk map is synthesized. By merging and encoding risk areas at different times and spatial locations according to risk levels and occurrence sequence, a spatiotemporal evolution map of structural risk that comprehensively reflects the process of load-bearing capacity loss, key weak points, and failure sequence of tunnel lining throughout the entire fire process is generated.

7. The method for predicting the damage evolution of concrete lining in tunnel fires according to claim 6, characterized in that, The bearing capacity field is extracted based on the damage evolution path, including: Damage state parameters are analyzed based on the damage evolution path. By extracting the values ​​of continuous damage variables characterizing material performance degradation in the spatial and temporal dimensions from the damage evolution path, the spatiotemporal evolution matrix of damage degree at each point is obtained. Based on the spatiotemporal evolution matrix, a damage-stiffness reduction relationship mapping is performed. By establishing a quantitative mapping function between damage variables and material elastic modulus, each damage value in the spatiotemporal evolution matrix is ​​converted into a corresponding axial stiffness and bending stiffness reduction coefficient, thus obtaining the spatiotemporal distribution field of the reduction coefficient. The bearing capacity is calculated based on the spatiotemporal distribution field of the reduction coefficient. The maximum resistance that the section can provide when bearing the design load combination is solved based on the principle of structural mechanics, and the spatiotemporal distribution field of the remaining bearing capacity of the lining is obtained.

8. A system for predicting the damage evolution of concrete lining in tunnel fires, characterized in that, include: The acquisition module is used to acquire geometric feature data of the tunnel, fire image data, and high-temperature performance data of the lining concrete. The construction module is used to construct the thermal flow field based on the geometric feature data and the fire image data. By simulating the movement of the fire buoyancy plume in the tunnel space and dynamically calibrating the heat source, a three-dimensional transient temperature field acting on the inner surface of the lining is obtained. The calculation module is used to calculate the internal forces of the cross section based on the three-dimensional transient temperature field and the high-temperature performance data of the material. By mapping the instantaneous temperature values ​​of each point in space to the corresponding material performance parameters, the internal forces of the cross section generated by the constraint of non-uniform thermal expansion are solved in the structural system considering the circumferential constraint and longitudinal continuity of the lining, so as to obtain the real-time internal force state of each cross section of the lining. The determination module is used to determine the damage initiation based on the real-time internal force state of each section of the lining. The damage initiation is determined by comparing the principal stress with the concrete strength threshold at the corresponding temperature, and the spatiotemporal distribution of the initial damage zone is obtained. The evolution module is used to perform damage-internal force coupled evolution based on the spatiotemporal distribution of the initial damage zone. By treating the damage zone as equivalent to stiffness reduction and iteratively solving for new internal forces and damage expansion, the damage evolution path is obtained. The generation module is used to generate a safety situation map based on the damage evolution path. By extracting the spatial distribution of the remaining bearing capacity of the lining section at different times and identifying the area range and time node that has dropped below the safety threshold, a spatiotemporal evolution map of structural risk is obtained.

9. The tunnel fire concrete lining damage evolution prediction system according to claim 8, characterized in that, The building module includes: The first construction unit is used to construct physical control equations based on the geometric feature data. By establishing a set of control equations describing the flow and heat transfer of buoyancy-driven fluid constrained by the wall in the narrow space of the tunnel, a basic dynamic model without fire source information is obtained. The second construction unit is used to perform model correction based on the basic dynamic model and the fire image data. By analyzing the spatiotemporal evolution information of the flame and smoke front in the fire image data, it calculates the heat release rate time history parameter that conforms to the development law of the plume in the tunnel, and substitutes the heat release rate time history parameter as a dynamic source term into the basic dynamic model to obtain the tunnel fire model. The third building unit is used to perform numerical analysis of the temperature field based on the tunnel fire model. By calculating the spatiotemporal distribution of gas temperature in the tunnel space, the three-dimensional transient temperature field acting on the inner surface of the lining is obtained.

10. The tunnel fire concrete lining damage evolution prediction system according to claim 8, characterized in that, The computing module includes: The first calculation unit is used to perform temperature-material parameter field mapping based on the three-dimensional transient temperature field and the material high-temperature performance data. By querying and interpolating the instantaneous temperature values ​​of each point in space into the material high-temperature performance data, the corresponding elastic modulus and thermal expansion coefficient are obtained, thus obtaining the time-varying field of material parameters at discrete points in space. The second calculation unit is used to construct the constraint structure system based on the time-varying field of the material parameters, and to establish the structural mechanics control equations reflecting the circumferential closed constraint and longitudinal continuous constraint of the tunnel based on the shell geometry and connection characteristics of the tunnel lining, so as to obtain the parameterized mechanical model of the tunnel lining. The third calculation unit is used to solve the thermally induced internal force field based on the parameterized mechanical model of the tunnel lining. By taking the time-varying field of the material parameters and the three-dimensional transient temperature field as input, it numerically solves the structural mechanics control equations, calculates the structural internal forces caused by non-uniform thermal expansion under constraint conditions, and obtains the real-time internal force state of each section of the lining.

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