A data warehouse for double-tunnel construction safety

Abstract

The application provides a kind of multi-dimensional database for double tunnel construction safety, comprising the following steps: data acquisition, through the application based on web data management system, the recursive collection, extraction and conversion process of data acquisition is executed;Determine double Gaussian ground subsidence tank model;Data warehouse utilizes the double Gaussian ground subsidence tank model for multidimensional modeling.Data warehouse is a kind of subject-oriented, integrated, non-volatile and time-varying data collection to support management decision-making.It supports information processing by providing a solid platform for integrating and historical data.The application proposes a kind of multi-dimensional data model containing space-time dimension and context dimension, which is used to establish a safety-oriented database, and helps information retrieval and safety decision-making based on a large amount of data.

Description

Technical Field

[0001] This invention relates to a data warehouse for safety in dual-tunnel construction. Background Technology

[0002] China is currently enjoying a boom in subway construction. However, most cities lack prior experience in subway tunnel construction. Several serious malfunctions during tunnel construction have resulted in deaths and significant property damage. Most of these failures are attributed to inadequate geotechnical risk management. Therefore, safety management, especially geotechnical risk control, has become one of the major issues in subway tunnel engineering.

[0003] Incomplete information is a major factor leading to accidents in subway tunnel engineering. Safety risk control requires comprehensive analysis of instrument data and other safety-related data. Based on a multidimensional data model, a safety-oriented geotechnical instrument database is established, integrating all data from heterogeneous data sources. This not only allows for rapid retrieval of on-site information but also enables further analysis. Ground settlement, as one of the most important safety indicators, is analyzed using the database. A double Gaussian model is proposed to describe the settlement trough in dual tunnels. Furthermore, a modified trough width parameter is proposed to represent the extent of the safety risk zone. Summary of the Invention

[0004] This invention proposes a multidimensional data model incorporating spatiotemporal and contextual dimensions for building a security-oriented data warehouse. The data warehouse facilitates information retrieval and security decision-making based on large amounts of data. The proposed double Gaussian model effectively represents the ground settlement trough above the twin tunnels, with ground settlement used to represent the depth and width of the trough. The logistic growth curve effectively represents the increase in ground loss over time. The first derivative of ground loss reflects the evolution of security risk, while the second derivative helps identify the moment of highest security risk. To achieve the above objectives, the technical solution adopted by this invention is as follows:

[0005] A data warehouse for safe construction of dual tunnels includes the following steps:

[0006] S1: Data Acquisition: By applying a web-based data management system, the recursive process of data acquisition, extraction, and transformation is executed;

[0007] S2: Determine the ground settlement trough model: Spatially define the ground settlement monitoring data in the data warehouse as a double Gaussian ground settlement trough model. The double Gaussian ground settlement trough model is characterized by the first Gaussian function representing the settlement trough caused by the previous tunnel. The second Gaussian function characterizing the additional settlement caused by the subsequent tunnel. Superposition constitutes, and its expression is: ,in, This represents the total ground subsidence. and These represent the maximum ground disturbances caused by the preceding and following tunnels, respectively. The horizontal distance between the centerlines of the preceding and following tunnels. and These are the original parameters for the shape of the settling tank. This represents the distance between two tunnels recorded in the data warehouse.

[0008] S3: The data warehouse uses the aforementioned double Gaussian ground settlement trough model for multidimensional modeling.

[0009] The established data warehouse uses a web-based data management system to perform a recursive process of data collection, extraction, and transformation.

[0010] Step S1 data acquisition includes the following steps:

[0011] S101: Data Collection: Designers, contractors, surveyors, and resident engineers provide a large amount of basic and specific data for the metro construction and are authorized to upload the data to an online database in the form of electronic documents. These documents include: the designer's architectural construction drawings, structural construction drawings, electrical construction drawings, plumbing construction drawings, heating, ventilation and air conditioning construction drawings, interior decoration construction drawings, and geological reports; the contractor's construction schedule; the surveyor's instrument data, including monitoring reports on ground settlement, tunnel top displacement, tunnel convergence, and settlement of adjacent facilities; and the resident engineer's inspection reports.

[0012] S102: Data Extraction: Text reports, including construction drawings, commencement reports, construction contracts, monitoring reports, and completion reports, are classified as unstructured data without a predefined data model. Therefore, text analysis techniques are required to extract and represent implicit soil thickness data, which is difficult for computer programs to automatically identify, and the conclusions in monitoring reports in a structured form. Fact tables store physical information describing factual events. All instrument data, including ground settlement, tunnel top displacement, tunnel convergence, and settlement of adjacent facilities, are stored as factual data in different fact tables. Dimension tables provide the context and meaning of factual data to facilitate exploration of the data from multiple perspectives, including spatiotemporal dimensions and tunnel geometry and hydrogeological conditions, which are considered contextual dimensions in the data warehouse.

[0013] S103: Data Transformation: Different participants may have different naming standards and units of measurement in their reports. Therefore, all data should be analyzed and transformed. Data with different names but referring to the same thing should be labeled as the same thing and all data should be changed to a unified name and stored in the data warehouse. All length units should be changed to mm, time units to h, date format should be xxxx year xx month xx day xx hour, and time should be in 24-hour format.

[0014] Furthermore, the spatial model of the instrument data in the data warehouse determined in step S2 is a double Gaussian model. When the previous tunnel has just passed, the ground settlement is fitted using the first Gaussian function, representing the settlement trough caused by the previous tunnel. Then, after the next tunnel passes, this portion of settlement is subtracted from the final settlement to obtain the additional settlement. The second Gaussian function is then used to fit the additional settlement. Finally, the two curves are superimposed to display the entire ground settlement trough. The ground settlement trough is represented by a double Gaussian model, as shown below:

[0015]

[0016] in This represents the total ground subsidence. and These can be considered as the maximum ground disturbances caused by the preceding and following tunnels, respectively. It is the horizontal distance between the centerlines of the preceding and following tunnels. and These are the original parameters for the shape of the settling tank. This refers to the distance between two tunnels recorded in the database.

[0017] slot width parameters This is considered a usable measure for defining the extent of safety risk areas because most ground settlement occurs on each cross-section above the twin tunnels. Within a distance, The larger the value, the wider the ground settlement trough. It can be obtained using the following formula:

[0018]

[0019]

[0020] and represents the original parameters for the shape of the settlement trough, and u represents the distance between the two tunnels recorded in the data warehouse.

[0021] However, i is considered a measure of static risk, and in addition, formation loss is introduced. To more comprehensively represent the width and depth of the settling tank;

[0022]

[0023]

[0024] In the formula, and These are the trench width parameters for the two tunnels, respectively. It is worth noting that m is used as and The unit mm is used as and Since the unit is 1000, the integral result should be divided by 1000 to get the result. unit.

[0025] First derivative representation The growth rate reflects the evolution of security risks. The moment with the highest growth rate indicates the highest security risk, which can be detected through... The second derivative is easily determined; furthermore, by stabilizing the ground settlement trough... Dividing by the excavation area of ​​the two tunnels, the ground loss rate can be obtained. For every 1% increase in the ground loss rate, the settlement value at that location will increase by 1 mm.

[0026] Furthermore, in step S3, the data warehouse utilizes the aforementioned double Gaussian ground settlement trough model for multidimensional modeling. This involves applying the proposed model to the instrument data in the data warehouse based on a nonlinear least squares optimization method.

[0027] The objective function F will be:

[0028]

[0029]

[0030] It is in the Ground subsidence observed at each subsidence marker, therefore... It is the first The deviation of each data point from the fitted curve;

[0031]

[0032] It is the residual vector of the settlement markers in each ground array, optimized using a nonlinear least squares method. Iterative minimization and finding unknown parameters , These are solutions to the system of linear equations, as shown below:

[0033]

[0034] Where k is the iteration step and J is the Jacobian matrix, as shown below:

[0035]

[0036] scalar This is the damping factor, which controls the amplitude and direction of each major iteration. If the descent rate is fast, a smaller damping factor can be used. However, if the rate of descent during iteration is insufficient, it can be increased. ,and It tends to move in the direction of a steeper descent. In this work, the initial damping coefficient... It is 0.001.

[0037] The proposed model can be applied to instrument data in a data warehouse using an optimization method based on nonlinear least squares, thus completing the modeling of a multi-dimensional data warehouse. Attached Figure Description

[0038] Figure 1 - This is a simplified flowchart of the present invention.

[0039] Figure 2 - A schematic diagram of a typical settlement trough above a double tunnel. Detailed Implementation

[0040] Incomplete information is a major factor leading to accidents in subway tunnel engineering. Safety risk control requires comprehensive analysis of instrument data and other safety-related data. This invention provides a method for analyzing the safety risks of ground settlement caused by underground engineering construction based on a multi-dimensional monitoring database. It establishes a safety-oriented geotechnical instrument database, integrating all data from heterogeneous data sources. This not only allows for rapid retrieval of on-site information but also enables further analysis. The following embodiments further illustrate this invention in detail. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of the invention.

[0041] The above technical solution will be described in detail below with reference to specific implementation methods.

[0042] Example:

[0043] A company is undertaking a 2.1km long urban twin-tunnel construction project with complex geological conditions. Facing immense pressure in safety management during subway construction, the company has decided to implement a proposed safety-oriented data warehouse. The established data warehouse utilizes a web-based data management system to execute a recursive data acquisition, extraction, and transformation process. This includes the following steps:

[0044] S101: Data Collection: Designers, contractors, surveyors, and resident engineers provide a large amount of basic and specific data for the metro construction and are authorized to upload the data to an online database in the form of electronic documents. These documents include: the designer's architectural construction drawings, structural construction drawings, electrical construction drawings, plumbing construction drawings, heating, ventilation and air conditioning construction drawings, interior decoration construction drawings, and geological reports; the contractor's construction schedule; the surveyor's instrument data, including monitoring reports on ground settlement, tunnel top displacement, tunnel convergence, and settlement of adjacent facilities; and the resident engineer's inspection reports.

[0045] S102: Data Extraction: Text reports, including construction drawings, commencement reports, construction contracts, monitoring reports, and completion reports, are classified as unstructured data without a predefined data model. Therefore, text analysis techniques are required to extract and represent implicit soil thickness data, which is difficult for computer programs to automatically identify, and the conclusions in monitoring reports in a structured form. Fact tables store physical information describing factual events. All instrument data, including ground settlement, tunnel top displacement, tunnel convergence, and settlement of adjacent facilities, are stored as factual data in different fact tables. Dimension tables provide the context and meaning of factual data to facilitate exploration of the data from multiple perspectives, including spatiotemporal dimensions and tunnel geometry and hydrogeological conditions, which are considered contextual dimensions in the data warehouse.

[0046] S103: Data Transformation: Different participants may have different naming standards and units of measurement in their reports. Therefore, all data should be analyzed and transformed. Data with different names but referring to the same thing should be labeled as the same thing and all data should be changed to a unified name and stored in the data warehouse. All length units should be changed to mm, time units to h, date format should be xxxx year xx month xx day xx hour, and time should be in 24-hour format.

[0047] The collected data were then fitted to the determined double Gaussian model of the ground settlement trough using an optimization method based on nonlinear least squares. In this tunnel project analysis, an optimization method based on nonlinear least squares was used to fit 3638 sets of samples. The root mean square error (RMSE) and correlation coefficient (R) were used to verify the fitting results. 83% of the data showed good fitting results when r>0, indicating that the proposed double Gaussian model fit the settlement data well.

[0048] Although embodiments of the present invention have been disclosed above, they are not limited to the applications listed in the specification and embodiments. It can be applied to various fields suitable for the present invention. Further modifications can be readily implemented by those skilled in the art.

Claims

1. A data warehouse for safety in dual-tunnel construction, characterized in that: Includes the following steps: S1: Data Acquisition: By applying a web-based data management system, the recursive process of data acquisition, extraction, and transformation is executed; S2: Determine the ground settlement trough model: Spatially define the ground settlement monitoring data in the data warehouse as a double Gaussian ground settlement trough model. The double Gaussian ground settlement trough model is characterized by the first Gaussian function representing the settlement trough caused by the previous tunnel. The second Gaussian function characterizing the additional settlement caused by the subsequent tunnel. Superposition constitutes, and its expression is: ,in, This represents the total ground subsidence. and These represent the maximum ground disturbances caused by the preceding and following tunnels, respectively. The horizontal distance between the centerlines of the preceding and following tunnels. and These are the original parameters for the shape of the settling tank. This represents the distance between two tunnels recorded in the data warehouse. S3: The data warehouse uses the aforementioned double Gaussian ground settlement trough model for multidimensional modeling.

2. A data warehouse for safe construction of dual tunnels according to claim 1, characterized in that: Step S1, data acquisition, involves using a web-based data management system to perform a recursive data acquisition, extraction, and transformation process, including the following steps: S101: Data Collection: Designers, contractors, surveyors, and resident engineers provide a large amount of basic and specific data for the metro construction and are authorized to upload the data to an online database in the form of electronic documents. These documents include: the designer's architectural construction drawings, structural construction drawings, electrical construction drawings, plumbing construction drawings, heating, ventilation and air conditioning construction drawings, interior decoration construction drawings, and geological reports; the contractor's construction schedule; the surveyor's instrument data, including monitoring reports on ground settlement, tunnel top displacement, tunnel convergence, and settlement of adjacent facilities; and the resident engineer's inspection reports. S102: Data Extraction: Text reports, including construction drawings, commencement reports, construction contracts, monitoring reports, and completion reports, are classified as unstructured data without a predefined data model. Therefore, text analysis techniques are required to extract and represent implicit soil thickness data, which is difficult for computer programs to automatically identify, and the conclusions in monitoring reports in a structured form. Fact tables store physical information describing factual events. All instrument data, including ground settlement, tunnel top displacement, tunnel convergence, and settlement of adjacent facilities, are stored as factual data in different fact tables. Dimension tables provide the context and meaning of factual data to facilitate exploration of the data from multiple perspectives, including spatiotemporal dimensions and tunnel geometry and hydrogeological conditions, which are considered contextual dimensions in the data warehouse. S103: Data Transformation: Different participants may have different naming standards and units of measurement in their reports. Therefore, all data should be analyzed and transformed. Data with different names but referring to the same thing should be labeled as the same thing and all data should be changed to a unified name and stored in the data warehouse. All length units should be changed to mm, time units to h, date format should be xxxx year xx month xx day xx hour, and time should be in 24-hour format.

3. A data warehouse for safe construction of dual tunnels according to claim 2, characterized in that: The spatial model of the instrument data in the data warehouse determined in step S2 is a double Gaussian model: When a tunnel has just passed, the ground settlement is fitted using the first Gaussian function, which represents the settlement trough caused by the previous tunnel. Then, after the next tunnel passes, this portion of the settlement is subtracted from the final settlement to obtain the additional settlement. The additional settlement is then fitted using the second Gaussian function. Finally, the two curves are superimposed to display the entire ground settlement trough. The ground settlement trough is represented by a double Gaussian model, as shown below: in This represents the total ground subsidence. and These can be considered as the maximum ground disturbances caused by the preceding and following tunnels, respectively. It is the horizontal distance between the centerlines of the preceding and following tunnels. and These are the original parameters for the shape of the settling tank. This refers to the distance between two tunnels recorded in the database. slot width parameters This is considered a usable measure for defining the extent of safety risk areas because most ground settlement occurs on each cross-section above the twin tunnels. Within a distance, The larger the value, the wider the ground settlement trough. It can be obtained using the following formula: and These are the original parameters for the shape of the settling tank. This represents the distance between two tunnels recorded in the data warehouse. but It is considered a measure of static risk, in addition to which formation loss is introduced. To more comprehensively represent the width and depth of the settling tank; In the formula, and These are the trench width parameters for the two tunnels, respectively. It is worth noting that m is used as and The unit mm is used as and Since the unit is 1000, the integral result should be divided by 1000 to get the result. Units; First derivative representation The growth rate reflects the evolution of security risks. The moment with the highest growth rate indicates the highest security risk, which can be detected through... The second derivative is easily determined; furthermore, by stabilizing the ground settlement trough... Dividing by the excavation area of ​​the two tunnels, the ground loss rate can be obtained. For every 1% increase in the ground loss rate, the settlement value at that location will increase by 1 mm.

4. A data warehouse for safe construction of dual tunnels according to claim 3, characterized in that: Step S3 involves multi-dimensional modeling of the data warehouse using the aforementioned double Gaussian ground subsidence trough model. This is achieved by applying the proposed model to the instrument data in the data warehouse based on a nonlinear least squares optimization method. The objective function F will be: It is in the Ground subsidence observed at each subsidence marker, therefore... It is the first The deviation of each data point from the fitted curve; It is the residual vector of the settlement markers in each ground array, optimized using a nonlinear least squares method. Iterative minimization and finding unknown parameters , These are solutions to the system of linear equations, as shown below: Where k is the iteration step and J is the Jacobian matrix, as shown below: scalar It is the damping factor, which controls the amplitude and direction of each major iteration. If the descent rate is very fast, a smaller damping factor can be used. However, if the rate of descent during iteration is insufficient, it can be increased. ,and It tends to move in a steeper descent direction; in this work, the initial damping coefficient... It is 0.001; The proposed model can be applied to instrument data in a data warehouse using an optimization method based on nonlinear least squares, thus completing the modeling of a multi-dimensional data warehouse.

Images