CN114139381A - General investigation and evaluation method for pile foundation damage considering uncertainty of pile soil parameters - Google Patents
General investigation and evaluation method for pile foundation damage considering uncertainty of pile soil parameters Download PDFInfo
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Abstract
The invention provides a general survey and evaluation method for pile foundation damage considering uncertainty of pile soil parameters. The method comprises the following steps: collecting field geological data before pile foundation construction and vibration data caused by a train; collecting samples with randomly distributed soil parameters and pile parameters; a theoretical analysis model of the vehicle-bridge-pile-soil dynamic interaction is built based on the combination of a thin layer method with an ideal matching layer and a volume method; and solving the dynamic response of the foundation bearing platform and the ground vibration response of the field around the pile foundation by using a vehicle-bridge-pile-soil dynamic interaction theoretical analysis model, and obtaining the damage assessment and general investigation analysis results of the pile foundation through a deep learning algorithm. The invention considers the uncertainty of the soil parameters around the pile foundation and the parameters of the pile in space, utilizes the random distribution sample to make the vehicle-bridge-pile-soil dynamic interaction model have probability significance, and evaluates and generally inspects the damage through the damage probability distribution curve of the pile foundation.
Description
Technical Field
The invention relates to the technical field of bridge detection, in particular to a pile foundation damage general investigation and evaluation method considering uncertainty of pile soil parameters.
Background
The pile foundation is used as an important component of a bridge lower structure and can stably transmit load to a surrounding soil layer. Which is described in detail below. In the past 40 years, the foundation construction of China is rapidly developed, domestic expressways, express railways and urban rail lines are widely constructed in various regions, and pile foundations have the advantages of high bearing capacity, high shock resistance, low settlement and the like, so that the pile foundations are widely adopted in the construction of the highways and railways in China as main supporting structures. For the existing bridge, the general survey and detection of the actual working state of the pile foundation structure are carried out, and the bearing condition after the long-term operation is evaluated, so that the important social and economic benefits are achieved, on one hand, the technical guarantee can be provided for the safe operation of the existing bridge, and on the other hand, the limited maintenance and reinforcement cost can be reasonably planned.
The method is different from an upper structure which is easy to ensure inspection and supervision and construction quality, and the upper structure is used as a pile foundation of a hidden project, and the construction quality is difficult to control due to the restriction of various conditions. A large amount of pile foundations are not detected in early engineering construction, and the pile foundation structure after operation is easily influenced by the long-term effects of being close to newly-built structure foundation treatment, operation medium bridge abutment soil pressure and the like, and even causes structural defects. In addition, the safety reserve of the vertical bearing capacity of the pile foundation can be reduced due to the increase of the passing speed of the existing line and the increase of the number of traveling vehicles, so that the requirements for rapid nondestructive general investigation evaluation and reinforcement maintenance of the vertical bearing capacity of the existing operation bridge pile foundation are gradually increased in recent years.
At present, the pile foundation detection methods commonly used in the prior art engineering include a high strain detection method, a low strain dynamic load method, a sound wave nondestructive detection technology, a core drilling detection method and the like.
(1) High strain detection method: the method comprises the steps of adopting a heavy hammer to impact a pile top, and enabling impact pulses of the heavy hammer to be transmitted downwards along a pile body to enable pile-soil to generate corresponding displacement so as to excite the soil resistance around the pile and the pile end bearing force, thereby judging whether the bearing capacity of a pile foundation can meet the actual engineering requirement, judging the ultimate bearing capacity of the pile, and reasonably detecting the integrity of the pile foundation. In the high-strain detection method, the instantaneous damage to the soil around the pile and the accuracy of detection signals become important conditions for detecting and analyzing the bearing capacity of a single pile by the dynamic method.
(2) Low strain detection method: the working principle is that the top of the pile foundation is enabled to obtain a certain degree of vertical shock excitation force action, and a stress wave which is transmitted downwards is formed through the pile body. When the pile body has defects, reflected waves are generated. The reflected wave is transmitted to the top of the pile foundation, and the sensor at the top of the pile foundation smoothly receives the signal, so that a dynamic waveform is formed. And judging the pile foundation quality according to the characteristics of the stress waves collected by the reflected waves. The low strain detection method mainly comprises a hydroelectric effect method, a reflection wave method, a dynamic parameter method, a resonance method and the like, and has the main advantages of high economy, convenience, rapidness, capability of carrying out real-time judgment on site and the like. The method has certain difficulty in judging the defects of the pile bottom of the long pile foundation (more than 50.0m) because the propagation characteristic of the elastic wave and the excitation energy are limited to a certain extent.
(3) The sound wave nondestructive testing technology comprises the following steps: the method is developed from the traditional acoustic detection technology, and whether the pile foundation has defects or not is judged by accurately analyzing the stress in the impact process. If the pile foundation stress wave is in a uniform propagation development state, and the wave velocity and the wave peak value are in an unchanged state, the integrity of the pile foundation is better.
(4) Core drilling detection method: the method adopts a professional drilling machine and an artificial diamond drill bit to determine a core sample on the structural concrete, is used for detecting indexes such as the length, the material strength, the sediment thickness and the like of a concrete pouring pile or a cement pile, and judges or identifies the rock-soil property of a pile end bearing layer. Although the method is not limited by factors such as fields and conditions and is particularly suitable for detecting the large-diameter pile, the method needs to destroy a local pile body to obtain core sample sampling detection, and belongs to semi-nondestructive detection.
The above-mentioned pile foundation detection method commonly used in the prior art engineering has the following disadvantages:
the prior art has the defects that: the pile foundation detection technology is basically that exciting force is additionally applied to the top of a pile or the upper part of a bearing platform, the method is convenient to detect the pile foundation which is just constructed, but the method is not easy to operate the existing railway bridge foundation which is in operation and is not easy to realize under certain conditions.
The prior art has the defects that: the property of the field soil is complex and variable, however, the property of the field soil in the detection is generally considered to be a definite value, which causes great inaccuracy.
The prior art has the defects of 3: in the process of pouring the pile foundation concrete, due to the influence of comprehensive factors such as human factors, non-human factors and the like, certain uncertainty is generated on the uniformity of the pile foundation quality in the depth direction, however, in the existing detection, the parameter of the pile foundation is generally regarded as a constant from top to bottom, and the influence of the uncertainty of the pile foundation parameter on the detection result is easily ignored.
Disclosure of Invention
The embodiment of the invention provides a pile foundation damage general survey and evaluation method considering uncertainty of pile soil parameters, so as to effectively complete a quick evaluation general survey task of a large-range pile foundation.
In order to achieve the purpose, the invention adopts the following technical scheme.
A pile foundation damage general investigation and evaluation method considering uncertainty of pile soil parameters comprises the following steps:
collecting field geological data before pile foundation construction and vibration data caused by a train;
collecting samples with randomly distributed soil parameters and pile parameters;
a theoretical analysis model of the vehicle-bridge-pile-soil dynamic interaction is built based on the combination of a thin layer method with an ideal matching layer and a volume method;
taking the field geological data and train-induced vibration data, soil parameter random distribution samples and pile parameter random distribution samples as the input of the train-bridge-pile-soil dynamic interaction theoretical analysis model, and solving the dynamic response of a foundation bearing platform and the ground vibration response of the field around the pile foundation;
and obtaining damage assessment and general investigation analysis results of the pile foundation through a deep learning algorithm based on the dynamic response of the foundation cap and the ground vibration response of the field around the pile foundation.
Preferably, the collecting of the field geological data and the train-induced vibration data before the pile foundation construction comprises:
acquiring site geological data before pile foundation construction, wherein the site geological data comprise geological survey parameters of site soil, standard penetration test parameters and structural parameters of a bridge;
selecting a pile foundation and a bearing platform of a bridge to be evaluated, arranging sensors on four angular point positions of the bearing platform and near-field ground around the bearing platform, and acquiring vibration data caused by a train through the sensors under the normal operation condition of the train.
Preferably, the collecting of the randomly distributed soil parameter sample comprises:
selecting the elastic modulus of a field soil layer as a random variable, considering the random distribution of the random variable along with depth and the uncertainty of observed data of a data test along with the field, establishing a prior probability distribution model of the field soil, optimizing the prior probability distribution model through a field soil standard penetration test or multi-channel surface wave analysis field test data, and obtaining a plurality of soil parameter random distribution samples which accord with the posterior distribution through the optimized prior probability distribution model by utilizing Bayesian theorem and Markov chain-Monte Carlo simulation.
Preferably, the prior probability distribution model of the field soil consists of inherent variability and measurement uncertainty of the field soil;
the elastic modulus of the soil layer is expressed by a lognormal variable, and the probability distribution is as follows:
wherein EuNon-drainage young's modulus as a soil layer parameter; z is a standard normal random variable; mu and sigma are respectively EuMean and variance of; mu.sNAnd σNAre each ln (E)u) Mean and variance of.
The probability distribution of the measurement uncertainty of the regression model between the elastic modulus and the SPT is as follows:
wherein, mumaxAnd muminRespectively represent EuMaximum and minimum values of the mean of (a); sigmamaxAnd σminRespectively represent EuThe maximum and minimum of the variance of (c).
And (3) combining the formula (1) and the formula (2) to obtain a prior probability distribution model of the site soil.
Preferably, the randomly distributed samples of acquisition pile parameters comprise:
respectively taking the elastic modulus of the pile foundation, the distribution of damage along the length of the pile and the degree of damage as random variables, obtaining pile parameter random samples considering multiple random variables by utilizing the random variables according to the damage of a single pile and the damage of a plurality of piles through Markov chain-Montacharlo simulation analysis, and grading the damage of the pile foundation according to the pile parameter random samples.
Preferably, the step of solving the dynamic response of the foundation cap and the ground vibration response of the field around the pile foundation by using the field geological data and train-induced vibration data, the soil parameter random distribution sample and the pile parameter random distribution sample as the input of the train-bridge-pile-soil dynamic interaction theoretical analysis model comprises the following steps:
the vehicle-bridge-pile-soil dynamic interaction theoretical analysis model comprises an upper vehicle-bridge-pier sub model and a lower cushion cap-pile foundation-field soil sub model, wherein for the upper sub model, wheel sets, a bogie and a vehicle body in the vehicle model are all regarded as rigid bodies, the wheel sets only consider sinking and floating movement, the vehicle body and the bogie consider sinking and floating and nodding movement, and meanwhile, a wheel-rail close contact model is considered;
taking field geological data before pile foundation construction, vibration data caused by a train, soil parameter random distribution samples and pile parameter random distribution samples as input of a train-bridge-pile-soil dynamic interaction theoretical analysis model, and constructing a virtual excitation input form of an axle time-varying system according to track irregularity, solving an axle time-varying system motion equation by a separation iteration method to obtain an excitation force transmitted to a pile foundation bearing platform through a pier, a dynamic response of the pile foundation bearing platform and a ground vibration response of a field around the pile foundation;
for the lower pile foundation and the field soil model, the randomness of the elastic modulus of the soil and the randomness of the elastic modulus of the piles are considered by utilizing a thin layer method and a volume method with ideal matching layers, the dynamic response of the pile foundation bearing platform obtained by the upper structure and the ground vibration response of the field around the pile foundation are input into the lower pile foundation and the field soil model, and the probability distribution of the pile-soil dynamic impedance function and the dynamic response probability distribution of the pile foundation bearing platform and the field at any position are calculated.
Preferably, the obtaining of the damage assessment and census analysis result of the pile foundation through the deep learning algorithm based on the dynamic response of the foundation cap and the ground vibration response of the field around the pile foundation includes:
respectively selecting the dynamic response of the pile foundation bearing platform and the ground vibration response of a field around the pile foundation as indexes, using a large amount of field vibration test data and theoretical analysis data caused by a train as sample parameters of a deep learning algorithm, and obtaining a probability distribution curve 1 and a probability distribution curve 2 for evaluating the damage of the pile foundation through a random forest algorithm in the deep learning;
the probability distribution curve 1 is characterized in that dynamic response of a bridge pile foundation bearing platform is used as a measurement index, vibration frequency of the bearing platform is used as an abscissa, vibration frequency spectrum of the bearing platform is used as an ordinate, and a plurality of crossed threshold curves with 95% confidence intervals are given according to damage classification of a pile foundation; the probability distribution curve 2 is characterized in that ground vibration response of a field around a pile is used as a measurement index, the vibration frequency of the field is used as an abscissa, a vibration frequency spectrum of the field is used as an ordinate, and a plurality of crossed threshold curves with 95% confidence intervals are given according to damage classification of a pile foundation.
According to the technical scheme provided by the embodiment of the invention, uncertainty of soil parameters around a pile foundation and parameters of the pile is considered in space, a vehicle-bridge-pile-soil dynamic interaction model has probability significance by utilizing a random distribution sample, and damage is evaluated and generally checked through a pile foundation damage probability distribution curve, so that the nondestructive testing technology is more suitable for engineering practice.
Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is an implementation schematic diagram of a pile foundation damage general survey and evaluation method considering uncertainty of pile soil parameters according to an embodiment of the present invention;
fig. 2 is a processing flow chart of a pile foundation damage census and evaluation method considering uncertainty of pile soil parameters according to an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.
As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. It will be understood that when an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may also be present. Further, "connected" or "coupled" as used herein may include wirelessly connected or coupled. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
For the convenience of understanding the embodiments of the present invention, the following description will be further explained by taking several specific embodiments as examples in conjunction with the drawings, and the embodiments are not to be construed as limiting the embodiments of the present invention.
According to the embodiment of the invention, the excitation load is not additionally applied, the excitation equipment is not needed, and the train load normally operated above the existing bridge is used as the excitation source, so that the implementation and the operation of pile foundation detection are facilitated. The embodiment of the invention considers the randomness of the elastic modulus of the field soil, and simultaneously optimizes the probability by using the result of the standard penetration test by using the Bayes' theorem to obtain the posterior probability which is more in line with the reality. The invention simultaneously considers the double random variables of the randomness of the elastic modulus of the pile body material and the randomness of the damage depth of the pile foundation in the pile foundation pouring and using processes, so that the pile foundation detection is more fit for the actual engineering.
The invention aims to provide a pile foundation damage general investigation and evaluation method considering uncertainty of pile soil parameters aiming at the defect of evaluating pile foundation damage by the existing low-strain dynamic load method so as to effectively complete a quick evaluation general investigation task of a large-range pile foundation.
The method simultaneously adopts three methods of field test testing, theoretical analysis and computer deep learning intelligent analysis, considers the influence of uncertainty of the pile driving soil parameters through a Bayesian principle and Markov chain-Monte Carlo simulation, and carries out deep learning on data by means of a large number of samples and computer intelligent technologies, thereby generally surveying and evaluating the damage of the pile foundation in the probability sense, and avoiding the problem that the pile foundation structure is damaged when the residual bearing capacity of the pile foundation is evaluated by using a static load test method, a high-strain dynamic pile test method and the like in the specification.
The implementation principle of the pile foundation damage general survey and evaluation method considering uncertainty of pile soil parameters provided by the embodiment of the invention is shown in fig. 1, the specific processing flow is shown in fig. 2, and the method comprises the following processing steps:
and S10, collecting field geological data before pile foundation construction and vibration data caused by the train.
The method comprises the steps of obtaining site geological data before pile foundation construction, wherein the site geological data comprise a geological survey parameter of site soil, a standard penetration test parameter and a structural parameter of a bridge, serving as input parameters for post-stage pile foundation damage theoretical analysis, and providing original sample data for prior distribution of elastic modulus in the site soil parameters. When the survey data of the site soil around the pile foundation to be evaluated is not available, the parameter data of the similar or the same site geological condition can be used for replacing the survey data.
Selecting a pile foundation and a bearing platform of a bridge to be evaluated, and arranging sensors on four angular point parts of the bearing platform and the near-field ground within 5m of the periphery of the bearing platform. The sensor can be a displacement sensor and/or an acceleration sensor. And acquiring vibration data caused by the train through a sensor under the normal operation condition of the train.
And step S20, collecting a soil parameter random distribution sample.
The uncertainty of the site soil parameters around the pile foundation to be evaluated is taken into account. As the soil is naturally occurring, the uncertainty in site soil parameters is divided into intrinsic and translational variability: firstly, the inherent variability of the soil body properties in space such as depth cannot be reduced along with the increase of cognition; secondly, the regression model between the soil body elastic modulus and the standard penetration test has conversion uncertainty.
Selecting the elastic modulus of a field soil layer as a random variable, considering the random distribution of the random variable along with the depth and the uncertainty of observed data of a data test along with the land, establishing a prior probability distribution model of the field soil, optimizing the prior probability distribution model through a field soil standard penetration test or MASW (Multi Analysis of Surface Waves) field test data, and obtaining a plurality of random distribution samples of the soil parameters which accord with the post-test distribution through the optimized prior probability distribution model by utilizing Bayesian theorem and Markov chain-Monte Carlo simulation.
The prior probability distribution model of the field soil consists of the inherent variability and the measurement uncertainty of the field soil.
Because the soil is naturally formed, the soil property has inherent variability in space, the variability cannot be reduced along with the increase of cognition, and is constantly greater than 0, in order to simulate the randomness, the elastic modulus of the soil layer is expressed by a lognormal variable, and the probability distribution is as follows:
wherein EuNon-drainage young's modulus as a soil layer parameter; z is a standard normal random variable; mu and sigma are respectively EuMean and variance of; mu.sNAnd σNAre each ln (E)u) Mean and variance of.
The regression model between elastic modulus and the SPT also has a conversion uncertainty, and this variability can be described by a uniform distribution with the following probability distribution:
wherein, mumaxAnd muminRespectively represent EuMaximum and minimum values of the mean of (a); sigmamaxAnd σminRespectively represent EuThe maximum and minimum of the variance of (c).
And (3) combining the formula (1) and the formula (2) to obtain the prior probability distribution model of the site soil.
And step S30, collecting pile parameter random distribution samples.
The pile foundation to be evaluated is generally determined by the mark number of the concrete material, but the elastic modulus of the pile foundation has certain uncertainty within a certain range under the influence of the transportation, stirring, construction, underground corrosion and the like of the concrete, so the elastic modulus of the pile foundation is taken as a random variable. In addition, in order to select the situations of a plurality of damages, the distribution of the damages along the pile length and the degree of the damages are respectively used as another two random variables. The method comprises the steps of considering the damage of a single pile, the damage of a plurality of piles and other different conditions, and obtaining pile parameter random samples considering multiple random variables by means of Markov chain-Monte Carlo simulation analysis. And grading the pile foundation damage according to the generated random sample data.
And step S40, establishing a vehicle-bridge-pile-soil dynamic interaction probability analysis model.
A theoretical analysis model of the interaction of the vehicle-bridge-pile-soil power is established based on the combination of a thin layer method with an ideal matching layer and a volume method. The vehicle-bridge-pile-soil dynamic interaction theoretical analysis model comprises an upper vehicle-bridge-pier sub model and a lower bearing platform-pile foundation-field soil sub model. For the upper sub-model, the wheel set, the bogie and the vehicle body in the vehicle model are all regarded as rigid bodies, the wheel set only considers the ups and downs movement, the vehicle body and the bogie consider the ups and downs and the nodding movement, and meanwhile, the wheel-rail close contact model is considered. And simultaneously, according to the virtual excitation input form of the axle time-varying system constructed according to the track irregularity, solving the motion equation of the axle time-varying system by a separation iteration method to obtain the excitation force transmitted to the pile foundation bearing platform through the bridge pier, the dynamic response of the pile foundation bearing platform and the ground vibration response of the field around the pile foundation.
For the lower sub-model, the randomness of the elastic modulus of the soil and the randomness of the elastic modulus of the pile are considered by utilizing a thin layer method and a volume method with ideal matching layers, the exciting force on the pile foundation bearing platform, the dynamic response of the pile foundation bearing platform and the ground vibration response of the field around the pile foundation, which are obtained by the upper structure model, are input into the pile foundation and the field soil model at the lower part, the probability distribution of the pile-soil dynamic impedance function and the dynamic response probability distribution at any position of the pile foundation bearing platform and the field around the pile foundation are calculated, and then the confidence interval of the dynamic response of the pile foundation bearing platform and the confidence interval of the near-field ground vibration response around the pile with 95% confidence coefficient are obtained.
And step S50, pile foundation damage assessment and census analysis based on the computer deep learning algorithm.
The dynamic response of the pile foundation bearing platform and the ground vibration response of the field around the pile foundation are respectively selected as indexes, a large amount of field vibration test data and theoretical analysis data caused by the train are used as sample parameters of a deep learning algorithm, and a probability distribution curve 1 and a probability distribution curve 2 for evaluating the damage of the pile foundation are obtained through a random forest algorithm in the deep learning.
The computer deep learning algorithm of the invention is based on data mining technology and neural network intelligent training, takes sample data containing pile soil random parameters, structures and field vibration response as a training set, establishes a computer deep learning prediction model through mathematical algorithm operations such as sampling, subset training, prediction, matching and the like, and statistically analyzes the internal relation and rule between random variables composed of a plurality of uncertain parameters and a pile foundation damage probability distribution curve in the evaluation method to obtain the probability distribution curve which can be used for pile foundation evaluation. The probability distribution curve 1 is characterized in that dynamic response of a bridge pile foundation bearing platform is used as a measurement index, vibration frequency of the bearing platform is used as an abscissa, vibration frequency spectrum of the bearing platform is used as an ordinate, and a plurality of crossed threshold curves with 95% confidence intervals are given according to damage classification of a pile foundation. The probability distribution curve 2 is characterized in that ground vibration response of a field around a pile is used as a measurement index, the vibration frequency of the field is used as an abscissa, a vibration frequency spectrum of the field is used as an ordinate, and a plurality of crossed threshold curves with 95% confidence intervals are given according to damage classification of a pile foundation. In order to ensure the accuracy of damage prediction evaluation, the probability distribution curve 1 and the probability distribution curve 2 are used for comprehensive comparison and analysis, and whether the pile foundation to be evaluated is damaged or not and the rough condition of the damage are evaluated and generally checked.
In summary, the technical scheme of the embodiment of the invention is a low-strain detection method, which directly utilizes the train load of the existing bridge in operation without using additional exciting force; the evaluation index of the invention is not the stress wave signal at the top of the pile foundation, but the dynamic response of the field soil at the top of the bearing platform and around the pile group foundation. Therefore, the invention utilizes the actual conditions of the site, can consider the uncertainty of the pile soil parameters, greatly simplifies the means and the setting of the pile foundation detection, and can play the effect of nondestructive rapid general survey.
Compared with other nondestructive testing methods for pile foundation damage, the method does not need to additionally apply excitation load, so that special excitation equipment does not need to be equipped or designed, train load normally operated above the existing bridge is used as an excitation source, and implementation and operation of a pile foundation field test are greatly facilitated.
The invention considers the uncertainty of the soil parameters around the pile foundation and the parameters of the pile in space, utilizes the random distribution samples to make the vehicle-bridge-pile-soil dynamic interaction model have probability significance, and evaluates and generally checks the damage through the damage probability distribution curve of the pile foundation, so that the nondestructive testing technology is more suitable for the actual engineering.
Those of ordinary skill in the art will understand that: the figures are merely schematic representations of one embodiment, and the blocks or flow diagrams in the figures are not necessarily required to practice the present invention.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for apparatus or system embodiments, since they are substantially similar to method embodiments, they are described in relative terms, as long as they are described in partial descriptions of method embodiments. The above-described embodiments of the apparatus and system are merely illustrative, and the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (7)
1. A pile foundation damage general investigation and evaluation method considering uncertainty of pile soil parameters is characterized by comprising the following steps:
collecting field geological data before pile foundation construction and vibration data caused by a train;
collecting samples with randomly distributed soil parameters and pile parameters;
a theoretical analysis model of the vehicle-bridge-pile-soil dynamic interaction is built based on the combination of a thin layer method with an ideal matching layer and a volume method;
taking the field geological data and train-induced vibration data, soil parameter random distribution samples and pile parameter random distribution samples as the input of the train-bridge-pile-soil dynamic interaction theoretical analysis model, and solving the dynamic response of a foundation bearing platform and the ground vibration response of the field around the pile foundation;
and obtaining damage assessment and general investigation analysis results of the pile foundation through a deep learning algorithm based on the dynamic response of the foundation cap and the ground vibration response of the field around the pile foundation.
2. The method of claim 1, wherein said collecting of field geological data and train induced vibration data prior to pile foundation construction comprises:
acquiring site geological data before pile foundation construction, wherein the site geological data comprise geological survey parameters of site soil, standard penetration test parameters and structural parameters of a bridge;
selecting a pile foundation and a bearing platform of a bridge to be evaluated, arranging sensors on four angular point positions of the bearing platform and near-field ground around the bearing platform, and acquiring vibration data caused by a train through the sensors under the normal operation condition of the train.
3. The method of claim 1, wherein said collecting randomly distributed samples of soil parameters comprises:
selecting the elastic modulus of a field soil layer as a random variable, considering the random distribution of the random variable along with depth and the uncertainty of observed data of a data test along with the field, establishing a prior probability distribution model of the field soil, optimizing the prior probability distribution model through a field soil standard penetration test or multi-channel surface wave analysis field test data, and obtaining a plurality of soil parameter random distribution samples which accord with the posterior distribution through the optimized prior probability distribution model by utilizing Bayesian theorem and Markov chain-Monte Carlo simulation.
4. The method of claim 3, wherein said prior probability distribution model of the site-soil consists of site-soil inherent variability and measurement uncertainty;
the elastic modulus of the soil layer is expressed by a lognormal variable, and the probability distribution is as follows:
wherein EuNon-drainage young's modulus as a soil layer parameter; z is a standard normal random variable; mu and sigma are respectively EuMean and variance of; mu.sNAnd σNAre each ln (E)u) Mean and variance of.
The probability distribution of the measurement uncertainty of the regression model between the elastic modulus and the SPT is as follows:
wherein, mumaxAnd muminRespectively represent EuMaximum and minimum values of the mean of (a); sigmamaxAnd σminRespectively represent EuThe maximum and minimum of the variance of (c).
And (3) combining the formula (1) and the formula (2) to obtain a prior probability distribution model of the site soil.
5. The method of claim 1, wherein randomly distributing samples of the acquired pile parameters comprises:
respectively taking the elastic modulus of the pile foundation, the distribution of damage along the length of the pile and the degree of damage as random variables, obtaining pile parameter random samples considering multiple random variables by utilizing the random variables according to the damage of a single pile and the damage of a plurality of piles through Markov chain-Montacharlo simulation analysis, and grading the damage of the pile foundation according to the pile parameter random samples.
6. The method of any one of claims 1 to 5, wherein said taking said field geological data and train induced vibration data, soil parameter random distribution samples and pile parameter random distribution samples as inputs to said theoretical analysis model of vehicle-bridge-pile-soil dynamic interaction to solve the dynamic response of the foundation cap and the ground vibration response of the field surrounding the pile foundation comprises:
the vehicle-bridge-pile-soil dynamic interaction theoretical analysis model comprises an upper vehicle-bridge-pier sub model and a lower cushion cap-pile foundation-field soil sub model, wherein for the upper sub model, wheel sets, a bogie and a vehicle body in the vehicle model are all regarded as rigid bodies, the wheel sets only consider sinking and floating movement, the vehicle body and the bogie consider sinking and floating and nodding movement, and meanwhile, a wheel-rail close contact model is considered;
taking field geological data before pile foundation construction, vibration data caused by a train, soil parameter random distribution samples and pile parameter random distribution samples as input of a train-bridge-pile-soil dynamic interaction theoretical analysis model, and constructing a virtual excitation input form of an axle time-varying system according to track irregularity, solving an axle time-varying system motion equation by a separation iteration method to obtain an excitation force transmitted to a pile foundation bearing platform through a pier, a dynamic response of the pile foundation bearing platform and a ground vibration response of a field around the pile foundation;
for the lower pile foundation and the field soil model, the randomness of the elastic modulus of the soil and the randomness of the elastic modulus of the piles are considered by utilizing a thin layer method and a volume method with ideal matching layers, the dynamic response of the pile foundation bearing platform obtained by the upper structure and the ground vibration response of the field around the pile foundation are input into the lower pile foundation and the field soil model, and the probability distribution of the pile-soil dynamic impedance function and the dynamic response probability distribution of the pile foundation bearing platform and the field at any position are calculated.
7. The method of claim 6, wherein the obtaining of the damage assessment and census analysis of the pile foundation by a deep learning algorithm based on the dynamic response of the foundation cap and the ground vibration response of the field around the pile foundation comprises:
respectively selecting the dynamic response of the pile foundation bearing platform and the ground vibration response of a field around the pile foundation as indexes, using a large amount of field vibration test data and theoretical analysis data caused by a train as sample parameters of a deep learning algorithm, and obtaining a probability distribution curve 1 and a probability distribution curve 2 for evaluating the damage of the pile foundation through a random forest algorithm in the deep learning;
the probability distribution curve 1 is characterized in that dynamic response of a bridge pile foundation bearing platform is used as a measurement index, vibration frequency of the bearing platform is used as an abscissa, vibration frequency spectrum of the bearing platform is used as an ordinate, and a plurality of crossed threshold curves with 95% confidence intervals are given according to damage classification of a pile foundation; the probability distribution curve 2 is characterized in that ground vibration response of a field around a pile is used as a measurement index, the vibration frequency of the field is used as an abscissa, a vibration frequency spectrum of the field is used as an ordinate, and a plurality of crossed threshold curves with 95% confidence intervals are given according to damage classification of a pile foundation.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117077250A (en) * | 2023-07-17 | 2023-11-17 | 浙江大学 | Static and dynamic force similar model pile design method for centrifugal model experiment |
| CN117454725A (en) * | 2023-12-26 | 2024-01-26 | 浙江远算科技有限公司 | Offshore wind power foundation seismic load simulation method and equipment based on superunit condensation |
| CN117688480A (en) * | 2024-02-04 | 2024-03-12 | 四川华腾公路试验检测有限责任公司 | Bridge damage identification method based on damage frequency panorama and random forest |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117077250A (en) * | 2023-07-17 | 2023-11-17 | 浙江大学 | Static and dynamic force similar model pile design method for centrifugal model experiment |
| CN117454725A (en) * | 2023-12-26 | 2024-01-26 | 浙江远算科技有限公司 | Offshore wind power foundation seismic load simulation method and equipment based on superunit condensation |
| CN117454725B (en) * | 2023-12-26 | 2024-03-29 | 浙江远算科技有限公司 | Offshore wind power foundation seismic load simulation method and equipment based on superunit condensation |
| CN117688480A (en) * | 2024-02-04 | 2024-03-12 | 四川华腾公路试验检测有限责任公司 | Bridge damage identification method based on damage frequency panorama and random forest |
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