Method and device for predicting service life of pile foundation, storage medium, electronic device and computer program product

Abstract

The invention discloses a method and device for predicting the service life of a pile foundation, a storage medium, an electronic device and a computer program product, and relates to the field of offshore wind power, the method for predicting the service life of the pile foundation comprises the steps that a target model is obtained, the target model is used for predicting the failure probability of the pile foundation meeting the preset condition within one time, and the failure probability of the pile foundation meeting the preset condition is obtained; the target model is constructed through M groups of data, the ith group of data in the M groups of data comprises N bearing capacities corresponding to the ith time of the specified pile foundation, the N bearing capacities obey normal distribution, the time is used for reflecting the mounting duration of the pile foundation, and the specified pile foundation meets a preset condition; predicting the prediction failure probability of the target pile foundation at the target time through the target model; and under the condition that the predicted failure probability is larger than a preset threshold value, it is determined that the bearing capacity of the target pile foundation in the target time is smaller than the preset bearing capacity, and under the condition that the bearing capacity of the pile foundation is smaller than the preset bearing capacity, the pile foundation reaches the service life.

Description

Technical Field

[0001] The present application relates to the field of offshore wind power, and more specifically, to a method and device for predicting the service life of a pile foundation, a storage medium, an electronic device, and a computer program product. Background Art

[0002] As a key structural component supporting offshore wind turbines, the pile foundation of an offshore wind farm is immersed in seawater for a long time and faces a harsh corrosive environment. The loss of material thickness caused by corrosion will lead to a reduction in the bearing capacity of the pile foundation, thus affecting its service life.

[0003] The main methods currently used for predicting the life of offshore wind power pile foundations are: life prediction methods based on empirical formulas. This method uses empirical data of the corrosion environment to predict the life of the pile foundation through empirical formulas. However, this method does not consider the random characteristics of the bearing capacity, only provides an average life prediction, and lacks reliability analysis; structural life assessment methods based on deterministic analysis. This method uses finite element analysis (FEA) combined with a deterministic corrosion model for structural analysis, but does not consider the change of corrosion rate over time and the random change of bearing capacity. The result may be conservative or radical, and the accuracy is low; life prediction methods based on probability methods combined with corrosion models. This method attempts to combine probability statistics and corrosion models to predict the life of pile foundations, but most methods do not systematically consider the random distribution characteristics of bearing capacity, making it difficult to accurately predict the life of pile foundations. In the prior art, life prediction usually ignores the randomness of bearing capacity and the time-varying characteristics of corrosion rate, so the accuracy and reliability are poor.

[0004] With regard to the problem of low accuracy in predicting the service life of pile foundations in related technologies, no effective solution has been proposed so far.

[0005] Therefore, it is necessary to improve the related technology to overcome the above-mentioned defects in the related technology. Summary of the invention

[0006] The embodiments of the present application provide a method and device for predicting the service life of a pile foundation, a storage medium, an electronic device, and a computer program product, so as to at least solve the problem of low accuracy in predicting the service life of a pile foundation.

[0007] According to one aspect of an embodiment of the present application, a method for predicting the service life of a pile foundation is provided, comprising: obtaining a target model, wherein the target model is used to predict the failure probability of a pile foundation that meets preset conditions at a time, and the failure probability is used to reflect the probability that the bearing capacity of the pile foundation that meets the preset conditions at the time is less than the preset bearing capacity, and the target model is constructed by M groups of data, and the i-th group of data in the M groups of data includes N bearing capacities corresponding to the specified pile foundation at the i-th time, and the N bearing capacities obey a normal distribution, and each bearing capacity is related to the corrosion rate of the specified pile foundation, and the corrosion rate is related to the environment in which the specified pile foundation is located and the specified pile foundation. The materials of the pile foundation have an associated relationship, M and N are positive integers greater than or equal to 2, the time is used to reflect the installation time of the pile foundation, i is an integer greater than or equal to 1 and less than or equal to M, and the designated pile foundation meets the preset conditions; the predicted failure probability of the target pile foundation at the target time is predicted by the target model, wherein the target pile foundation meets the preset conditions, and the failure probability includes the predicted failure probability; when the predicted failure probability is greater than a preset threshold, it is determined that the bearing capacity of the target pile foundation at the target time is less than the preset bearing capacity, wherein when the bearing capacity of the pile foundation is less than the preset bearing capacity, the pile foundation reaches its service life.

[0008] In an exemplary embodiment, obtaining a target model includes: obtaining the M groups of data, calculating M parameter values ​​based on the M groups of data, wherein an i-th parameter value among the M parameter values ​​is calculated based on an i-th group of data among the M groups of data, and the i-th parameter value is used to reflect the failure probability of the specified pile foundation at the i-th time; and constructing the target model based on the M parameter values ​​and the M times corresponding to the M groups of data.

[0009] In an exemplary embodiment, M parameter values ​​are calculated based on the M groups of data, including: calculating the i-th parameter value in the following manner to calculate the M parameter values: determining P carrying capacities in the i-th group of data, wherein the P carrying capacities are all less than the preset carrying capacity, and P is an integer greater than or equal to 1 and less than or equal to N; dividing the P by the N to obtain the i-th parameter value.

[0010] In an exemplary embodiment, the method further includes: obtaining N bearing capacities in the i-th group of data by: determining a corrosion rate function corresponding to the specified pile foundation, wherein the corrosion rate function is used to reflect the corrosion rate of the specified pile foundation at different times, and the corrosion rate function corresponding to the specified pile foundation is associated with the environment in which the specified pile foundation is located and the material of the specified pile foundation; obtaining N initial bearing capacities, wherein the N initial bearing capacities obey a normal distribution; calculating the i-th bearing capacity of the N bearing capacities according to the i-th initial bearing capacity of the N initial bearing capacities by the following formula to obtain the N bearing capacities: Wherein, LBC(ti) is the i-th load capacity, LBC i is the i-th initial bearing capacity, k is the influencing factor, the influencing factor is associated with the environment in which the specified pile foundation is located and the material of the specified pile foundation, CR(τ) is the corrosion rate function, and ti is the i-th time.

[0011] In an exemplary embodiment, determining the corrosion rate function corresponding to the specified pile foundation includes: detecting the corrosion current and corrosion depth corresponding to the specified pile foundation at different times; determining a first parameter value according to the corrosion current corresponding to the specified pile foundation at different times, and determining a second parameter value according to the corrosion depth corresponding to the specified pile foundation at different times; determining the corrosion rate function corresponding to the specified pile foundation as CR(t)=a*t b ; Wherein, t is time, a is the first parameter value, and b is the second parameter value.

[0012] In an exemplary embodiment, predicting the predicted failure probability of the target pile foundation at the target time by using the target model includes: predicting the predicted failure probability of the target pile foundation at the target time by using the target model using the following formula: Wherein, F(t) is the predicted failure probability of the target pile foundation at the target time, e is the base of the natural logarithm, λ is the shape parameter, m is the scale parameter, and both λ and m are obtained by fitting the M groups of data.

[0013] According to another aspect of an embodiment of the present application, a prediction device for the service life of a pile foundation is also provided, comprising: a first acquisition module, for acquiring a target model, wherein the target model is used to predict the failure probability of a pile foundation that meets preset conditions at a time, and the failure probability is used to reflect the probability that the bearing capacity of the pile foundation that meets the preset conditions at the time is less than the preset bearing capacity, and the target model is constructed by M groups of data, and the i-th group of data in the M groups of data includes N bearing capacities corresponding to the specified pile foundation at the i-th time, and the N bearing capacities obey a normal distribution, and each bearing capacity is related to the corrosion rate of the specified pile foundation, and the corrosion rate is related to the environment in which the specified pile foundation is located and the specified pile foundation. The materials of the pile foundation have an associated relationship, M and N are positive integers greater than or equal to 2, the time is used to reflect the installation time of the pile foundation, i is an integer greater than or equal to 1 and less than or equal to M, and the designated pile foundation meets the preset condition; a prediction module is used to predict the predicted failure probability of the target pile foundation at the target time through the target model, wherein the target pile foundation meets the preset condition, and the failure probability includes the predicted failure probability; a determination module is used to determine that the bearing capacity of the target pile foundation at the target time is less than the preset bearing capacity when the predicted failure probability is greater than a preset threshold, wherein when the bearing capacity of the pile foundation is less than the preset bearing capacity, the pile foundation reaches its service life.

[0014] According to another aspect of the embodiments of the present application, a computer-readable storage medium is provided, the computer-readable storage medium comprising a stored program, wherein the program is configured to execute the above-mentioned method for predicting the service life of a pile foundation when running.

[0015] According to another aspect of the embodiments of the present application, there is also provided an electronic device, including a memory and a processor, wherein the memory stores a computer program, wherein the processor is configured to execute the above-mentioned method for predicting the service life of the pile foundation through the computer program.

[0016] According to another aspect of the embodiments of the present application, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the method for predicting the service life of the pile foundation is implemented.

[0017] In this application, the target model is used to predict the probability that the bearing capacity of the target pile foundation is less than the preset bearing capacity at the target time, and when the probability is greater than a certain value, it is determined that the pile foundation has reached its service life. Since the change of the corrosion rate of the pile foundation over time and the randomness of the bearing capacity of the pile foundation are taken into account when constructing the target model, the influence of random factors on the prediction results is greatly reduced, so that the service life of the pile foundation can be predicted more accurately, thereby solving the problem of low accuracy in predicting the service life of the pile foundation. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0019] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0020] Figure 1 It is a hardware structure block diagram of a mobile terminal of a method for predicting the service life of a pile foundation according to an embodiment of the present application;

[0021] Figure 2 is a flow chart of a method for predicting the service life of a pile foundation according to an embodiment of the present application;

[0022] Figure 3 It is a structural block diagram of a device for predicting the service life of a pile foundation according to an embodiment of the present application. DETAILED DESCRIPTION

[0023] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0024] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0025] The method embodiments provided in the embodiments of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Taking running on a mobile terminal as an example, Figure 11 is a hardware structure block diagram of a mobile terminal for predicting the service life of a pile foundation according to an embodiment of the present application. Figure 1 As shown, the mobile terminal may include one or more ( Figure 1 Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor (MP) or a programmable logic device (FPGA)) and a memory 104 for storing data, wherein the mobile terminal may also include a transmission device 106 and an input / output device 108 for communication functions. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the mobile terminal. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations are shown.

[0026] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the prediction method of the service life of the pile foundation in the embodiment of the present application. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, to implement the above method. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories may be connected to the mobile terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof.

[0027] The transmission device 106 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of the mobile terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0028] In order to solve the above problem, this embodiment provides a method for predicting the service life of a pile foundation, including but not limited to being applied to the above mobile terminal. Figure 2is a flow chart of a method for predicting the service life of a pile foundation according to an embodiment of the present application, such as Figure 2 As shown, the process includes the following steps S202-S206:

[0029] Step S202: obtaining a target model, wherein the target model is used to predict the failure probability of a pile foundation that meets a preset condition at a time, and the failure probability is used to reflect the probability that the bearing capacity of the pile foundation that meets the preset condition at the time is less than the preset bearing capacity, and the target model is constructed by M groups of data, and the i-th group of data in the M groups of data includes N bearing capacities corresponding to the specified pile foundation at the i-th time, and the N bearing capacities obey a normal distribution, and each bearing capacity is related to the corrosion rate of the specified pile foundation, and the corrosion rate is associated with the environment in which the specified pile foundation is located and the material of the specified pile foundation, M and N are positive integers greater than or equal to 2, and the time is used to reflect the installation duration of the pile foundation, and i is an integer greater than or equal to 1 and less than or equal to M, and the specified pile foundation meets the preset condition;

[0030] Optionally, the preset conditions include, but are not limited to: the material of the pile foundation (such as steel, concrete, carbon fiber composite material, etc.) is a specified material, and the coordinate range of the pile foundation is a specified range.

[0031] It should be noted that the corrosion rates of different pile foundations that meet the preset conditions are the same.

[0032] It should be noted that the load-bearing capacity (LBC) of a pile foundation refers to the maximum load that the pile foundation can withstand under preset conditions. When the pile foundation's load-bearing capacity is less than the preset load-bearing capacity, it will no longer be able to be used, i.e., it will fail.

[0033] Step S204: predicting the predicted failure probability of the target pile foundation at the target time by using the target model, wherein the target pile foundation meets the preset condition and the failure probability includes the predicted failure probability;

[0034] Optionally, the corrosion rate of the target pile foundation is the same as the corrosion rate function corresponding to the specified pile foundation.

[0035] Step S206: When the predicted failure probability is greater than a preset threshold, determining that the bearing capacity of the target pile foundation at the target time is less than a preset bearing capacity, wherein when the bearing capacity of the pile foundation is less than the preset bearing capacity, the pile foundation reaches its service life.

[0036] Optionally, when the predicted failure probability is 50% and the preset threshold is 40%, it is determined that the target pile foundation has reached its service life.

[0037] It should be noted that when the bearing capacity of the pile foundation is less than the preset bearing capacity, the pile foundation cannot be used any more, that is, the pile foundation has reached its service life.

[0038] In the above steps, the target model is used to predict the probability that the bearing capacity of the target pile foundation is less than the preset bearing capacity at the target time, and when the probability is greater than a certain value, it is determined that the pile foundation has reached its service life. Since the change of the corrosion rate of the pile foundation over time and the randomness of the bearing capacity of the pile foundation are taken into account when constructing the target model, the influence of random factors on the prediction results is greatly reduced, so that the service life of the pile foundation can be predicted more accurately, thereby solving the problem of low accuracy in predicting the service life of the pile foundation.

[0039] In an exemplary embodiment, obtaining the target model may be achieved by following the steps S11-S12:

[0040] Step S11: acquiring the M groups of data, and calculating M parameter values ​​according to the M groups of data, wherein the i-th parameter value among the M parameter values ​​is calculated according to the i-th group of data among the M groups of data, and the i-th parameter value is used to reflect the failure probability of the specified pile foundation at the i-th time;

[0041] It should be noted that, M groups of data of a designated pile foundation at M times are obtained, wherein the i-th group of data in the M groups of data is the data of the designated pile foundation at the i-th time.

[0042] Step S12: constructing the target model according to the M parameter values ​​and the M times corresponding to the M groups of data.

[0043] Optionally, M parameter values ​​are fitted by Weibull distribution, so as to construct the target model according to the M parameter values ​​and M times corresponding to the M groups of data.

[0044] It should be noted that, through the above steps S11-S12, the target model can more comprehensively consider various factors that affect the bearing capacity and corrosion rate of the pile foundation, including environmental changes, material aging and maintenance effects, so as to dynamically evaluate the degradation state of the pile foundation, rather than just providing static or average prediction results, thereby improving the accuracy of life prediction.

[0045] In an exemplary embodiment, calculating M parameter values ​​according to the M groups of data can be implemented by the following steps: calculating the i-th parameter value in the following manner (steps S21-S22) to calculate the M parameter values:

[0046] Step S21: determining P carrying capacities in the i-th group of data, wherein the P carrying capacities are all less than the preset carrying capacity, and P is an integer greater than or equal to 1 and less than or equal to N;

[0047] Step S22: Divide the P by the N to obtain the i-th parameter value.

[0048] Optionally, the i-th group of data includes 100 bearing capacities corresponding to the specified pile foundation at the i-th time. When 50 of the 100 bearing capacities are less than the preset bearing capacity, the i-th parameter value is 50% (50÷100*100%=50%).

[0049] In an exemplary embodiment, the method further comprises the following steps: obtaining N carrying capacities in the i-th group of data by the following manner (steps S31-S33):

[0050] Step S31: determining a corrosion rate function corresponding to the designated pile foundation, wherein the corrosion rate function is used to reflect the corrosion rate of the designated pile foundation at different times, and the corrosion rate function corresponding to the designated pile foundation is associated with the environment in which the designated pile foundation is located and the material of the designated pile foundation;

[0051] It should be noted that different pile foundations with the same environment and material have the same corrosion rate function.

[0052] Step S32: Obtain N initial carrying capacities, wherein the N initial carrying capacities obey a normal distribution;

[0053] Optionally, N different initial carrying capacities obeying a normal distribution are randomly generated through Monte Carlo simulation.

[0054] Step S33: Calculate the i-th carrying capacity among the N carrying capacities according to the i-th initial carrying capacity among the N initial carrying capacities by the following formula to obtain the N carrying capacities:

[0055]

[0056] Wherein, LBC(ti) is the i-th load capacity, LBC i is the i-th initial bearing capacity, k is the influencing factor, the influencing factor is associated with the environment in which the specified pile foundation is located and the material of the specified pile foundation, CR(τ) is the corrosion rate function, and ti is the i-th time.

[0057] Optionally, the N carrying capacities obey a normal distribution.

[0058] Optionally, influencing factors corresponding to different environments and different materials are obtained through laboratory tests or material databases.

[0059] In an exemplary embodiment, determining the corrosion rate function corresponding to the specified pile foundation may be achieved by following the steps S41-S43:

[0060] Step S41: detecting the corrosion current and corrosion depth of the designated pile foundation at different times;

[0061] Optionally, the corrosion current corresponding to the designated pile foundation at different times is detected by linear polarization resistance technology, electrochemical noise measurement, corrosion potential measurement and the like.

[0062] Optionally, the corrosion depth of the designated pile foundation at different times is detected by ultrasonic detection, magnetic flux loss detection, X-ray detection, electrochemical impedance spectroscopy, using a corrosion probe, or the like.

[0063] Step S42: determining a first parameter value according to the corrosion current corresponding to the designated pile foundation at different times, and determining a second parameter value according to the corrosion depth corresponding to the designated pile foundation at different times;

[0064] Optionally, a regression analysis (such as linear regression, polynomial regression and time series regression) is performed on the corrosion current corresponding to the specified pile foundation at different times to determine the first parameter value, and a regression analysis is performed on the corrosion depth corresponding to the specified pile foundation at different times to determine the second parameter value.

[0065] Step S43: Determine the corrosion rate function corresponding to the specified pile foundation as CR(t)=a*t b ; Wherein, t is time, a is the first parameter value, and b is the second parameter value.

[0066] In an exemplary embodiment, predicting the predicted failure probability of the target pile foundation at the target time by using the target model can be achieved by the following steps: predicting the predicted failure probability of the target pile foundation at the target time by using the target model using the following formula:

[0067]

[0068] Wherein, F(t) is the predicted failure probability of the target pile foundation at the target time, e is the base of the natural logarithm, λ is the shape parameter, m is the scale parameter, and both λ and m are obtained by fitting the M groups of data.

[0069] It should be noted that by predicting the service life of the target pile foundation, the maintenance team can more accurately identify high-risk pile foundations that are about to reach the end of their service life, thereby concentrating resources for targeted inspections and maintenance. This improves the safety of the pile foundation while avoiding unnecessary comprehensive inspections and improving the efficiency of maintenance work.

[0070] Obviously, the above-described embodiments are only embodiments of a part of the present invention, rather than all embodiments. In order to better understand the above method, the above process is described below in conjunction with embodiments, but it is not intended to limit the technical solutions of the embodiments of the present invention, specifically:

[0071] 1. Establishing corrosion rate model:

[0072] Determine the corrosion rate CR(t) as a function of time t, where the corrosion rate can be regressed and analyzed through monitoring data (monitoring actual corrosion current, periodically monitoring corrosion depth, etc.), and its change pattern over time can be obtained by fitting: CR(t) = a·t b , where a is obtained by fitting the monitored corrosion current data, and b is obtained by fitting the monitored corrosion depth data.

[0073] 2. Establish a random model of carrying capacity: Assume that the initial carrying capacity LBC0 obeys the normal distribution N(μ0,σ0), and its degradation process obeys the random process:

[0074]

[0075] Here, k is an influencing factor that depends on the properties of the material and the environmental conditions.

[0076] 3. Calculate the failure probability of the pile foundation at M times:

[0077] Based on the random model of corrosion rate and carrying capacity, a large number of random samples are generated at the i-th time of M times using Monte Carlo simulation. One sample includes one carrying capacity. The failure probability of the i-th time among the M times is determined. When the carrying capacity included in a sample is lower than a critical value, the sample is determined to be failed.

[0078] The formula for calculating the failure probability corresponding to the i-th time is as follows:

[0079]

[0080] 4. Lifespan prediction:

[0081] By fitting the failure probability data and M times through Weibull distribution, the probability distribution of failure time is obtained:

[0082]

[0083] Among them, λ is the shape parameter and m is the scale parameter, both of which are obtained based on the fitting data.

[0084] It should be noted that this application proposes a statistical algorithm for predicting the life of offshore wind power underwater steel structures based on a random bearing capacity model. The algorithm incorporates the randomness of corrosion rate and bearing capacity into the model, and uses Monte Carlo simulation and Weibull distribution analysis to achieve accurate prediction of the structure life. It makes up for the shortcomings of the existing technology in dealing with the randomness of bearing capacity and the time-varying characteristics of corrosion rate, and significantly improves the reliability and accuracy of life prediction. The algorithm can be widely used in the actual engineering of pile foundation maintenance and management of offshore wind farms.

[0085] It should be noted that the present application also has the following advantages: (1) A life prediction model that takes into account the randomness of bearing capacity: Compared with the prior art, the present application more comprehensively reflects the actual situation of the pile foundation by introducing a random model of bearing capacity; (2) An algorithm that combines Monte Carlo simulation with Weibull distribution analysis: This algorithm generates a large number of random samples to obtain the probability distribution of failure time, thereby improving the accuracy of life prediction; (3) A dynamic coupling model of corrosion rate and bearing capacity degradation: The model proposed in the present application can dynamically track the impact of corrosion progression on the bearing capacity of the structure, providing a scientific basis for engineering maintenance and management.

[0086] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods of each embodiment of the present application.

[0087] In this embodiment, a prediction device for the service life of a pile foundation is also provided, and the device is used to implement the above-mentioned embodiments and preferred embodiments, and the descriptions that have been made will not be repeated. As used below, the term "module" can implement a combination of software and / or hardware of a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0088] Figure 3 : is a structural block diagram of a device for predicting the service life of a pile foundation according to an embodiment of the present application, the device comprising:

[0089] The first acquisition module 302 is used to acquire a target model, wherein the target model is used to predict the failure probability of a pile foundation that meets a preset condition at a time, and the failure probability is used to reflect the probability that the bearing capacity of the pile foundation that meets the preset condition at the time is less than the preset bearing capacity, and the target model is constructed by M groups of data, and the i-th group of data in the M groups of data includes N bearing capacities corresponding to the specified pile foundation at the i-th time, and the N bearing capacities obey a normal distribution, and each bearing capacity is related to the corrosion rate of the specified pile foundation, and the corrosion rate is associated with the environment in which the specified pile foundation is located and the material of the specified pile foundation, M and N are positive integers greater than or equal to 2, and the time is used to reflect the installation time of the pile foundation, and i is an integer greater than or equal to 1 and less than or equal to M, and the specified pile foundation meets the preset condition;

[0090] A prediction module 304 is used to predict the predicted failure probability of a target pile foundation at a target time by using the target model, wherein the target pile foundation meets the preset condition and the failure probability includes the predicted failure probability;

[0091] The determination module 306 is used to determine that the bearing capacity of the target pile foundation at the target time is less than the preset bearing capacity when the predicted failure probability is greater than a preset threshold, wherein when the bearing capacity of the pile foundation is less than the preset bearing capacity, the pile foundation reaches the end of its service life.

[0092] The above-mentioned device uses the target model to predict the probability that the bearing capacity of the target pile foundation is less than the preset bearing capacity at the target time, and determines that the pile foundation has reached its service life when the probability is greater than a certain value. Since the change of the corrosion rate of the pile foundation over time and the randomness of the bearing capacity of the pile foundation are taken into account when constructing the target model, the influence of random factors on the prediction results is greatly reduced, so that the service life of the pile foundation can be predicted more accurately, thereby solving the problem of low accuracy in predicting the service life of the pile foundation.

[0093] In an exemplary embodiment, the first acquisition module 302 is also used to acquire the M groups of data, and calculate M parameter values ​​based on the M groups of data, wherein the i-th parameter value among the M parameter values ​​is calculated based on the i-th group of data among the M groups of data, and the i-th parameter value is used to reflect the failure probability of the specified pile foundation at the i-th time; the target model is constructed based on the M parameter values ​​and the M times corresponding to the M groups of data.

[0094] In an exemplary embodiment, the first acquisition module 302 is also used to calculate the i-th parameter value in the following manner to obtain the M parameter values: determine P carrying capacities in the i-th group of data, wherein the P carrying capacities are all less than the preset carrying capacity, and P is an integer greater than or equal to 1 and less than or equal to N; divide the P by the N to obtain the i-th parameter value.

[0095] In an exemplary embodiment, the above-mentioned device also includes: a second acquisition module, which is used to obtain N bearing capacities in the i-th group of data in the following manner: determining the corrosion rate function corresponding to the specified pile foundation, wherein the corrosion rate function is used to reflect the corrosion rate of the specified pile foundation at different times, and the corrosion rate function corresponding to the specified pile foundation is associated with the environment in which the specified pile foundation is located and the material of the specified pile foundation; obtaining N initial bearing capacities, wherein the N initial bearing capacities obey a normal distribution; calculating the i-th bearing capacity of the N bearing capacities according to the i-th initial bearing capacity of the N initial bearing capacities through the following formula to obtain the N bearing capacities: Wherein, LBC(ti) is the i-th load capacity, LBC i is the i-th initial bearing capacity, k is the influencing factor, the influencing factor is associated with the environment in which the specified pile foundation is located and the material of the specified pile foundation, CR(τ) is the corrosion rate function, and ti is the i-th time.

[0096] In an exemplary embodiment, the second acquisition module is further used to detect the corrosion current and corrosion depth corresponding to the specified pile foundation at different times; determine the first parameter value according to the corrosion current corresponding to the specified pile foundation at different times, and determine the second parameter value according to the corrosion depth corresponding to the specified pile foundation at different times; determine the corrosion rate function corresponding to the specified pile foundation as CR(t)=a*t b ; Wherein, t is time, a is the first parameter value, and b is the second parameter value.

[0097] In an exemplary embodiment, the prediction module 304 is further configured to predict the predicted failure probability of the target pile foundation at the target time by using the target model using the following formula: Wherein, F(t) is the predicted failure probability of the target pile foundation at the target time, e is the base of the natural logarithm, λ is the shape parameter, m is the scale parameter, and both λ and m are obtained by fitting the M groups of data.

[0098] An embodiment of the present application further provides a computer-readable storage medium, in which a computer program is stored, wherein the computer program is configured to execute the steps of any of the above method embodiments when running.

[0099] Optionally, in this embodiment, the storage medium may be configured to store a computer program for performing the following steps:

[0100] S1, obtaining a target model, wherein the target model is used to predict the failure probability of a pile foundation that meets preset conditions at a time, and the failure probability is used to reflect the probability that the bearing capacity of the pile foundation that meets the preset conditions at the time is less than the preset bearing capacity, and the target model is constructed by M groups of data, and the i-th group of data in the M groups of data includes N bearing capacities corresponding to the specified pile foundation at the i-th time, and the N bearing capacities obey a normal distribution, and each bearing capacity is related to the corrosion rate of the specified pile foundation, and the corrosion rate is associated with the environment in which the specified pile foundation is located and the material of the specified pile foundation, M and N are positive integers greater than or equal to 2, and the time is used to reflect the installation duration of the pile foundation, and i is an integer greater than or equal to 1 and less than or equal to M, and the specified pile foundation meets the preset conditions;

[0101] S2, predicting the predicted failure probability of the target pile foundation at the target time by using the target model, wherein the target pile foundation meets the preset condition and the failure probability includes the predicted failure probability;

[0102] S3, when the predicted failure probability is greater than a preset threshold, determining that the bearing capacity of the target pile foundation at the target time is less than the preset bearing capacity, wherein when the bearing capacity of the pile foundation is less than the preset bearing capacity, the pile foundation reaches its service life.

[0103] In an exemplary embodiment, the computer-readable storage medium may include, but is not limited to, various media that can store computer programs, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk.

[0104] For specific examples in this embodiment, reference may be made to the examples described in the above embodiments and exemplary implementation modes, and this embodiment will not be described in detail herein.

[0105] An embodiment of the present application further provides a computer program product, including a computer program, and when the computer program is executed by a processor, the steps in any one of the above method embodiments are performed.

[0106] An embodiment of the present application further provides an electronic device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0107] Optionally, in this embodiment, the processor may be configured to perform the following steps through a computer program:

[0108] S1, obtaining a target model, wherein the target model is used to predict the failure probability of a pile foundation that meets preset conditions at a time, and the failure probability is used to reflect the probability that the bearing capacity of the pile foundation that meets the preset conditions at the time is less than the preset bearing capacity, and the target model is constructed by M groups of data, and the i-th group of data in the M groups of data includes N bearing capacities corresponding to the specified pile foundation at the i-th time, and the N bearing capacities obey a normal distribution, and each bearing capacity is related to the corrosion rate of the specified pile foundation, and the corrosion rate is associated with the environment in which the specified pile foundation is located and the material of the specified pile foundation, M and N are positive integers greater than or equal to 2, and the time is used to reflect the installation duration of the pile foundation, and i is an integer greater than or equal to 1 and less than or equal to M, and the specified pile foundation meets the preset conditions;

[0109] S2, predicting the predicted failure probability of the target pile foundation at the target time by using the target model, wherein the target pile foundation meets the preset condition and the failure probability includes the predicted failure probability;

[0110] S3, when the predicted failure probability is greater than a preset threshold, determining that the bearing capacity of the target pile foundation at the target time is less than the preset bearing capacity, wherein when the bearing capacity of the pile foundation is less than the preset bearing capacity, the pile foundation reaches its service life.

[0111] In an exemplary embodiment, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0112] For specific examples in this embodiment, reference may be made to the examples described in the above embodiments and exemplary implementation modes, and this embodiment will not be described in detail herein.

[0113] Obviously, those skilled in the art should understand that the above modules or steps of the present application can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order from that herein, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present application is not limited to any specific combination of hardware and software.

[0114] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for predicting the service life of a pile foundation, characterized in that: include: Obtain a target model, wherein the target model is used to predict the failure probability of a pile foundation that meets preset conditions at a time, the failure probability is used to reflect the probability that the bearing capacity of the pile foundation that meets the preset conditions at the time is less than the preset bearing capacity, the target model is constructed by M groups of data, the i-th group of data in the M groups of data includes N bearing capacities corresponding to the specified pile foundation at the i-th time, the N bearing capacities obey a normal distribution, each bearing capacity is related to the corrosion rate of the specified pile foundation, the corrosion rate is associated with the environment in which the specified pile foundation is located and the material of the specified pile foundation, M and N are positive integers greater than or equal to 2, the time is used to reflect the installation duration of the pile foundation, i is an integer greater than or equal to 1 and less than or equal to M, and the specified pile foundation meets the preset conditions; Predicting a predicted failure probability of a target pile foundation at a target time by using the target model, wherein the target pile foundation satisfies the preset condition and the failure probability includes the predicted failure probability; When the predicted failure probability is greater than a preset threshold, it is determined that the bearing capacity of the target pile foundation at the target time is less than the preset bearing capacity, wherein when the bearing capacity of the pile foundation is less than the preset bearing capacity, the pile foundation reaches its service life.

2. The method according to claim 1, characterized in that Get the target model, including: Acquire the M groups of data, and calculate M parameter values ​​according to the M groups of data, wherein the i-th parameter value among the M parameter values ​​is calculated according to the i-th group of data among the M groups of data, and the i-th parameter value is used to reflect the failure probability of the specified pile foundation at the i-th time; The target model is constructed according to the M parameter values ​​and the M times corresponding to the M groups of data.

3. The method according to claim 2, characterized in that M parameter values ​​are calculated based on the M groups of data, including: The i-th parameter value is calculated in the following manner to obtain the M parameter values: Determine P carrying capacities in the i-th group of data, wherein the P carrying capacities are all less than the preset carrying capacity, and P is an integer greater than or equal to 1 and less than or equal to N; Divide the P by the N to obtain the i-th parameter value.

4. The method according to claim 1, characterized in that: The method further comprises: The N carrying capacities in the i-th group of data are obtained by: Determine a corrosion rate function corresponding to the specified pile foundation, wherein the corrosion rate function is used to reflect the corrosion rate of the specified pile foundation at different times, and the corrosion rate function corresponding to the specified pile foundation is associated with the environment in which the specified pile foundation is located and the material of the specified pile foundation; Obtaining N initial carrying capacities, wherein the N initial carrying capacities obey a normal distribution; The i-th carrying capacity among the N carrying capacities is calculated according to the i-th initial carrying capacity among the N initial carrying capacities by the following formula to obtain the N carrying capacities: Wherein, LBC(ti) is the i-th load capacity, LBC i is the i-th initial carrying capacity, k is an influencing factor, and the influencing factor is associated with the environment in which the designated pile foundation is located and the material of the designated pile foundation. CR(τ) is a corrosion rate function, and ti is the i-th time.

5. The method according to claim 4, characterized in that Determining the corrosion rate function corresponding to the specified pile foundation includes: Detecting the corrosion current and corrosion depth of the designated pile foundation at different times; Determine a first parameter value according to the corrosion current corresponding to the specified pile foundation at different times, and determine a second parameter value according to the corrosion depth corresponding to the specified pile foundation at different times; Determine the corrosion rate function corresponding to the specified pile foundation as CR(t)=a*t b ; Wherein, t is time, a is the first parameter value, and b is the second parameter value.

6. The method according to claim 1, characterized in that Predicting the predicted failure probability of the target pile foundation at the target time by the target model includes: The predicted failure probability of the target pile foundation at the target time is predicted by the target model using the following formula: Wherein, F(t) is the predicted failure probability of the target pile foundation at the target time, e is the base of the natural logarithm, λ is the shape parameter, m is the scale parameter, and both λ and m are obtained by fitting the M groups of data.

7. A device for predicting the service life of a pile foundation, characterized in that: include: A first acquisition module is used to acquire a target model, wherein the target model is used to predict the failure probability of a pile foundation that meets a preset condition at a time, and the failure probability is used to reflect the probability that the bearing capacity of the pile foundation that meets the preset condition at the time is less than the preset bearing capacity, and the target model is constructed by M groups of data, and the i-th group of data in the M groups of data includes N bearing capacities corresponding to the specified pile foundation at the i-th time, and the N bearing capacities obey a normal distribution, and each bearing capacity is related to the corrosion rate of the specified pile foundation, and the corrosion rate is associated with the environment in which the specified pile foundation is located and the material of the specified pile foundation, M and N are positive integers greater than or equal to 2, and the time is used to reflect the installation duration of the pile foundation, and i is an integer greater than or equal to 1 and less than or equal to M, and the specified pile foundation meets the preset condition; A prediction module, used to predict the predicted failure probability of a target pile foundation at a target time through the target model, wherein the target pile foundation meets the preset condition and the failure probability includes the predicted failure probability; A determination module is used to determine that the bearing capacity of the target pile foundation at the target time is less than the preset bearing capacity when the predicted failure probability is greater than a preset threshold, wherein when the bearing capacity of the pile foundation is less than the preset bearing capacity, the pile foundation reaches its service life.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein the program executes the method according to any one of claims 1 to 6 when executed.

9. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to execute the method according to any one of claims 1 to 6 through the computer program.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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