Risk assessment system for construction of long tunnel crossing fault fracture zone

Abstract

The invention relates to the technical field of tunnel construction management, and discloses a risk assessment system for long tunnel crossing fault fracture zone construction, which comprises a data acquisition module and a risk assessment module. According to the system, a data set is acquired through the data acquisition module, all possible risk factors are comprehensively acquired, a high-quality data assessment result is more accurate, the risk assessment module is provided with an LSTM algorithm model, an assessment data set, a tunnel progress data set and risk indexes are generated through analysis of the algorithm model, and the risk assessment module is provided with a construction period step. Each step corresponds to different construction contents, the risk assessment module judges the construction period step of the current time point according to the tunnel progress data set, if the tunnel progress data set spans from the previous step to the next step, the LSTM algorithm model correspondingly carries out training updating, the risk assessment module compares the risk index with a risk threshold, and the risk assessment module carries out risk assessment. And corresponding reports are output, so that the safety of tunnel site construction is greatly improved, and the robustness of an intelligent updating system is high.

Description

Technical Field

[0001] The invention relates to the technical field of tunnel construction management, and in particular to a risk assessment system for the construction of a long tunnel passing through a fault fracture zone. Background Art

[0002] The construction of long and large tunnels through fault fracture zones is a very challenging engineering task. Fault fracture zones are usually composed of a large amount of rock fragments, soil and water. Due to the unstable geological conditions of fault fracture zones, various unforeseen problems may be encountered during the construction process. Therefore, the construction of long and large tunnels through fault fracture zones requires special construction technologies and methods to ensure the safety and smooth progress of the project. First of all, a detailed geological survey is required before construction. This includes a detailed investigation and analysis of the geological structure, rock type, groundwater level, etc. of the fault fracture zone. This information is crucial for determining the construction plan and method. For example, if the fault fracture zone is mainly composed of soft rock, then special construction methods such as shield method or New Austrian Tunneling Method may be required. Secondly, effective support measures need to be taken during the construction process. Due to the unstable geological conditions of the fault fracture zone, problems such as ground subsidence and collapse may occur during the construction process. Therefore, sufficient support structures need to be set up inside the tunnel to prevent these problems from occurring. These support structures may include steel arch frames, concrete linings, anchor rods, etc. In addition, the construction speed needs to be strictly controlled during the construction process. Too fast a construction speed may cause problems such as ground subsidence and collapse. Strict monitoring is also required during the construction process. This includes real-time monitoring of the geological conditions inside the tunnel, the stability of the support structure, ground subsidence, etc. Once a problem is found, immediate measures need to be taken to deal with it. Finally, after the construction is completed, strict acceptance is required. This includes a comprehensive inspection and evaluation of the tunnel's structural stability, usage functions, etc. Only through acceptance can the safety and normal use of the tunnel be ensured.

[0003] At present, the risk assessment system for the construction of traditional long tunnels through fault fracture zones faces the complexity and variability of geological conditions. The collected geological data are prone to incomplete, outdated or inaccurate problems, resulting in a high probability of misleading results in subsequent risk analysis. In addition, with the advancement of construction and the acquisition of new geological information, the risk assessment model needs to be manually updated and maintained. If it is not updated in time, the robustness of the system will also be reduced. Summary of the invention

[0004] In view of the shortcomings of the prior art, the present invention provides a risk assessment system for the construction of long and large tunnels crossing fault and broken zones, which has the advantages of more accurate assessment results due to high-quality data and high robustness of the intelligent update system. It solves the problems of unstable data quality and low system robustness of traditional risk assessment systems for the construction of long and large tunnels crossing fault and broken zones.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A risk assessment system for the construction of a long tunnel passing through a fault zone, comprising:

[0007] Data collection module and risk assessment module;

[0008] The data acquisition module includes a construction data unit, a geological data unit and a meteorological data unit. The construction data unit collects construction data sets in real time by connecting to a database through a network, and arranges the data sets in chronological order from early to late. The construction data sets include construction data of long and large tunnels at all time points. The geological data unit collects geological data sets in real time by connecting to a geological monitoring device through a network, and arranges the data sets in chronological order from early to late. The geological data sets include geological data of the area where the long and large tunnel is located. The meteorological data unit collects meteorological data sets by connecting to a big data platform through a network, and arranges the data sets in chronological order from early to late. The meteorological data sets include meteorological and hydrological data of the area where the long and large tunnel is located. The data acquisition module transmits the construction data sets, geological data sets and meteorological data sets to the risk assessment module through the network;

[0009] The risk assessment module includes a quality analysis unit, a construction analysis unit and a risk analysis unit. The risk assessment module is provided with an LSTM algorithm model. The quality analysis unit substitutes the construction data set, the geological data set and the meteorological data set into the LSTM algorithm model, analyzes and generates an evaluation data group Pgsj. The construction analysis unit substitutes the construction data set and the evaluation data group Pgsj into the LSTM algorithm model, analyzes and generates a tunnel progress data group Sdjd. The risk analysis unit substitutes the tunnel progress data group Sdjd and the evaluation data group Pgsj into the LSTM algorithm model, analyzes and generates a risk index Fxzs. The risk assessment module is provided with a construction period ladder according to the construction data set. The risk assessment module determines the construction period ladder at the current time point according to the tunnel progress data group Sdjd, and updates the LSTM algorithm model accordingly. The risk assessment module is provided with a risk threshold, compares the risk index Fxzs with the risk threshold, and outputs a corresponding assessment report or safety management report.

[0010] Furthermore, the expression of the construction data set is {S1 t 、S2 t 、S3 t 、...、Sn t},S1 t To Sn t The construction data of the long and large tunnels at each time point correspond in sequence. The construction data include construction scale, design plan and construction method. t represents the time point when the database updates the construction data. 1 to n represent that there are n groups of long and large tunnel construction data updated in the database.

[0011] Furthermore, the expression of the geological data set is {D1 m 、D2 m 、D3 m ,...,Dz m}, D1 m To Dz m It corresponds to the geological data of the area where the long tunnel is located each time the geological monitoring device monitors, the geological data includes fault activity, rock burst data and groundwater data, m represents the time point when the geological monitoring device monitors the geological data, and 1 to z represent that there are z groups of geological data monitored by the geological monitoring device.

[0012] Furthermore, the expression of the meteorological data set is {Q1 s 、Q2 s 、Q3 s , ..., Qx s}, Q1 s To Qx s They correspond to the meteorological and hydrological data provided by the big data platform each time, s represents the time point when the big data platform provides the meteorological and hydrological data, and 1 to x represent that there are x groups of meteorological and hydrological data provided by the big data platform.

[0013] Furthermore, the calculation formula of the evaluation data group Pgsj is as follows:

[0014]

[0015] Pgsj=∑ LSTM [(Sn t -Sn-1 t )+DZ+Qx s ]

[0016] In the formula, Pgsj represents the evaluation data group, BD represents the fixed parameters of the LSTM algorithm model to determine whether the geological data is abnormal. Indicates that the LSTM algorithm model selects abnormal geological data DZ and Sn-1 in the geological data set in chronological order. t Indicates the construction data of the long tunnel at a time point before the current time, Sn t -Sn-1 t represents the difference in the long tunnel construction data between two time points, that is, the latest updated long tunnel construction content at the current time point, ∑ LSTM [(Sn t -Sn-1 t )+DZ+Qx s] indicates that the LSTM algorithm model selects the latest updated long and large tunnel construction content, abnormal geological data and meteorological and hydrological data at the latest time point from the construction data set, geological data set and meteorological data set in chronological order.

[0017] Furthermore, the calculation formula of the tunnel progress data group Sdjd is as follows:

[0018]

[0019] In the formula, Sdjd represents the tunnel progress data set, ZS represents the design scheme for long and large tunnels in the construction data set, It means that the LSTM algorithm model analyzes the proportion of the completed project to the scheduled progress of the long and large tunnel design plan based on the latest updated construction content at the current time point.

[0020] Furthermore, the risk index Fxzs is calculated as follows:

[0021]

[0022] In the formula, Fxzs represents the risk index, α represents the weight of the tunnel progress data group in the risk index calculation formula, and β represents the weight of the evaluation data group in the risk index calculation formula. It means that the LSTM algorithm model algorithm calculates the weighted average of the tunnel progress data group and the evaluation data group according to the α weight and the β weight, which is the risk index corresponding to the construction of the long tunnel crossing the fault fracture zone at the current time point.

[0023] Furthermore, the risk assessment module is provided with a construction period ladder according to the long and large tunnel design scheme in the construction data set, and the construction period ladder includes a first-level ladder, a second-level ladder, a third-level ladder and a fourth-level ladder, and each level corresponds to different construction contents. The risk assessment module determines the construction period ladder at the current time point according to the tunnel progress data group Sdjd. If the tunnel progress data group Sdjd jumps from the previous level to the next level, the LSTM algorithm model is trained and updated according to the construction data set, geological data set and meteorological data set at all time points.

[0024] Furthermore, when the risk index Fxzs exceeds the risk threshold, the risk assessment module determines that there are abnormalities in the long and large tunnel construction data, geological data, and meteorological and hydrological data, and outputs a high-risk assessment report.

[0025] Furthermore, when the risk index Fxzs does not exceed the risk threshold, the risk assessment module determines that there is no abnormality in the long and large tunnel construction data, geological data, and meteorological and hydrological data, and outputs a safety management report.

[0026] Compared with the prior art, the present invention provides a risk assessment system for the construction of a long tunnel passing through a fault fracture zone, which has the following beneficial effects:

[0027] 1. The present invention sets a construction data unit, a geological data unit and a meteorological data unit through a data acquisition module. The construction data unit collects a construction data set in real time by connecting to a database through a network, and arranges the data set in chronological order from early to late. The construction data set includes the construction data of the long and large tunnel at all time points. The geological data unit collects a geological data set in real time by connecting to a geological monitoring device through a network, and arranges the data set in chronological order from early to late. The geological monitoring device includes a seismograph, a strain monitor and a groundwater monitor. The geological data set includes the geological data of the area where the long and large tunnel is located. The geological data unit collects a geological data set in real time by connecting to a geological monitoring device through a network, and arranges the data set in chronological order from early to late. The geological monitoring device includes a seismograph, a strain monitor and a groundwater monitor. The geological data set includes the geological data of the area where the long and large tunnel is located. The meteorological data unit collects a meteorological data set by connecting to a big data platform through a network, and arranges the data set in chronological order from early to late. The meteorological data set includes the meteorological and hydrological data of the area where the long and large tunnel is located. All possible risk factors are fully collected, the data traceability is strong, and the evaluation results of high-quality data are more accurate.

[0028] 2. The present invention sets a quality analysis unit, a construction analysis unit and a risk analysis unit through a risk assessment module. The risk assessment module is provided with an LSTM algorithm model. The quality analysis unit analyzes and generates an evaluation data group Pgsj, and screens out the latest updated long tunnel construction content, abnormal geological data and meteorological and hydrological data at the latest time point in the construction data set, geological data set and meteorological data set. The construction analysis unit analyzes and generates a tunnel progress data group Sdjd, and analyzes and obtains the proportion of the completed engineering part to the scheduled progress of the long tunnel design scheme, which is convenient for subsequent rapid judgment of the level of the construction period ladder, targeted management, and higher efficiency and accuracy of risk assessment. The risk analysis unit analyzes and generates a risk index Fxzs, digitally assesses construction risks in multiple aspects, and the risk assessment module is provided with a construction period ladder. The risk assessment module has a ladder, and each ladder corresponds to different construction content. The risk assessment module determines the construction period ladder at the current time point according to the tunnel progress data group Sdjd. If the tunnel progress data group Sdjd crosses from the previous ladder to the next ladder, the LSTM algorithm model is trained and updated according to the construction data set, geological data set and meteorological data set of all time points. The risk assessment module is set with a risk threshold, and the risk index Fxzs is compared with the risk threshold. When the risk index Fxzs exceeds the risk threshold, the risk assessment module determines that the construction data, geological data and meteorological and hydrological data of the long and large tunnel are abnormal, and outputs a high-risk assessment report, which converts the formulated risk response strategy into a specific construction plan and operation guide, greatly improving the safety of on-site construction of long and large tunnels, and the intelligent update system has high robustness. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is a schematic diagram of the system flow of the present invention. DETAILED DESCRIPTION

[0030] The following will be combined with the drawings in 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.

[0031] Due to the complexity and variability of geological conditions, the traditional risk assessment system for long tunnels crossing fault fracture zones is prone to incomplete, outdated or inaccurate geological data, which leads to a high probability of misleading results in subsequent risk analysis. In addition, as the construction progresses and new geological information is obtained, the risk assessment model needs to be manually updated and maintained. If it is not updated in time, the robustness of the system will also decrease. Therefore, a risk assessment system for long tunnels crossing fault fracture zones is provided. Please refer to Figure 1 ,The system includes a data acquisition module and a risk assessment module;

[0032] The data acquisition module includes a construction data unit, a geological data unit, and a meteorological data unit. The construction data unit collects construction data sets in real time through a network connection to the database and arranges them in chronological order from early to late. The construction data set includes the construction data of the long tunnel at all time points. The expression of the construction data set is {S1 t 、S2 t 、S3 t 、...、Sn t},S1 t To Sn t The construction data of the long and large tunnels at each time point correspond in sequence. The construction data includes the construction scale, design scheme and construction method. The construction scale includes the length, width and height of the tunnel, and the estimated start and end time of the construction. The design scheme includes the design structure of the long and large tunnel, and the construction methods such as shield method, drilling and blasting method, and tunnel boring machine method. t represents the time point when the database updates the construction data. Whenever the construction of the long and large tunnel begins, the database will synchronously store the new construction data. 1 to n represents that there are n groups of long and large tunnel construction data updated by the database.

[0033] The geological data unit collects geological data sets in real time through a network connection with geological monitoring devices, and arranges them in chronological order from early to late. The geological monitoring devices include seismographs, strain monitors, and groundwater monitors. The geological data sets include geological data of the area where the long tunnel is located. The expression of the geological data set is {D1m 、D2 m 、D3 m ,...,Dz m}, D1 m To Dz m The corresponding geological data of the area where the long tunnel is located each time the geological monitoring device monitors the geological data include fault activity, rock burst data and groundwater data, specifically the geological structure, rock type and groundwater conditions of the fault fracture zone. m represents the time point when the geological monitoring device monitors the geological data. 1 to z represent that there are z groups of geological data monitored by the geological monitoring device.

[0034] The meteorological data unit collects meteorological data sets through the network connection big data platform and arranges them in chronological order from early to late. The meteorological data set includes the meteorological and hydrological data of the area where the long and large tunnel is located. The expression of the meteorological data set is {Q1 s 、Q2 s 、Q3 s ,...,Qx s}, Q1 s To Qx s The meteorological and hydrological data are provided by the big data platform each time. The meteorological and hydrological data include the average temperature, precipitation, humidity, and evaporation in different seasons in the region. s represents the time point when the big data platform provides meteorological and hydrological data. 1 to x represents that there are x groups of meteorological and hydrological data provided by the big data platform. In the construction of long and large tunnels, meteorological and hydrological data are crucial to ensure construction safety and quality. These data provide detailed information about the climate and hydrological conditions in the area where the tunnel is located, which helps engineers better plan construction plans and deal with possible environmental risks.

[0035] The data acquisition module transmits the construction data set, geological data set and meteorological data set to the risk assessment module through the network, comprehensively collecting all possible risk factors, with strong data traceability and more accurate assessment results with high-quality data;

[0036] The risk assessment module includes a quality analysis unit, a construction analysis unit and a risk analysis unit. The risk assessment module is provided with an LSTM algorithm model. The quality analysis unit substitutes the construction data set, the geological data set and the meteorological data set into the LSTM algorithm model to analyze and generate the assessment data group JPgsj. The calculation formula is as follows:

[0037]

[0038] JPgsj=∑ LsTM [(Sn t -Sn-1 t )+DZ+Qx s ]

[0039] In the formula, Pgsj represents the evaluation data group, and BD represents the fixed parameters of the LSTM algorithm model to determine whether the geological data is abnormal. The fixed parameters are specifically the geological fault activity, rock burst data and groundwater data within the safe range. If the actual geological data exceeds the fixed parameters, it means that geological disasters may occur at the current time point, posing a threat to the safety of long tunnel construction. Indicates that the LSTM algorithm model selects abnormal geological data DZ and Sn-1 in the geological data set in chronological order. t Indicates the construction data of the long tunnel at a time point before the current time, Sn t -Sn-1 t represents the difference in the long tunnel construction data between two time points, that is, the latest updated long tunnel construction content at the current time point, ∑ LSTM [(Sn t -Sn-1 t )+DZ+Qx s ] indicates that the LSTM algorithm model selects the latest updated long and large tunnel construction content, abnormal geological data and the latest meteorological and hydrological data from the construction data set, geological data set and meteorological data set in chronological order;

[0040] The construction analysis unit substitutes the construction data set and the evaluation data group Pgsj into the LSTM algorithm model, analyzes and generates the tunnel progress data group Sdjd, and its calculation formula is as follows:

[0041]

[0042] In the formula, Sdjd represents the tunnel progress data set, ZS represents the design scheme for long and large tunnels in the construction data set, It means that the LSTM algorithm model algorithm analyzes the proportion of the completed engineering part to the scheduled progress of the long tunnel design scheme according to the latest updated construction content at the current time point, which is convenient for subsequent rapid judgment of the level of the construction period ladder, targeted management, and higher efficiency and accuracy of risk assessment;

[0043] The risk analysis unit substitutes the tunnel progress data group Sdjd and the evaluation data group Pgsj into the LSTM algorithm model, analyzes and generates the risk index Fxzs, the calculation formula of which is as follows:

[0044]

[0045] In the formula, Fxzs represents the risk index, α represents the weight of the tunnel progress data group in the risk index calculation formula, represents the weight of the evaluation data group in the risk index calculation formula, α and β are constants, and α+β=1, It means that the LSTM algorithm model algorithm calculates the weighted average of the tunnel progress data group and the evaluation data group according to the α weight and the β weight, which is the risk index corresponding to the construction of the long tunnel crossing the fault fracture zone at the current time point, and digitally evaluates the construction risks in multiple aspects;

[0046] The risk assessment module is set with a construction period ladder according to the long and large tunnel design scheme in the construction data set. The construction period ladder includes the first step, the second step, the third step and the fourth step, and each step corresponds to different construction contents. For example, the first step corresponds to the construction period of the front section of the tunnel, the second step corresponds to the construction period of the middle section of the tunnel, the third step corresponds to the construction period of the rear section of the tunnel, and the fourth step corresponds to the acceptance period after the completion of the tunnel. The risk assessment module determines the construction period ladder at the current time point according to the tunnel progress data group Sdjd. If the tunnel progress data group Sdjd crosses from the previous step to the next step, the LSTM algorithm model is trained and updated according to the construction data set, geological data set and meteorological data set at all time points. The risk assessment module is set with a risk threshold and the risk index is The number Fxzs is compared with the risk threshold. When the risk index Fxzs exceeds the risk threshold, the risk assessment module determines that there are anomalies in the construction data, geological data, and meteorological and hydrological data of the long and large tunnel, and outputs a high-risk assessment report, converting the formulated risk response strategy into a specific construction plan and operation guide, which greatly improves the safety of on-site construction of the long and large tunnel. When the risk index Fxzs does not exceed the risk threshold, the risk assessment module determines that there are no anomalies in the construction data, geological data, and meteorological and hydrological data of the long and large tunnel, and outputs a safety management report. Regular review and evaluation effectively enhances the preventive nature of geological disasters, reduces project delays, and effectively protects construction equipment and tunnel structures from damage. The LSTM algorithm model has higher accuracy in risk assessment, and the intelligent update system is highly robust.

[0047] 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 risk assessment system for the construction of a long tunnel passing through a fault zone, characterized in that: Including data collection module and risk assessment module; The data acquisition module includes a construction data unit, a geological data unit and a meteorological data unit. The construction data unit collects construction data sets in real time by connecting to a database through a network, and arranges the data sets in chronological order from early to late. The construction data sets include construction data of long and large tunnels at all time points. The geological data unit collects geological data sets in real time by connecting to a geological monitoring device through a network, and arranges the data sets in chronological order from early to late. The geological data sets include geological data of the area where the long and large tunnel is located. The meteorological data unit collects meteorological data sets by connecting to a big data platform through a network, and arranges the data sets in chronological order from early to late. The meteorological data sets include meteorological and hydrological data of the area where the long and large tunnel is located. The data acquisition module transmits the construction data sets, geological data sets and meteorological data sets to the risk assessment module through the network; The risk assessment module includes a quality analysis unit, a construction analysis unit and a risk analysis unit. The risk assessment module is provided with an LSTM algorithm model. The quality analysis unit substitutes the construction data set, the geological data set and the meteorological data set into the LSTM algorithm model, analyzes and generates an evaluation data group Pgsj. The construction analysis unit substitutes the construction data set and the evaluation data group Pgsj into the LSTM algorithm model, analyzes and generates a tunnel progress data group Sdjd. The risk analysis unit substitutes the tunnel progress data group Sdjd and the evaluation data group Pgsj into the LSTM algorithm model, analyzes and generates a risk index Fxzs. The risk assessment module is provided with a construction period ladder according to the construction data set. The risk assessment module determines the construction period ladder at the current time point according to the tunnel progress data group Sdjd, and updates the LSTM algorithm model accordingly. The risk assessment module is provided with a risk threshold, compares the risk index Fxzs with the risk threshold, and outputs a corresponding assessment report or safety management report.

2. According to claim 1, a risk assessment system for the construction of a long tunnel passing through a fault zone is characterized by: The expression of the construction data set is {S1 t 、S2 t 、S3 t 、...、Sn t },S1 t To Sn t The construction data of the long and large tunnels at each time point correspond in sequence. The construction data include construction scale, design plan and construction method. t represents the time point when the database updates the construction data. 1 to n represent that there are n groups of long and large tunnel construction data updated in the database.

3. The risk assessment system for the construction of a long tunnel passing through a fault zone according to claim 2 is characterized in that: The expression of the geological data set is {D1 m 、D2 m 、D3 m ,...,Dz m }, D1 m To Dz m It corresponds to the geological data of the area where the long tunnel is located each time the geological monitoring device monitors, the geological data includes fault activity, rock burst data and groundwater data, m represents the time point when the geological monitoring device monitors the geological data, and 1 to z represent that there are z groups of geological data monitored by the geological monitoring device.

4. A risk assessment system for the construction of a long tunnel passing through a fault zone according to claim 3, characterized in that: The expression of the meteorological data set is {Q1 s 、Q2 s 、Q3 s ,...,Qx s }, Q1 s To Qx s They correspond to the meteorological and hydrological data provided by the big data platform each time, s represents the time point when the big data platform provides the meteorological and hydrological data, and 1 to x represent that there are x groups of meteorological and hydrological data provided by the big data platform.

5. A risk assessment system for the construction of a long tunnel passing through a fault zone according to claim 4, characterized in that: The calculation formula of the evaluation data set Pgsj is as follows: Pgsj=∑ LSTM [(Sn t -Sn-1 t )+DZ+Qx s ] In the formula, Pgsj represents the evaluation data group, BD represents the fixed parameters of the LSTM algorithm model to determine whether the geological data is abnormal. Indicates that the LSTM algorithm model selects abnormal geological data DZ and Sn-1 in the geological data set in chronological order. t Indicates the construction data of the long tunnel at a time point before the current time, Sn t -Sn-1 t represents the difference in the long tunnel construction data between two time points, that is, the latest updated long tunnel construction content at the current time point, ∑ LSTM [(Sn t -Sn-1 t )+DZ+Qx s ] indicates that the LSTM algorithm model selects the latest updated long and large tunnel construction content, abnormal geological data and meteorological and hydrological data at the latest time point from the construction data set, geological data set and meteorological data set in chronological order.

6. A risk assessment system for the construction of a long tunnel passing through a fault zone according to claim 5, characterized in that: The calculation formula of the tunnel progress data group Sdjd is as follows: In the formula, Sdjd represents the tunnel progress data set, ZS represents the design scheme for long and large tunnels in the construction data set, It means that the LSTM algorithm model analyzes the proportion of the completed project to the scheduled progress of the long and large tunnel design plan based on the latest updated construction content at the current time point.

7. A risk assessment system for the construction of a long tunnel passing through a fault zone according to claim 6, characterized in that: The calculation formula of the risk index Fxzs is as follows: In the formula, Fxzs represents the risk index, α represents the weight of the tunnel progress data group in the risk index calculation formula, and β represents the weight of the evaluation data group in the risk index calculation formula. It means that the LSTM algorithm model algorithm calculates the weighted average of the tunnel progress data group and the evaluation data group according to the α weight and the β weight, which is the risk index corresponding to the construction of the long tunnel crossing the fault fracture zone at the current time point.

8. The risk assessment system for the construction of a long tunnel passing through a fault zone according to claim 7 is characterized in that: The risk assessment module is provided with a construction period ladder according to the long and large tunnel design scheme in the construction data set, and the construction period ladder includes a first-level ladder, a second-level ladder, a third-level ladder and a fourth-level ladder, and each level corresponds to different construction contents. The risk assessment module determines the construction period ladder at the current time point according to the tunnel progress data group Sdjd. If the tunnel progress data group Sdjd jumps from the previous level to the next level, the LSTM algorithm model is trained and updated according to the construction data sets, geological data sets and meteorological data sets at all time points.

9. A risk assessment system for the construction of a long tunnel passing through a fault zone according to claim 8, characterized in that: When the risk index Fxzs exceeds the risk threshold, the risk assessment module determines that there are abnormalities in the long and large tunnel construction data, geological data, and meteorological and hydrological data, and outputs a high-risk assessment report.

10. A risk assessment system for the construction of a long tunnel passing through a fault zone according to claim 9, characterized in that: When the risk index Fxzs does not exceed the risk threshold, the risk assessment module determines that there is no abnormality in the long and large tunnel construction data, geological data, and meteorological and hydrological data, and outputs a safety management report.

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