Quality evaluation and analysis method for excavation support of underground powerhouse

Abstract

The invention provides an underground powerhouse excavation support quality evaluation and analysis method, and relates to the field of underground powerhouses, and the method comprises the following steps: S1, data collection and preprocessing: building a basic database, collecting geological exploration data and on-site monitoring data of an underground powerhouse to obtain geological parameter data # imgabs0 #, preprocessing the geological parameter data # imgabs1 #, and storing the preprocessed geological parameter data # imgabs0 # into a database; setting a standard feature vector as an evaluation index; and S2, model selection and integration strategy: selecting a physical model and a machine learning model according to the preprocessed geological parameter data # imgabS2. Through the cooperation of the above structures, the method has the following beneficial effects: firstly, the crustal stress distribution of each part of the underground powerhouse can be accurately mastered by integrating a multi-factor crustal stress calculation model; secondly, a real-time monitoring system can be combined with big data and an artificial intelligence algorithm, support hidden dangers can be found in time and dynamically optimized, and surrounding rock instability is effectively prevented; and 3, the multi-source data integration platform provides comprehensive and accurate information, assists a decision maker in comprehensive analysis, and avoids one-sided decision making.

Description

Technical Field

[0001] The invention relates to the field of underground powerhouses, and in particular to a method for evaluating and analyzing the excavation and support quality of underground powerhouses. Background Art

[0002] In the construction of underground powerhouses, the technical problems faced are complex, diverse and interrelated. The geostress environment is complex and changeable. The magnitude and direction of geostress in different regions and depths vary significantly. The geostress in plate boundaries or areas with frequent geological tectonic activities is large and complex in direction. Stress concentration will occur in mountainous and canyon areas due to the influence of topography and landforms. The history of geological tectonic movement also causes uneven distribution of geostress fields. The lithology and strength of the surrounding rocks vary greatly. Different lithology surrounding rocks such as igneous rocks, sedimentary rocks, and metamorphic rocks have their own characteristics. Their strength is affected by internal factors such as mineral composition, particle structure, and degree of cementation, as well as external factors such as groundwater erosion. The support strength is closely related to the stability of the surrounding rock. When the support strength is insufficient, the weak surrounding rock is prone to plastic deformation under high geostress, and the surrounding rock with developed joints and fissures will collapse, threatening the safety and progress of construction.

[0003] Due to the different ground stress environment of underground powerhouses and the different lithology and strength of the surrounding rocks, the support strength required to maintain the stability of the surrounding rocks also changes accordingly. Insufficient support strength may lead to local instability, collapse, or excessive deformation of the surrounding rocks, or even overall destruction. Excessive support strength will cause unnecessary waste. Summary of the invention

[0004] In view of the deficiencies in the above-mentioned prior art, the technical problem to be solved by the present invention is to provide a method for evaluating and analyzing the quality of excavation and support of underground power plants, which can realize accurate excavation and support design, ensure that the support structure can effectively maintain the stability of the surrounding rock, and prevent the surrounding rock from becoming unstable, collapsing or excessively deforming.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is: the present invention provides an underground powerhouse excavation support quality evaluation and analysis method, comprising the following steps: S1. Data collection and preprocessing: Establish a basic database and collect geological exploration data and field monitoring data of underground powerhouses to obtain geological parameter data , and the geological parameter data Perform preprocessing and set the standard feature vector as the evaluation index; S2. Model selection and integration strategy: Based on the preprocessed geological parameter data Select a physical model and a machine learning model, and determine the corresponding integration strategy based on the selected physical model and machine learning model; S3, model training and iteration: Train the selected physical model and machine learning model, and alternately train the deep learning model and the physical model. Use the prediction results of the deep learning model to optimize the parameters of the physical model, and use the output results of the physical model to guide the training of the deep learning model. S4. Integrated prediction and performance verification: Integrate the prediction results of the trained deep learning model and the physical model to obtain an integrated model, and use actual engineering data to verify the prediction performance of the integrated model.

[0006] The preferred solution also includes the following steps: S5. Application and continuous optimization: Deploy the trained deep learning model, physical model and integrated model to the actual engineering environment, so that it can receive the geological parameter data of the engineering site in real time. , and make predictions and analyses. During the deployment process, the model is seamlessly integrated with the data acquisition system and monitoring system of the engineering site to obtain the required data and output the prediction results. The prediction performance of the integrated model is evaluated based on the prediction results and the set standard feature vectors to obtain the performance evaluation results. According to the performance evaluation results, the integrated model, deep learning model and physical model are adjusted and optimized as necessary; The deep learning model is deployed on the server at the project site, and the geological parameter data collected by the sensor is received in real time through the network connection. , the physical model can be used to adjust parameters according to the actual situation on site and then perform calculations and analysis. The two work together to provide decision-making for engineering construction; Continue to collect new prediction results, organize and store them in the basic database, repeat steps S1 to S5, and use the new data to update and optimize the deep learning model and physical model; In a preferred solution, in step S1, the stratum lithology, geological structure and ground stress conditions are determined based on geological survey data, and on-site monitoring data are collected through sensors to obtain construction dynamics; Among them, the on-site monitoring data include surrounding rock deformation, stress changes and support structure force data; Preprocessing includes the geological parameter data Cleaning and conversion, which is used to organize data to remove duplicate, erroneous or incomplete records, convert non-numeric data into a form suitable for analysis, and standardize data from different sources, including unifying physical units and normalizing numerical ranges; Set the standard eigenvector as the evaluation index, including the anchor support strength index of the standard parameters The range is 1.17 to 1.24, standard parameter anchor support strength index Range: 1.55 to 1.7; in, and It is the range value under ideal conditions determined based on a large amount of engineering experience and theoretical analysis, and is used to measure the difference between actual engineering and standard conditions.

[0007] In the preferred solution, in step S2, the physical model adopts a physical model based on finite element analysis to simulate the physical behavior of anchor rods and anchor cables under extremely high ground stress, and the physical model is used to simulate the physical behavior of anchor rods and anchor cables under extremely high ground stress according to the geological parameter data of the underground powerhouse. Set model parameters and define boundary conditions in the physical model based on finite element analysis to simulate the constraints in actual engineering; Build another deep learning model and choose convolutional neural network as the deep learning architecture; The convolutional layer of the convolutional neural network can be used to automatically extract local features from the physical model based on finite element analysis, the pooling layer is used to reduce the data dimension, and the fully connected layer is used to integrate the features and make the final prediction. The key features are input into the deep learning model, and the key features are extracted from the calculation results of the physical model based on finite element analysis, including stress distribution characteristic diagram stress and deformation trend data deformation. The key features are used as additional input of the deep learning model to achieve an organic combination of the physical model based on finite element analysis and the deep learning model, so that the deep learning model can learn the relevant features of the physical process simulated by the physical model, and enhance the deep learning model's prediction ability for complex geological and stress conditions.

[0008] In the preferred solution, in step S3, the specific training steps are as follows: Using the collected and pre-processed geological parameter data Train a deep learning model to predict the support strength index of anchor bolts and cables, and use the preprocessed geological parameter data As a dataset , and set the data set The total sample size is , divided into training sets according to the ratio of 7:2:1 , validation set and test set ,Right now , , , through the training set The initially constructed deep learning model is trained in multiple rounds under supervision and validated on the validation set. Monitor the training results and use the test set Evaluate model performance; in, , , ; Define the loss function: ; in, is the actual support strength index in the training set, is the prediction function corresponding to the deep learning model, For the training set Middle Input geological parameter data A sample of The parameters of the deep learning model are obtained by inputting geological parameter data And parameters , output the corresponding predicted value , and calculate the predicted value With actual value The average of the sum of squared differences of quantized model training sets The prediction error on ; Use the stochastic gradient descent algorithm to update the model parameters. For the convolution layer parameters and , and its update formula is: ; ; in, is the convolution kernel weight, is the convolution layer bias, is the learning rate, and They are the loss functions For the convolutional layer parameters and The gradient of the training set is calculated and multiplied by the learning rate to update the adjustment parameters so that the deep learning model The loss function value on gradually decreases; Fully connected layer parameters and The update formula is as follows: ; ; in, is the weight of the fully connected layer, is the bias of the fully connected layer; During the training process, in the validation set To monitor the model performance on the validation set, calculate the loss on the validation set: ; in, For the validation set Sample size, For the validation set The actual support strength index, For the validation set Middle Input geological parameter data sample; When the validation set When the loss stops decreasing or starts to increase for several consecutive rounds, early stopping can be used to stop training to prevent overfitting and appropriately reduce the learning rate. Then continue training; The physical model is based on finite element analysis, and its basic equation is based on the principle of continuum mechanics. The stress-strain relationship of the surrounding rock of the underground powerhouse follows the generalized Hooke's law; Assume that the calculation result of the physical model based on finite element analysis is ; in, Represents the input parameters of the physical model based on finite element analysis, including geometry, material properties, boundary conditions, Represents the parameters of the physical model based on finite element analysis, including material parameters and boundary condition parameters; Use experimental data to train physical models based on finite element analysis and obtain experimental results , the experimental results Comparison with the physical model calculation results based on finite element analysis In contrast, the error function defined by the physical model based on finite element analysis is The formula is as follows: ; in, To compare the number of data points, It means that the first The strain value corresponding to the data point is Indicates the value calculated based on the physical model of finite element analysis and The corresponding The strain value of each data point; Adjust the material parameters and boundary condition parameters in the physical model based on finite element analysis according to the errors; Alternately train the deep learning model and the physical model based on finite element analysis, and use the anchor and cable support strength index results predicted by the deep learning model , As a reference input in physical models based on finite element analysis; Assume that the equivalent stiffness adjustment formula of the updated anchor rod and anchor cable in the physical model based on finite element analysis is: ; ; in, and are the equivalent stiffness of anchor rods before and after updating, and are the equivalent stiffness of the anchor cable before and after the update, is the adjustment factor, used to control the amplitude of the adjustment. is the anchor support strength index of standard parameters, is the anchor support strength index of standard parameters, and The function is to adjust the equivalent stiffness of anchors and cables in the physical model based on finite element analysis according to the difference between the anchor and cable support strength index predicted by the deep learning model and the anchor and cable support strength index of standard parameters; Recalculate the physical model based on finite element analysis to obtain new physical model results, and feed back the relevant features in the physical model results to the deep learning model as a basis for adjusting the training data; Assume that the physical model result fed back to the deep learning model is , then the training data of the deep learning model can be updated as: ; Continue to train the deep learning model based on new training data, and repeat this process over and over again.

[0009] In the preferred solution, in step S4, the prediction result of the deep learning model is assumed to be , the prediction results of the physical model based on finite element analysis are , using the weighted average integration strategy, the integrated prediction results The formula is as follows: ; Weight coefficient and Based on the deep learning model and the physical model based on finite element analysis, the The performance of the above is determined; Calculate the mean squared error of the deep learning model on the validation set and the mean square error of the physical model based on finite element analysis on the validation set ; The weight coefficient calculation formula is: ; ; Through weight allocation, models with smaller mean square errors can be given larger weights to reduce the error and uncertainty of a single model; Use historical data to verify the prediction performance of the integrated model, and calculate the mean square error between the predicted value and the actual value The formula is: ; in, To verify the number of data, is the actual support strength index, For the ensemble model predicted values ​​and; The formula for calculating the mean absolute error between the predicted values ​​and the actual values ​​is: ; Draw a scatter plot of predicted values ​​and actual values. If the data points are closely distributed on the diagonal If the prediction error is close to , it means that the prediction accuracy of the integrated model is high. The prediction error of the integrated model is compared with that of the individual deep learning model and the physical model to evaluate the effectiveness of the integrated strategy. and , it means that integrated prediction improves the prediction performance.

[0010] In the preferred solution, in step S5, when the integrated model is actually applied, according to the integrated prediction results Determine the bolt support strength index predicted by the integrated model and anchor support strength index ; Calculate the bolt support strength index predicted by the integrated model and anchor support strength index Bolt support strength index with standard parameters and standard parameter anchor support strength index The Euclidean distance of For the anchor support strength index, the Euclidean distance calculation formula is: ; Euclidean distance Formula used to quantify the bolt support strength index predicted by the integrated model Bolt support strength index with standard parameters the degree of deviation from the range; For the anchor support strength index, the Euclidean distance calculation formula is: ; Euclidean distance Formula used to quantify the bolt support strength index predicted by the integrated model Bolt support strength index with standard parameters the degree of deviation from the range; According to the calculated Euclidean distance and Convert to confidence ; Based on the basic database, select values ​​that distinguish between obvious deviations and relatively close to the standard , and set is the preset maximum Euclidean distance threshold; Based on the anchor support strength index The corresponding confidence , the calculation formula is expressed as: ; Based on the anchor support strength index The corresponding confidence , the calculation formula is expressed as: ; Among them, the confidence The interval range is from 0 to 1; Confidence The closer it is to 1, the stronger the anchor support strength index predicted by the current anchor. Bolt support strength index with standard parameters If the deviation from the range is small, Bolt support strength index under standard parameters Range, conversely, confidence The closer it is to 0, the stronger the anchor support strength index predicted by the current anchor. Bolt support strength index with standard parameters The greater the deviation from the range, the Bolting strength index that deviates from standard parameters scope; Confidence The closer it is to 1, the stronger the anchor support strength index predicted by the current anchor cable. Anchor support strength index with standard parameters If the deviation from the range is small, Anchor support strength index under standard parameters Range, conversely, confidence The closer it is to 0, the stronger the anchor support strength index predicted by the current anchor. Anchor support strength index with standard parameters The greater the deviation from the range, the Anchor support strength index that deviates from standard parameters scope.

[0011] The present invention provides an underground powerhouse excavation support quality evaluation and analysis method, which has the following beneficial effects through the cooperation between the above structures: First, the geostress calculation model that can integrate multiple factors can accurately grasp the geostress distribution of various parts of the underground powerhouse, and determine the adaptive support strength accordingly to avoid improper support. At the same time, it can accurately measure the mechanical parameters of the surrounding rock and build a personalized support design model. It can customize the optimal support plan for surrounding rocks of different lithology and improve the coordination between support and surrounding rock. Second, the real-time monitoring system combined with big data and artificial intelligence algorithms can timely detect support hazards and dynamically optimize them, effectively preventing surrounding rock instability. In addition, through the combination of reasonable support structure and waterproofing and drainage measures, it can resist natural erosion, maintain surrounding rock stability, and extend the service life of the project; Third, the multi-source data integration platform provides comprehensive and accurate information to assist decision makers in comprehensive analysis and avoid one-sided decision-making. At the same time, it helps to compare different plans through quantitative evaluation models, quickly determine the optimal plan, improve decision-making efficiency and rationality, and ensure the smooth progress of the project. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The present invention will be further described below in conjunction with the accompanying drawings and embodiments: Figure 1 It is the main structural diagram of the process of the present invention. DETAILED DESCRIPTION

[0013] In order to better understand the purpose, structure and function of the present invention, the embodiments and features in the embodiments of the present application can be combined with each other without conflict. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0014] Example 1 like Figure 1 As shown, this embodiment provides an underground powerhouse excavation support quality evaluation and analysis method, which includes the following steps: S1. Data collection and preprocessing: Establish a basic database and collect geological exploration data and field monitoring data of underground powerhouses to obtain geological parameter data , and the geological parameter data Perform preprocessing and set the standard feature vector as the evaluation index; In a preferred solution, in step S1, the stratum lithology, geological structure and ground stress conditions are determined based on geological survey data, and on-site monitoring data are collected through sensors to obtain construction dynamics; Among them, the on-site monitoring data include surrounding rock deformation, stress changes and support structure force data; Preprocessing includes the geological parameter data Cleaning and conversion, which is used to organize data to remove duplicate, erroneous or incomplete records, convert non-numeric data into a form suitable for analysis, and standardize data from different sources, including unifying physical units and normalizing numerical ranges; Set the standard eigenvector as the evaluation index, including the anchor support strength index of the standard parameters The range is 1.17 to 1.24, standard parameter anchor support strength index Range: 1.55 to 1.7; in, and It is the range value under ideal conditions determined based on a large amount of engineering experience and theoretical analysis, and is used to measure the difference between actual engineering and standard conditions.

[0015] S2. Model selection and integration strategy: Based on the preprocessed geological parameter data Select a physical model and a machine learning model, and determine the corresponding integration strategy based on the selected physical model and machine learning model; In the preferred solution, in step S2, the physical model adopts a physical model based on finite element analysis to simulate the physical behavior of anchor rods and anchor cables under extremely high ground stress, and the physical model is used to simulate the physical behavior of anchor rods and anchor cables under extremely high ground stress according to the geological parameter data of the underground powerhouse. Set model parameters and define boundary conditions in the physical model based on finite element analysis to simulate the constraints in actual engineering; Build another deep learning model and choose convolutional neural network as the deep learning architecture; The convolutional layer of the convolutional neural network can be used to automatically extract local features from the physical model based on finite element analysis, the pooling layer is used to reduce the data dimension, and the fully connected layer is used to integrate the features and make the final prediction. The key features are input into the deep learning model, and the key features are extracted from the calculation results of the physical model based on finite element analysis, including stress distribution characteristic diagram stress and deformation trend data deformation. The key features are used as additional input of the deep learning model to achieve an organic combination of the physical model based on finite element analysis and the deep learning model, so that the deep learning model can learn the relevant features of the physical process simulated by the physical model, and enhance the deep learning model's prediction ability for complex geological and stress conditions.

[0016] S3, model training and iteration: Train the selected physical model and machine learning model, and alternately train the deep learning model and the physical model. Use the prediction results of the deep learning model to optimize the parameters of the physical model, and use the output results of the physical model to guide the training of the deep learning model. In the preferred solution, in step S3, the specific training steps are as follows: Using the collected and pre-processed geological parameter data Train a deep learning model to predict the support strength index of anchor bolts and cables, and use the preprocessed geological parameter data As a dataset , and set the data set The total sample size is , divided into training sets according to the ratio of 7:2:1 , validation set and test set ,Right now , , , through the training set The initially constructed deep learning model is trained in multiple rounds under supervision and validated on the validation set. Monitor the training results and use the test set Evaluate model performance; in, , , ; Define the loss function: ; in, is the actual support strength index in the training set, is the prediction function corresponding to the deep learning model, For the training set Middle Input geological parameter data A sample of The parameters of the deep learning model are obtained by inputting geological parameter data And parameters , output the corresponding predicted value , and calculate the predicted value With actual value The average of the sum of squared differences of quantized model training sets The prediction error on ; Use the stochastic gradient descent algorithm to update the model parameters. For the convolution layer parameters and , and its update formula is: ; ; in, is the convolution kernel weight, is the convolution layer bias, is the learning rate, and They are the loss functions For the convolutional layer parameters and The gradient of the training set is calculated and multiplied by the learning rate to update the adjustment parameters so that the deep learning model The loss function value on gradually decreases; Fully connected layer parameters and The update formula is as follows: ; ; in, is the weight of the fully connected layer, is the bias of the fully connected layer; During the training process, in the validation set To monitor the model performance on the validation set, calculate the loss on the validation set: ; in, For the validation set Sample size, For the validation set The actual support strength index, For the validation set Middle Input geological parameter data sample; When the validation set When the loss stops decreasing or starts to increase for several consecutive rounds, early stopping can be used to stop training to prevent overfitting and appropriately reduce the learning rate. Then continue training; The physical model is based on finite element analysis, and its basic equation is based on the principle of continuum mechanics. The stress-strain relationship of the surrounding rock of the underground powerhouse follows the generalized Hooke's law; Assume that the calculation result of the physical model based on finite element analysis is ; in, Represents the input parameters of the physical model based on finite element analysis, including geometry, material properties, boundary conditions, Represents the parameters of the physical model based on finite element analysis, including material parameters and boundary condition parameters; Use experimental data to train physical models based on finite element analysis and obtain experimental results , the experimental results Comparison with the physical model calculation results based on finite element analysis In contrast, the error function defined by the physical model based on finite element analysis is The formula is as follows: ; in, To compare the number of data points, It means that the first The strain value corresponding to the data point is Indicates the value calculated based on the physical model of finite element analysis and The corresponding The strain value of each data point; Adjust the material parameters and boundary condition parameters in the physical model based on finite element analysis according to the errors; Alternately train the deep learning model and the physical model based on finite element analysis, and use the anchor and cable support strength index results predicted by the deep learning model , As a reference input in physical models based on finite element analysis; Assume that the equivalent stiffness adjustment formula of the updated anchor rod and anchor cable in the physical model based on finite element analysis is: ; ; in, and are the equivalent stiffness of anchor rods before and after updating, and are the equivalent stiffness of the anchor cable before and after the update, is the adjustment factor, used to control the amplitude of the adjustment. is the anchor support strength index of standard parameters, is the anchor support strength index of standard parameters, and The function is to adjust the equivalent stiffness of anchors and cables in the physical model based on finite element analysis according to the difference between the anchor and cable support strength index predicted by the deep learning model and the anchor and cable support strength index of standard parameters; Recalculate the physical model based on finite element analysis to obtain new physical model results, and feed back the relevant features in the physical model results to the deep learning model as a basis for adjusting the training data; Assume that the physical model result fed back to the deep learning model is , then the training data of the deep learning model can be updated as: ; Continue to train the deep learning model based on new training data, and repeat this process over and over again.

[0017] S4. Integrated prediction and performance verification: Integrate the prediction results of the trained deep learning model and the physical model to obtain an integrated model, and use actual engineering data to verify the prediction performance of the integrated model.

[0018] In the preferred solution, in step S4, the prediction result of the deep learning model is assumed to be , the prediction results of the physical model based on finite element analysis are , using the weighted average integration strategy, the integrated prediction results The formula is as follows: ; Weight coefficient and Based on the deep learning model and the physical model based on finite element analysis, the The performance of the above is determined; Calculate the mean squared error of the deep learning model on the validation set and the mean square error of the physical model based on finite element analysis on the validation set ; The weight coefficient calculation formula is: ; ; Through weight allocation, models with smaller mean square errors can be given larger weights to reduce the error and uncertainty of a single model; Use historical data to verify the prediction performance of the integrated model, and calculate the mean square error between the predicted value and the actual value The formula is: ; in, To verify the number of data, is the actual support strength index, For the ensemble model predicted values ​​and; The formula for calculating the mean absolute error between the predicted values ​​and the actual values ​​is: ; Draw a scatter plot of predicted values ​​and actual values. If the data points are closely distributed on the diagonal If the prediction error is close to , it means that the prediction accuracy of the integrated model is high. The prediction error of the integrated model is compared with that of the individual deep learning model and the physical model to evaluate the effectiveness of the integrated strategy. and , it means that integrated prediction improves the prediction performance.

[0019] S5. Application and continuous optimization: Deploy the trained deep learning model, physical model and integrated model to the actual engineering environment, so that it can receive the geological parameter data of the engineering site in real time. , and make predictions and analyses. During the deployment process, the model is seamlessly integrated with the data acquisition system and monitoring system of the engineering site to obtain the required data and output the prediction results. The prediction performance of the integrated model is evaluated based on the prediction results and the set standard feature vectors to obtain the performance evaluation results. According to the performance evaluation results, the integrated model, deep learning model and physical model are adjusted and optimized as necessary; The deep learning model is deployed on the server at the project site, and the geological parameter data collected by the sensor is received in real time through the network connection. , the physical model can be used to adjust parameters according to the actual situation on site and then perform calculations and analysis. The two work together to provide decision-making for engineering construction; Continue to collect new prediction results, organize and store them in the basic database, repeat steps S1 to S5, and use the new data to update and optimize the deep learning model and physical model; In the preferred solution, in step S5, when the integrated model is actually applied, according to the integrated prediction results Determine the bolt support strength index predicted by the integrated model and anchor support strength index ; Calculate the bolt support strength index predicted by the integrated model and anchor support strength index Bolt support strength index with standard parameters and standard parameter anchor support strength index The Euclidean distance of For the anchor support strength index, the Euclidean distance calculation formula is: ; Euclidean distance Formula used to quantify the bolt support strength index predicted by the integrated model Bolt support strength index with standard parameters the degree of deviation from the range; For the anchor support strength index, the Euclidean distance calculation formula is: ; Euclidean distance Formula used to quantify the bolt support strength index predicted by the integrated model Bolt support strength index with standard parameters the degree of deviation from the range; According to the calculated Euclidean distance and Convert to confidence ; Based on the basic database, select values ​​that distinguish between obvious deviations and relatively close to the standard , and set is the preset maximum Euclidean distance threshold; Based on the anchor support strength index The corresponding confidence , the calculation formula is expressed as: ; Based on the anchor support strength index The corresponding confidence , the calculation formula is expressed as: ; Among them, the confidence The interval range is from 0 to 1; Confidence The closer it is to 1, the stronger the anchor support strength index predicted by the current anchor. Bolt support strength index with standard parameters If the deviation from the range is small, Bolt support strength index under standard parameters Range, conversely, confidence The closer it is to 0, the stronger the anchor support strength index predicted by the current anchor. Bolt support strength index with standard parameters The greater the deviation from the range, the Bolting strength index that deviates from standard parameters scope; Confidence The closer it is to 1, the stronger the anchor support strength index predicted by the current anchor cable. Anchor support strength index with standard parameters If the deviation from the range is small, Anchor support strength index under standard parameters Range, conversely, confidence The closer it is to 0, the stronger the anchor support strength index predicted by the current anchor. Anchor support strength index with standard parameters The greater the deviation from the range, the Anchor support strength index that deviates from standard parameters scope.

[0020] Although the present application has been described in conjunction with specific features and embodiments thereof, it is obvious that various modifications and combinations may be made thereto without departing from the spirit and scope of the present application. Accordingly, this specification and the drawings are merely exemplary illustrations of the present application as defined by the appended claims, and are deemed to have covered any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

[0021] It should also be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so as to describe the embodiments of the present invention described herein.

Claims

1. A method for evaluating and analyzing the quality of underground powerhouse excavation support, characterized in that: The following steps are involved: S1. Data collection and preprocessing: Establish a basic database and collect geological exploration data and field monitoring data of underground powerhouses to obtain geological parameter data , and the geological parameter data Perform preprocessing and set the standard feature vector as the evaluation index; S2. Model selection and integration strategy: Based on the preprocessed geological parameter data Select a physical model and a machine learning model, and determine the corresponding integration strategy based on the selected physical model and machine learning model; S3, model training and iteration: Train the selected physical model and machine learning model, and alternately train the deep learning model and the physical model. Use the prediction results of the deep learning model to optimize the parameters of the physical model, and use the output results of the physical model to guide the training of the deep learning model. S4. Integrated prediction and performance verification: Integrate the prediction results of the trained deep learning model and the physical model to obtain an integrated model, and use actual engineering data to verify the prediction performance of the integrated model.

2. The underground powerhouse excavation support quality evaluation and analysis method according to claim 1 is characterized in that: The following steps are also included: S5. Application and continuous optimization: Deploy the trained deep learning model, physical model and integrated model to the actual engineering environment, so that it can receive the geological parameter data of the engineering site in real time. , and make predictions and analyses. During the deployment process, the model is seamlessly integrated with the data acquisition system and monitoring system of the engineering site to obtain the required data and output the prediction results. The prediction performance of the integrated model is evaluated based on the prediction results and the set standard feature vectors to obtain the performance evaluation results. According to the performance evaluation results, the integrated model, deep learning model and physical model are adjusted and optimized as necessary; The deep learning model is deployed on the server at the project site, and the geological parameter data collected by the sensor is received in real time through the network connection. , the physical model can be used to adjust parameters according to the actual situation on site and then perform calculations and analysis. The two work together to provide decision-making for engineering construction; Continue to collect new prediction results, organize and store them in the basic database, repeat steps S1 to S5, and use the new data to update and optimize the deep learning model and physical model.

3. The underground powerhouse excavation support quality evaluation and analysis method according to claim 2 is characterized in that: In step S1, the stratum lithology, geological structure and ground stress are determined based on the geological survey data, and the on-site monitoring data are collected through sensors to obtain the construction dynamics; Among them, the on-site monitoring data include surrounding rock deformation, stress changes and support structure force data; Preprocessing includes the geological parameter data Cleaning and conversion, which is used to organize data to remove duplicate, erroneous or incomplete records, convert non-numeric data into a form suitable for analysis, and standardize data from different sources, including unifying physical units and normalizing numerical ranges; Set the standard eigenvector as the evaluation index, including the anchor support strength index of the standard parameters The range is 1.17 to 1.24, standard parameter anchor support strength index Range: 1.55 to 1.7; in, and It is the range value under ideal conditions determined based on a large amount of engineering experience and theoretical analysis, and is used to measure the difference between actual engineering and standard conditions.

4. The underground powerhouse excavation support quality evaluation and analysis method according to claim 3 is characterized in that: In step S2, the physical model adopts a physical model based on finite element analysis to simulate the physical behavior of anchor rods and cables under extremely high ground stress. Set model parameters and define boundary conditions in the physical model based on finite element analysis to simulate the constraints in actual engineering; Build another deep learning model and choose convolutional neural network as the deep learning architecture; The convolutional layer of the convolutional neural network can be used to automatically extract local features from the physical model based on finite element analysis, the pooling layer is used to reduce the data dimension, and the fully connected layer is used to integrate the features and make the final prediction. The key features are input into the deep learning model, and the key features are extracted from the calculation results of the physical model based on finite element analysis, including stress distribution characteristic diagram stress and deformation trend data deformation. The key features are used as additional input of the deep learning model to achieve an organic combination of the physical model based on finite element analysis and the deep learning model, so that the deep learning model can learn the relevant features of the physical process simulated by the physical model, and enhance the deep learning model's prediction ability for complex geological and stress conditions.

5. The underground powerhouse excavation support quality evaluation and analysis method according to claim 4 is characterized in that: In step S3, the specific training steps are as follows: Using the collected and pre-processed geological parameter data Train a deep learning model to predict the support strength index of anchor bolts and cables, and use the preprocessed geological parameter data As a dataset , and set the data set The total sample size is , divided into training sets according to the ratio of 7:2:1 , validation set and test set ,Right now , , , through the training set The initially constructed deep learning model is trained in multiple rounds under supervision and validated on the validation set. Monitor the training results and use the test set Evaluate model performance; in, , , ; Define the loss function: ; in, is the actual support strength index in the training set, is the prediction function corresponding to the deep learning model, For the training set Middle Input geological parameter data A sample of The parameters of the deep learning model are obtained by inputting geological parameter data And parameters , output the corresponding predicted value , and calculate the predicted value With actual value The average of the sum of squared differences of quantized model training sets The prediction error on ; Use the stochastic gradient descent algorithm to update the model parameters. For the convolution layer parameters and , and its update formula is: ； ； in, is the convolution kernel weight, is the convolution layer bias, is the learning rate, and They are the loss functions For the convolutional layer parameters and The gradient of the training set is calculated and multiplied by the learning rate to update the adjustment parameters so that the deep learning model The loss function value on gradually decreases; Fully connected layer parameters and The update formula is as follows: ； ； in, is the weight of the fully connected layer, Bias for the fully connected layer; During the training process, in the validation set To monitor the model performance on the validation set, calculate the loss on the validation set: ； in, For the validation set Sample size, For the validation set The actual support strength index, For the validation set Middle Input geological parameter data sample; When the validation set When the loss stops decreasing or starts to increase for several consecutive rounds, early stopping can be used to stop training to prevent overfitting and appropriately reduce the learning rate. Then continue training; The physical model is based on finite element analysis, and its basic equation is based on the principle of continuum mechanics. The stress-strain relationship of the surrounding rock of the underground powerhouse follows the generalized Hooke's law; Assume that the calculation result of the physical model based on finite element analysis is ; in, Represents the input parameters of the physical model based on finite element analysis, including geometry, material properties, boundary conditions, Represents the parameters of the physical model based on finite element analysis, including material parameters and boundary condition parameters; Use experimental data to train physical models based on finite element analysis and obtain experimental results , the experimental results Comparison with the physical model calculation results based on finite element analysis In contrast, the error function defined by the physical model based on finite element analysis is The formula is as follows: ； in, To compare the number of data points, It means that the first The strain value corresponding to the data point is Indicates the value calculated based on the physical model of finite element analysis and The corresponding The strain value of each data point; Adjust the material parameters and boundary condition parameters in the physical model based on finite element analysis according to the errors; Alternately train the deep learning model and the physical model based on finite element analysis, and use the anchor and cable support strength index results predicted by the deep learning model , As a reference input in physical models based on finite element analysis; Assume that the equivalent stiffness adjustment formula of the updated anchor rod and anchor cable in the physical model based on finite element analysis is: ； ； in, and are the equivalent stiffness of anchor rods before and after updating, and are the equivalent stiffness of the anchor cable before and after the update, is the adjustment factor, which is used to control the adjustment amplitude. is the anchor support strength index of standard parameters, is the anchor support strength index of standard parameters, and The function is to adjust the equivalent stiffness of anchors and cables in the physical model based on finite element analysis according to the difference between the anchor and cable support strength index predicted by the deep learning model and the anchor and cable support strength index of standard parameters; Recalculate the physical model based on finite element analysis to obtain new physical model results, and feed back the relevant features in the physical model results to the deep learning model as a basis for adjusting the training data; Assume that the physical model result fed back to the deep learning model is , then the training data of the deep learning model can be updated as: ； Continue to train the deep learning model based on new training data, and repeat this process over and over again.

6. The underground powerhouse excavation support quality evaluation and analysis method according to claim 5 is characterized in that: In step S4, let the prediction result of the deep learning model be , the prediction results of the physical model based on finite element analysis are , using the weighted average integration strategy, the integrated prediction results The formula is as follows: ； Weight coefficient and Based on the deep learning model and the physical model based on finite element analysis, the The performance of the above is determined; Calculate the mean squared error of the deep learning model on the validation set and the mean square error of the physical model based on finite element analysis on the validation set ; The weight coefficient calculation formula is: ； ； Through weight allocation, models with smaller mean square errors can be given larger weights to reduce the error and uncertainty of a single model; Use historical data to verify the prediction performance of the integrated model, and calculate the mean square error between the predicted value and the actual value The formula is: ； in, To verify the amount of data, is the actual support strength index, For the ensemble model predicted values ​​and; The formula for calculating the mean absolute error between the predicted values ​​and the actual values ​​is: ； Draw a scatter plot of predicted values ​​and actual values. If the data points are closely distributed on the diagonal If the prediction error is close to , it means that the prediction accuracy of the integrated model is high. The prediction error of the integrated model is compared with that of the individual deep learning model and the physical model to evaluate the effectiveness of the integrated strategy. and , it means that integrated prediction improves the prediction performance.

7. The underground powerhouse excavation support quality evaluation and analysis method according to claim 6 is characterized in that: In step S5, when the integrated model is actually applied, the integrated prediction results are Determine the bolt support strength index predicted by the integrated model and anchor support strength index ; Calculate the bolt support strength index predicted by the integrated model and anchor support strength index Bolt support strength index with standard parameters and standard parameter anchor support strength index The Euclidean distance of For the anchor support strength index, the Euclidean distance calculation formula is: ； Euclidean distance Formula used to quantify the bolt support strength index predicted by the integrated model Bolt support strength index with standard parameters the degree of deviation from the range; For the anchor support strength index, the Euclidean distance calculation formula is: ； Euclidean distance Formula used to quantify the bolt support strength index predicted by the integrated model Bolt support strength index with standard parameters the degree of deviation from the range; According to the calculated Euclidean distance and Convert to confidence ; Based on the basic database, select values ​​that distinguish between obvious deviations and relatively close to the standard , and set is the preset maximum Euclidean distance threshold; Based on the anchor support strength index The corresponding confidence , the calculation formula is expressed as: ； Based on the anchor cable support strength index The corresponding confidence , the calculation formula is expressed as: ； Among them, the confidence The interval range is from 0 to 1; Confidence The closer it is to 1, the stronger the anchor support strength index predicted by the current anchor. Bolt support strength index with standard parameters If the deviation from the range is small, Bolt support strength index under standard parameters Range, conversely, confidence The closer it is to 0, the stronger the anchor support strength index predicted by the current anchor. Bolt support strength index with standard parameters The greater the deviation from the range, the Bolting strength index deviating from standard parameters scope; Confidence The closer it is to 1, the stronger the anchor support strength index predicted by the current anchor cable. Anchor support strength index with standard parameters If the deviation from the range is small, Anchor support strength index under standard parameters Range, conversely, confidence The closer it is to 0, the stronger the anchor support strength index predicted by the current anchor. Anchor support strength index with standard parameters The greater the deviation from the range, the Anchor support strength index that deviates from standard parameters scope.

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