Existing building apparent damage trend prediction method

Abstract

The invention discloses an existing building apparent damage trend prediction method, and relates to the technical field of building detection, and the method comprises the following steps: S1, defining damage features and classification; s2, selecting a proper sensor; s3, establishing a multi-dimensional data acquisition system; s4, designing a 4D network model; s5, carrying out model training and tuning; s6, constructing a dynamic association mechanism; s7, result verification and reliability evaluation; s8, constructing a visualization and interaction platform; and S9, a continuous learning and model updating mechanism. According to the method, through real-time data input and dynamic updating of the model state, the change of the apparent damage of the building can be captured in time. Attention on key damage features is enhanced by using an attention mechanism, and prediction precision and real-time performance are improved. Through the innovative method, the building damage monitoring efficiency and accuracy can be remarkably improved, more reliable data support is provided for building safety management, the service life of the building is finally prolonged, and the maintenance cost is reduced.

Description

Technical Field

[0001] The invention relates to the technical field of building detection, and in particular to a method for predicting apparent damage trend of existing buildings. Background Art

[0002] With the acceleration of urbanization, the safety and durability of existing buildings have become important social issues. Existing building damage monitoring technologies mainly rely on regular manual inspections or simple sensor data analysis, which leads to a delayed response to damage status and lacks real-time and comprehensiveness. In addition, traditional methods often find it difficult to effectively integrate multi-dimensional data and cannot dynamically capture the changes in building damage over time and space, thus affecting the accurate prediction of damage trends.

[0003] In order to solve the above problems, we proposed a method for predicting the apparent damage trend of existing buildings. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a method for predicting the apparent damage trend of an existing building to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solution: a method for predicting the apparent damage trend of an existing building, comprising the following steps: S1. Define injury characteristics and classification; Identify the damage characteristics of existing buildings that need to be monitored, and develop damage classification standards through literature research and historical damage records to effectively distinguish different types of damage when modeling; S2. Select a suitable sensor; According to the damage characteristics to be monitored, select appropriate sensors to collect relevant data of the damage characteristics. The sensors include image recognition equipment, laser scanners, and strain sensors to ensure that the selected sensors can reflect the damage status of the existing buildings in real time and accurately. S3. Establish a multi-dimensional data collection system; Combine sensor data, environmental factors and historical damage records to form a monitoring database, establish a multi-dimensional data collection system, comprehensively analyze multi-dimensional data, and understand the influencing factors and development trends of damage; S4, design 4D network model; After the data processing is ready, a 4D network model suitable for building damage prediction is designed. The 4D network model combines the spatiotemporal characteristics to dynamically capture the changes of damage status over time and space. The 4D network model also introduces an attention mechanism to enhance the model's ability to focus on key damage features and improve the accuracy of prediction. S5, model training and tuning; The designed 4D network model is trained using the processed data, the performance of the model is evaluated using cross-validation, and the model parameters, such as learning rate and batch size, are adjusted based on the evaluation results; S6, construction of dynamic association mechanism; Build a dynamic association mechanism for the dynamic association of damage status information, network models and monitoring solutions. This mechanism updates the model status based on real-time data input and adjusts the monitoring solution through a feedback loop to ensure that the model always adapts to the latest building conditions. S7, result verification and reliability assessment; After the model training is completed, the prediction results are evaluated using an independent validation set; S8, build visualization and interaction platform; Build a visualization and interactive platform, use data visualization technology to present prediction results, real-time monitoring data and historical damage records in the form of charts, and provide user interaction functions so that users can customize monitoring plans according to actual needs; S9. Continuous learning and model updating mechanism.

[0006] Further optimizing the technical solution, in step S1, the damage characteristics of the existing building include appearance cracks, deformation, corrosion, shedding, water penetration and deterioration; in, Appearance cracks include crack width, depth, and direction; Deformation includes displacement, tilt, and depression; Corrosion includes corrosion degree and distribution; Shedding includes material flaking and fragment distribution; Water penetration includes water marks and humidity changes; Deterioration includes loss of material strength and signs of aging.

[0007] To further optimize the technical solution, in step S3, the multi-dimensional data collection system includes a database system and a data processing platform; Database system: Choose a suitable database management system such as MySQL or PostgreSQL to support storage and query of large-scale data; Data processing platform, using data processing platforms such as Apache Spark for data cleaning, integration and analysis, providing flexible analysis capabilities and efficient data processing capabilities; The multi-dimensional data collection system collects multi-dimensional data into the database system through the data collection interface to ensure the real-time update and accessibility of the data; Using data analysis tools, statistical analysis and correlation analysis are performed on multi-dimensional data to identify potential influencing factors of damage and generate a damage development trend model.

[0008] To further optimize the technical solution, in step S4, the 4D network model combines time T, space X, Y, and feature dimension F to predict the damage trend. The 4D network model consists of the following parts: Input layer: receives multi-dimensional data, including sensor data, environmental factors, and historical damage records; Convolutional layer: used to extract spatial features and identify damage patterns; Cyclic layer: captures time series data and identifies trends in damage over time; Attention mechanism: Enhance the model's attention to key damage features and improve prediction accuracy; Output layer: outputs the prediction results of future damage status.

[0009] To further optimize the technical solution, the 4D network model includes the following processes when being constructed: Assume a 4D tensor ,in, For time, is the spatial coordinate X axis, is the spatial coordinate Y axis, is the feature dimension; The formula of the 4D network model is as follows: ; in, is the output of the convolutional layer, is the convolution kernel, and its size is set according to actual needs. is the bias term; Use a recurrent layer to process time series data: ; in, It is the output of the recurrent layer, indicating the change of damage characteristics over time.

[0010] To further optimize this technical solution, the attention mechanism introduced by the 4D network model is as follows: ; in, is the attention weight, is the learned weight matrix, is the feature dimension; Finally, the prediction result of the output layer is expressed as: ; in, For damage status prediction.

[0011] To further optimize the technical solution, in step S5, cross-validation is used to evaluate the performance of the model, including the following specific process: Data segmentation: Divide the preprocessed dataset into training set, validation set and test set; Model training: Use the training set to train the 4D network model; Cross-validation: Use k-fold cross-validation, such as k=5, to divide the training set into k subsets, and perform training k times in a cycle, each time using one subset as the validation set and the other subsets as the training set; Performance evaluation: Calculate the performance indicators of the model through the validation set, such as root mean square error and accuracy; Parameter tuning: adjust model parameters based on evaluation results; Final training: After multiple rounds of training and tuning, the entire training set is trained using the finalized hyperparameters.

[0012] To further optimize the technical solution, in the model training, the loss function in the training process selects mean square error or cross entropy loss, and the formula is as follows: ; in, For loss, is the sample size, is the true value, is the predicted value; The parameter tuning includes: Learning rate: Use learning rate scheduling strategies such as cosine annealing to dynamically adjust the learning rate; Batch size: Try different batch sizes to optimize training speed and stability; Regularization: Add L1 or L2 regularization terms to prevent the model from overfitting.

[0013] To further optimize the technical solution, in step S6, the dynamic association mechanism includes the following specific processes when being constructed: Real-time data input: real-time monitoring data input, including sensor data, environmental factors and historical damage records; Status update: When new data is input, the model status is updated and the new damage status is calculated by the following formula: : ; in, is the damage status at the current moment, is the damage status at the previous moment, is the damage change calculated based on the new input data; Feedback loop: Compare the model's prediction results with the actual monitoring data and calculate the prediction error. The calculation formula is as follows: ; in, is the prediction error, is the actual monitoring value, is the model prediction value; Monitoring plan adjustment: Dynamically adjust the monitoring plan based on the prediction error. If the error exceeds the set threshold, increase the sensor collection frequency or change the monitoring point to obtain more accurate data. Regular model retraining: Set up regular retraining of the model and use the latest monitoring data to update the model weights to keep it accurate and effective.

[0014] To further optimize the technical solution, in step S7, the prediction result is compared with the actual monitoring result to analyze the accuracy and recall rate of the model to ensure the reliability of the prediction result of the model; And conduct long-term follow-up monitoring and make a comprehensive evaluation of the model's prediction results in different time periods.

[0015] Compared with the prior art, the present invention provides a method for predicting the apparent damage trend of existing buildings, which has the following beneficial effects: This method for predicting the apparent damage trend of existing buildings can timely capture changes in the apparent damage of buildings through real-time data input and dynamic updating of model status. At the same time, the attention mechanism is used to strengthen the focus on key damage characteristics, improving the accuracy and real-time performance of the prediction. This innovative method can significantly improve the efficiency and accuracy of building damage monitoring, provide more reliable data support for building safety management, and ultimately extend the service life of buildings and reduce maintenance costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A schematic diagram of a flow chart of a method for predicting apparent damage trend of existing buildings proposed by the present invention; Figure 2 A schematic diagram of a multi-dimensional data collection system in a method for predicting apparent damage trend of existing buildings proposed by the present invention; Figure 3 A schematic diagram of constructing a 4D network model in a method for predicting apparent damage trend of existing buildings proposed by the present invention; Figure 4 A schematic diagram of the process of model training and tuning in a method for predicting apparent damage trend of existing buildings proposed by the present invention; Figure 5 This is a schematic diagram of constructing a dynamic correlation mechanism in a method for predicting apparent damage trends of existing buildings proposed in the present invention. DETAILED DESCRIPTION

[0017] The following will be combined with the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] Example: See also Figure 1 , a method for predicting the apparent damage trend of an existing building, comprising the following steps: S1. Define injury characteristics and classification Identify the damage characteristics of existing buildings that need to be monitored, and develop damage classification standards through literature research and historical damage records to effectively distinguish different types of damage when modeling.

[0019] In this embodiment, in step S1, the damage characteristics of the existing building include appearance cracks, deformation, corrosion, shedding, water penetration and deterioration.

[0020] in, Appearance cracks include crack width, depth, and direction; Deformation includes displacement, tilt, and depression; Corrosion includes corrosion degree and distribution; Shedding includes material flaking and fragment distribution; Water penetration includes water marks and humidity changes; Deterioration includes loss of material strength and signs of aging.

[0021] Based on the above damage characteristics, appropriate monitoring methods are selected. The monitoring methods corresponding to the damage characteristics are shown in Table 1. Table 1 can help clearly define the damage characteristics and monitoring methods, which is convenient for subsequent data analysis and model construction.

[0022]

[0023] Table 1 S2. Choose the right sensor According to the damage characteristics that need to be monitored, appropriate sensors are selected to collect relevant data on the damage characteristics. The sensors include image recognition equipment, laser scanners, and strain sensors, which are used to ensure that the selected sensors can reflect the damage status of existing buildings in real time and accurately.

[0024] S3. Establish a multi-dimensional data collection system By combining sensor data, environmental factors and historical damage records, a monitoring database is formed, a multi-dimensional data collection system is established, and multi-dimensional data is comprehensively analyzed to understand the influencing factors and development trends of damage.

[0025] like Figure 2 As shown, in this embodiment, in step S3, the multi-dimensional data collection system includes a database system and a data processing platform; Database system: Choose a suitable database management system such as MySQL or PostgreSQL to support storage and query of large-scale data; Data processing platform, using data processing platforms such as Apache Spark for data cleaning, integration and analysis, providing flexible analysis capabilities and efficient data processing capabilities; The multi-dimensional data collection system collects multi-dimensional data into the database system through the data collection interface to ensure the real-time update and accessibility of the data; Using data analysis tools, statistical analysis and correlation analysis are performed on multi-dimensional data to identify potential influencing factors of damage and generate a damage development trend model.

[0026] In this embodiment, a multi-dimensional data collection system is shown in Table 2.

[0027] Table 2 S4. Design 4D network model After the data processing is ready, a 4D network model suitable for building damage prediction is designed. The 4D network model combines the spatiotemporal characteristics to dynamically capture the changes of damage status over time and space. The 4D network model also introduces an attention mechanism to enhance the model's ability to focus on key damage features and improve the accuracy of prediction.

[0028] like Figure 3 As shown, in this embodiment, in step S4, the 4D network model combines time T, space X, Y, and feature dimension F to predict the damage trend. The 4D network model consists of the following parts: Input layer: receives multi-dimensional data, including sensor data, environmental factors, and historical damage records; Convolutional layer: used to extract spatial features and identify damage patterns; Cyclic layer: captures time series data and identifies trends in damage over time; Attention mechanism: Enhance the model's attention to key damage features and improve prediction accuracy; Output layer: outputs the prediction results of future damage status.

[0029] The 4D network model is constructed by the following process: Assume a 4D tensor ,in, For time, is the spatial coordinate X axis, is the spatial coordinate Y axis, is the feature dimension; The formula of the 4D network model is as follows: ; in, is the output of the convolutional layer, is the convolution kernel, and its size is set according to actual needs. is the bias term; Use a recurrent layer to process time series data: ; in, It is the output of the recurrent layer, indicating the change of damage characteristics over time.

[0030] The attention mechanism introduced by the 4D network model is as follows: ; in, is the attention weight, is the learned weight matrix, is the feature dimension; Finally, the prediction result of the output layer is expressed as: ; in, For damage status prediction.

[0031] Based on the above model, when using: Step 1: Data Preparation During the data preparation phase, ensure that the data format conforms to the 4D tensor The data should be normalized for better calculations in the model.

[0032] Step 2: Model training Use the defined model structure and train it with historical data. Use the back propagation algorithm to optimize the model parameters. , , . Cross-validation can be used to evaluate the model effect during training.

[0033] Step 3: Dynamic Input After the model training is completed, the real-time monitoring data can be dynamically input into the model. Update the model state. After each new data input, new features are extracted through the convolutional layer, the time state is updated through the recurrent layer, and the key features are strengthened using the attention mechanism.

[0034] Step 4: Predict Output Finally, the prediction result is obtained through the output layer , providing future damage trend prediction. This process can provide important basis for subsequent maintenance decisions and risk assessment.

[0035] S5. Model training and tuning The designed 4D network model is trained using the processed data, cross-validation is used to evaluate the performance of the model, and the model parameters, such as learning rate and batch size, are adjusted according to the evaluation results.

[0036] like Figure 4 As shown, in this embodiment, in step S5, cross-validation is used to evaluate the performance of the model, including the following specific processes: Data segmentation: The preprocessed data set is divided into training set, validation set and test set. Usually 70% is used for training, 15% for validation and 15% for testing. This can ensure the generalization ability of the model. Model training: Use the training set to train the 4D network model; Cross-validation: Use k-fold cross-validation, such as k=5, to divide the training set into k subsets, and perform training k times in a cycle, using one subset as the validation set each time and the other subsets as the training set, to more comprehensively evaluate the model performance and avoid overfitting; Performance evaluation: Calculate the performance indicators of the model through the validation set, such as root mean square error and accuracy, to ensure the predictive ability of the model. At the same time, draw a learning curve to observe the changes in training and validation errors to determine whether further tuning is needed; Parameter tuning: adjust model parameters based on evaluation results; Final training: After multiple rounds of training and tuning, the entire training set is trained using the finalized hyperparameters.

[0037] Through the above steps, the predictive ability of the 4D network model can be effectively improved, providing reliable support for the monitoring and evaluation of building damage trends.

[0038] In the model training, the loss function in the training process selects mean square error or cross entropy loss, and the formula is as follows: ; in, For loss, is the sample size, is the true value, is the predicted value; The parameter tuning includes: Learning rate: Use learning rate scheduling strategies such as cosine annealing to dynamically adjust the learning rate; Batch size: Try different batch sizes to optimize training speed and stability; Regularization: Add L1 or L2 regularization terms to prevent the model from overfitting.

[0039] S6. Construction of dynamic association mechanism A dynamic association mechanism is constructed to dynamically associate damage status information, network models and monitoring schemes. The mechanism updates the model status based on real-time data input and adjusts the monitoring scheme through a feedback loop to ensure that the model always adapts to the latest building conditions.

[0040] like Figure 5 As shown, in this embodiment, in step S6, the dynamic association mechanism includes the following specific processes when being constructed: Real-time data input: real-time monitoring data input, including sensor data, environmental factors and historical damage records; Status update: When new data is input, the model status is updated and the new damage status is calculated by the following formula: : ; in, is the damage status at the current moment, is the damage status at the previous moment, is the damage change calculated based on the new input data; Feedback loop: Compare the model's prediction results with the actual monitoring data and calculate the prediction error. The calculation formula is as follows: ; in, is the prediction error, is the actual monitoring value, is the model prediction value; the error is used to adjust the monitoring plan and model parameters to ensure their continuous optimization; Monitoring plan adjustment: Dynamically adjust the monitoring plan based on the predicted error. If the error exceeds the set threshold, increase the sensor collection frequency or change the monitoring point to obtain more accurate data. This adjustment ensures that the monitoring plan adapts to changes in the building status and detects potential damage in a timely manner. Regular model retraining: Set up regular model retraining and use the latest monitoring data to update the model weights to keep it accurate and effective. This retraining can be based on the performance evaluation results of the model to effectively improve the prediction ability.

[0041] Through the dynamic association mechanism, damage status information, network models and monitoring solutions can be interconnected in real time to ensure the accuracy and timeliness of building damage prediction. The implementation of this mechanism will provide strong support for building maintenance decisions.

[0042] S7. Result verification and reliability assessment After the model training is completed, the prediction results are evaluated using an independent validation set.

[0043] In this embodiment, in step S7, the prediction result is compared with the actual monitoring result to analyze the accuracy and recall rate of the model to ensure the reliability of the prediction result of the model; And conduct long-term follow-up monitoring and make a comprehensive evaluation of the model's prediction results in different time periods.

[0044] S8. Build a visualization and interactive platform Build a visualization and interactive platform, use data visualization technology to present prediction results, real-time monitoring data and historical damage records in the form of charts, and provide user interaction functions so that users can customize monitoring plans according to actual needs.

[0045] S9. Continuous learning and model updating mechanism Establish a continuous learning and model updating mechanism to regularly retrain and optimize the model by continuously introducing new monitoring data and feedback.

[0046] In this embodiment, this mechanism can ensure that the model remains efficient and accurate during long-term use, and adapts to the changing building conditions and external environment. This self-updating ability is the key to achieving long-term effective prediction.

[0047] The beneficial effects of the present invention are: This method for predicting the apparent damage trend of existing buildings can timely capture changes in the apparent damage of buildings through real-time data input and dynamic updating of model status. At the same time, the attention mechanism is used to strengthen the focus on key damage characteristics, improving the accuracy and real-time performance of the prediction. This innovative method can significantly improve the efficiency and accuracy of building damage monitoring, provide more reliable data support for building safety management, and ultimately extend the service life of buildings and reduce maintenance costs.

[0048] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0049] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for predicting the apparent damage trend of an existing building, characterized in that: The following steps are involved: S1. Define injury characteristics and classification; Identify the damage characteristics of existing buildings that need to be monitored, and develop damage classification standards through literature research and historical damage records to effectively distinguish different types of damage when modeling; S2. Select a suitable sensor; According to the damage characteristics to be monitored, select appropriate sensors to collect relevant data of the damage characteristics. The sensors include image recognition equipment, laser scanners, and strain sensors to ensure that the selected sensors can reflect the damage status of the existing buildings in real time and accurately. S3. Establish a multi-dimensional data collection system; Combine sensor data, environmental factors and historical damage records to form a monitoring database, establish a multi-dimensional data collection system, comprehensively analyze multi-dimensional data, and understand the influencing factors and development trends of damage; S4, design 4D network model; After the data processing is ready, a 4D network model suitable for building damage prediction is designed. The 4D network model combines the spatiotemporal characteristics to dynamically capture the changes of damage status over time and space. The 4D network model also introduces an attention mechanism to enhance the model's ability to focus on key damage features and improve the accuracy of prediction. S5, model training and tuning; The designed 4D network model is trained using the processed data, the performance of the model is evaluated using cross-validation, and the model parameters, such as learning rate and batch size, are adjusted based on the evaluation results; S6, construction of dynamic association mechanism; Build a dynamic association mechanism for the dynamic association of damage state information, network models and monitoring solutions. This mechanism updates the model state based on real-time data input and adjusts the monitoring solution through a feedback loop to ensure that the model always adapts to the latest building conditions. S7, result verification and reliability assessment; After the model training is completed, the prediction results are evaluated using an independent validation set; S8, build visualization and interaction platform; Build a visualization and interactive platform, use data visualization technology to present prediction results, real-time monitoring data and historical damage records in the form of charts, and provide user interaction functions so that users can customize monitoring plans according to actual needs; S9, continuous learning and model updating mechanism; Establish a continuous learning and model updating mechanism to regularly retrain and optimize the model by continuously introducing new monitoring data and feedback.

2. The method for predicting apparent damage trend of existing buildings according to claim 1, characterized in that: In step S1, the damage characteristics of the existing building include appearance cracks, deformation, corrosion, shedding, water penetration and deterioration; in, Appearance cracks include crack width, depth, and direction; Deformation includes displacement, tilt, and depression; Corrosion includes corrosion degree and distribution; Shedding includes material flaking and fragment distribution; Water penetration includes water marks and humidity changes; Deterioration includes loss of material strength and signs of aging.

3. The method for predicting apparent damage trend of existing buildings according to claim 1, characterized in that: In step S3, the multi-dimensional data collection system includes a database system and a data processing platform; Database system: Choose a suitable database management system such as MySQL or PostgreSQL to support storage and query of large-scale data; Data processing platform, using data processing platforms such as Apache Spark for data cleaning, integration and analysis, providing flexible analysis capabilities and efficient data processing capabilities; The multi-dimensional data collection system collects multi-dimensional data into the database system through the data collection interface to ensure the real-time update and accessibility of the data; Using data analysis tools, statistical analysis and correlation analysis are performed on multi-dimensional data to identify potential influencing factors of damage and generate a damage development trend model.

4. The method for predicting apparent damage trend of existing buildings according to claim 1, characterized in that: In step S4, the 4D network model combines time T, space X, Y, and feature dimension F to predict the damage trend. The 4D network model consists of the following parts: Input layer: receives multi-dimensional data, including sensor data, environmental factors, and historical damage records; Convolutional layer: used to extract spatial features and identify damage patterns; Cyclic layer: captures time series data and identifies trends in damage over time; Attention mechanism: Enhance the model's attention to key damage features and improve prediction accuracy; Output layer: outputs the prediction results of future damage status.

5. The method for predicting apparent damage trend of existing buildings according to claim 4, characterized in that: The 4D network model is constructed by the following process: Assume a 4D tensor ,in, For time, is the spatial coordinate X axis, is the spatial coordinate Y axis, is the feature dimension; The formula of the 4D network model is as follows: ； in, is the output of the convolutional layer, is the convolution kernel, and its size is set according to actual needs. is the bias term; Use a recurrent layer to process time series data: ； in, It is the output of the recurrent layer, indicating the change of damage characteristics over time.

6. The method for predicting apparent damage trend of existing buildings according to claim 4, characterized in that: The attention mechanism introduced by the 4D network model is as follows: ； in, is the attention weight, is the learned weight matrix, is the feature dimension; Finally, the prediction result of the output layer is expressed as: ； in, For damage status prediction.

7. The method for predicting apparent damage trend of existing buildings according to claim 1 is characterized in that: In step S5, cross-validation is used to evaluate the performance of the model, including the following specific process: Data segmentation: Divide the preprocessed dataset into training set, validation set and test set; Model training: Use the training set to train the 4D network model; Cross-validation: Use k-fold cross-validation, such as k=5, to divide the training set into k subsets, and perform training k times in a cycle, each time using one subset as the validation set and the other subsets as the training set; Performance evaluation: Calculate the performance indicators of the model through the validation set, such as root mean square error and accuracy; Parameter tuning: adjust model parameters based on evaluation results; Final training: After multiple rounds of training and tuning, the entire training set is trained using the finalized hyperparameters.

8. The method for predicting apparent damage trend of existing buildings according to claim 7, characterized in that: In the model training, the loss function in the training process selects mean square error or cross entropy loss, and the formula is as follows: ； in, For loss, is the sample size, is the true value, is the predicted value; The parameter tuning includes: Learning rate: Use learning rate scheduling strategies such as cosine annealing to dynamically adjust the learning rate; Batch size: Try different batch sizes to optimize training speed and stability; Regularization: Add L1 or L2 regularization terms to prevent the model from overfitting.

9. The method for predicting apparent damage trend of existing buildings according to claim 1, characterized in that: In step S6, when constructing the dynamic association mechanism, the following specific processes are included: Real-time data input: real-time monitoring data input, including sensor data, environmental factors and historical damage records; Status update: When new data is input, the model status is updated and the new damage status is calculated by the following formula: : ； in, is the damage status at the current moment, is the damage status at the previous moment, is the damage change calculated based on the new input data; Feedback loop: Compare the model's prediction results with the actual monitoring data and calculate the prediction error. The calculation formula is as follows: ； in, is the prediction error, is the actual monitoring value, is the model prediction value; Monitoring plan adjustment: Dynamically adjust the monitoring plan based on the prediction error. If the error exceeds the set threshold, increase the sensor collection frequency or change the monitoring point to obtain more accurate data. Regular model retraining: Set up regular retraining of the model and use the latest monitoring data to update the model weights to keep it accurate and effective.

10. The method for predicting apparent damage trend of existing buildings according to claim 1, characterized in that: In step S7, the prediction results are compared with the actual monitoring results to analyze the accuracy and recall of the model to ensure the reliability of the prediction results of the model; And conduct long-term follow-up monitoring and make a comprehensive evaluation of the model's prediction results in different time periods.

Images