CN115062881B - Tunnel structure property prediction method and device, computer equipment and storage medium - Google Patents
Tunnel structure property prediction method and device, computer equipment and storage medium Download PDFInfo
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Abstract
The invention provides a tunnel structure character prediction method, a tunnel structure character prediction device, computer equipment and a storage medium. Relates to the technical field of tunnel and underground engineering construction. The prediction method comprises the steps of obtaining performance state evaluation index data and performance state degradation influence factor data of the tunnel structure, and obtaining performance state prediction data of the tunnel structure according to the performance state evaluation index data and the performance state degradation influence factor data. The method comprehensively considers various influence factors of the tunnel structure performance degradation in the actual operation environment, establishes the performance state evaluation index of the tunnel structure by combing key disease indexes influencing the performance state of the tunnel structure, finely predicts the tunnel structure according to the index and various influence factors, fully considers the heterogeneity, the linkage, the uncertainty and the multistage property of the tunnel structure performance state degradation process, and can provide accurate and scientific decision support for the maintenance and the like of the tunnel structure.
Description
Technical Field
The invention relates to the technical field of tunnel and underground engineering construction, in particular to a method and a device for predicting tunnel structural properties, computer equipment and a storage medium.
Background
The tunnel structure is an underground structure and is a main stressed structure for ensuring the safe operation of road traffic and the stability of a structural system. Generally, under the common influence of various factors such as construction quality, external construction disturbance, earth surface load, hydrogeological environment, vehicle vibration, material degradation and the like, diseases such as uneven settlement, peeling, cracks, water leakage and the like inevitably occur during the operation of the main body structure of the tunnel, the performance is gradually degraded, and the trend of gradual increase occurs along with the increase of service life.
To the operation detection and monitoring work of tunnel structure, the tradition mode is mainly carried out by manual equipment instrument or instrument, but this kind of mode exists detection precision, efficiency and accuracy and hangs down, and has personnel's safety problem. In recent years, automatic tunnel defect detection technologies have been developed, and currently, tunnel structure detection technologies can be broadly classified into automatic detection technologies based on camera measurement, automatic detection technologies based on laser scanning, and hidden defect detection technologies based on radar or ultrasonic waves, according to differences in sensing technologies. Carry on these technologies on patrolling and examining the equipment, carry out comprehensive automation and patrol and examine, can realize the real-time high-efficient detection to tunnel structure better. However, the real-time monitoring mode can only detect the disease when the disease occurs, and the time for maintenance and overhaul is shorter; and the real-time detection method has higher requirements on equipment distribution and equipment precision, so the cost is higher.
With the rapid development of urban rail transit in large scale, the prediction of the performance of the tunnel structure becomes an urgent need, so that how to accurately predict the performance state of the tunnel structure becomes a big problem in theory and engineering practice.
Disclosure of Invention
The invention solves the problem of accurately predicting the performance state of the tunnel structure.
In order to solve the above problems, the present invention provides a method, an apparatus, a computer device, and a storage medium for predicting tunnel structural properties.
In one aspect, the present invention provides a method for predicting tunnel structural properties, where the method includes:
acquiring performance state evaluation index data of a tunnel structure;
acquiring performance state degradation influence factor data of a tunnel structure;
and acquiring performance state prediction data of the tunnel structure according to the performance state evaluation index data of the tunnel structure and the performance state degradation influence factor data of the tunnel structure.
In some embodiments, the prediction method further comprises: dividing the tunnel structure into a plurality of tunnel structure units according to a preset length;
the acquiring of the performance state evaluation index data of the tunnel structure includes: acquiring performance state evaluation index data of a plurality of tunnel structure units;
the acquiring the data of the performance state degradation influence factors of the tunnel structure comprises: acquiring performance state degradation influence factor data of a plurality of tunnel structure units;
the acquiring performance state prediction data of the tunnel structure according to the performance state evaluation index data of the tunnel structure and the performance state degradation influence factor data of the tunnel structure includes:
acquiring performance state prediction data of each tunnel structure unit according to the performance state evaluation index data of the tunnel structure unit and the performance state degradation influence factor data of the tunnel structure unit,
and acquiring the performance state prediction data of the tunnel structure according to the performance state prediction data of each tunnel structure unit.
In some embodiments, said obtaining performance state assessment indicator data for a plurality of said tunnel structure units comprises:
acquiring full life cycle data of each tunnel structure unit;
and acquiring performance state evaluation index data of each tunnel structure unit according to the full life cycle data of each tunnel structure unit.
In some embodiments, the performance state degradation affecting factors of the tunnel structure unit include: a heterogeneity factor and a degree of influence of the heterogeneity factor on degradation of the tunnel structural unit;
the acquiring performance state degradation influence factor data of a plurality of the tunnel structure units includes:
obtaining the value vector of the heterogeneity factor,
and acquiring a parameter vector of the degradation influence degree.
In some embodiments, the obtaining the performance state prediction data of each of the tunnel structure units according to the performance state assessment index data of the tunnel structure units and the performance state degradation influence factor data of the tunnel structure units includes:
discretizing the performance state evaluation index data of the tunnel structure unit;
acquiring a performance prediction function of each state grade of each tunnel structure unit;
and acquiring the service life or the residual service life of each state grade of each tunnel structure unit according to the performance prediction function.
In some embodiments, said obtaining a performance prediction function for each state level of each said tunnel structure unit comprises:
acquiring a service life distribution function of each state grade of each tunnel structure unit;
acquiring a service life distribution density function of each state grade of each tunnel structure unit;
according to the service life distribution function, acquiring a reliability function of each state grade of each tunnel structure unit at each moment;
obtaining a failure rate function of each state grade of each tunnel structure unit at each moment according to the service life distribution density function and the reliability function, wherein the failure rate function is as follows:
wherein the content of the first and second substances,
a function representing the rate of failure of the power converter,
a shape parameter representing a weibull distribution,
a scale parameter indicating a weibull distribution, t an instant, k the tunnel structure unit, and i a state level of the tunnel structure.
In some embodiments, the obtaining the performance prediction function for each state level of each tunnel structure unit further includes:
and obtaining the scale parameters of the Weibull distribution according to the value vectors of the heterogeneity factors and the parameter vectors of the degradation influence degree.
In some embodiments, the scale parameters of the weibull distribution are:
wherein the content of the first and second substances,
a value vector representing a heterogeneity factor in the acquired performance state degradation influencing factor data,
and a parameter vector representing a degree of influence of the heterogeneity factor on degradation of the state level of the tunnel structure unit.
Compared with the prior art, the tunnel structure character prediction method has the advantages that:
the method for predicting the tunnel structure character comprehensively considers various influence factors of tunnel structure performance deterioration in an actual operation environment, combs key disease indexes influencing the tunnel structure performance state, sets a performance state evaluation index of the tunnel structure, and finely predicts the tunnel structure according to the index and various influence factors, wherein the prediction is higher in accuracy due to the fact that heterogeneity, linkage, uncertainty and multistage of the tunnel structure performance state deterioration process are fully considered, and accurate and scientific decision support can be provided for maintenance and repair of the tunnel structure.
In another aspect, a tunnel structure property prediction apparatus is provided, including:
an obtaining unit for obtaining performance status evaluation index data of the tunnel structure,
the acquiring unit is further configured to acquire performance state degradation influence factor data of the tunnel structure;
and the analysis unit is used for acquiring the performance state prediction data of the tunnel structure according to the performance state evaluation index data of the tunnel structure and the performance state degradation influence factor data of the tunnel structure.
In another aspect, a computer device is provided, which includes a memory, a processor and a computer program stored on the memory and executable on the processor, and the processor implements the steps of the prediction method when executing the computer program.
In another aspect, a computer-readable storage medium is provided, on which a computer program is stored, which computer program, when being executed by a processor, carries out the steps of the prediction method described above.
The advantages of the tunnel structure property prediction apparatus, the computer device and the computer readable storage medium of the present invention over the prior art are the same as the advantages of the tunnel structure property prediction method over the prior art, and are not described herein again.
Drawings
Fig. 1 is an application environment diagram of a tunnel structural property prediction method in an embodiment of the present invention;
FIG. 2 is a schematic flowchart of a tunnel structural property prediction method according to an embodiment of the present invention;
fig. 3 is a schematic diagram of a tunnel structural property prediction method according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of obtaining performance status assessment indicator data of a tunnel structure unit according to an embodiment of the present invention;
FIG. 5 is a schematic diagram illustrating performance state prediction data obtained for each tunnel fabric unit according to an embodiment of the present invention;
FIG. 6 is a schematic diagram of a tunnel structure property prediction apparatus according to an embodiment of the present invention;
fig. 7 is an internal structural diagram of a computer device in an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
Fig. 1 is an application environment diagram of a tunnel structure property prediction method in the embodiment of the present invention. Referring to fig. 1, the method for predicting tunnel structural properties is applied to a system for predicting tunnel structural properties. The tunnel structure property prediction system includes a terminal 110 and a server 120. The terminal 110 and the server 120 are connected through a network. The terminal 110 may be a desktop terminal or a mobile terminal, and the mobile terminal may be at least one of a mobile phone, a tablet computer, a notebook computer, and the like. The server 120 may be implemented as a stand-alone server or a server cluster composed of a plurality of servers.
As shown in fig. 2, in one embodiment, a method for predicting tunnel structural behavior is provided. The embodiment is mainly illustrated by applying the method to the terminal 110 (or the server 120) in fig. 1. Referring to fig. 2, the method for predicting tunnel structural properties specifically includes the following steps:
In the embodiment of the invention, the tunnel can be a subway tunnel, a highway tunnel and a municipal tunnel. Taking a subway tunnel structure as an example, the subway tunnel structure is an engineering structure with linear, continuous, long and strip-shaped layout, spans various complex geological environments, is influenced by various factors such as people, objects, pipes, rings and the like, such as design factors, construction factors, external construction factors, geological environment factors and the like, and has the characteristics of heterogeneity, uncertainty, multistage, linkage and the like of the performance state of the tunnel structure in the degradation process.
The heterogeneity of tunnel structure property degradation means that tunnel structures at different spatial positions are affected by different factors to different degrees, so that degradation rules, such as degradation speed and service life of the tunnel structures, are also different. The uncertainty of the tunnel structure property degradation means that a change process of a state index of a tunnel structure with time is random, that is, a survival time (a time that the state index changes from one level to another level) of each state level is a random variable, and a degradation path or a degradation amplitude of the tunnel structure between two state indexes has various possibilities and is uncertain, so that the degradation process of the state is uncertain. The multistage property of the tunnel structure deterioration means that the deterioration rules of the tunnel structure are different in different deterioration stages. The linkage of the property degradation of the tunnel structure means that the degradation processes of the tunnel structures at different positions are mutually influenced.
The method for predicting the tunnel structure property comprehensively considers various influence factors of tunnel structure property degradation in an actual operation environment, establishes a performance state evaluation index of the tunnel structure by combing key disease indexes influencing the performance state of the tunnel structure, finely predicts the tunnel structure according to the index and various influence factors, fully considers heterogeneity, linkage, uncertainty and multistage of the tunnel structure performance state degradation process, and can provide accurate and scientific decision support for maintenance and repair of the tunnel structure.
In some embodiments, as shown in fig. 3, the prediction method includes:
303, acquiring performance state prediction data of each tunnel structure unit according to the performance state evaluation index data of the tunnel structure unit and the performance state degradation influence factor data of the tunnel structure unit;
and 304, acquiring performance state prediction data of the tunnel structure according to the performance state prediction data of each tunnel structure unit.
Due to the heterogeneity of the degradation process of the performance state of the tunnel structure and the inconsistency of the degradation rules of the tunnel structure at different spatial positions, the condition of over-repair or under-repair and the like is often caused if the uniform rule is used for describing the degradation of the tunnel structure. Therefore, in order to consider the influence of the tunnel structure degradation heterogeneity, in the embodiment of the present invention, the tunnel structure is divided into a plurality of continuous tunnel structure units by a certain length unit, and a personalized analysis is performed on the degradation process of the tunnel structure unit at each specific spatial position, so as to implement a refined prediction.
Illustratively, the tunnel structure mainly comprises a tunnel lining structure, and for open cut method tunnels and mine method tunnels, the length of 1 structural casting between two construction joints is preferably taken as a unit, for shield tunnels, 3-5 ring segments are preferably taken as a unit, and for immersed tube tunnels, 1 segment is preferably taken as a unit. By dividing the tunnel structure units and carrying out personalized modeling analysis on the tunnel structure, the problem of heterogeneity in the degradation process of the performance state of the tunnel structure is solved.
In some embodiments, as shown in fig. 4, the acquiring the performance status assessment index data of the tunnel structure unit includes:
The automatic detection technology for tunnel diseases can be divided into an automatic detection technology based on camera measurement, an automatic detection technology based on laser scanning and a hidden disease detection technology based on radar or ultrasonic waves according to different sensing technologies. The continuous development and maturity of the tunnel structure detection technologies provide multi-source sensing data for the performance state prediction of the tunnel structure in the embodiment, the embodiment sufficiently studies and collects various data of the whole life cycle of the tunnel structure, extracts key variables for representing the safety of the tunnel structure from a large amount of historical data of the whole life cycle of the tunnel structure, establishes key state evaluation indexes related to various performance state evolution rules, and further quantitatively analyzes various factors influencing the performance state degradation of the tunnel structure by using the whole life cycle data of the tunnel structure.
Illustratively, taking a subway tunnel structure as an example, the subway tunnel structure full-life cycle data includes completion acceptance data (including ledger data), inspection and detection data, disease data, repair data, traffic operation data, external construction data, ground traffic load, natural environment and geological condition data, and the like, each of which is closely related to the performance evolution process of the tunnel structure. When the degradation rule of the performance state of the subway tunnel structure is researched and analyzed, the full life cycle data of the subway tunnel structure is firstly researched and collected, and the data source point, the data format, the data standard, the effect on the prediction of the performance state of the tunnel structure and the like are sorted and analyzed. And acquiring the full life cycle data of each tunnel structure unit based on the divided tunnel structure units according to the full life cycle data of the tunnel structure.
Illustratively, the performance state assessment index data of the tunnel structure unit includes a tunnel main body structure technical condition index based on the scheduled inspection data, a tunnel settlement amount based on the monitoring data, and the like. Based on various data of the whole life cycle of the tunnel structure, the performance state evaluation index of the tunnel structure unit can be obtained, and the evaluation index can also be used for quantitatively analyzing various factors influencing the degradation of the performance state of the tunnel structure, wherein the factors influencing the degradation process of the tunnel structure unit are various, such as tunnel structure design attributes, geological conditions, traffic loads, environmental characteristics and the like, and the heterogeneous factors have different degradation influence degrees on the tunnel structure unit. Therefore, when acquiring the data of the performance state degradation influence factors of the plurality of tunnel structure units, the value vector of the heterogeneity factor and the parameter vector of the degradation influence degree of the heterogeneity factor on the tunnel structure units are mainly acquired.
In the embodiment of the invention, information such as tunnel structure state data, geographic environment factors, transportation organization factors, maintenance management factors and the like is detected and monitored in the tunnel structure full life cycle data, various influence factors of the unit are quantitatively assigned based on the tunnel structure unit, the action mechanism of the various influence factors on the evolution process of the tunnel structure performance state is quantitatively analyzed on the basis, and key influence factors are refined, so that the performance state degradation influence factor data of each tunnel structure unit is obtained.
In some embodiments, as shown in fig. 5, the obtaining the performance status prediction data of each tunnel structure unit according to the performance status evaluation index data of the tunnel structure unit and the performance status degradation influence factor data of the tunnel structure unit includes:
the state grades of the tunnel structure units are divided according to management standards made for different maintenance decisions in engineering practice.
Due to the characteristic that the deterioration of the tunnel structure presents a bathtub curve, an early failure period, an accidental failure period and a wear failure period can be divided, and the failure rate of the tunnel structure unit in each period is respectively reduced, unchanged and increased along with time. Therefore, in order to accurately describe the degradation condition of the tunnel structure unit, the state index of the tunnel structure unit is discretized, the state grades are divided for the tunnel structure unit according to the full life cycle of the tunnel structure unit, and the service life or the residual service life of each state grade of each tunnel structure unit is calculated through the performance prediction function of each state grade of each tunnel structure. The present embodiment discretely divides the state of each tunnel structure unit into a plurality of state levels, and each state level is regarded as a degradation stage, so that the degradation process of the tunnel structure unit is divided into a plurality of stages, the modeling analysis is refined, and the multi-stage characteristics in the degradation process of the performance state of the tunnel structure are considered.
Since the degradation process of the performance state of the tunnel structure also has uncertainty, that is, the change process of the state index of the tunnel structure over time is random and uncertain, and the lifetime of each state level is a random variable, a random method needs to be adopted to describe the change process. In this embodiment, a weibull distribution function is selected to describe the distribution of the lifetime of each state level of each tunnel structure unit, and a random process method based on weibull distribution is used to describe the lifetime distribution of each degradation stage of each tunnel structure unit, thereby considering the uncertainty in the degradation process of the performance and state of the tunnel structure.
Specifically, the obtaining of the performance prediction function of each state level of each tunnel structure unit includes:
step 5021, obtaining a distribution function of each state grade life of each tunnel structure unit.
Life of tunnel structure unit k with state class i
The distribution function of (a) is:
step 5022, acquiring a life distribution density function of each state grade of each tunnel structure unit.
Life of tunnel structure unit k with state class i
The distribution density function of (a) is:
step 5023, obtaining a reliability function of each state grade of each tunnel structure unit at each moment according to the service life distribution function.
The reliability function at time t for a state class i of tunnel structure unit k is:
step 5024, obtaining a failure rate function of each state grade of each tunnel structure unit at each moment according to the service life distribution density function and the reliability function.
The failure rate function at time t with the state rank i of the tunnel structure unit k is:
wherein k represents a certain tunnel structure unit, i represents the state grade of the tunnel structure, t represents the time, t is more than or equal to 0,
，
a shape parameter representing a weibull distribution,
a scale parameter representing a weibull distribution,
indicating the lifetime of the tunnel structure unit k at the state level i,
a distribution function representing the lifetime at time t of the state class i of the tunnel-structure-unit k,
a distribution density function representing the lifetime of the tunnel-structure-unit k at time t with a state level i,
a reliability function at time t representing a state level i of the tunnel structure unit k,
a failure rate function at time t representing a state level i of tunnel structure unit k. When in use
Failure rate function > 1
Monotonically increasing; when in use
Failure rate function < 1
Monotonically decreasing; when in use
Failure rate function of =1
=
Is constant, independent of t, alsoIt is said to have "memoryless", where the lifetime of the state class i follows an exponential distribution.
In some embodiments, in order to quantitatively analyze the linkage of the degradation of the tunnel structure unit and the degree of influence of other heterogeneity factors on the degradation process of the tunnel structure unit, in this embodiment, various factors and the like associated with the degradation of the tunnel structure unit k and located at the same spatial position are regarded as heterogeneity factors influencing the degradation of the tunnel structure unit k, and constitute the heterogeneity factors influencing the degradation of the tunnel structure together with transportation organization factors, design factors, maintenance factors, natural environment factors and the like. On the basis of quantifying the heterogeneity factor, a function containing the heterogeneity factor is used for representing a scale parameter of Weibull distribution
Linkage in the degradation process of the performance state of the tunnel structure is considered.
The obtaining the performance prediction function of each state level of each tunnel structure unit further comprises:
and obtaining the scale parameters of the Weibull distribution according to the value vectors of the heterogeneity factors and the parameter vectors of the degradation influence degree.
To ensure
>0，
Comprises the following steps:
wherein the content of the first and second substances,
a value vector representing a heterogeneous factor in the acquired performance state degradation influence factor data, and
representing the value vector of the selected G heterogeneity factors influencing the degradation of the state grade i of the tunnel structure unit k,
a parameter vector representing the degree of influence of the heterogeneity factor on the degradation of the state level of the tunnel structural unit, and
and the parameter vector represents the degree of influence of various heterogeneity factors on the degradation of the state level i of the tunnel structure unit k.
In order to take into account the effect of the maintenance history on the repair cycle of the tunnel structural unit, a heterogeneity factor
Is taken as
The number of times the tunnel structure unit k has performed a maintenance activity after the moment. At this time, various types of heterogeneity factors are included
State class i life of tunnel structure unit k
Distribution function of
Distribution density function
Reliability function
And failure rate function
Respectively as follows:
therefore, according to the service life distribution function, transition probabilities among different state levels and expected service lives can be calculated, the remaining service life is further evaluated, and the service life or the remaining service life of each state level of each tunnel structure unit is obtained.
In the embodiment of the invention, an individualized prediction model of the evolution rule of the unit diseases of the tunnel structure section is constructed based on a multi-stage Weibull distribution method, and the heterogeneity, linkage, uncertainty and multistage of the performance state degradation process are fully considered. Aiming at the unknown parameters of the model, the parameters in the model can be learned and mined by using big data technologies such as Bayesian estimation, reinforcement learning and the like, so that the accuracy of model prediction is improved.
To sum up, the prediction method of the embodiment of the present invention first obtains the key indexes and their influence factors of the performance state of the tunnel structure, and after dividing the tunnel structure interval into a plurality of continuous small segments of equal length, quantizes the performance state indexes and their deterioration influence factors of each segment by using the full life cycle data, and on this basis, adopts a multi-stage weibull distribution to construct an individualized random prediction model of the performance state indexes of each segment, thereby implementing accurate prediction of the performance state deterioration process of the tunnel structure.
Corresponding to the tunnel structure property prediction method, the embodiment of the invention also provides a tunnel structure property prediction device. Fig. 6 is a schematic diagram of a tunnel structural property prediction apparatus according to an embodiment of the present invention, and as shown in fig. 6, the tunnel structural property prediction apparatus includes:
an obtaining unit 601, configured to obtain performance status assessment index data of the tunnel structure,
the acquiring unit 601 is further configured to acquire performance state degradation influencing factor data of the tunnel structure;
an analyzing unit 602, configured to obtain performance state prediction data of the tunnel structure according to the performance state evaluation index data of the tunnel structure and the performance state degradation influence factor data of the tunnel structure.
In some embodiments, the prediction apparatus further includes a dividing unit 603 configured to divide the tunnel structure into a plurality of tunnel structure units according to a preset length.
In some embodiments, the obtaining unit 601 further includes: and acquiring the performance state evaluation index data of the tunnel structure unit and the performance state degradation influence factor data of the tunnel structure unit.
Wherein the acquiring unit acquires the performance state evaluation index data of the tunnel structure unit includes:
acquiring full life cycle data of each tunnel structure unit;
and acquiring performance state evaluation index data of each tunnel structure unit according to the full life cycle data of each tunnel structure unit.
Wherein the acquiring unit acquires the data of the performance state degradation influence factors of the tunnel structure unit includes: and acquiring a value vector of the heterogeneity factor and acquiring a parameter vector of the degradation influence degree.
In some embodiments, the analysis unit 602 is further configured to obtain performance status prediction data of each tunnel structure unit according to the performance status assessment indicator data of the tunnel structure units and the performance status degradation influence factor data of the tunnel structure units.
Wherein the analyzing unit is configured to obtain the performance state prediction data of each tunnel structure unit according to the performance state assessment index data and the performance state degradation influence factor data of the tunnel structure unit, and includes:
discretizing the performance state evaluation index data of the tunnel structure unit;
acquiring a performance prediction function of each state grade of each tunnel structure unit, wherein the state grades of the tunnel structure units are divided according to management standards made for different maintenance decisions in engineering practice;
acquiring the service life or the residual service life of each state grade of each tunnel structure unit according to the performance prediction function;
in some embodiments, obtaining the performance prediction function for each state level of each of the tunnel structure units comprises:
acquiring a service life distribution function of each state grade of each tunnel structure unit;
acquiring a service life distribution density function of each state grade of each tunnel structure unit;
according to the service life distribution function, acquiring a reliability function of each state grade of each tunnel structure unit at each moment;
and acquiring the failure rate function of each state grade of each tunnel structure unit at each moment according to the service life distribution density function and the reliability function. Wherein the failure rate function is:
wherein the content of the first and second substances,
a function representing the rate of failure of the power converter,
a shape parameter representing a weibull distribution,
a scale parameter representing a weibull distribution, t represents a time, k represents the tunnel structure unit, and i represents a state level of the tunnel structure.
In some embodiments, the obtaining the performance prediction function for each state level of each tunnel structure unit further includes:
and obtaining the scale parameters of the Weibull distribution according to the value vectors of the heterogeneity factors and the parameter vectors of the degradation influence degree.
The scale parameters of the Weibull distribution are as follows:
wherein, the first and the second end of the pipe are connected with each other,
a value vector representing a heterogeneity factor in the acquired performance state degradation influence factor data, (b) a value vector representing a heterogeneity factor in the acquired performance state degradation influence factor data, (c) a value vector representing a heterogeneity factor in the acquired performance state degradation influence factor data, and (d)
) ' means
The transpose of (a) is performed,
and a parameter vector representing a degree of influence of the heterogeneity factor on degradation of the state level of the tunnel structure unit.
In the embodiment of the invention, heterogeneity factors and influence degrees of the heterogeneity factors influencing the performance state of the tunnel structure are introduced into the scale parameters of Weibull distribution, so that a failure rate function, a reliability function and the like are obtained, heterogeneity, linkage, uncertainty and multistage of the performance state degradation process are fully considered, and the prediction effect is more real and accurate.
Taking the subway tunnel structure as an example, the state grade of the subway tunnel structure is divided into 5 grades, which are respectively very healthy, medium healthy, unhealthy and dangerous. The method comprises the steps of dividing a tunnel structure into a plurality of tunnel structure units according to a preset length, and predicting each tunnel structure unit according to the tunnel structure property prediction method in a certain period of time to obtain that the certain tunnel structure unit is in an abnormal health grade at present, wherein the lasting time of the abnormal health grade is N years.
FIG. 7 is a diagram illustrating an internal structure of a computer device in one embodiment. The computer device may specifically be the terminal 110 (or the server 120) in fig. 1. As shown in fig. 7, the computer apparatus includes a processor, a memory, a network interface, an input device, and a display screen connected through a system bus. Wherein the memory includes a non-volatile storage medium and an internal memory. The non-volatile storage medium of the computer device stores an operating system and may further store a computer program that, when executed by the processor, causes the processor to implement the tunnel structure property prediction method. The internal memory may also have a computer program stored therein, which, when executed by the processor, causes the processor to perform the method for predicting a tunnel structural property. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on the shell of the computer equipment, an external keyboard, a touch pad or a mouse and the like.
Those skilled in the art will appreciate that the architecture shown in fig. 7 is merely a block diagram of some of the structures associated with the inventive arrangements and is not intended to limit the computing devices to which the inventive arrangements may be applied, as a particular computing device may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the following steps when executing the computer program: acquiring performance state evaluation index data and performance state degradation influence factor data of a tunnel structure; and acquiring performance state prediction data of the tunnel structure according to the performance state evaluation index data and the performance state degradation influence factor data.
In one embodiment, the processor, when executing the computer program, further implements the steps of the tunnel structure property prediction method described above.
In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored which, when executed by a processor, performs the steps of: acquiring performance state evaluation index data and performance state degradation influence factor data of a tunnel structure; and acquiring performance state prediction data of the tunnel structure according to the performance state evaluation index data and the performance state degradation influence factor data.
In one embodiment, the computer program, when executed by the processor, further implements the steps of the tunnel structure property prediction method described above.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above may be implemented by a computer program, which may be stored in a non-volatile computer readable storage medium, and when executed, may include the processes of the embodiments of the methods described above. Any reference to memory, storage, databases, or other media used in embodiments provided herein may include non-volatile and/or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), rambus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
It is noted that, in this document, relational terms such as "first" and "second," and the like, may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising a … …" does not exclude the presence of another identical element in a process, method, article, or apparatus that comprises the element.
The foregoing are merely exemplary embodiments of the present invention, which enable those skilled in the art to understand or practice the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (6)
1. A method for predicting tunnel structural properties, comprising:
dividing the tunnel structure into a plurality of tunnel structure units according to a preset length,
obtaining performance state evaluation indexes of a plurality of tunnel structure units to obtain performance state evaluation index data of the tunnel structure,
obtaining the performance state degradation influencing factors of a plurality of the tunnel structure units to obtain the performance state degradation influencing factor data of the tunnel structure,
acquiring performance state prediction data of the tunnel structure according to the performance state evaluation index data of the tunnel structure and the performance state degradation influence factor data of the tunnel structure;
wherein the obtaining performance state assessment index data of the plurality of tunnel structure units comprises:
acquiring full-life cycle data of each tunnel structure unit,
acquiring performance state evaluation index data of each tunnel structure unit according to the full life cycle data of each tunnel structure unit;
the performance state degradation influencing factors of the tunnel structure unit comprise: a heterogeneity factor and a degree of influence of the heterogeneity factor on degradation of the tunnel structure unit;
the acquiring performance state degradation influence factor data of a plurality of the tunnel structure units includes:
obtaining the value vector of the heterogeneity factor,
obtaining a parameter vector of the degradation influence degree;
the acquiring performance state prediction data of the tunnel structure according to the performance state evaluation index data of the tunnel structure and the performance state degradation influence factor data of the tunnel structure includes:
acquiring performance state prediction data of each tunnel structure unit according to the performance state evaluation index data of the tunnel structure unit and the performance state degradation influence factor data of the tunnel structure unit,
acquiring performance state prediction data of the tunnel structure according to the performance state prediction data of each tunnel structure unit;
wherein the obtaining of the performance state prediction data of each tunnel structure unit according to the performance state evaluation index data of the tunnel structure unit and the performance state degradation influence factor data of the tunnel structure unit includes:
discretizing the performance state evaluation index data of the tunnel structure unit,
obtaining a performance prediction function for each state level of each of the tunnel structure units,
acquiring the service life or the residual service life of each state grade of each tunnel structure unit according to the performance prediction function;
wherein the obtaining of the performance prediction function of each state level of each tunnel structure unit comprises: acquiring a failure rate function of each state grade of each tunnel structure unit at each moment,
the failure rate function is:
wherein the content of the first and second substances,
a function representing the rate of failure of the power supply,
a shape parameter representing a weibull distribution,
a scale parameter representing a Weibull distribution, t represents a time, k represents the tunnel structure unit, and i represents a state level of the tunnel structure;
and obtaining the scale parameters of the Weibull distribution according to the value vectors of the heterogeneity factors and the parameter vectors of the degradation influence degree.
2. The prediction method according to claim 1, wherein the obtaining the performance prediction function for each state level of each tunnel structure unit comprises:
acquiring a service life distribution function of each state grade of each tunnel structure unit;
acquiring a service life distribution density function of each state grade of each tunnel structure unit;
according to the service life distribution function, acquiring a reliability function of each state grade of each tunnel structure unit at each moment;
and acquiring the failure rate function of each state grade of each tunnel structure unit at each moment according to the service life distribution density function and the reliability function.
3. The prediction method according to claim 2, wherein the scale parameters of the Weibull distribution are:
wherein the content of the first and second substances,
a value vector representing a heterogeneity factor in the acquired performance state degradation influence factor data,
and a parameter vector representing a degree of influence of the heterogeneity factor on degradation of the state level of the tunnel structure unit.
4. A tunnel structural property prediction device is characterized by comprising:
an obtaining unit for obtaining performance status evaluation index data of the tunnel structure,
the acquiring of the performance state evaluation index data of the tunnel structure includes: obtaining performance state evaluation indexes of a plurality of tunnel structure units,
the obtaining of the performance state evaluation indexes of the plurality of tunnel structure units comprises: acquiring full life cycle data of each tunnel structure unit, and acquiring performance state evaluation index data of each tunnel structure unit according to the full life cycle data of each tunnel structure unit;
the obtaining unit is further adapted to obtain performance state degradation influencing factor data of the tunnel structure,
the acquiring the data of the performance state degradation influence factors of the tunnel structure comprises: acquiring the performance state degradation influencing factors of a plurality of the tunnel structure units,
the performance state degradation influence factors of the tunnel structure unit include: heterogeneity factors and the degree of influence of the heterogeneity factors on the deterioration of the tunnel structure unit,
the acquiring performance state degradation influence factor data of a plurality of the tunnel structure units includes: obtaining a value vector of the heterogeneity factor and a parameter vector of the degradation influence degree;
an analyzing unit for obtaining performance state prediction data of the tunnel structure according to the performance state evaluation index data of the tunnel structure and the performance state degradation influence factor data of the tunnel structure,
the acquiring performance state prediction data of the tunnel structure according to the performance state evaluation index data of the tunnel structure and the performance state degradation influence factor data of the tunnel structure includes: acquiring performance state prediction data of each of the tunnel structure units based on the performance state evaluation index data of the tunnel structure units and the performance state degradation influence factor data of the tunnel structure units, acquiring performance state prediction data of the tunnel structure based on the performance state prediction data of each of the tunnel structure units,
the acquiring performance state prediction data of each tunnel structure unit according to the performance state evaluation index data of the tunnel structure unit and the performance state degradation influence factor data of the tunnel structure unit includes: discretizing the performance state evaluation index data of the tunnel structure units to obtain a performance prediction function of each state level of each tunnel structure unit, and obtaining the service life or residual service life of each state level of each tunnel structure unit according to the performance prediction function,
wherein the obtaining of the performance prediction function of each state level of each tunnel structure unit comprises: acquiring a failure rate function of each state grade of each tunnel structure unit at each moment,
the failure rate function is:
wherein the content of the first and second substances,
a function representing the rate of failure of the power converter,
a shape parameter representing a weibull distribution,
a scale parameter representing a Weibull distribution, t represents a time, k represents the tunnel structure unit, and i represents a state level of the tunnel structure;
and obtaining the scale parameters of the Weibull distribution according to the value vectors of the heterogeneity factors and the parameter vectors of the degradation influence degree.
5. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the prediction method according to any one of claims 1 to 3 when executing the computer program.
6. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the prediction method according to any one of claims 1 to 3.
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| CN115985137B (en) * | 2023-03-10 | 2023-08-01 | 深圳市城市交通规划设计研究中心股份有限公司 | Risk assessment method for tunnel traffic operation, electronic equipment and storage medium |
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104063612A (en) * | 2014-07-02 | 2014-09-24 | 武汉理工大学 | Tunnel engineering risk situation fuzzy evaluation method and system |
| CN111325403A (en) * | 2020-02-26 | 2020-06-23 | 长安大学 | Method for predicting remaining life of electromechanical equipment of highway tunnel |
| CN112418645A (en) * | 2020-11-18 | 2021-02-26 | 云南航天工程物探检测股份有限公司 | Tunnel engineering full life cycle safety evaluation method |
| WO2022121272A1 (en) * | 2020-12-09 | 2022-06-16 | 山东大学 | Real-time jamming prediction method and system suitable for tbm tunneling |
| CN114882933A (en) * | 2022-05-16 | 2022-08-09 | 深圳忆联信息系统有限公司 | Low-power-consumption forward and backward reliability detection method and device of SSD, computer equipment and storage medium |
-
2022
- 2022-08-19 CN CN202211000361.7A patent/CN115062881B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104063612A (en) * | 2014-07-02 | 2014-09-24 | 武汉理工大学 | Tunnel engineering risk situation fuzzy evaluation method and system |
| CN111325403A (en) * | 2020-02-26 | 2020-06-23 | 长安大学 | Method for predicting remaining life of electromechanical equipment of highway tunnel |
| CN112418645A (en) * | 2020-11-18 | 2021-02-26 | 云南航天工程物探检测股份有限公司 | Tunnel engineering full life cycle safety evaluation method |
| WO2022121272A1 (en) * | 2020-12-09 | 2022-06-16 | 山东大学 | Real-time jamming prediction method and system suitable for tbm tunneling |
| CN114882933A (en) * | 2022-05-16 | 2022-08-09 | 深圳忆联信息系统有限公司 | Low-power-consumption forward and backward reliability detection method and device of SSD, computer equipment and storage medium |
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