A method for intelligently evaluating damage of main cable after bridge vehicle fire

Abstract

The application discloses a kind of bridge vehicle fire post-bridge cable damage intelligent evaluation method, belong to bridge fire damage evaluation field, including the following steps: first, obtain the working condition parameters including fire source intensity, fire source distance and bridge deck wind speed;Then verify the working condition parameters, load the bridge main cable internal temperature inversion model and the bridge main cable surface temperature prediction model according to the main cable protection type, corresponding output main cable internal time-temperature prediction value, main cable surface maximum temperature prediction value;Main cable internal time-temperature prediction value, main cable surface maximum temperature prediction value are compared with main cable material safety threshold, and damage classification is carried out.The application constructs end-to-end pipeline from disaster description to damage evaluation report, automatically completes complex analysis work, avoids the error introduced due to manual operation and interpretation, realizes the order of magnitude breakthrough of evaluation efficiency, is suitable for bridge post-fire evaluation scene, provides key time window for bridge safety condition rapid judgment and repair decision.

Description

Technical Field

[0001] This invention relates to fire assessment technology, specifically to a method for intelligent assessment of main cable damage after a bridge vehicle fire. Background Technology

[0002] Long-span cable-stayed bridges are widely used in important projects such as crossing the sea and rivers. They can directly cross wide waters and closely connect areas that originally needed to be detoured or separated by the sea, greatly shortening the time and space distance. When a vehicle traveling on a bridge catches fire, the high-temperature flames and hot smoke produced not only directly damage the bridge deck structure but also cause long-term thermal damage to critical load-bearing components such as the main cable. As the main load-bearing element of the bridge, the internal temperature changes of the main cable directly determine the degree of degradation of the material's mechanical properties, thus affecting post-disaster load-bearing capacity assessment and repair decisions.

[0003] In post-fire damage assessment of bridges, accurately reconstructing the temperature and time-related changes within the main cable during the fire process is crucial. Considering the enclosed structure of the main cable and the inability to directly measure its internal temperature, traditional post-fire assessment methods primarily rely on two technical approaches: one is to obtain surface temperature data based on thermocouples or infrared thermometry and extrapolate the internal temperature using empirical formulas; the other is to establish a sophisticated physical simulation model, such as using COMSOL Multiphysics to create an equivalent medium model of the "steel wire-void" and simulating the heat transfer process by calculating the equivalent thermal conductivity. However, these methods have significant limitations in post-fire assessment scenarios. 1. Insufficient real-time performance restricts post-disaster assessment: Post-disaster assessment often requires determining the structural safety status as soon as possible. Traditional finite element / multiphysics simulation often takes a long time to complete a single calculation, while the main cable temperature inversion needs to consider the entire process of "rapid heating-slow climbing-gradual cooling", which is difficult to meet the timeliness requirements of rapid post-disaster assessment. 2. Limited accuracy and traceability of internal damage assessment: Surface temperature measurement cannot capture the thermal inertia effect and heat transfer hysteresis characteristics inside the main cable. For example, in physical simulation, the equivalent thermal conductivity changes in a complex manner with temperature. The uncertainty of complex variable physical property parameters and protective material parameters further introduces deviations, leading to deviations in damage assessment. 3. Insufficient level of intelligence: Existing post-disaster assessments rely heavily on expert experience to interpret simulation results, lacking automated internal temperature reconstruction capabilities. This makes it difficult to quickly obtain stable conclusions under multiple scenarios and uncertain parameters. In particular, when it is necessary to quickly analyze the damage impact of various fire scenarios on the main cable, the workload of traditional methods increases exponentially. 4. Uncertainty of operating parameters is difficult to handle: Post-disaster site information is often incomplete, and key operating parameters often present ranges or multiple possible combinations. If only a single temperature curve is output based on a single point parameter, it is easy to make the assessment too optimistic or too conservative, and lack risk boundaries for decision-making. Currently, LSTM networks have shown advantages in capturing dynamic heat transfer characteristics, while generative adversarial networks have potential in terms of data generation realism. However, there is still a lack of mature solutions for combining them with post-disaster information extraction, parameter verification, parameter range sampling, multi-curve generation, and joint internal and external temperature classification to form an end-to-end assessment pipeline from disaster description to damage classification and report generation. Therefore, we need to develop an intelligent assessment method for main cable damage for post-disaster damage assessment. Summary of the Invention

[0004] The purpose of this invention is to provide an intelligent assessment method for main cable damage after a bridge vehicle fire. It not only constructs an end-to-end pipeline from disaster description to damage assessment report, which can automatically complete complex analysis work, greatly improving the ease of use and popularization of the technology, effectively avoiding errors introduced by manual operation and interpretation, but also achieves an order-of-magnitude breakthrough in assessment efficiency. It is applicable to post-fire assessment scenarios for bridges, providing a critical time window for rapid judgment of bridge safety status and repair decisions.

[0005] To achieve the above objectives, the present invention provides an intelligent assessment method for main cable damage after a bridge vehicle fire, which specifically includes the following steps: S1. Based on the large language model, the key working condition parameters and main cable protection type of the bridge cable vehicle fire scenario are identified, and working condition parameters including fire source intensity, fire source distance and bridge deck wind speed are obtained. S2: Load the bridge main cable internal temperature inversion model and the bridge main cable surface temperature prediction model according to the main cable protection type; verify the working condition parameters and, based on the verified working condition parameters, the bridge main cable internal temperature inversion model is used to output the predicted value of the main cable internal time-temperature, and the bridge main cable surface temperature prediction model is used to output the predicted value of the highest temperature on the main cable surface. S3. The predicted time-temperature inside the main cable, the predicted maximum temperature on the surface of the main cable, and the safety threshold of the main cable material are compared to classify the damage. S4 integrates the operating condition parameter dataset, the predicted time-temperature value inside the main cable, the predicted maximum temperature on the main cable surface, and the damage level to generate a report containing parameter vectors, the time-temperature curve inside the main cable, the maximum temperature on the main cable surface, the damage level, and repair recommendations.

[0006] In some examples of the present invention, the construction of the bridge main cable surface temperature prediction model in step S2 specifically includes the following steps: Step A-1: ​​Construct a bridge vehicle fire simulation model and output peak temperature-height data of the main cable surface under different working conditions using the simulation model. The peak temperature-height data output by the simulation model, the experimental data of the main cable surface obtained by the preset test platform, and the corresponding working parameters are used to construct a dataset. After preprocessing, the dataset is divided into a training set, a validation set, and a test set. Step A-2: Construct a bridge main cable surface temperature prediction model based on a conditional generative adversarial network. This model has a generator A and a discriminator A. The generator A is configured to generate predicted temperature curves based on noise vectors and normalized working condition parameters. The discriminator A is configured to distinguish between the generated temperature curves and the actual temperature curves and output a discrimination probability value. Train the bridge main cable surface temperature prediction model using a loss function on the training set. During the training process, monitor the training effect and adjust the training parameters through the validation set. Evaluate the performance of the bridge main cable surface temperature prediction model on the test set to obtain the trained bridge main cable surface temperature prediction model. Step A-3: Input the operating condition parameters from step S2 into the trained bridge main cable surface temperature prediction model and output the predicted value of the highest surface temperature of the main cable.

[0007] In some examples of the present invention, generator A in step A-2 includes an input layer, three first fully connected hidden layers, and a first output layer; the input layer is a concatenated vector of receiving noise vector and normalized working condition parameters, used for data input and feature initialization; the three first fully connected hidden layers are connected sequentially, each followed by a batch normalization layer and a LeakyReLU activation function, used for feature extraction and nonlinear transformation at different levels, respectively; the first output layer includes a first fully connected layer and a Tanh activation function, used to output temperature prediction values ​​for multiple standard altitude points; The discriminator A is a two-stream network structure, including a conditional stream, a data stream, a concatenation layer, two second fully connected hidden layers, and a second output layer. The conditional stream includes a second fully connected layer and a LeakyReLU activation function, used to process the operating condition vector and extract conditional features. The data stream includes a third fully connected layer and a LeakyReLU activation function, used to process the temperature curve vector and extract data features. The concatenation layer concatenates the feature vectors output from the conditional stream and the data stream into a fused feature vector. The two second fully connected hidden layers are connected sequentially, with each second fully connected hidden layer followed by a LeakyReLU activation function, used for feature fusion and discriminant analysis, respectively. The second output layer includes a fourth fully connected layer and a Sigmoid activation function, used to output the discriminant probability value.

[0008] In some examples of this invention, the generator A employs a weighted combination loss function L. total From the counter-loss Ladv and physical constraint loss L smooth Weighted according to the following formula: Among them, the resistance loss L adv The calculation formula is: Among them, physical constraint loss L smooth The calculation formula is: Where λ is the hyperparameter in the prediction model; E represents the mathematical expectation in the prediction model; P Z (z) represents the probability distribution of random noise; P data (c) represents the data distribution under actual operating conditions; D(.) is the output probability of discriminator A; G(.) is the output of generator A; N is the length of the temperature sequence; T i This represents the predicted temperature value at the i-th elevation point; The expression for the loss function used by discriminator A in the cable surface temperature prediction model is as follows: in, Discriminator A represents the probability of outputting "true" under given operating conditions.

[0009] In some examples of the present invention, the construction of the inversion model of the internal temperature of the main cable of the bridge in step S2 specifically includes the following steps: Step B-1: Input the working condition parameters into the bridge vehicle fire simulation model, and output the time-temperature data of the main cable surface by the simulation model. Input the time-temperature data of the main cable surface into the main cable heat transfer simulation model, and output the time-temperature data of the main cable interior; Step B-2: The above-output time-temperature data inside the main cable, experimental data inside the main cable, and corresponding operating parameters are used to construct a dataset. After preprocessing, the dataset is divided into a training set, a validation set, and a test set. Step B-3: Construct a main cable heat transfer inversion model based on LSTM conditional generative adversarial network. Use a weighted combination loss function that includes adversarial loss and physical constraints to train the generator B of the LSTM conditional generative adversarial network model to obtain the trained main cable heat transfer inversion model. Step B-3: Construct a main cable heat transfer inversion model based on LSTM conditional generative adversarial network. Use a weighted combined loss function that includes adversarial loss and physical constraints. Train the main cable heat transfer inversion model with the training set. During the training process, monitor the training effect through the validation set and adjust the training parameters. Evaluate the performance of the main cable heat transfer inversion model on the test set to obtain the trained main cable heat transfer inversion model. Step B-4: Input the operating condition parameters from step S2 into the trained main cable heat transfer inversion model and output the predicted time-temperature values ​​inside the main cable.

[0010] In some examples of the present invention, the heat transfer simulation model of the main cable is derived using a rectangular finite element model. The rectangular finite element model consists of the main cable steel wires and the air gaps between the steel wires. The internal steel wire dimensions and porosity are consistent with the experimental model. There is surface-to-surface thermal radiation between the steel wire gaps. The radiating surface is defined as a diffuse reflective surface and its emissivity is set. The radiation direction is set to be controlled by opacity. The opacity of the steel wires and the air is set to opaque and transparent, respectively. The air is set to be non-flowing, and the thermal convection of the air is ignored. The main cable structure is regarded as a porous medium of "steel wire-void". By fitting the equivalent thermal conductivity and correcting the equivalent specific heat capacity, a simplified equivalent model of the main cable structure of steel wire-void is established. The input is the time-temperature data of the main cable surface, and the output is the time-temperature data of the main cable interior.

[0011] In some examples of this invention, the equivalent thermal conductivity of the simplified equivalent model is derived through a rectangular finite element model, and its calculation formula is as follows: in, The average heat flux density is denoted as H; H is the thickness of the main cable protective layer; T1 and T2 are the upper and lower boundary temperatures, respectively. Numerical fitting was performed on the equivalent thermal conductivity, and a fourth-order polynomial was used to obtain the formula for calculating the equivalent thermal conductivity as a function of temperature: in, , , , For temperature coefficient, It is a constant. The ambient temperature; The equivalent specific heat capacity is corrected to 60%-80% of the original specific heat capacity based on the porosity of the model.

[0012] In some examples of the present invention, in step B-3, the main cable heat transfer inversion model has a generator B and a discriminator B; Generator B uses an LSTM autoregressive structure, specifically including the following steps: The input layer receives the concatenated vector [z;c], where c is a conditional scalar defined by operating parameters, and z is a noise vector; The initial hidden state and initial cell state of the LSTM are generated through a fully connected layer; At each time step, the predicted temperature from the previous time step is concatenated with the conditional scalar c as input. The hidden state and cell state are updated through the LSTM gating mechanism. The output layer uses the Sigmoid activation function to generate normalized temperature values, thus obtaining the complete main cable temperature prediction sequence. Discriminator B uses a conditional LSTM structure, specifically including the following steps: At each time step, the temperature value is concatenated with the conditional scalar c to form the input sequence. Temporal features are extracted by an LSTM encoder, and the final hidden state outputs the probability of authenticity through a fully connected layer and a Sigmoid activation function.

[0013] In some examples of this invention, generator B introduces a reconstruction term of the main cable temperature sequence on top of the adversarial loss, and the weighted combined loss function is: The discriminator B loss uses the standard binary cross-entropy form: in, This represents the mathematical expectation in the inversion model; z is the noise vector; c is the conditional scalar; This represents the probability function indicating the authenticity of the output of discriminator B. This represents the main cable temperature sequence function generated by generator B. P represents the hyperparameters in the inversion model; y represents the true main cable temperature sequence sampled from the real data distribution; P represents the hyperparameters in the inversion model. data This is the data distribution of the actual main cable temperature sequence.

[0014] In some examples of the present invention, in the damage grading of step S3, the external temperature for damage grading is the predicted value of the highest surface temperature of the main cable. The internal temperature is measured using the highest surface temperature of the outer steel wire of the main cable. Measured by the predicted time-temperature value inside the main cable; The safety threshold for main cable materials includes the first surface temperature threshold. Second surface temperature threshold and the temperature threshold of the outer steel wire Set the over-threshold time threshold. Cumulative time exceeding the threshold The cumulative duration during which the surface temperature of the outer steel wire of the main cable exceeds the outer steel wire temperature threshold. when If so, it is determined to be undamaged; when ,and If so, it is determined to be a minor injury; when ,and If so, it is determined to be a minor injury; when ,and If so, it is determined to be a moderate injury; when ,and If so, it is determined to be a severe injury.

[0015] In some examples of the present invention, a smart assessment method for main cable damage after a bridge vehicle fire has the following advantages: 1. By inputting the working condition parameters into the trained bridge main cable surface temperature prediction model and the bridge main cable internal temperature inversion model, a generative adversarial network model is generated for forward prediction. It can generate the predicted value of the highest surface temperature of the main cable and the predicted value of the internal time-temperature of the main cable within milliseconds. It can quickly obtain stable conclusions under multiple scenarios and multiple uncertain parameters, achieving an order-of-magnitude breakthrough in assessment efficiency. It is suitable for post-fire assessment scenarios of bridges, providing a key time window for rapid judgment of bridge safety status and repair decisions. 2. Construct a heat transfer inversion model for the main cable based on LSTM conditional generative adversarial network. This model can not only invert the temperature-time change curve inside the main cable, solving the problem of inversion where the internal temperature of the main cable cannot be directly measured; but also train the model with different operating parameters so that it can cover unknown operating conditions far beyond the training sample range, rather than the calculation results of a single operating condition. The output predicted values ​​of the maximum surface temperature and internal temperature of the main cable can provide data support, making it more universal and secure. 3. A dual-model calling framework of "internal temperature inversion and surface maximum temperature prediction" is adopted to characterize the internal temperature rise process of the main cable and the maximum surface temperature of the cable respectively. In the damage analysis stage, joint comparison and fusion classification are carried out to make the damage judgment closer to the real heat-heat transfer-degradation mechanism. 4. It can automatically generate structured and traceable assessment reports, forming a closed loop from data analysis to decision support: it not only includes parameter ranges, sampling strategies and curve results, but also provides grading criteria, damage probability / confidence level and targeted repair suggestions, which can be directly used for post-disaster disposal and subsequent review. 5. The bridge main cable surface temperature prediction model provides a new paradigm of "physical mechanism + data-driven"; the bridge main cable internal temperature inversion model can ensure that the inverted main cable internal temperature time series change conforms to the physical law of "rapid heating-slow climbing-gradual cooling", ensuring a high degree of consistency between the generated sequence and the real data, and achieving high evaluation accuracy while greatly improving evaluation efficiency. 6. Compare the external and internal temperatures of the main cable with the safety threshold of the main cable material, and compare the cumulative time exceeding the threshold with the threshold time. Classify the damage of the main cable into different levels according to different value ranges. Avoid using a single parameter for comparison, which may result in an excessively large range of classification. The damage classification is clearer and provides a reliable reference for the degree of damage to the main cable. Attached Figure Description

[0016] Figure 1 This is an overall flowchart of the present invention; Figure 2 This is a flowchart of the bridge main cable internal temperature inversion model and the bridge main cable surface temperature prediction model in this invention.

[0017] Figure 3 This is a schematic diagram of the structure of generator A according to Embodiment 2 of the present invention; Figure 4 This is a schematic diagram of the discriminator A according to Embodiment 2 of the present invention; Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages 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. The same reference numerals in the drawings represent the same components. It should be noted that the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the described embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0019] Unless otherwise defined, the technical or scientific terms used herein shall have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms “first,” “second,” and similar terms used in this patent application specification and claims do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Similarly, “an” or “a” and similar terms do not necessarily indicate a quantity limitation. Terms such as “comprising” or “including” mean that the element or object preceding the word encompasses the element or object listed following the word and its equivalents, without excluding other elements or objects. Terms such as “connected” or “linked” are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as “upper,” “lower,” “left,” and “right” are used only to indicate relative positional relationships; these relative positional relationships may change accordingly when the absolute position of the described object changes. Example 1

[0020] like Figure 1 , Figure 2 As shown, the present invention provides an intelligent assessment method for main cable damage after a bridge vehicle fire, which specifically includes the following steps: S1. Based on the large language model, the key working condition parameters and main cable protection type of the bridge cable vehicle fire scenario are identified, and working condition parameters including fire source intensity, fire source distance and bridge deck wind speed are obtained. S2: Verify the operating parameters and load the bridge main cable internal temperature inversion model and the bridge main cable surface temperature prediction model according to the main cable protection type; based on the verified operating parameters, the bridge main cable internal temperature inversion model is used to output the predicted value of the main cable internal time-temperature, and the bridge main cable surface temperature prediction model is used to output the predicted value of the highest temperature on the main cable surface. S3. The predicted time-temperature inside the main cable, the predicted maximum temperature on the surface of the main cable, and the safety threshold of the main cable material are compared to classify the damage. S4 integrates the operating condition parameter dataset, the predicted time-temperature value inside the main cable, the predicted maximum temperature value on the surface of the main cable, and the damage level to generate a report containing parameter vectors, the time-temperature curve inside the main cable, the maximum temperature on the surface of the main cable, the damage level, and repair suggestions. Specifically, the system matched with the intelligent assessment method for main cable damage after bridge vehicle fire can be deployed in a local server cluster under the internal network. It includes a large language module, a bridge main cable internal temperature inversion module, a bridge main cable surface temperature prediction module, etc. The parameters, calculation data and knowledge base content of each module are stored in local storage devices to ensure offline processing and privacy security throughout the data processing chain. Each module is encapsulated using containerization technology and exchanges data and schedules processes through application programming interfaces (APIs), achieving high cohesion and low coupling between modules. Furthermore, the data transmission component can use either wireless or wired connections. Wireless transmission utilizes LoRa wireless communication technology, suitable for long-distance bridges and environments with heavy traffic, with a transmission distance of up to 500 meters. Wired transmission uses high-temperature resistant cables for areas with weak wireless signals, ensuring continuous data transmission and avoiding wireless interference. The data transmission component also includes signal boosters, deployed every 500 meters to improve signal strength and reduce data packet loss. In step S1, the large language model confirms through multiple rounds of human-computer interaction. Utilizing its semantic understanding capabilities, the model identifies implicit or explicit fire scenario parameter entities in the user input, allowing the user to select the main cable protection type for the bridge based on existing protection types. The large language model can be a specialized model obtained by fine-tuning existing open-source pre-trained models with text and code instructions from the bridge engineering field. This model is suitable for bridge fire damage assessment and possesses professional bridge fire post-assessment terminology understanding and logical reasoning capabilities. For unclear key condition parameters, follow-up questioning logic is initiated. For ambiguous or unreasonable condition parameters, confirmation and correction logic is initiated, ultimately outputting a complete and error-free set of standardized working condition parameters. For example, keyword extraction can be performed based on the large language model. This extraction method converts speech to text, then calibrates the text to extract keywords such as "fire" and "location," determining the main cable protection type and outputting standardized working condition parameters for fire source intensity, bridge deck wind speed, and fire source distance. Standardized operating condition parameters include: fire source intensity, fire source distance, and bridge deck wind speed. Among them, fire source intensity is a range value, which is obtained by sampling the fire source intensity parameter in a uniform distribution within this range; fire source distance is the distance between the fire source location and the cable; bridge deck wind speed is a range value, which is obtained by sampling the bridge deck wind speed in a uniform distribution within this range. The three operating condition parameters are arranged and combined to achieve comprehensive coverage of the possibilities of fire scenarios through this hybrid sampling strategy. In step S2, the local knowledge base can be called to verify the working condition parameters. The local knowledge base may include standard data such as bridge professional terminology database and working condition parameter terminology database. It can verify the working condition parameter dataset based on test data, historical data, etc., remove fuzzy and inaccurate working condition parameters, and generate a first command and a second command applicable to the main cable protection type. The first command is used to load the internal temperature inversion model of the bridge main cable. The inversion model outputs the predicted value of the internal time-temperature of the main cable based on the working condition parameter dataset. The second command is used to load the surface temperature prediction model of the bridge main cable. The prediction model outputs the predicted value of the highest surface temperature of the main cable based on the working condition parameter dataset. Among them, the experimental data can be obtained from the Chinese invention patent "Real-time Mixed Test Platform and Implementation Method of Vehicle-Fire-Wind Force for Substructure of Long-span Bridge" authorized by our research group with the authorization announcement number CN120741035B. That is, to conduct full-scale or large-scale model fire resistance test of cable under controllable parameter wind, fire and force coupling conditions. The repeatable, measurable and high-fidelity fire environment provided by the platform can greatly ensure the diversity and reliability of the original data. This example integrates large language model interaction, knowledge base parameter verification and scheduling, which can transform post-disaster natural language descriptions into structured chemical condition parameter datasets and correct missing / ambiguous parameters, reducing reliance on expert experience and software operation, and improving engineering usability and accessibility. By jointly comparing and fusing the predicted time-temperature values ​​inside the main cable and the predicted maximum surface temperature values ​​of the main cable during the damage analysis stage, the damage assessment can be more closely aligned with the actual heat-transfer-degradation mechanism. In addition, it can automatically generate structured and traceable assessment reports, forming a closed loop from data analysis to decision support: it not only includes parameter ranges, sampling strategies and curve results, but also provides grading criteria, damage probability / confidence levels and targeted repair suggestions, which can be directly used for post-disaster response and subsequent review. Example 2

[0021] The bridge main cable surface temperature prediction model in step S2 of Example 1 specifically includes the following steps: Step A-1: ​​Construct a bridge vehicle fire simulation model and output peak temperature-height data of the main cable surface under different working conditions using the simulation model. The surface peak temperature-height data output by the simulation model, the experimental data of the main cable surface obtained by the preset test platform, and the corresponding working parameters are used to construct a dataset. After preprocessing, the dataset is divided into a training set, a validation set, and a test set. Step A-2: Construct a bridge main cable surface temperature prediction model based on a conditional generative adversarial network. This model has a generator A and a discriminator A. The generator A is configured to generate predicted temperature curves based on noise vectors and normalized working condition parameters. The discriminator A is configured to distinguish between the generated temperature curves and the actual temperature curves and output a discrimination probability value. Train the bridge main cable surface temperature prediction model using a loss function on the training set. During the training process, monitor the training effect and adjust the training parameters through the validation set. Evaluate the performance of the bridge main cable surface temperature prediction model on the test set to obtain the trained bridge main cable surface temperature prediction model. Step A-3: Input the working condition parameters from step S2 into the trained bridge main cable surface temperature prediction model, output the main cable surface height-temperature change prediction data, and obtain the predicted value of the highest temperature on the main cable surface. Specifically, in step A-1, a bridge vehicle fire simulation model is constructed, and the ambient temperature field on the cable surface is obtained by simulation calculation using the simulation model; The two-dimensional cable plane is simplified into a one-dimensional vertical rod. The arbitrary position of the cable can be represented by the height of the vertical rod. The two-dimensional spatial coordinates are simplified into one-dimensional positions. The vehicle is simplified into a cube with a fire source on its surface. For different vehicle types, the maximum recommended grid size is given: 20cm for a maximum heat release rate of 0-5MW; 25cm for a maximum heat release rate of 5-10MW; 30cm for a maximum heat release rate of 10-15MW; 35cm for a maximum heat release rate of 15-20MW; and 50cm for a maximum heat release rate of 20-200MW. The maximum heat release rate of the simulated flame is used to replace the highest temperature of the simulated actual flame over 1.5 hours for 8-12 minutes; temperature measuring points are set at equal intervals along the height direction on the surface of the vertical rod according to the required accuracy. The operating parameters include fire source intensity, bridge deck wind speed, and fire source distance. The fire source intensity is sampled densely with a uniform distribution in the range of 0-50MW, and sparsely with a uniform distribution in the range of 50-200MW. The bridge deck wind speed is sampled uniformly in the range of 0-15m / s. The fire source distance is the distance between the emergency lane and the adjacent lane from the edge of the main cable. Experimental data on the surface of the main cable can be obtained by conducting experiments on the main cable under certain operating parameters, thereby obtaining experimental data on the peak temperature-height of the main cable surface. This experimental data, along with the corresponding operating parameters, the peak temperature-height data output by the simulation model, and the corresponding operating parameters, are combined to construct a dataset. By combining simulation and experimental data, the output results of the bridge main cable surface temperature prediction model are made more accurate, avoiding the problem of model deviation caused by the same type of dataset. In some embodiments, such as Figure 3 , Figure 4 As shown, in step A-2, generator A includes an input layer, three first fully connected hidden layers, and a first output layer. The input layer is a concatenated vector of the received noise vector and normalized working condition parameters, used for data input and feature initialization. The three first fully connected hidden layers are connected sequentially, with each first fully connected hidden layer followed by a batch normalization layer and a LeakyReLU activation function, used for feature extraction and nonlinear transformation at different levels, respectively. The first output layer includes a first fully connected layer and a Tanh activation function, used to output the predicted temperature values ​​at multiple standard altitude points. The discriminator A is a two-stream network structure, including a conditional stream, a data stream, a concatenation layer, two second fully connected hidden layers, and a second output layer. The conditional stream includes a second fully connected layer and a LeakyReLU activation function, used to process the operating condition vector and extract conditional features. The data stream includes a third fully connected layer and a LeakyReLU activation function, used to process the temperature curve vector and extract data features. The concatenation layer concatenates the feature vectors output from the conditional stream and the data stream into a fused feature vector. The two second fully connected hidden layers are connected sequentially, with each second fully connected hidden layer followed by a LeakyReLU activation function, used for feature fusion and discriminant analysis, respectively. The second output layer includes a fourth fully connected layer and a Sigmoid activation function, used to output the discriminant probability value. Furthermore, the weighted combination loss function L used by generator A total From the counter-loss L adv and physical constraint loss L smooth Weighted according to the following formula: Among them, the resistance loss L adv The calculation formula is: Among them, physical constraint loss L smooth The calculation formula is: Where λ is the hyperparameter in the prediction model; E represents the mathematical expectation in the prediction model; P Z (z) represents the probability distribution of random noise; P data (c) represents the data distribution under actual working conditions; D(.) represents the output probability of discriminator A; G(.) represents the output of generator A; N represents the length of the temperature sequence; Ti represents the predicted temperature value at the i-th elevation point; The expression for the loss function used by discriminator A in the cable surface temperature prediction model is as follows: in, Discriminator A represents the probability of outputting "true" under given operating conditions.

[0022] An optimization algorithm based on gradient descent and its hyperparameter configuration strategy are adopted. The network is trained by alternating iterative updates of discriminator A and generator A. The determination coefficient R is used on the test set. 2 The model was evaluated using mean absolute error (MAE); during training, the training effect was monitored using a validation set to adjust the training parameters, with a hyperparameter learning rate of 2×10⁻⁶. -4The minimum number of training epochs is 500 to ensure prediction accuracy, and the maximum is 1000 to prevent overfitting. The coefficient of determination R is used on the test set. 2 The model is evaluated using the mean absolute error (MAE) and the coefficient of determination (R²). 2 >0.9, mean absolute error (MAE) <50℃; In this example, a pre-trained conditional generative adversarial network model is used for forward prediction, which can generate a high-precision peak temperature-height curve in milliseconds, improving computational efficiency by several orders of magnitude. By training with different operating parameters, the model can cover unknown operating conditions far beyond the training sample range, rather than the calculation results of a single operating condition, making it more universal and safer. In addition, the large-scale model technology of conditional generative adversarial networks was innovatively applied to the specific engineering scenario of fire protection for bridge cable vehicles. This not only solved the efficiency bottleneck of traditional methods, but more importantly, it provided a new paradigm of "physical mechanism + data-driven", offering a brand-new technical path and solution. Specific examples: Step A-1: ​​In the bridge vehicle fire simulation model, the two-dimensional cable plane is simplified into a one-dimensional vertical bar, and the height of the vertical bar is used to represent any position of the cable. The vehicle is simplified into a cube, and a fire source is set on its surface. Temperature measurement points are arranged along the height direction on the surface of the vertical bar according to the accuracy requirements. At the same time, the recommended maximum grid size is selected according to the fire source intensity, and the simulation method of the maximum heat release rate flame of 10 min is used to replace the actual flame of 1.5 h with the highest temperature to obtain the temperature data in the stable stage. The working condition parameter matrix and temperature value matrix are normalized. Then, the training set, validation set and test set are divided into 8:1:1.

[0023] Step A-2, Generator A structure: The input is a concatenated vector of a 100-dimensional noise vector and a 3-dimensional normalized working condition parameter vector; it passes through fully connected hidden layers with 256, 512, and 256 nodes in sequence, followed by batch normalization and LeakyReLU after each layer; the output layer is a 36-node fully connected layer with Tanh activation to output the temperature prediction values ​​of 36 standard altitude points. Discriminator A structure: It adopts a dual-branch structure. The conditional flow uses a 64-node fully connected layer to process the 3D operating condition vector, and the data flow uses a 64-node fully connected layer to process the 36-dimensional temperature curve. The outputs of the two branches are concatenated into a 128-dimensional vector, and then passed through 256 and 128-node fully connected layers (each layer is followed by batch normalization and LeakyReLU). Finally, the discriminant probability value is output through Sigmoid. During the training phase, a weighted combined loss function, incorporating adversarial loss and physical constraints, is used to train the model. Training employs an alternating iterative update strategy of "discriminator first, generator follow-up," and the performance is monitored on the validation set to adjust the training parameters. Example hyperparameter: learning rate. Training rounds: 700; Prediction accuracy: MAE=18.40℃, RMSE=24.98℃, R2=0.9908; Step A-3: Input the working condition parameters from step S2 into the trained bridge main cable surface temperature prediction model, output the main cable surface height-temperature change prediction data, and obtain the predicted value of the highest temperature on the main cable surface. Example 3

[0024] The inversion model for the internal temperature of the main cable of the bridge in step S2 of Example 1 specifically includes the following steps: Step B-1: Input different working condition parameters into the bridge vehicle fire simulation model in Example 2, and output the time-temperature data of the main cable surface by simulation calculation of the simulation model. Input the time-temperature data of the main cable surface into the main cable heat transfer simulation model, and output the time-temperature data of the main cable interior; Step B-2: The above-output time-temperature data inside the main cable, experimental data inside the main cable, and corresponding operating parameters are used to construct a dataset. After preprocessing, the dataset is divided into a training set, a validation set, and a test set. Step B-3: Construct a main cable heat transfer inversion model based on LSTM conditional generative adversarial network. Use a weighted combined loss function that includes adversarial loss and physical constraints. Train the main cable heat transfer inversion model with the training set. During the training process, monitor the training effect through the validation set and adjust the training parameters. Evaluate the performance of the main cable heat transfer inversion model on the test set to obtain the trained main cable heat transfer inversion model. Step B-4: Input the operating parameters from step S2 into the trained main cable heat transfer inversion model and output the predicted time-temperature value inside the main cable. This example demonstrates a main cable heat transfer inversion model based on an LSTM-based conditional generative adversarial network. It not only inverts the time-temperature change curve within the main cable, solving the problem of inverting the inability to directly measure the internal temperature of the main cable, but also achieves a breakthrough in evaluation efficiency through forward prediction using a pre-trained conditional generative adversarial network-based main cable heat transfer inversion model. This model is suitable for post-fire assessment scenarios of bridges, providing a crucial time window for rapid judgment of bridge safety and repair decisions. Furthermore, by using time-temperature data from the main cable surface, it simulates and obtains internal time-temperature data to form a dataset. Only the operating parameters of a vehicle fire need to be input to directly generate the internal temperature field of the main cable, solving the problem of the infeasibility of deploying temperature measurement equipment along the entire main cable and avoiding the need for additional temperature measurement equipment and the difficulty in deploying such equipment. In some examples of the present invention, in step B-1, the heat transfer simulation model of the main cable is a simplified equivalent model, which regards the main cable structure as a "steel wire-void" porous medium. By fitting the equivalent thermal conductivity and correcting the equivalent specific heat capacity, a simplified equivalent model of the steel wire-void main cable structure is established. Finally, the time-temperature data of the main cable surface is input into the heat transfer simulation model of the main cable, and the time-temperature data of the main cable interior is output. The simplified equivalent model is derived using a rectangular finite element model. The rectangular finite element model consists of the main cable steel wires and the air gaps between the steel wires. The internal steel wire dimensions and porosity are consistent with the experimental model. There is surface-to-surface thermal radiation between the steel wire gaps. The radiating surface is defined as a diffuse reflective surface and its emissivity is set. The radiation direction is set to be controlled by opacity. The opacities of the steel wires and air are set to opaque and transparent, respectively. The air is set to be non-flowing, and the thermal convection of the air is ignored. The upper and lower boundaries of the model are set to fixed temperatures T1 and T2, respectively, and the left and right boundaries are adiabatic. The average heat flux density q of the model can be obtained through finite element analysis, and the equivalent thermal conductivity can be obtained according to Fourier's law. ; The equivalent thermal conductivity is obtained through back-calculation using rectangular finite element analysis, and the calculation formula is as follows: in, The average heat flux density is denoted as H; H is the thickness of the main cable protective layer; T1 and T2 are the upper and lower boundary temperatures, respectively. Numerical fitting was performed on the equivalent thermal conductivity, and a fourth-order polynomial was used to obtain the formula for calculating the equivalent thermal conductivity as a function of temperature: in, , , , For temperature coefficient, It is a constant. The ambient temperature; In step B-2, the experimental data inside the main cable can be obtained by conducting experiments on the main cable under certain operating parameters to obtain the time-temperature experimental data inside the main cable. This experimental data, along with the corresponding operating parameters, the surface peak temperature-height data output by the simulation model, and the corresponding operating parameters, are combined to construct a dataset. By combining the simulation and experimental data, the output results of the bridge main cable internal temperature inversion model are made more accurate, avoiding the problem of model deviation caused by the same type of dataset. In some examples of the present invention, the main cable heat transfer inversion model in step B-3 has a generator B and a discriminator B, and adopts an alternating iterative update strategy of discriminator B first and generator B following, with 300-500 training rounds, and the minimum mean absolute error of the validation set is used as the early stopping criterion. In the main cable heat transfer inversion model, generator B adopts an LSTM autoregressive structure, specifically including the following steps: The input layer receives the concatenated vector [z;c], where c is a conditional scalar defined by operating parameters, and z is a noise vector; The initial hidden state and initial cell state of LSTM are generated through a fully connected layer. At each time step, the predicted temperature of the previous time step is concatenated with the conditional scalar c as input. The hidden state and cell state are updated through the LSTM gating mechanism. The output layer uses the Sigmoid activation function to generate normalized temperature values, thus obtaining the complete main cable temperature prediction sequence. Discriminator B uses a conditional LSTM structure, specifically including the following steps: At each time step, the temperature value is concatenated with the conditional scalar c to form the input sequence. Temporal features are extracted by an LSTM encoder, and the final hidden state outputs the probability of authenticity through a fully connected layer and a Sigmoid activation function.

[0025] Specifically, a training dataset was constructed based on 109 sets of main cable heat transfer simulation results, with a time range of 0 to 90 minutes and a sampling time step of 1 minute. After resampling and interpolation, a main cable temperature sequence y with a length of T=91 was obtained. Each set of data corresponds to a set of operating parameters. The operating parameters are used as the conditional scalar c of the sequence-level condition input. After normalization of each sample sequence, a "condition-sequence" sample pair (c,y) is formed. Generator B uses an LSTM autoregression structure with noise and conditions; for each sample, the conditional scalar c and the noise vector z with dimension d_z=32 are concatenated in the feature dimension to form a joint vector [z;c]; First, the joint vector [z;c] is mapped through a fully connected layer to generate the initial hidden state and the initial cell state: in, , For the LSTM hidden unit dimension, the split() function splits the fully connected layer output into hidden state h0 and cell state c0, W. init This is the initialization of the weight matrix, b init It is a bias term; Generator B generates the main cable temperature prediction sequence stepwise in an autoregressive manner. That is, at each time step The temperature value generated at the previous moment The current input of the gating mechanism, together with the conditional scalar c. If the temperature value of the previous time step is set as the normalized value corresponding to the initial ambient temperature of the main cable, then the current input... : The current input x t Feeding into LSTM gating mechanism update ,Right now: Among them, the input gate Controls the extent to which the current input is written into the cell state; forget gate Determines which dimensions the memory from the previous moment is retained; Output gate Determine which information in the cell state is visible to the current hidden state; Here, is the Sigmoid activation function; tanh(·) is the hyperbolic tangent function; ⊙ represents the Hadamard element-wise multiplication; W ix W fx W ox W cx W represents the input weight matrix. ih W fh W oh W ch Let b represent the hidden state weight matrix of the previous time step. i b f b o b c Indicates bias; h t-1 c t-1 The previous hidden state and cell state; h t c t The current hidden state and cell state are hidden. Candidate cell state; Introducing cell state c at each time step t and input gate Forgotten Gate and output gate Three types of gating mechanisms can automatically "remember" key stages (such as the rapid heating period) related to the temperature rise of the main cable and "forget" disturbances that are less related to the final result during the long-term temperature evolution from 0 to 90 minutes, thereby realizing time-series modeling of the entire process of "rapid heating, slow climbing, and gradual cooling" of the main cable temperature. Then, the normalized temperature prediction value for this moment is given by outputting a fully connected layer and sigmoid activation: in, , This is the transpose of the output weight matrix. As a bias, during training, the value is adjusted based on the global minimum-maximum. After inverse normalization to the physical temperature range, and through autoregression cycles T=91, the main cable temperature prediction sequence is obtained: Generator B adopts an autoregressive generation method that uses the output of the previous time step as the input of the next time step, so that generator B naturally satisfies the time smoothness and continuity constraints in numerical terms, and can better reproduce the characteristics of gradual temperature rise and slow temperature drop of the main cable in the solid heat transfer finite element model. Discriminator B uses an LSTM structure to encode the entire time series, concatenating the predicted temperature at each moment with the conditional scalar c to form the input vector. Repeat the concatenation operation along the time dimension to obtain the input sequence. ; input vector The data is fed into the LSTM encoder sequentially. in, This represents the cell state at the previous moment. The hidden state from the previous time step is considered a high-dimensional feature representation of the entire sequence under a given condition. Then, it is passed through two fully connected layers and a sigmoid activation layer to output the "realism" probability. in The LeakyReLU activation function is used. It is considered as a high-dimensional feature representation of the entire sequence under a given working condition, and W1 is the weight matrix of the first fully connected layer. b1 and b2 are the transpose of the weight matrix of the second fully connected layer, and b1 and b2 are the biases of each fully connected layer.

[0026] Discriminator B introduces conditional scalars explicitly at the input layer. It can learn the physical correspondence between "operating parameters - internal temperature rise curve of main cable". When the heating amplitude or cooling rate of a certain generated sequence does not match the current operating parameters, the discriminator B will give a lower authenticity score, thereby pushing the generator B to move closer to the real COMSOL distribution in adversarial training.

[0027] Generator B introduces a reconstruction term for the main cable temperature sequence on top of the adversarial loss. The weighted combined loss function of adversarial loss and physical constraints can be written as: The discriminator B loss uses the standard binary cross-entropy form: in, This represents the mathematical expectation in the inversion model; z is the noise vector; c is the conditional scalar; This represents the probability function indicating the authenticity of the output of discriminator B. This represents the internal temperature sequence function of the main cable generated by generator B. y represents the hyperparameters in the inversion model; y represents the actual main cable temperature sequence sampled from the real data distribution; Pdata represents the data distribution of the actual main cable temperature sequence, representing the overall characteristics of the temperature data obtained from COMSOL simulation or experiment. During the training phase of the heat transfer inversion model for the main cable, the Adam optimization algorithm can be used, with a learning rate set to 2×10. -4 During the evaluation phase of the main cable heat transfer inversion model, the mean square error (MSE), mean absolute error (MAE), and coefficient of determination were calculated using the inversely normalized temperature sequences of the validation and test sets. The minimum MAE of the validation set is used as the criterion for selecting the optimal model. At the same time, comparison charts of "real curves and generated curves" under several typical working conditions are output to intuitively verify the model's fitting effect on the characteristics of the main cable's temperature rise and cooldown stages. Example 4

[0028] In step S3, when performing damage grading, the damage grading standard is determined by referring to both external and internal temperatures, that is, the external temperature is the predicted value of the highest surface temperature of the main cable. The measurement is performed using the surface temperature of the outer steel wire of the main cable, measured by the predicted time-temperature value inside the main cable, and using the highest surface temperature of the outer steel wire of the main cable as the measurement. The comparison revealed that the safety threshold for the main cable material includes the first surface temperature threshold. Second surface temperature threshold and the temperature threshold of the outer steel wire ; In surface temperature grading, a first surface temperature threshold is preset. With the second surface temperature threshold This is used to characterize the degree of thermal effect on the surface of the main cable; in the internal temperature grading, the temperature threshold of the outer steel wire is preset. This is used to characterize the initial level of the thermal effect of the outer steel wire; an additional threshold time for exceeding the threshold is set. Cumulative time exceeding the threshold The cumulative duration during which the surface temperature of the outer steel wire of the main cable exceeds the outer steel wire temperature threshold. The temperature thresholds mentioned above are determined by the material library and / or specification library, and the over-threshold time thresholds are determined by the material library and / or test calibration. When the predicted maximum surface temperature of the main cable is not higher than the first surface temperature threshold, that is... If so, then there is no loss; When the predicted maximum surface temperature of the main cable is higher than the first surface temperature threshold but not higher than the second surface temperature threshold, and the maximum surface temperature of the outer steel wire of the main cable is not higher than the outer steel wire temperature threshold, i.e. ,and If so, it is considered a minor injury; When the predicted maximum surface temperature of the main cable is higher than the second surface temperature threshold, and the maximum surface temperature of the outer steel wire of the main cable is not higher than the outer steel wire temperature threshold, i.e. ,and If so, it is considered a minor injury; When the highest surface temperature of the outer steel wire of the main cable exceeds the outer steel wire temperature threshold, and the cumulative time exceeding the threshold does not exceed the threshold time, i.e. ,and The injury was moderate. When the highest surface temperature of the outer steel wire of the main cable exceeds the outer steel wire temperature threshold, and the cumulative time exceeding the threshold is greater than the threshold time, i.e. ,and This constitutes severe injury; Specifically, based on literature review, 95℃ is the temperature limit for changes in the physicochemical properties of the outermost wrapping tape or topcoat of the main cable; the temperature limit for irreversible damage to the aerogel felt is set at 600℃ based on literature review; and the temperature limit for no damage to the high-strength steel wire of the main cable is set at 300℃ based on literature review and experimental research, thus establishing the first surface temperature threshold. 95℃, second surface temperature threshold The temperature threshold is 600℃ and the outer steel wire temperature. 300℃; Therefore, if the predicted maximum surface temperature of the main cable is not higher than 95℃, it is considered undamaged. When the predicted maximum surface temperature of the main cable is higher than 95℃ but not higher than 600℃, and the maximum surface temperature of the outer steel wire of the main cable is not higher than 300℃, it is considered minor damage. When the predicted maximum surface temperature of the main cable is higher than 600℃ and the maximum surface temperature of the outer steel wire of the main cable is not higher than 300℃, it is considered minor damage. When the highest surface temperature of the outer steel wire of the main cable exceeds 300℃ and the cumulative time exceeding the threshold does not exceed the threshold time threshold, it is considered moderate damage. When the highest surface temperature of the outer steel wire of the main cable exceeds 300℃ and the cumulative time exceeding the threshold exceeds the threshold time, it is considered severe damage. The over-threshold time threshold is determined by the steel wire material library and / or experimental calibration.

[0029] The foregoing description, with reference to preferred embodiments, details an exemplary implementation of the intelligent assessment method for main cable damage after a bridge vehicle fire proposed in this invention. However, those skilled in the art will understand that various modifications and alterations can be made to the above specific embodiments without departing from the concept of this invention, and various combinations can be made to the various technical features and structures proposed in this invention without exceeding the protection scope of this invention, which is determined by the appended claims.

Claims

1. A method for intelligent assessment of main cable damage after a bridge vehicle fire, characterized in that, Specifically, the following steps are included: S1. Based on the large language model, the key working condition parameters and main cable protection type of the bridge cable vehicle fire scenario are identified, and working condition parameters including fire source intensity, fire source distance and bridge deck wind speed are obtained. S2: Load the bridge main cable internal temperature inversion model and the bridge main cable surface temperature prediction model according to the main cable protection type; verify the working condition parameters and, based on the verified working condition parameters, configure the bridge main cable internal temperature inversion model to output the main cable internal time-temperature prediction value, and configure the bridge main cable surface temperature prediction model to output the main cable surface maximum temperature prediction value. S3. The predicted time-temperature inside the main cable, the predicted maximum temperature on the surface of the main cable, and the safety threshold of the main cable material are compared to classify the damage. S4 integrates the operating condition parameter dataset, the predicted time-temperature value inside the main cable, the predicted maximum temperature on the main cable surface, and the damage level to generate a report containing parameter vectors, the time-temperature curve inside the main cable, the maximum temperature on the main cable surface, the damage level, and repair recommendations.

2. The intelligent assessment method for main cable damage after a bridge vehicle fire according to claim 1, characterized in that, The construction of the bridge main cable surface temperature prediction model in step S2 specifically includes the following steps: Step A-1: ​​Construct a bridge vehicle fire simulation model and output peak temperature-height data of the main cable surface under different working conditions using the simulation model. The peak temperature-height data output by the simulation model, the experimental data of the main cable surface obtained by the preset test platform, and the corresponding working parameters are used to construct a dataset. After preprocessing, the dataset is divided into a training set, a validation set, and a test set. Step A-2: Construct a bridge main cable surface temperature prediction model based on a conditional generative adversarial network. This model has a generator A and a discriminator A. The generator A is configured to generate predicted temperature curves based on noise vectors and normalized working condition parameters. The discriminator A is configured to distinguish between the generated temperature curves and the actual temperature curves and output a discrimination probability value. Train the bridge main cable surface temperature prediction model using a loss function on the training set. During the training process, monitor the training effect and adjust the training parameters through the validation set. Evaluate the performance of the bridge main cable surface temperature prediction model on the test set to obtain the trained bridge main cable surface temperature prediction model. Step A-3: Input the operating condition parameters from step S2 into the trained bridge main cable surface temperature prediction model and output the predicted value of the highest surface temperature of the main cable.

3. The intelligent assessment method for main cable damage after a bridge vehicle fire according to claim 2, characterized in that, The generator A in step A-2 includes an input layer, three first fully connected hidden layers, and a first output layer. The input layer is a concatenated vector of a noise vector and normalized working condition parameters, used for data input and feature initialization. The three first fully connected hidden layers are connected sequentially, with each first fully connected hidden layer followed by a batch normalization layer and a LeakyReLU activation function, used for feature extraction and nonlinear transformation at different levels, respectively. The first output layer includes a first fully connected layer and a Tanh activation function, used to output the predicted temperature values ​​at multiple standard altitude points. The discriminator A is a two-stream network structure, including a conditional stream, a data stream, a splicing layer, two second fully connected hidden layers, and a second output layer. The conditional stream includes a second fully connected layer and a LeakyReLU activation function, which are used to process the operating condition vector and extract conditional features. The data stream includes a third fully connected layer and a LeakyReLU activation function, which are used to process the temperature curve vector and extract data features. The concatenation layer concatenates the feature vectors output from the conditional stream and the data stream into a fused feature vector; the two second fully connected hidden layers are connected sequentially, with each second fully connected hidden layer followed by a LeakyReLU activation function, which are used for feature fusion and discriminant analysis, respectively; the second output layer includes a fourth fully connected layer and a Sigmoid activation function, which are used to output the discriminant probability value.

4. The intelligent assessment method for main cable damage after a bridge vehicle fire according to claim 3, characterized in that, The weighted combination loss function L used by generator A total From the counter-loss L adv and physical constraint loss L smooth Weighted according to the following formula: Among them, the resistance loss L adv The calculation formula is: Among them, physical constraint loss L smooth The calculation formula is: Where z is the noise vector; c is the conditional scalar; λ is the hyperparameter in the prediction model; E represents the mathematical expectation in the prediction model; P Z (z) represents the probability distribution of random noise; P data (c) represents the data distribution under actual operating conditions; D(.) is the output probability of discriminator A; G(.) is the output of generator A; N is the length of the temperature sequence; T i This represents the predicted temperature value at the i-th elevation point; The expression for the loss function used by discriminator A in the cable surface temperature prediction model is as follows: in, Discriminator A represents the probability of outputting "true" under given operating conditions.

5. The intelligent assessment method for main cable damage after a bridge vehicle fire according to claim 1, characterized in that, The construction of the temperature inversion model inside the main cable of the bridge in step S2 specifically includes the following steps: Step B-1: Input the working condition parameters into the bridge vehicle fire simulation model, and output the time-temperature data of the main cable surface by the simulation model. Input the time-temperature data of the main cable surface into the main cable heat transfer simulation model, and output the time-temperature data of the main cable interior; Step B-2: The above-output time-temperature data inside the main cable, experimental data inside the main cable, and corresponding operating parameters are used to construct a dataset. After preprocessing, the dataset is divided into a training set, a validation set, and a test set. Step B-3: Construct a main cable heat transfer inversion model based on LSTM conditional generative adversarial network. Use a weighted combined loss function that includes adversarial loss and physical constraints. Train the main cable heat transfer inversion model with the training set. During the training process, monitor the training effect through the validation set and adjust the training parameters. Evaluate the performance of the main cable heat transfer inversion model on the test set to obtain the trained main cable heat transfer inversion model. Step B-4: Input the operating condition parameters from step S2 into the trained main cable heat transfer inversion model and output the predicted time-temperature values ​​inside the main cable.

6. The intelligent assessment method for main cable damage after a bridge vehicle fire according to claim 5, characterized in that, In step B-1, the heat transfer simulation model of the main cable is derived using a rectangular finite element model. The rectangular finite element model consists of the main cable steel wires and the air between the steel wires. The internal steel wire dimensions and porosity are consistent with the experimental model. There is surface-to-surface thermal radiation between the steel wires. The radiating surface is defined as a diffuse reflective surface, and the emissivity is set. The radiation direction is set to be controlled by the opacity. The opacity of the steel wires and the air is set to opaque and transparent, respectively. The air is set to be non-flowing, and the thermal convection of the air is ignored. The main cable structure is regarded as a porous medium of "steel wire-void". By fitting the equivalent thermal conductivity and correcting the equivalent specific heat capacity, a simplified equivalent model of the main cable structure of steel wire-void is established. The input is the time-temperature data of the main cable surface, and the output is the time-temperature data of the main cable interior.

7. The intelligent assessment method for main cable damage after a bridge vehicle fire according to claim 6, characterized in that, The equivalent thermal conductivity of the simplified equivalent model is derived through the rectangular finite element model, and its calculation formula is as follows: in, The average heat flux density is denoted as H; H is the thickness of the main cable protective layer; T1 and T2 are the upper and lower boundary temperatures, respectively. Numerical fitting was performed on the equivalent thermal conductivity, and a fourth-order polynomial was used to obtain the formula for calculating the equivalent thermal conductivity as a function of temperature: in, , , , For temperature coefficient, It is a constant. Ambient temperature; The equivalent specific heat capacity is corrected to 60%-80% of the original specific heat capacity based on the porosity of the model.

8. The intelligent assessment method for main cable damage after a bridge vehicle fire according to claim 5, characterized in that, In step B-3, the main cable heat transfer inversion model has a generator B and a discriminator B; Generator B uses an LSTM autoregressive structure, specifically including the following steps: The input layer receives the concatenated vector [z;c], where c is a conditional scalar defined by operating parameters, and z is a noise vector; The initial hidden state and initial cell state of the LSTM are generated through a fully connected layer; At each time step, the predicted temperature from the previous time step is concatenated with the conditional scalar c as input. The hidden state and cell state are updated through the LSTM gating mechanism. The output layer uses the Sigmoid activation function to generate normalized temperature values, thus obtaining the complete main cable temperature prediction sequence. Discriminator B uses a conditional LSTM structure, specifically including the following steps: At each time step, the temperature value is concatenated with the conditional scalar c to form the input sequence. Temporal features are extracted by an LSTM encoder, and the final hidden state outputs the probability of authenticity through a fully connected layer and a Sigmoid activation function.

9. The intelligent assessment method for main cable damage after a bridge vehicle fire according to claim 8, characterized in that, The generator B introduces a reconstruction term of the main cable temperature sequence on top of the adversarial loss, and the weighted combined loss function is: The discriminator B loss uses the standard binary cross-entropy form: in, This represents the mathematical expectation in the inversion model; z is the noise vector; c is the conditional scalar; This represents the probability function indicating the authenticity of the output of discriminator B. This represents the main cable temperature sequence function generated by generator B. P represents the hyperparameters in the inversion model; y represents the true main cable temperature sequence sampled from the real data distribution; P represents the hyperparameters in the inversion model. data This is the data distribution of the actual main cable temperature sequence.

10. The intelligent assessment method for main cable damage after a bridge vehicle fire according to claim 1, characterized in that, In step S3, the external temperature for damage grading is the predicted value of the highest surface temperature of the main cable. The internal temperature is measured using the highest surface temperature of the outer steel wire of the main cable. Measured by the predicted time-temperature value inside the main cable; The safety threshold for main cable materials includes the first surface temperature threshold. Second surface temperature threshold and the temperature threshold of the outer steel wire ; Set the over-threshold time threshold Cumulative time exceeding the threshold The cumulative duration during which the surface temperature of the outer steel wire of the main cable exceeds the outer steel wire temperature threshold. when If so, it is determined to be undamaged; when ,and If so, it is determined to be a minor injury; when ,and If so, it is determined to be a minor injury; when ,and If so, it is determined to be a moderate injury; when ,and If so, it is determined to be a severe injury.

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