Urban rail transit environment vibration standard exceeding risk management system and method

Abstract

The invention relates to an urban rail transit environment vibration standard exceeding risk management system and method, and the system comprises a processor which comprises a screening unit, an evaluation unit and a decision-making unit. The screening unit is used for screening vibration sensitive points of input vibration data, particularly screening sensitive points of which predicted vibration response values exceed the standard or are close to exceed the standard, and taking the sensitive points as key sensitive points; an evaluation unit calculates an overproof probability based on the predicted vibration response value of the screened key sensitive point and a probability density function, and compares the overproof probability with a control probability of vibration standard reaching corresponding to the importance level of the sensitive target; under the condition that no damping measure is set, if the exceeding probability is smaller than the control probability, the decision-making unit does not need to adopt any damping measure; under the condition that different grades of vibration reduction measures are set and the exceeding probability is smaller than the control probability, the grades of the vibration reduction measures do not need to be improved; otherwise, the grade of the vibration reduction measures is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of urban rail transit, and in particular to a risk management system and method for excessive environmental vibration in urban rail transit. Background Art

[0002] Urban rail transit systems are a crucial component of my country's modern public transportation system. The environmental vibration issues they generate during operation are becoming a growing concern in the engineering field. Vibration primarily originates from the transfer of mechanical energy generated by the dynamic interaction between wheels and rails. Its formation mechanism involves the coupled influence of multiple factors, including the dynamics of the rail vehicle system, the dynamic response of the track structure, and the propagation characteristics of waves in the strata. During vibration propagation, train operating parameters (such as speed and axle load), geological conditions (soil properties and wave impedance characteristics), and building structural characteristics (foundation type and natural frequency) all have an impact. Vibration propagates through the composite medium of track, foundation, and building, ultimately affecting sensitive objects.

[0003] Currently, assessments of excessive vibration levels often rely on deterministic analysis methods, typically comparing maximum vibration values ​​with standard limits. While simple, this approach overlooks factors such as the time-varying nature of train loads, the frequency response characteristics of the track system, and the anisotropy of the geological medium, leading to significant fluctuations in assessment results. In actual projects, vibration reduction measures designed based on this static model are often prone to two problems: over-protection, resulting in wasted resources; and under-protection, requiring repeated treatment. Under such complex conditions, the problems of under- or over-protection are particularly pronounced.

[0004] To overcome the limitations of traditional methods, it is necessary to establish a dynamic risk assessment system based on probability and statistics. This system should comprehensively consider three aspects: first, the random variations in train operating parameters; second, the inherent uncertainty transmission relationship between the track and foundation system; and third, the distribution of the building structure's sensitivity to vibration. Using methods such as stochastic process analysis and Monte Carlo simulation, a probabilistic model of vibration response can be constructed, shifting from a simple judgment of "whether the limit is exceeded" to a probabilistic assessment of vibration risk. Furthermore, by integrating intelligent monitoring technology, the vibration propagation path can be tracked in real time, establishing an integrated dynamic control mechanism of "prediction-monitoring-control."

[0005] Current research trends are moving toward multidisciplinary integration. On a theoretical level, there's a need to strengthen the modeling of the nondeterministic propagation of waves in stratified media. In terms of technological application, breakthroughs are being made in AI-based vibration signature recognition and the development of adaptive vibration reduction devices. In engineering implementation, the practical applications of novel constrained damping rails and three-dimensional vibration isolation supports are being actively explored. These advances are expected to propel environmental vibration control from a traditional passive response model to a more proactive and intelligent approach.

[0006] For example, CN117634684A discloses a method and system for predicting the environmental vibration impact of urban rail transit based on GIS spatial analysis, including: S1. Collecting target parameters of the target rail transit; S2. Using the target rail transit as a reference, selecting a prediction area and randomly selecting prediction points within the prediction area; S3. Extracting the design parameters and environmental parameters of the prediction points based on the target parameters, and calculating the vibration propagation correction value of the prediction points based on the design parameters and environmental parameters of the prediction points based on GIS spatial analysis; S4. Constructing an environmental vibration prediction model; inputting the vibration propagation correction value of the prediction point into the environmental vibration prediction model to calculate the Z vibration level at the prediction point. This technical solution proposes a vibration propagation correction value for the prediction point based on the Z vibration level of the prediction point, but the final output Z vibration level is still a fixed value.

[0007] CN118013774A discloses a random prediction method, computer equipment, and readable storage medium for rail transit vibration environmental impact assessment. The random prediction method includes the following steps: obtaining the vibration response at the predicted point on the ground under the action of the vibration source load; calculating the maximum Z vibration level and weighted frequency-divided vibration level at the surface pickup point; obtaining the correction function of the maximum Z vibration level of the tunnel structure and the correction function of the frequency-divided vibration level; using data processing software to process the vibration response results of the surface point to obtain the maximum Z vibration level and the frequency-divided vibration level, and obtaining the final vibration response result after tunnel structure correction; obtaining the maximum Z vibration level response surface prediction model and the frequency-divided vibration level response surface prediction model; and realizing the fixed value prediction and probabilistic prediction functions of the surface vibration. This technical solution performs probabilistic prediction of the surface Z vibration level based on the response surface prediction method, and provides prediction intervals with different confidence levels for specific working conditions.

[0008] CN117807672A discloses a reliability design method for track vibration reduction measures. This method identifies multiple factors that influence the reliability of vibration reduction measures. It uses a probability density function to represent the vibration reduction effect of each factor based on the frequency-divided track source intensity. Based on a combined total probability formula and a probability density function integration method, the reliability of the probability density function of each factor influencing the track vibration reduction effect is calculated, and the reliability design of the track vibration reduction measures is then performed. This method applies the probability density function to the design of vibration and noise reduction measures, introducing the influence of uncertainty.

[0009] The technical solutions of CN118013774A and CN117807672A mentioned above both involve probabilistic prediction. Under the framework of probabilistic prediction, whether or not the standard is exceeded will no longer be a deterministic judgment, but an event that occurs at a certain probability level. Therefore, in-depth research on the possibility of exceeding the standard (i.e., the risk of exceeding the standard) of vibration in the rail transit environment, including but not limited to risk identification, risk quantification, and risk control, is crucial to ensuring the safe and stable operation of the rail transit system. Through a systematic risk identification process, it is possible to identify factors that may cause vibration to exceed the standard; with the help of precise risk quantification methods, the probability of these risks occurring and their potential impact can be assessed; and effective risk control strategies can help formulate preventive measures, reduce the occurrence of exceeding the standard incidents, and ensure the quality of life of residents along the line and the safety of surrounding buildings. Such research not only helps to improve the overall safety of rail transit projects, but also achieves cost savings and maximizes social benefits during the planning and operation stages.

[0010] This invention proposes a method and system for managing the risk of excessive vibration in rail transit environments, taking into account the random nature of vibration. This invention enables the assessment and management of environmental vibration during the construction and operation phases of rail transit, improving the accuracy and applicability of risk assessments for excessive vibration, thereby achieving the coordinated development of social and economic benefits.

[0011] In addition, on the one hand, there are differences in understanding among those skilled in the art; on the other hand, the applicant studied a large number of documents and patents when making the present invention, but due to space limitations, not all details and contents are listed in detail. However, this does not mean that the present invention does not have the characteristics of these prior arts. On the contrary, the present invention already has all the characteristics of the prior art, and the applicant reserves the right to add relevant prior art to the background technology. Summary of the Invention

[0012] The current assessment system for vibration anomalies mostly uses traditional deterministic analysis methods, the core logic of which is to make judgments by comparing peak vibration parameters with standard thresholds. Although this type of method has the advantage of convenient operation, it has three significant defects: it fails to consider the time evolution of train dynamic loads, the dynamic response characteristics of track structures, and the heterogeneous characteristics of stratum media. This static assessment model exposes two major contradictions in actual engineering applications - it may lead to excessive protection and waste of resources, and it may also require repeated corrections due to insufficient protection strength. Especially in complex engineering environments such as soft soil base areas and subway transfer nodes, this assessment deviation will be significantly amplified, directly affecting the economy and reliability of the vibration reduction plan.

[0013] This invention aims to construct a dynamic risk assessment method and model based on probability theory, which requires the integration of three core elements: first, the random fluctuation characteristics of train operating parameters must be quantified; second, the uncertainty coupling mechanism of the track-foundation system must be analyzed; and third, the differentiated sensitivity of building structures to vibration responses must be evaluated. By introducing random process modeling and Monte Carlo numerical simulation technology, a statistical distribution model of vibration response can be established, upgrading the traditional binary judgment (exceeding the standard / not exceeding the standard) to a quantitative analysis of risk probability. Synchronously integrating intelligent sensor networks can achieve real-time dynamic tracking of vibration propagation paths, thereby forming a closed-loop dynamic control system of "prediction and warning-state monitoring-active control."

[0014] The present invention provides a risk management system for exceeding the standard for urban rail transit environment vibration from a first aspect, comprising a processor, wherein the processor comprises a screening unit, an evaluation unit and a decision unit. The screening unit screens vibration sensitive points for input vibration data, and in particular screens vibration sensitive points whose predicted vibration response values ​​exceed the standard or are close to exceeding the standard and uses them as key sensitive points; the evaluation unit calculates the probability of exceeding the standard based on the predicted vibration response values ​​and probability density functions of the screened key sensitive points, and compares the probability of exceeding the standard with the control probability corresponding to the importance level of the sensitive target; the decision unit does not need to adopt any vibration reduction measures if the probability of exceeding the standard is less than the control probability when no vibration reduction measures are set; if different levels of vibration reduction measures are set, there is no need to increase the level of vibration reduction measures if the probability of exceeding the standard is less than the control probability; otherwise, the level of vibration reduction measures is increased.

[0015] The present invention breaks through the static limitations of traditional deterministic assessment methods by constructing a dynamic management system consisting of a screening unit, an evaluation unit, and a decision-making unit. The screening unit adopts a probabilistic screening strategy to accurately identify high-risk vibration sensitive points; the evaluation unit quantifies the risk of exceeding the standard based on the probability density function, and dynamically adjusts the control threshold in combination with the sensitive target level; the decision-making unit effectively avoids the contradiction between excessive protection and insufficient protection in traditional methods through a graded response mechanism of multi-level vibration reduction measures. The system significantly improves the accuracy of economic assessment of vibration risks under complex working conditions (such as soft soil foundations and transfer hubs), ensuring that vibration reduction measures meet reliability requirements while avoiding waste of resources.

[0016] According to a preferred embodiment, the evaluation unit is further configured to: in the operation stage, the verification unit constructs a joint probability distribution function based on the measured vibration response value and the predicted vibration response value of the key sensitive point, and determines a joint probability density function, a first marginal probability density function and a second marginal probability density function, the first marginal probability density function corresponds to the probability distribution of the predicted vibration response value, and the second marginal probability density function corresponds to the probability distribution of the measured vibration response value; the equal probability curve of the joint probability density function is in the plane coordinate system x formed by the predicted vibration response value and the measured vibration response value mOx p The decision unit is based on the sign sent by the evaluation unit and the straight line x p =x m The accuracy of the predicted vibration response value is determined by the degree of position deviation; p Represents the predicted vibration response value; x m Indicates the measured vibration response value.

[0017] By introducing the joint probability distribution function, the system can quantify the dynamic correlation between the predicted value and the measured value, breaking through the error amplification defect of the traditional static comparison method. The geometric characteristics of the elliptical marker intuitively represent the degree of deviation of the prediction accuracy. p =x m Deviation analysis of the straight line can dynamically correct the confidence interval of the prediction model. This technical approach effectively addresses the assessment bias caused by ground medium heterogeneity and track-foundation coupling uncertainty, providing real-time data support for the dynamic optimization of vibration reduction measures.

[0018] According to a preferred embodiment, the evaluation unit is further configured to: calculate the residual risk coefficient based on the joint probability density function and send it to the decision unit; when the residual risk coefficient is greater than the control probability, the decision unit triggers an alarm signal to remind that the vibration reduction measures adopted during the construction period are not effective.

[0019] The introduction of a residual risk factor enables quantitative verification of the effectiveness of vibration reduction measures during the construction phase, transcending the traditional binary determination of "whether or not the standard has been exceeded." When the residual risk factor exceeds the control probability threshold, an early warning mechanism is triggered, promptly identifying insufficient vibration reduction effectiveness during the design phase and mitigating systemic risks caused by geological complexity or model simplification. This technical approach effectively enhances the risk controllability of vibration reduction solutions throughout their lifecycle.

[0020] According to a preferred embodiment, the evaluation unit is further configured to: calculate the vibration reduction performance deviation that characterizes the difference between the actual effectiveness and expected effectiveness of the vibration reduction measures and send it to the decision unit; when the vibration reduction performance deviation is greater than a preset deviation threshold, the decision unit triggers a maintenance signal.

[0021] Real-time monitoring of vibration reduction performance deviations and a threshold alarm mechanism address the inability of traditional methods to quantify the actual effectiveness of measures. By dynamically comparing expected performance with measured deviations, the system accurately identifies performance degradation or construction deviations in vibration reduction structures, providing a basis for preventive maintenance decisions during the operation phase and significantly reducing the risk of performance degradation due to long-term operation.

[0022] According to a preferred embodiment, the decision-making unit is further configured to: divide the plane coordinate system composed of the predicted vibration response value and the measured vibration response value into four zones based on the standard limit values ​​of key sensitive points; in the line planning stage, when the main part of the elliptical sign body is located in the third zone close to the origin and no migration occurs, if the residual risk coefficient is less than 50% of the control probability, it is judged that no subsequent monitoring and governance work is required.

[0023] Based on a dynamic zoning strategy, this invention enables full-cycle risk management from route planning to operation. By combining the position of the elliptical marker with the residual risk factor, it can intelligently determine whether to terminate monitoring, avoiding the redundant investment in low-risk areas required by traditional methods.

[0024] According to a preferred embodiment, the decision unit is further configured to: during the operation phase, when the elliptical marker moves from the first zone before vibration reduction to the third zone after vibration reduction, and the residual risk coefficient γ is less than 0.1, the decision unit records the vibration reduction measures as a successful case; wherein the positions of the first zone and the third zone are relative and not adjacent.

[0025] By dynamically tracking the sign's migration trajectory, the system can quantitatively evaluate the actual noise reduction effects of vibration reduction measures. When the sign moves from a high-risk area to a safe zone and the residual risk coefficient approaches zero, not only does this validate the effectiveness of the vibration reduction measures, but it also establishes a reusable library of successful cases, providing benchmarks for subsequent projects and significantly improving the scalability of the vibration reduction solution.

[0026] According to a preferred embodiment, the decision-making unit is further configured to: in the operation phase, when the elliptical sign body is suddenly located in the second zone of the plane coordinate system composed of the predicted vibration response value and the measured vibration response value, the sign body and the straight line x p =x m The deviation distance is large, which is the reason for the inaccurate prediction in the decision-making unit traceability design stage; among them, the second zone is close to the axis of the measured vibration value and far away from the origin.

[0027] By tracing the source of abnormal marker deviations, the system can accurately pinpoint the root cause of prediction model inaccuracies. When a marker intrudes into a high-risk area, it automatically triggers a design parameter verification process. This addresses the problem of prediction failures caused by traditional methods due to the time-varying nature of train loads or uncertainties in ground response, achieving a closed-loop management system for risk warning and model correction.

[0028] According to a preferred embodiment, the decision unit is further configured as follows: during the operation phase, when the elliptical marker moves from a first zone far away from the origin and not close to the axis to a second zone close to the longitudinal axis of the measured vibration response value in the plane coordinate system formed by the predicted vibration response value and the measured vibration response value, the decision unit traces the reason for the large difference between the predicted insertion loss and the actual insertion loss; or, when the elliptical marker moves from a fourth zone far away from the origin and close to the horizontal axis of the predicted vibration response value to a third zone close to the origin in the plane coordinate system formed by the predicted vibration response value and the measured vibration response value, the decision unit traces the reason for the large difference between the predicted insertion loss and the actual insertion loss; or, when the elliptical marker moves from the second zone close to the longitudinal axis of the measured vibration response value to the third zone close to the origin in the plane coordinate system formed by the predicted vibration response value and the measured vibration response value, the decision unit traces the reason for the large difference between the predicted insertion loss of the new measure and the actual insertion loss of the new measure.

[0029] Through multi-dimensional traceability analysis of marker migration paths, we achieve in-depth diagnosis of discrepancies between the predicted model and actual performance. Whether it's an insertion loss prediction error or insufficient effectiveness of a new measure, the system automatically locates the root cause based on marker migration characteristics. This overcomes the technical bottleneck of traditional static assessments, which cannot distinguish between prediction error types, and provides precise data support for iterative model optimization.

[0030] From a second aspect, the present invention provides a method for managing the risk of excessive vibration in an urban rail transit environment, the method comprising: screening vibration sensitive points on input vibration data, in particular screening vibration sensitive points whose predicted vibration response values ​​exceed or are close to exceeding the standard and using them as key sensitive points; calculating the probability of exceeding the standard based on the predicted vibration response values ​​and probability density functions of the screened key sensitive points, and comparing the probability of exceeding the standard with the control probability corresponding to the importance level of the sensitive target; in the case where no vibration reduction measures are set, if the probability of exceeding the standard is less than the control probability, no vibration reduction measures need to be adopted; in the case where vibration reduction measures of different levels are set, if the probability of exceeding the standard is less than the control probability, there is no need to increase the level of the vibration reduction measures; otherwise, the level of the vibration reduction measures is increased.

[0031] The method of the present invention breaks through the limitations of the "peak-threshold" binary judgment in traditional deterministic analysis methods by introducing a dynamic evaluation mechanism that uses a probability density function to calculate the probability of exceeding the standard. The screening unit accurately identifies high-risk sensitive points, solving the problem of the original method ignoring the time-varying characteristics of train loads, track dynamic responses, and stratum heterogeneity. The control probability hierarchical decision-making mechanism based on importance levels effectively avoids the contradiction between excessive protection and insufficient protection, especially in complex working conditions such as soft soil foundations, and significantly improves the economy and reliability of vibration reduction measures. This method realizes a paradigm shift from static threshold comparison to dynamic risk probability analysis, providing a scientific decision-making basis for rail transit vibration control.

[0032] According to a preferred embodiment, the method further includes: in the operation stage, constructing a joint probability distribution function based on the measured vibration response values ​​and the predicted vibration response values ​​of the key sensitive points, and determining a joint probability density function, a first marginal probability density function, and a second marginal probability density function, wherein the first marginal probability density function corresponds to the probability distribution of the predicted vibration response value, and the second marginal probability density function corresponds to the probability distribution of the measured vibration response value; and the equal probability curve of the joint probability density function is in the plane coordinate system x formed by the predicted vibration response value and the measured vibration response value. m Ox p The sign is presented as an elliptical body; based on the sign and the straight line x p =x m The accuracy of the predicted vibration response value is determined by the degree of position deviation; p Represents the predicted vibration response value; x m Indicates the measured vibration response value.

[0033] By constructing a joint probability distribution function of predicted values ​​and measured values, this method solves the problem of prediction inaccuracy caused by formation medium heterogeneity and system uncertainty in traditional static assessment. The geometric characteristics of the elliptical marker intuitively represent the confidence interval of the prediction accuracy. p =x m Deviation analysis of the straight line allows for dynamic correction of prediction model parameters. This technical approach not only quantifies the coupling uncertainty of the track-subgrade system but also adaptively compensates for the time-varying nature of train loads through marker position migration, effectively improving the robustness of the prediction model. In complex engineering environments, this method provides real-time data support for the dynamic optimization of vibration reduction measures, significantly reducing the cost of addressing prediction errors associated with traditional methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 This is a flow chart of the method for managing the risk of excessive vibration in urban rail transit environments provided by the present invention;

[0035] Figure 2 This is a simplified module connection diagram of the urban rail transit environmental vibration exceeding standard risk management system provided by the present invention;

[0036] Figure 3 It is a schematic diagram of the definition of the probability of exceeding the standard provided by the present invention;

[0037] Figure 4 The plane coordinate system x provided by the present invention is composed of the predicted vibration response value and the measured vibration response value. m Ox p Schematic diagram of;

[0038] Figure 5 It is a partition diagram of a plane coordinate system composed of predicted vibration response values ​​and measured vibration response values ​​provided by the present invention;

[0039] Figure 6 is a schematic diagram of a marker provided by the present invention in a first working condition;

[0040] Figure 7 is a schematic diagram of a marker provided by the present invention in a second working condition;

[0041] Figure 8 is a schematic diagram of the marker provided by the present invention in the third working condition;

[0042] Figure 9 is a schematic diagram of the migration of the marker in the fourth working condition provided by the present invention;

[0043] Figure 10 This is a schematic diagram of the migration of the marker in the fifth working condition provided by the present invention.

[0044] Figure 11 It is a schematic diagram of the migration of the sign body after adopting the new vibration reduction measure provided by the present invention;

[0045] Figure 12 This is a risk response flow chart for the construction period provided by the present invention.

[0046] Reference Signs List

[0047] 1: Probability density function curve; 2: Standard limit; 3: Area of ​​probability of exceeding the standard; 100: Processor; 110: Screening unit; 120: Evaluation unit; 130: Decision unit; 200: Memory. DETAILED DESCRIPTION

[0048] The following is a detailed description with reference to the accompanying drawings.

[0049] Example 1

[0050] Regarding the existing assessment system for excessive vibration, the current engineering field generally adopts the classical deterministic analysis paradigm, the core of which is to make a judgment by comparing the maximum vibration amplitude with the standard limit. Although this assessment method is easy to implement, it has three limitations: it fails to reflect the temporal dynamic characteristics of the train running load, does not take into account the dynamic response law of the track structure, and does not consider the spatial heterogeneity characteristics of the stratum medium. In practical applications, this static assessment model often leads to two extreme situations: either waste of resources due to excessive protection, or repeated treatment due to insufficient protection strength. Especially in complex working conditions such as soft soil foundation areas and subway transfer hubs, the above-mentioned assessment deviation will show an exponential amplification trend, seriously affecting the economy and reliability of vibration reduction measures.

[0051] This invention aims to develop a dynamic risk assessment system and model based on probability statistics, integrating three key dimensions: first, quantifying the time-varying random characteristics of train operating parameters; second, analyzing the uncertain coupling mechanism of the track-foundation system; and third, establishing a spatial distribution model of building structure vibration sensitivity. By applying stochastic process modeling and Monte Carlo simulation techniques, a probability distribution function for vibration response can be constructed, achieving a transition from traditional binary judgment (exceeding the standard) to quantitative analysis of risk probability. Simultaneously, in conjunction with an intelligent monitoring system, dynamic tracking of vibration propagation paths is implemented, ultimately forming a dynamic closed-loop control mechanism of "prediction and warning - real-time monitoring - active regulation."

[0052] To address the deficiencies of the prior art, the present invention provides an urban rail transit environmental vibration exceeding standard risk management system, which is executed by a processor 100. The processor 100 can be a physical hardware with information processing capabilities, such as a server, a central processing unit (CPU), or a dedicated integrated circuit.

[0053] Preferably, the processor 100 may be provided with a plurality of functional modules in the form of program codes. Figure 2 As shown, the functional module includes a screening unit 110 , an evaluation unit 120 and a decision unit 130 .

[0054] Preferably, if Figure 2 As shown, the system of the present invention may also be composed of a processor 100 and a memory 200. The memory 200 is used to store data received by the processor 100 and historical data during the information processing process. Preferably, the screening unit 110, the evaluation unit 120, and the decision unit 130 can be stored in the model storage area of ​​the memory 200 in the form of a screening model, an evaluation model, and a decision model, respectively. The processor 100 can call the model from the memory 200 via a circuit as needed. Preferably, the data storage area of ​​the memory 200 can store raw vibration data and key sensitive point data.

[0055] Preferably, the physical hardware of the memory 200 may be a mechanical hard disk (HDD), a solid-state drive (SSD), a hybrid hard disk (SSHD), an optical storage device, a flash memory storage device (U disk), etc.

[0056] The system of the present invention is used to implement the urban rail transit environmental vibration exceeding standard risk management method of the present invention.

[0057] like Figure 1As shown, the risk management method for exceeding the standard in the urban rail transit environment of the present invention includes: screening vibration sensitive points of the input vibration data, especially screening vibration sensitive points with predicted vibration response values ​​exceeding the standard or close to exceeding the standard and taking them as key sensitive points; calculating the probability of exceeding the standard based on the predicted vibration response values ​​and probability density functions of the screened key sensitive points, and comparing the probability of exceeding the standard with the control probability corresponding to the importance level of the sensitive target; in the case where no vibration reduction measures are set, if the probability of exceeding the standard is less than the control probability, no vibration reduction measures need to be adopted; in the case where vibration reduction measures of different levels are set, if the probability of exceeding the standard is less than the control probability, there is no need to increase the level of the vibration reduction measures; otherwise, the level of the vibration reduction measures is increased.

[0058] The screening unit 110 is used to screen the input vibration data for vibration sensitive points, especially to screen vibration sensitive points whose predicted vibration response values ​​exceed or are close to exceeding the standard and use them as key sensitive points.

[0059] Specifically, during the construction period, the screening unit 110 needs to timely screen the vibration sensitive points received through the receiving port of the processor 100 .

[0060] Based on the basic idea of ​​prediction uncertainty, the screening unit 110 needs to screen vibration sensitive points whose predicted vibration response values ​​exceed or are close to exceeding the standard during the screening process.

[0061] The definition formula for exceeding the standard is:

[0062] x p -s>0.

[0063] In the above formula, s represents the standard limit, x p This indicates the predicted vibration response value of a vibration-sensitive point during the construction period. Specific statistical indicators can include Z vibration level or frequency-divided vibration level.

[0064] Z-Vibration Level (Z-Vibration Level) refers to the weighted vibration energy in the vertical direction (Z-axis), measured in decibels (dB). Z-Vibration Level is the most commonly used indicator in environmental vibration assessments. It primarily reflects a building's or human's perception of vertical vibration, such as the vibration propagation caused by traffic (e.g., subways and highways).

[0065] Frequency-Banded Vibration Level (FBL) refers to the vibration energy within a specific frequency range (such as a 1 / 3 octave or 1 / 1 octave). Vibration has different sensitivities to different frequencies (for example, low-frequency vibrations are more noticeable to buildings, while high-frequency vibrations are more noticeable to humans). FBL can be used to analyze the frequency distribution of vibration energy in detail, determining whether specific frequency bands exceed standards.

[0066] The definition formula for close to exceeding the standard is:

[0067] s-x p <3σ.

[0068] In the above formula, s represents the standard limit, x p It represents the predicted vibration response value of a vibration sensitive point during the construction period, and σ represents the standard deviation of the predicted vibration response value.

[0069] Methods for screening vibration sensitive points by the screening unit 110 include but are not limited to the following empirical formula method, response surface prediction method and black box model.

[0070] The empirical formula method makes predictions based on the formula given in the Technical Guidelines for Environmental Impact Assessment of Urban Rail Transit, but it also takes into account the uncertainty of the predicted vibration response value and gives the standard deviation.

[0071] The response surface prediction method considering uncertainty is a response surface formula established through actual measurement or numerical simulation methods, which takes into account the uncertainty of input parameters and predicted vibration response values.

[0072] The black box model that considers uncertainty is to establish a mapping relationship between input parameters and output responses through actual measurement or numerical simulation methods, while taking into account the uncertainty of the output results.

[0073] The evaluation unit 120 calculates the exceedance probability based on the predicted vibration response values ​​of the selected key sensitive points and the probability density function, and compares the exceedance probability with the control probability corresponding to the importance level of the sensitive target.

[0074] Specifically, vibration-sensitive targets include residential areas, precision instruments, and historically significant buildings. Both international and domestic standards specify vibration response limits for these sensitive targets. In this paper, the standard limit for vibration response is denoted as s. Traditional deterministic predictions of vibration response values ​​can determine whether standards are exceeded based on regulatory limits during environmental impact assessments. However, in reality, subway environmental vibration indicators are inherently uncertain.

[0075] The present invention determines the precise probability of exceeding the standard based on the probability density function.

[0076] The calculation formula for the probability of exceeding the standard is:

[0077]

[0078] In the above formula, f(x p ) represents the predicted vibration response value x p Vibration level X p The probability density function, s represents the standard limit, P e Indicates the probability of exceeding the standard.

[0079] The evaluation unit 120 performs statistical analysis on the predicted vibration response value samples. The evaluation unit 120 tests the statistical model based on a hypothesis testing method. Alternative statistical models include but are not limited to a normal distribution model, a uniform distribution model, a lognormal distribution model, and the like.

[0080] When the significance index meets the requirements, it is considered to be in line with this distribution type. If it meets the normal distribution model, the probability density function is:

[0081]

[0082] In the above formula, σ p Represents the predicted vibration response value x p The standard deviation of p Represents the predicted vibration response value x p The average value of .

[0083] like Figure 3 As shown in the figure, the horizontal axis represents the statistical indicator, and the vertical axis represents the probability density function. The area enclosed by the probability density function curve 1 and the horizontal axis (statistical indicator) is 1. The vertical dashed line representing the standard limit 2 cuts through this enclosed area. The shaded area represents the area with a probability of exceeding the standard 3, and the remaining area represents the area with a probability of meeting the standard.

[0084] In the present invention, the control probability corresponding to the importance level of the sensitive target is preset and stored. Preferably, the control probability is determined in advance by professionals. If the probability of exceeding the standard is greater than the control probability, it means that the vibration meets the standard.

[0085] Specifically, factors that need to be considered in the process of formulating the protection level of sensitive targets include the actual function of residential buildings, the protection level of cultural relics buildings, the sensitivity of precision instruments to vibration, etc.

[0086] When grading residential buildings, the actual function of the building can be taken into consideration. For example, performance buildings and sanatorium buildings have higher grades, while ordinary residential areas have lower grades.

[0087] When classifying cultural relic buildings such as ancient buildings and museums displaying cultural relics, the protection level of the cultural relics recognized by the state should be taken into consideration.

[0088] When grading laboratories that contain precision instruments, the accuracy of the readings of the internal precision experimental instruments in a vibration environment can be considered. For example, physical and mechanical instruments are sensitive to vibration, while biochemical experimental instruments are not.

[0089] Based on the importance level of the pre-stored sensitive target, the evaluation unit 120 calculates the control probability P corresponding to the importance level. c For more important vibration sensitive points, the control probability of exceeding the standard P c The lower the control probability Pc The value of can be 10%, 5%, 1%, 0.1%, etc. Control probability P c The meaning of the indicator is that through vibration reduction measures, the probability of environmental vibration exceeding the standard does not exceed the control probability P c .

[0090] The meaning of the indicator is expressed by the formula:

[0091]

[0092] In the above formula, x p Represents the predicted vibration response value, f(x p ) represents the probability density function.

[0093] The decision unit 130 is used to retrieve a decision solution based on various data calculated by the evaluation unit 120 .

[0094] Preferably, the decision unit 130 sorts the candidate track vibration reduction measures in ascending order according to the vibration reduction amount, and records them as low-level vibration reduction, medium-level vibration reduction, high-level vibration reduction, and special vibration reduction.

[0095] Considering the uncertainty in the prediction process, the environmental vibration response values ​​of key sensitive points are represented by random variables that conform to a certain distribution model. The distribution model includes the mean value model, the standard deviation model, and the probability density function model.

[0096] Due to the uncertainty in practice, the predicted vibration response value x after vibration reduction measures p There is a possibility of exceeding the standard, but the predicted vibration response value x p The probability of exceeding the standard P e Requirements: P e <P c That is to say, the probability of exceeding the standard is less than the probability of control.

[0097] In the case where no vibration reduction measures are set, if the probability of exceeding the standard is less than the control probability, the decision unit 130 decides that no vibration reduction measures need to be taken.

[0098] In the case where vibration reduction measures of different levels are provided, when the probability of exceeding the standard is less than the control probability, the decision unit 130 decides that the level of the vibration reduction measure does not need to be increased; otherwise, the level of the vibration reduction measure is increased.

[0099] like Figure 12As shown in the figure, after setting the control probability based on the importance of vibration sensitivity, the probability distribution function of the vibration response value is predicted. The first check is performed to see if the probability of exceeding the standard reaches the control probability. If so, no vibration reduction measures are required; if not, low-level vibration reduction measures are used. The second check is performed to see if the probability of exceeding the standard reaches the control probability. If so, vibration reduction measures of that level are selected; if not, the level of vibration reduction measures is increased. The third check is performed to see if the probability of exceeding the standard reaches the control probability. If so, vibration reduction measures of that level are selected; if not, the level of vibration reduction measures is increased. This judgment cycle repeats until the probability of exceeding the standard reaches the control probability, at which point vibration reduction measures of that level are selected.

[0100] Specifically, if the control probability is not reached, the decision unit 130 makes the following decision: adopt low-level vibration reduction measures, calculate the probability of exceeding the standard of the environmental vibration response after adopting the low-level vibration reduction measures, and compare the probability of exceeding the standard with the control probability. If the probability of exceeding the standard is less than the control probability, that is, P e <P c The decision of the decision unit 130 is to adopt a low-level vibration reduction measure, such as installing ordinary fasteners.

[0101] If the control probability is still not reached, the decision unit 130 plans to adopt the intermediate vibration reduction measures. The decision unit 130 calculates the probability of exceeding the standard of the environmental vibration response after adopting the intermediate vibration reduction measures, and compares the probability of exceeding the standard with the control probability. If the probability of exceeding the standard is less than the control probability, that is, P e <P c The decision of the decision unit 130 is to adopt an intermediate vibration reduction measure, such as installing a vibration reduction fastener.

[0102] If the control probability is still not reached, the decision unit 130 plans to adopt advanced vibration reduction measures. The decision unit 130 calculates the probability of exceeding the standard of the environmental vibration response after adopting the advanced vibration reduction measures and compares the probability of exceeding the standard with the control probability. If the probability of exceeding the standard is less than the control probability, that is, P e <P c The decision of the decision unit 130 is to adopt advanced vibration reduction measures, such as installing trapezoidal sleeper tracks.

[0103] If the control probability is still not reached, the decision unit 130 plans to adopt special vibration reduction measures. e <P c The decision unit 130 decides to adopt special vibration reduction measures. If this is not possible, the special vibration reduction measures are implemented by rerouting the line or relocating sensitive objects. An example of a special vibration reduction measure is to install a steel spring floating plate track.

[0104] Entering the operation stage, the screening unit can obtain the measured vibration response value of the key sensitive point through actual measurement and send it to the evaluation unit 120. Assume that the measured vibration response value is a random variable X m .

[0105] Preferably, during the operational phase, assessment unit 120 constructs a joint probability distribution function based on the measured and predicted vibration response values ​​of key sensitive points. In other words, the present invention uses a joint probability distribution function of the predicted and measured vibration response values ​​to describe the risk of vibration exceeding the standard during the subway's operational period.

[0106] The evaluation unit 120 determines a joint probability density function, a first marginal probability density function, and a second marginal probability density function. The first marginal probability density function corresponds to the probability distribution of the predicted vibration response value. The second marginal probability density function corresponds to the probability distribution of the measured vibration response value.

[0107] Specifically, the binary random variables (X p ,X m )Use the joint probability distribution function F(x m ,x p ) to describe.

[0108]

[0109] In the above formula, h(x p ,x m ) represents the joint probability density function. Figure 4 is the plane coordinate system x consisting of the predicted vibration response value and the measured vibration response value m Ox p Schematic diagram of . Figure 4 As shown, the horizontal axis is the predicted vibration response value x p , the vertical axis is the measured vibration response value x m , the equal probability curve of the joint probability density function is an elliptical marker.

[0110]

[0111] In the above formula, ρ represents the correlation coefficient between the predicted and measured vibration response values, and a marginal probability density function can be obtained by integrating along a certain coordinate axis. p Represents the predicted vibration response value x p The standard deviation of p Represents the predicted vibration response value x p The average value of σ m Indicates the measured vibration response value x m The standard deviation of m Indicates the measured vibration response value x m The average value of .

[0112] The correlation coefficient ρ is calculated as follows:

[0113]

[0114] In the above formula, This represents the predicted and measured data pairs for similar vibration-sensitive points in similar projects from historical projects. Similar projects refer to projects with the same track type, vehicle type, speed, and similar ground conditions and track degradation. When data is lacking for new projects, a Bayesian prior distribution (ρ~Uniform(0,1)) can be used to predict data. The prediction process here is based on valid information from real-world operating conditions, assuming a positive correlation.

[0115] The first marginal probability density function is f(x p ), the second marginal probability density function is g(x m ), the joint probability density function is h(x p ,x m ), the three need to satisfy the following relationship:

[0116]

[0117] In the above formula, the evaluation unit 120 performs statistical analysis on the measured vibration response value data and uses hypothesis testing methods to test the statistical model. Alternative statistical models include, but are not limited to, normal distribution models, uniform distribution models, and lognormal distribution models. When the significance index meets the requirements, it is considered to conform to the distribution model. If the normal distribution model is satisfied, the second marginal probability density function is:

[0118]

[0119] In the above formula, σ m Indicates the standard deviation of the measured vibration response value, μ m Indicates the average value of the measured vibration response value.

[0120] like Figure 4 As shown in the figure, the equal probability curve of the joint probability density function is in the plane coordinate system x composed of the predicted vibration response value and the measured vibration response value. m Ox p The logo appears as an oval.

[0121] Specifically, the predicted and measured vibration response values ​​are placed in the plane coordinate system x m Ox p If the joint probability density function h(x p ,x m) satisfies the bivariate normal distribution. In this case, the equal probability curve is an ellipse, which is called an elliptical marker in the present invention. Figure 4 If other probability distribution functions are satisfied, the equal probability curve is no longer a standard ellipse, but this does not affect the technical solution of the present invention.

[0122] The projections of the elliptical marker on the two coordinate axes, namely the first marginal probability density function and the second marginal probability density function, correspond to the probability distributions of the predicted and measured vibration response values, respectively.

[0123] The evaluation unit 120 sends the data of the marker to the decision unit 130 .

[0124] The decision unit 130 determines the relationship between the marker sent by the evaluation unit 120 and the straight line x p =x m The accuracy of the predicted vibration response value is determined by the degree of position deviation; p Represents the predicted vibration response value; x m Indicates the measured vibration response value.

[0125] Specifically, in Figure 4 In the middle, the straight line x p =x m This indicates an absolutely accurate prediction. The relationship between the elliptical marker and the line indicates the accuracy of the predicted vibration response. The closer the marker's center point is to the line, and the smaller the angle θ between the marker's major axis and the line, the more accurate the predicted vibration response.

[0126] The decision unit 130 of the present invention predicts the accuracy of the vibration response value based on the root mean square error (RMSE):

[0127] According to a preferred embodiment, the evaluation unit 120 calculates the residual risk coefficient based on the joint probability density function. The calculation formula of the residual risk coefficient is:

[0128]

[0129] In the above formula, I∪IV represents the joint probability density function h(x p ,x m ) is the specific integral region I and IV. I∪II∪III∪IV represents the joint probability density function h(x p ,x m ), that is, the predicted vibration response value x p and the measured vibration response value x m All possible value range combinations of .

[0130] The evaluation unit 120 sends the residual risk coefficient γ to the decision unit 130 .

[0131] When the residual risk coefficient is greater than the control probability, that is, γ≥P c , the decision unit 130 triggers an alarm signal to remind that the vibration reduction measures adopted during the construction period are not effective and the design plan of the vibration reduction measures during the construction period should be re-evaluated.

[0132] According to a preferred embodiment, the evaluation unit 120 calculates a vibration reduction performance deviation that characterizes the difference between the actual performance of the vibration reduction measure and the expected performance. The calculation formula of the vibration reduction performance deviation δ is:

[0133]

[0134] In the above formula, ΔX p Indicates the predicted insertion loss, obtained through laboratory testing or analog testing, ΔX m represents the measured insertion loss, obtained through analog testing, and δ represents the difference between the actual effectiveness of the vibration reduction measures and the expected one.

[0135] The evaluation unit 120 sends the vibration reduction effectiveness deviation to the decision unit 130 .

[0136] If the vibration reduction performance deviation is greater than a preset deviation threshold, the decision unit 130 triggers a maintenance signal. Specifically, if the vibration reduction performance deviation is greater than the preset deviation threshold, the cause of the insufficient effectiveness of the vibration reduction measures should be traced and a maintenance process should be triggered.

[0137] The reasons for the insufficient effectiveness of vibration reduction measures include but are not limited to: differences between product laboratory testing and online service results, substandard product quality control during the manufacturing process of vibration reduction products, and substandard construction quality of vibration reduction products due to rushing to meet deadlines and lack of professionalism during the construction of vibration reduction measures.

[0138] If vibration levels are predicted to exceed standards during subway construction, it is often necessary to design vibration reduction measures to minimize vibration levels below standard limits after the subway begins operation. However, due to various uncertainties during the construction and operation periods, the vibration response of sensitive targets after completion is also uncertain, and this is also represented by a probability distribution function.

[0139] like Figure 5 As shown, the decision unit 130 transforms the plane coordinate system x composed of the predicted vibration response value and the measured vibration response value into a plane coordinate system x based on the standard limit of the key sensitive point. m Ox p Divided into four areas.

[0140] Specifically, the standard limit s is expressed in the plane coordinate system x m Ox pIn the figure, the horizontal axis represents the predicted vibration response value, and the vertical axis represents the measured vibration response value. This divides the two-dimensional plane into four areas, representing four situations: predicted to exceed the standard and the actual exceeded the standard, predicted to meet the standard but the actual exceeded the standard, predicted and actual met the standard, and predicted to exceed the standard but the actual met the standard. For convenience, these four areas are named as Zone I to Zone IV in Roman numerals in counterclockwise order, namely Zone 1, Zone 2, Zone 3, and Zone 4, as shown in the following example: Figure 5 shown.

[0141] The predicted and measured vibration response values ​​are represented in the above partition diagram by elliptical markers. The markers represent the contour plot of the probability density function of the predicted-measured joint distribution.

[0142] If a project implements vibration reduction measures, this will be reflected in the zoning diagram as the movement of the elliptical sign. If the predicted vibration response value does not exceed the standard, then no vibration reduction measures are required, and the corresponding sign does not need to be moved.

[0143] Based on this correspondence, decision unit 130 uses graphical analysis to trace the sign's movement trajectory and review the vibration reduction design during the construction phase, including product selection, parameter settings, and prediction methods, to accumulate experience for subsequent vibration reduction work. The sign's movement trajectory from pre-design to operation (including the case of no movement) falls into the following five operating conditions.

[0144] The first working condition: elimination of non-vibration sensitive points.

[0145] During the planning phase of a subway line, it's often necessary to screen for vibration-sensitive points along the line, such as residential areas, commercial areas, and scientific, educational, cultural, and health facilities that could be affected by vibration. More detailed predictions are required for key sensitive points of particular concern. If the predicted vibration response value for a key sensitive point meets the standard, no vibration reduction measures are recommended, and no exceeding of the standard occurs after the subway is put into operation, then it can be considered a relatively accurate prediction, and the correct engineering decision was made under the guidance of the accurate predicted vibration response value. At this point, the sign remains in the third zone (Zone III) and does not migrate.

[0146] That is to say, if Figure 6 As shown in the figure, during the route planning stage, when the main part of the elliptical sign (accounting for more than 50% of the sign) is located in the third zone close to the origin and no migration occurs, if the residual risk coefficient is less than 50% of the control probability, that is, γ < 0.5P c , the decision-making unit 130 determines that there is no need to carry out subsequent monitoring and governance work.

[0147] The decision unit 130 compares the marker and the line x p =x mCompare and calculate the prediction error RMSE to evaluate the accuracy of the prediction method and accumulate experience for the subsequent improvement of the prediction method.

[0148] The second working condition: ideal vibration reduction condition.

[0149] If the predicted vibration response value exceeds the standard, reasonable vibration reduction measures are designed and implemented, and the actual vibration response value during the operation phase meets the standard, then the decision unit 130 can determine that a successful vibration reduction project was implemented under the guidance of the accurate predicted vibration response value. The amount of vibration response reduction after the vibration reduction measures are adopted is called insertion loss. Figure 7 The symbol shifts to the lower left. The amount of migration of the symbol center on the horizontal axis is the predicted insertion loss, and the amount of migration of the symbol center on the vertical axis is the actual insertion loss.

[0150] like Figure 7 As shown, during the operation phase, when the elliptical marker moves from the first zone before vibration reduction to the third zone after vibration reduction, and the residual risk coefficient γ is less than 0.1, the decision unit 130 records the vibration reduction measure as a success case. Figure 7 In the embodiment, the first area and the third area are opposite to each other and are not adjacent to each other.

[0151] The decision unit 130 compares the vibration-damped marker and the straight line x p =x m The prediction error RMSE is compared and calculated to evaluate the accuracy of the prediction method. The decision unit 130 evaluates the actual vibration reduction capability of the vibration reduction measure by comparing the predicted insertion loss with the actual insertion loss, accumulating experience for subsequent improvement of the prediction method and selection of vibration reduction measures.

[0152] The third working condition: missing vibration sensitive points.

[0153] If the predicted vibration response value of a vibration sensitive point meets the standard, it is recommended that no vibration reduction measures are taken. However, if the actual vibration response value during operation exceeds the standard, adverse consequences may occur. This situation is called missing a vibration sensitive point. Figure 8 The manifestation is that the landmark body appears in the second zone (zone II).

[0154] like Figure 8 As shown in the figure, during the operation phase, when the elliptical sign suddenly locates in the second zone of the plane coordinate system composed of the predicted vibration response value and the measured vibration response value, the sign is in the second zone of the plane coordinate system. p =x m The deviation distance is large, and the decision unit 130 traces the cause of the inaccurate prediction in the design stage, accumulating experience for the subsequent iteration and improvement of the prediction method. Figure 8 In the graph, the second zone is close to the vertical axis of the measured vibration value and far away from the origin.

[0155] The reasons for inaccurate predictions in the design stage include: improper parameter selection when using the empirical formula method for prediction, improper parameter selection, grid size, boundary conditions, and model simplification when using the finite element method for prediction, failure to consider frequency domain differences and changes in long-term service performance during the prediction process, and failure to perform vehicle-track coupling dynamics calculations during the prediction process.

[0156] The fourth working condition: the performance of vibration reduction measures is insufficient.

[0157] For some vibration sensitive points, vibration reduction measures that are expected to meet the standards were designed and implemented under the conditions of predicted exceeding the standards, but exceeding the standards still occurred after operation. This situation is mainly caused by the insufficient performance of the vibration reduction measures. Figure 9 The manifestation is that the amount of movement of the marker along the horizontal axis is greater than the amount of movement along the vertical axis during the migration process, and it migrates from the first zone (zone I) to the second zone (zone II).

[0158] Specifically, during the operational phase, when the elliptical marker moves from a first zone, far from the origin and not close to the axis, to a second zone, close to the vertical axis of the measured vibration response values, in the plane coordinate system formed by the predicted and measured vibration response values, decision unit 130 traces the cause of the significant discrepancy between the predicted and actual insertion losses, accumulating experience for the selection, design, and implementation of subsequent vibration reduction measures. These causes include: discrepancies between laboratory test results and in-service performance of vibration reduction products, substandard manufacturing and construction quality of vibration reduction products, exaggerated product performance, and degradation of vibration reduction product performance over time.

[0159] The fifth condition: waste of investment.

[0160] Waste of investment means that the vibration response value is predicted to exceed the standard and corresponding vibration reduction measures are taken, but after operation it is found that the safety margin is too large. Even if the vibration reduction measures are not taken, the standard will not be exceeded, which will result in waste of investment. Figure 10 The manifestation is that the marker body migrates from the fourth zone (zone IV) to the third zone (zone III).

[0161] Although wasted investment will not lead to complaints of exceeding standards, it will increase construction costs and should be avoided as much as possible. This risk also occurs due to inaccurate predictions of vibration response values.

[0162] like Figure 10 As shown, when the elliptical marker moves from the fourth zone far away from the origin and close to the horizontal axis of the predicted vibration response value to the third zone close to the origin in the plane coordinate system composed of the predicted vibration response value and the measured vibration response value, the decision unit 130 traces the reason for the large difference between the predicted insertion loss and the actual insertion loss.

[0163] In the fifth working condition, the marker and the straight line x p =xm The deviation is large, located in the lower right corner of the coordinate system, and the prediction error (RMSE) is large. Decision unit 130 needs to specifically review the reasons for the inaccurate (overly conservative) prediction during the design phase to accumulate experience for subsequent improvements to the prediction method. These reasons include: overly conservative parameter selection when using the empirical formula method for prediction; inappropriate parameter selection, mesh size, boundary conditions, and model simplification when using the finite element method for prediction; overly conservative values ​​for long-term service performance parameters during the prediction process; and overly conservative parameter values ​​for vehicle-track coupled dynamics calculations during the prediction process.

[0164] It should be noted that all signage bodies may be distributed across regions, and the above five operating conditions are just simplified single modes. Analyze the operating conditions to which each key sensitive point will belong after construction. When the first and second operating conditions occur, accurate predictions of vibration response values ​​will guide decision makers to make the right decisions, namely, eliminating vibration-sensitive points and avoiding adverse environmental impacts caused by vibration through reasonable vibration reduction measures. If the prediction is inaccurate, three different types of risks will arise, namely the third to fifth operating conditions. If vibration reduction measures are not adopted or the level of vibration reduction measures is not high enough, it will lead to excessive vibration after operation, while blindly adopting high-level vibration reduction measures will increase construction costs.

[0165] Preferably, if the residual risk coefficient γ during the operation period ≥ P c , indicating that the third working condition caused by the omission of sensitive points during the construction period and the fourth working condition caused by the poor implementation of vibration reduction measures occurred. The decision unit 130 adopted new vibration reduction measures during the operation period to reduce vibration and moved the sign from Figure 11 The second zone (zone II) in the cell migrates to the third zone (zone III).

[0166] like Figure 11 As shown, in the plane coordinate system composed of the predicted vibration response value and the measured vibration response value, when the elliptical marker moves from the second zone close to the vertical axis of the measured vibration response value to the third zone close to the origin, the decision unit 130 traces the reason for the large difference between the predicted insertion loss of the new measure and the actual insertion loss of the new measure.

[0167] The decision unit 130 retroactively reduces the residual risk coefficient to below the control probability, that is, to achieve γ<P c .

[0168] There are three main types of vibration reduction measures during the operation period, namely vibration source modification and vibration reduction, transmission path isolation, and passive vibration isolation of sensitive targets.

[0169] The vibration source can be modified by replacing higher-grade vibration-damping fasteners, installing track dampers, etc.

[0170] The propagation path vibration isolation includes vibration isolation trenches, vibration isolation piles, wave damping blocks, etc.

[0171] Passive vibration isolation of sensitive targets includes installing vibration isolation supports on the foundation of buildings, room-within-room structures in buildings, vibration-damping floors, and vibration isolation tables for precision instruments.

[0172] After adopting the new vibration reduction measures, the probability of exceeding the standard is lower than the designed control probability.

[0173] Based on the above risk management methods for exceeding environmental vibration standards for urban rail transit, after the project is completed, the experiences and lessons learned from the entire risk management process should be summarized. During the rapid risk identification phase, it is necessary to summarize the degree of match between the risk-sensitive points obtained through various prediction methods and the final vibration response of sensitive points along the entire line. It is also necessary to evaluate whether the values ​​of various geotechnical and track performance parameters are consistent with the actual conditions in the area, and to accumulate relevant empirical parameters if necessary.

[0174] The accuracy of the probabilistic forecast results for specific targets should be summarized, and methods that can improve the accuracy of the forecasts should be summarized.

[0175] When discussing the risk control level of various key sensitive points, we should summarize whether the factors considered in the importance level of the risk points are comprehensive, and whether the control probability of various key sensitive points is too small or too large during the later operation period, so as to provide a reference for the next round of risk management.

[0176] When designing for vibration reduction, the performance of the reduction measures and the differences from the design values ​​need to be recorded.

[0177] When implementing vibration reduction measures during the operation phase, it is necessary to identify the specific reasons for exceeding standards at key sensitive points due to operation, and discuss whether these reasons can be prevented during the construction phase. By sorting out the issues at each step and making targeted modifications to each step, we can provide relevant prior information for the next round of risk management, reduce risk probability, and minimize risk management costs.

[0178] It should be noted that the above-mentioned specific embodiments are exemplary, and those skilled in the art can come up with various solutions inspired by the disclosure of the present invention, and these solutions also fall within the scope of the disclosure of the present invention and fall within the scope of protection of the present invention. Those skilled in the art should understand that the present invention specification and its drawings are illustrative and do not constitute a limitation on the claims. The scope of protection of the present invention is defined by the claims and their equivalents. The present invention specification contains multiple inventive concepts, such as "preferably" and "according to a preferred embodiment", which means that the corresponding paragraph discloses an independent concept, and the applicant reserves the right to file a divisional application based on each inventive concept.

Claims

1. An urban rail transit environmental vibration exceeding standard risk management system, characterized in that: A processor (100) is provided, wherein the processor (100) comprises: A screening unit (110) screens vibration sensitive points based on the input vibration data, and in particular screens vibration sensitive points whose predicted vibration response values ​​during the construction period exceed or are close to exceeding the standard and selects them as key sensitive points; An evaluation unit (120) calculates an exceeding probability based on the predicted vibration response value of the selected key sensitive points and a probability density function, and compares the exceeding probability with a control probability corresponding to an importance level of the sensitive target; A decision unit (130) is configured to, when no vibration reduction measures are set, if the probability of exceeding the standard is less than the control probability, not adopt any vibration reduction measures; when different levels of vibration reduction measures are set, if the probability of exceeding the standard is less than the control probability, not increase the level of the vibration reduction measures; otherwise, increase the level of the vibration reduction measures.

2. The system according to claim 1, wherein: The evaluation unit (120) is further configured to: During the operation phase, a joint probability distribution function is constructed based on the measured vibration response values ​​and the predicted vibration response values ​​of the key sensitive points, and a joint probability density function, a first marginal probability density function, and a second marginal probability density function are determined. The first marginal probability density function corresponds to the probability distribution of the predicted vibration response values, and the second marginal probability density function corresponds to the probability distribution of the measured vibration response values. The equal probability curve of the joint probability density function is in the plane coordinate system x formed by the predicted vibration response value and the measured vibration response value. m Ox p The logo body is presented as an oval; The decision unit (130) is based on the relationship between the marker sent by the evaluation unit (120) and the straight line x p =x m The accuracy of the predicted vibration response value is determined by the degree of position deviation; p Represents the predicted vibration response value; x m Indicates the measured vibration response value.

3. The system according to claim 1 or 2, characterized in that The evaluation unit (120) is further configured to: calculate a residual risk coefficient based on a joint probability density function and send the residual risk coefficient to the decision unit (130); When the residual risk coefficient is greater than the control probability, the decision unit (130) triggers an alarm signal to remind that the vibration reduction measures adopted during the construction period are not effective.

4. The system according to any one of claims 1 to 3, characterized in that: The evaluation unit (120) is further configured to: calculate a vibration reduction effectiveness deviation representing the difference between the actual effectiveness and the expected effectiveness of the vibration reduction measure and send the deviation to the decision unit (130); When the vibration reduction performance deviation is greater than a preset deviation threshold, the decision unit (130) triggers a maintenance signal.

5. The system according to any one of claims 1 to 4, characterized in that: The decision unit (130) is further configured to: The plane coordinate system consisting of predicted vibration response values ​​and measured vibration response values ​​is divided into four areas based on the standard limits of key sensitive points. During the line planning stage, when the main part of the elliptical marker is located in the third zone close to the origin and has not migrated, if the residual risk coefficient is less than 50% of the control probability, it is judged that no subsequent monitoring and governance work is required.

6. The system according to any one of claims 1 to 5, characterized in that: The decision unit (130) is further configured to: During the operation phase, when the elliptical marker moves from the first zone before vibration reduction to the third zone after vibration reduction, and the residual risk coefficient γ is less than 0.1, the vibration reduction measures are recorded as a successful case; The first area and the third area are located opposite to each other and are not adjacent to each other.

7. The system according to any one of claims 1 to 6, characterized in that: The decision unit (130) is further configured to: During the operation phase, when the elliptical sign is suddenly located in the second zone of the plane coordinate system composed of the predicted vibration response value and the measured vibration response value, the sign is at a certain angle to the straight line x. p =x m The deviation distance is large, and the decision-making unit (130) traces the cause of the inaccurate prediction in the design stage; The second area is close to the axis of the measured vibration value and far away from the origin.

8. The system according to any one of claims 1 to 7, characterized in that: The decision unit (130) is further configured to: During the operation phase, when the elliptical marker body migrates from a first zone far from the origin and not close to the axis to a second zone close to the longitudinal axis of the measured vibration response value in a plane coordinate system formed by the predicted vibration response value and the measured vibration response value, the decision unit (130) traces the cause of the large difference between the predicted insertion loss and the actual insertion loss; Alternatively, when the elliptical marker moves from a fourth zone, which is far from the origin and close to the horizontal axis of the predicted vibration response value, to a third zone, which is close to the origin, in a plane coordinate system formed by the predicted vibration response value and the measured vibration response value, the decision unit (130) traces the cause of the large difference between the predicted insertion loss and the actual insertion loss; Alternatively, when the elliptical marker moves from a second zone close to the vertical axis of the measured vibration response value to a third zone close to the origin in a plane coordinate system formed by the predicted vibration response value and the measured vibration response value, the decision unit (130) traces the reason for the large difference between the predicted insertion loss of the new measure and the actual insertion loss of the new measure.

9. A risk management method for excessive vibration in urban rail transit environment, characterized in that: The method comprises: Screen the input vibration data for vibration sensitive points, especially those where the predicted vibration response values ​​exceed or are close to exceeding the standard and identify them as key sensitive points; Calculating the exceedance probability based on the predicted vibration response value and probability density function of the selected key sensitive points, and comparing the exceedance probability with the control probability corresponding to the importance level of the sensitive target; In the case where no vibration reduction measures are set, if the probability of exceeding the standard is less than the control probability, no vibration reduction measures need to be adopted; in the case where different levels of vibration reduction measures are set, if the probability of exceeding the standard is less than the control probability, there is no need to increase the level of vibration reduction measures; otherwise, the level of vibration reduction measures is increased.

10. The method according to claim 9, characterized in that The method further comprises: During the operation phase, a joint probability distribution function is constructed based on the measured vibration response values ​​and the predicted vibration response values ​​of the key sensitive points, and a joint probability density function, a first marginal probability density function, and a second marginal probability density function are determined. The first marginal probability density function corresponds to the probability distribution of the predicted vibration response values, and the second marginal probability density function corresponds to the probability distribution of the measured vibration response values. The equal probability curve of the joint probability density function is in the plane coordinate system x formed by the predicted vibration response value and the measured vibration response value. m Ox p The logo body is presented as an oval; Based on the marker and the straight line x p =x m The accuracy of the predicted vibration response value is determined by the degree of position deviation; p Represents the predicted vibration response value; x m Indicates the measured vibration response value.

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