Tubular pile vertical bearing capacity probability analysis method considering non-uniformity characteristic of soil body

Abstract

The invention relates to a tubular pile vertical bearing capacity probability analysis method considering the non-uniformity characteristic of a soil body, belongs to the technical field of tubular pile foundation engineering, describes site soil body mechanical parameters by introducing a random field theory, and fully considers the randomness and spatial distribution characteristics of the soil body mechanical parameters. The method comprises the following steps: acquiring a data set of tubular pile size parameters and site soil mechanical parameters, calculating related statistical parameters and a spatial weight matrix, constructing multiple groups of site soil mechanical parameter random fields, calculating the vertical bearing capacity of each group of corresponding tubular piles one by one, and obtaining statistical distribution characteristics of the tubular piles; in this way, the defects of a traditional method in the aspect of considering randomness of mechanical parameters of a soil body are effectively overcome, a new way is provided for accurate evaluation of the vertical bearing capacity of the tubular pile, engineering design and construction can be more reliably served, and the safety and reliability of engineering are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of pipe pile foundation engineering, and particularly relates to a probabilistic analysis method for the vertical bearing capacity of pipe piles considering the non-uniformity characteristics of soil masses. Background Art

[0002] In the field of geotechnical engineering, as an important form of deep foundation, the vertical bearing capacity of pipe piles directly determines the stability of buildings, bridges and underwater structures. In actual engineering, due to the dual influence of geological deposition and construction activities, the non-uniformity characteristics of soil masses are significant. For example, at the interface between artificial fill and undisturbed soil in the filled area, a mechanical mutation layer is likely to form; the excavation project may damage the continuity of the original soil layer and exacerbate the spatial variability of soil parameters. This non-uniformity directly leads to a strong random distribution of the side friction and end resistance of the pile, posing a severe challenge to the bearing capacity calculation.

[0003] At present, there are significant limitations in the bearing capacity evaluation methods: For the static load test commonly used in the in-situ test method, although this test can directly obtain the foundation bearing capacity data, for underwater buildings or deeply buried underground structures, it is difficult to lay out the test equipment and the cost is extremely high. For example, the pile foundation of a cross-sea bridge needs to carry out tests in a tidal environment, and the water level change significantly affects the effective stress of the soil mass, and the obtained data has a very high dispersion. The empirical formula method highly depends on regional empirical parameters. In artificially modified sites such as filled areas, due to the lack of long-term observation data of the corresponding soil layers, the extrapolation error of the formula can reach more than 30%. In addition, although the numerical simulation method can construct a complex stratum model, there are still idealized assumptions in the simulation of key mechanisms such as the stress release effect caused by excavation and the soil consolidation process in the filled area. Existing methods simplify soil parameters into deterministic values and cannot quantify the probabilistic influence of the parameter spatial variation caused by filling / excavation projects on the foundation bearing capacity.

[0004] In addition, it is worth noting that when it comes to pipe piles with non-uniformity characteristics of soil masses, there is a significant size effect in the pile-soil interaction in non-uniform soils. For large-diameter pipe piles with a diameter exceeding 2m, the soil layers within the influence depth range may simultaneously include loose soil in the filled area, undisturbed clay layers and excavation disturbed areas. This composite stratum causes the "superposition principle" in traditional bearing capacity theories to fail. More seriously, for the foundation of underwater buildings, the coupling effect of hydrodynamic pressure and soil seepage needs to be considered, further amplifying the bearing capacity prediction error. Therefore, it is necessary to propose a probabilistic analysis method for the vertical bearing capacity of pipe piles considering the non-uniformity characteristics of soil masses, establish a probabilistic analysis method integrating the theory of geological random fields and construction disturbance effects, and provide a quantitative evaluation tool for the design of pipe pile foundations under complex working conditions. Summary of the Invention

[0005] To solve the above problems existing in the prior art, the present invention provides a probabilistic analysis method for the vertical bearing capacity of pipe piles considering the non-uniformity characteristics of soil masses, which solves the problems that the existing evaluation methods for the vertical bearing capacity of pipe piles cannot effectively quantify the randomness and spatial variability of soil parameters caused by the non-uniformity of soil masses, resulting in a large deviation between the calculation results and the actual situation, especially the insufficient adaptability to complex working conditions.

[0006] The object of the present invention can be achieved by the following technical solutions:

[0007] A probabilistic analysis method for the vertical bearing capacity of pipe piles considering the non-uniformity characteristics of soil masses includes the following steps:

[0008] S1: Obtain the data set of pipe pile size parameters and site soil mechanical parameters;

[0009] S2. Calculate the statistical parameters and spatial weight matrix of the data set of site soil mechanical parameters;

[0010] S3: Based on the statistical parameters and spatial weight matrix of the data set of site soil mechanical parameters, construct N groups of random fields of site soil mechanical parameters;

[0011] S4: According to the pipe pile size parameters and the constructed N groups of random fields of site soil mechanical parameters, calculate the vertical bearing capacity of the pipe pile corresponding to each group of random fields of site soil mechanical parameters one by one;

[0012] S5: Calculate the statistical distribution characteristics of the vertical bearing capacity of the pipe pile.

[0013] As a further solution of the present invention, the pipe pile size parameters in step S1 include the pile tip area A, the pile body length L, and the pile body perimeter u, and the site soil mechanical parameters include the side wall friction resistance and the tip resistance at each soil layer depth.

[0014] As a further solution of the present invention, the side wall friction resistance f s and the tip resistance q s at each soil layer depth h s are expressed as:

[0015] [h s , f s , q s = [(h s,0 , f s,0 , q s,0 ), (h s,1 , f s,1 , q s,1 ), (h s,2 , f s,2 , q s,2 ), …, (h s,n , f s,n , q s,n)].

[0016] As a further solution of the present invention, in the step S2, the statistical parameters of the site soil mechanical parameters include the standard deviation σ of the side wall friction resistance f , and the standard deviation σ of the tip resistance s .

[0017] As a further solution of the present invention, the spatial weight matrix in the step S2 is obtained according to the following formula: W = [w ij n×n , w ij = exp(-|h s,i - h s,j | / d), where d represents the correlation distance.

[0018] As a further solution of the present invention, the random field of the site soil mechanical parameters in the step S3 is obtained according to the following formula:

[0019] Where λ f is a normal distribution with a mean of 0 and a standard deviation of σ f , and λ q is a normal distribution with a mean of 0 and a standard deviation of σ s .

[0020] As a further solution of the present invention, N in the step S3 should be greater than or equal to 100.

[0021] As a further solution of the present invention, in the step S4, the vertical bearing capacity of the pipe pile is obtained according to the following formula: Where q z is the tip resistance corresponding to the depth position of the soil layer where the pile tip is located.

[0022] As a further solution of the present invention, the statistical distribution characteristics of the vertical bearing capacity of the pipe pile in the step S5 include the mean value of the vertical bearing capacity of the pipe pile, the 5% quantile value, and the 95% quantile value.

[0023] The beneficial effects of the present invention are:

[0024] ​By introducing the random field theory to describe the mechanical parameters of the soil mass on the site, the randomness and spatial distribution characteristics of the soil mechanical parameters are fully considered. A data set of pipe pile size parameters and soil mechanical parameters of the site is obtained, relevant statistical parameters and spatial weight matrices are calculated, multiple random fields of soil mechanical parameters of the site are constructed, the vertical bearing capacity of each corresponding pipe pile is calculated one by one, and its statistical distribution characteristics are obtained. In this way, the deficiencies of the traditional method in considering the randomness of soil mechanical parameters are effectively overcome, providing a new approach for the accurate evaluation of the vertical bearing capacity of pipe piles, which can serve engineering design and construction more reliably, improve the safety and reliability of the project. Compared with the traditional method, the calculation process of this method is simple, the mathematical theory basis is rigorous, and only a small amount of on-site exploration data is required to effectively identify the non-uniformity characteristics of the soil mechanical parameters of the site, calculate and obtain the bearing capacity characteristics of the pipe pile from the perspective of probability statistics, which can provide a better reference basis for engineering design and make up for the deficiencies of the existing methods in considering the complexity of the soil. Brief Description of the Drawings

[0025] For the convenience of those skilled in the art to understand, the present invention will be further described below with reference to the accompanying drawings.

[0026] Figure 1 is the overall flow chart of the present invention;

[0027] Figure 2 is a schematic diagram of the data set of soil mechanical parameters of the site of the present invention;

[0028] Figure 3 is a schematic diagram of the random field of soil mechanical parameters of the site of the present invention;

[0029] Figure 4 is a statistical distribution diagram of the vertical bearing capacity of the pipe pile of the present invention. Detailed Embodiment

[0030] To further elaborate on the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific embodiments, structures, features and their effects of the present invention are described in detail as follows.

[0031] Please refer to Figure 1 - Figure 4 , this embodiment provides a probabilistic analysis method for the vertical bearing capacity of pipe piles considering the non-uniformity characteristics of the soil, including the following steps:

[0032] S1: Obtain a data set of pipe pile size parameters and soil mechanical parameters of the site. The pipe pile size parameters are obtained from the design report. The end area A of the pipe pile is 0.502 m2, the pile body length L is 16 m, and the pile body perimeter is 2.512 m. The data set of soil mechanical parameters of the site is obtained from the on-site exploration report. The side wall friction resistance f at each soil layer depth h s ofs and the tip resistance q s The data can be obtained from Figure 2 represented.

[0033] S2: Calculate the statistical parameters and spatial weight matrix of the soil mechanical parameter dataset of the site. The statistical parameters of the soil mechanical parameters of the site are obtained by statistical analysis of the dataset. The standard deviation σ f of the sidewall friction resistance is 142.72 kPa, and the standard deviation σ s of the tip resistance is 10.64 MPa. Through the formula W = [w ij n×n , w ij = exp(-|h s,i - h s,j | / d), calculate the statistical correlation matrix of the soil mechanical parameters of the site, where the correlation distance is set to 1 m.

[0034] S3: Based on the statistical parameters and spatial weight matrix of the soil mechanical parameter dataset of the site, construct N groups of random fields of soil mechanical parameters of the site. Here, by constructing 100 groups of random fields of soil mechanical parameters of the site, through the obtained standard deviation of the sidewall friction resistance of 142.72 kPa and the standard deviation of the tip resistance of 10.64 MPa, according to the formula:

[0035] Construct 100 groups of random fields of soil mechanical parameters of the site, as shown in Figure 3 shown.

[0036] S4: According to the pipe pile size parameters and the constructed N groups of random fields of soil mechanical parameters of the site, that is, according to the pipe pile size parameters and the constructed 100 groups of random fields of soil mechanical parameters of the site, calculate the vertical bearing capacity of the pipe pile corresponding to each group of random fields of soil mechanical parameters of the site one by one. Based on the data of the constructed random fields of soil mechanical parameters and the tip resistance value q z at the depth of 16 m where the pile tip is located, according to the vertical bearing capacity formula of the pipe pile Calculate the distribution of the vertical bearing capacity of the pipe pile corresponding to 100 groups of random fields of soil mechanical parameters of the site as shown in Figure 4 shown.

[0037] S5: Calculate the statistical distribution characteristics of the vertical bearing capacity of the pipe pile. Based on the calculated distribution of the vertical bearing capacity of the pipe pile, it can be calculated that the mean value of the vertical bearing capacity of the pipe pile at this site is 16009.7 kN, the 5% percentile value of the bearing capacity is 15703.3 kN, and the 95% percentile value is 16209.6 kN.

[0038] ​There are significant limitations in current bearing capacity assessment methods: For the static load test, which is commonly used in on-site test methods, although this test can directly obtain the bearing capacity data of the foundation, for underwater buildings or deeply buried underground structures, it is difficult and costly to deploy test equipment. For example, pile foundations of cross-sea bridges need to conduct tests in a tidal environment, and the water level change significantly affects the effective stress of the soil, resulting in extremely high data dispersion. The empirical formula method highly depends on regional empirical parameters. In artificially modified sites such as filled areas, due to the lack of long-term observation data of the corresponding soil layers, the extrapolation error of the formula can reach more than 30%. In addition, although the numerical simulation method can construct a complex stratum model, there are still idealized assumptions in the simulation of key mechanisms such as the stress release effect caused by excavation and the soil consolidation process in the filled area. Existing methods simplify soil parameters to deterministic values and cannot quantify the probabilistic impact of parameter spatial variation caused by filling / excavation projects on the bearing capacity of the foundation. When it comes to pipe piles targeting the non-uniformity characteristics of soil, there is a significant size effect in the pile-soil interaction in non-uniform soil. For large-diameter pipe piles with a diameter exceeding 2m, the soil layers within the influence depth may simultaneously include loose soil in the filled area, undisturbed clay layers, and the excavation disturbed area. This composite stratum causes the "superposition principle" in traditional bearing capacity theory to fail. More seriously, the foundation of underwater buildings also needs to consider the coupling effect of hydrodynamic pressure and soil seepage, further amplifying the bearing capacity prediction error.

[0039] Based on the above problems, in this embodiment, stratified soil parameters are obtained to accurately capture the mechanical mutation at the boundary between the filled area and the undisturbed soil; the spatial correlation formula between soil layers is quantified using an exponential function W = [w ij n×n , w ij = exp(-|h s,i - h s,j ​| / d), where the relevant distance d controls the attenuation rate of the parameter and reflects the law of geological deposition; generate ≥ 100 groups of random fields that meet the requirements of the central limit theorem, covering the possible distributions of soil parameters through a large number of samples; calculate the 5%-95% quantiles to provide a basis for reliability design in engineering. This method transforms traditional deterministic analysis into a probability space problem, reducing the prediction error of the bearing capacity of complex soils such as the excavation disturbance area and the tidal zone. By introducing the random field theory to describe the mechanical parameters of the site soil, it fully considers the randomness and spatial distribution characteristics of the soil mechanical parameters, obtains the data sets of the pipe pile size parameters and the site soil mechanical parameters, calculates the relevant statistical parameters and the spatial weight matrix, constructs multiple groups of random fields of the site soil mechanical parameters, calculates the vertical bearing capacity of the pipe pile corresponding to each group one by one, and obtains its statistical distribution characteristics. In this way, it effectively overcomes the deficiencies of traditional methods in considering the randomness of soil mechanical parameters, providing a new way for the accurate evaluation of the vertical bearing capacity of pipe piles, and solving the problem that the existing methods for evaluating the vertical bearing capacity of pipe piles cannot effectively quantify the randomness and spatial variability of soil parameters caused by soil non-uniformity, resulting in a large deviation between the calculation results and the actual situation, especially the insufficient adaptability to complex working conditions.

[0040] Since there are often significant differences in the physical and mechanical properties of soils in different soil layers, and these differences contribute differently to the bearing capacity, while traditional methods simplify soil parameters to a single value. If only an average value is used to represent the properties of the entire soil, it cannot accurately reflect the actual impact of different soil layers on the bearing capacity, thus causing a deviation between the predicted result of the bearing capacity at the interface and the actual situation, leading to prediction errors. In addition, the non-linear characteristics of the soil are not fully considered, further increasing the inaccuracy of the prediction. To overcome this problem, in one embodiment, the pipe pile size parameters in step S1 include the pile tip area A, the pile body length L, and the pile body perimeter u, and the site soil mechanical parameters include the side wall friction resistance and the tip resistance at each soil layer depth. The geometric parameters of the pipe pile directly affect the pile-soil contact area and the end resistance, which are the basis for bearing capacity calculation. Defining soil parameters layer by layer, such as the side friction resistance and the tip resistance, can accurately reflect the mechanical mutation at the interface between the filled area and the undisturbed soil.

[0041] To better adapt to the non-linear mechanical behavior of loose soil in the filled area or excavated and disturbed soil, in one embodiment, at each soil layer depth h s the side wall friction resistance f s and the tip resistance q s are expressed as: [h s , f s , q s = [(h s,0 , f s,0 , q s,0 ), (h s,1 , f s,1 , qs,1 ),(h s,2 ,f s,2 ,q s,2 ),…,(h s,n ,f s,n ,q s,n )], by discretizing, each sampling point is independently modeled to solve the problem of poor adaptability of the "homogeneous soil hypothesis" in traditional methods to the interface between artificial fill and undisturbed soil; the tip resistance q s is assigned layer by layer to accurately reflect the sudden change of end resistance when the pile tip penetrates multiple layers of soil. For example, when the pile tip enters the dense sand layer from the loose fill, q s can suddenly increase by 2 - 3 times. The parameters at different depths are clearly expressed by mathematical expressions, which helps to process stratified data in a structured and systematic way, enabling the parameters of each soil layer to be independently processed, avoiding the errors caused by treating the entire soil layer as a homogeneous body in traditional methods. The discretization method is more suitable for computer simulation and automated processing, improving the operability and efficiency of the method.

[0042] Since traditional methods are based on frequentist statistics, which assume that parameters are fixed unknown constants rather than random variables, this treatment leads to the analysis focusing on the estimated values of the parameters while ignoring the random variability and uncertainty around the estimated values. Therefore, traditional methods often only give a point estimate rather than a probability interval, thus being unable to provide quantitative information about the uncertainty of the bearing capacity results and unable to give a confidence interval to evaluate the reliability of the results, ignoring the parameter randomness and being unable to evaluate the confidence interval of the bearing capacity results. In this regard, in one embodiment, in step S2, the statistical parameters of the in - situ soil mechanical parameters include the standard deviation σ f of the side - wall friction resistance, and the standard deviation σ s of the tip resistance. The statistical parameters here include the standard deviation, which provides a basis for subsequent probability analysis, enabling the quantification of the randomness and variability of soil parameters. The standard deviation is a key indicator for measuring the degree of data dispersion. By introducing the standard deviation, the natural fluctuations of soil parameters can be more accurately simulated when constructing the random field, thereby enhancing the reliability of the model. Quantifying the degree of dispersion of parameters by the standard deviation reflects the fluctuations of soil parameters caused by filling / excavation projects, providing input parameters for the subsequent normal - distribution random field and ensuring that the generated random field conforms to the actual statistical laws.

[0043] In addition, when simplifying spatial correlation by numerical simulation method, complex geological structures are often simplified into regular geometric shapes, ignoring continuous interfaces such as faults and weak zones in the actual geological conditions. This will lead to distortion of the stress release effect in the excavation area during simulation. Such simplification makes the stress transfer path inside the surrounding rock not conform to the actual situation, thus unable to accurately reflect the dynamic process of stress redistribution during the actual excavation process, resulting in a deviation between the simulation results and the actual engineering situation. To avoid this problem, in one embodiment, the spatial weight matrix in step S2 is obtained according to the following formula: W = [w ij n×n ,w ij = exp(-|h s,i - h s,j | / d), where d represents the correlation distance. This spatial weight matrix uses an exponential decay function, considering the spatial correlation between soil layers. The introduction of the correlation distance d can reflect the attenuation rate of soil parameters with depth. This design is more in line with the continuity characteristics of actual geological deposition, making the generated random field closer to the real situation and improving the physical rationality of the model. Among them, the correlation distance d controls the spatial correlation of soil parameters, and the weight matrix associates statistical parameters with spatial positions, simulating the spatial distribution pattern of soil inhomogeneity.

[0044] Since the deterministic method often cannot comprehensively cover the possible value ranges of parameters when dealing with complex systems such as seepage-soil coupling in underwater structures, mainly because there are many uncertain factors and complex non-linear relationships inside these systems. The deterministic model is usually based on a set of fixed parameters and assumptions, ignoring the parameter fluctuations and variabilities in the actual environment. In seepage-soil coupling, factors such as the properties of soil, the flow velocity and pressure of water may change with time and space, and the parameter fluctuations caused by these changes cannot be accurately characterized by a fixed set of parameters. Therefore, the application of the deterministic method in this field has certain limitations. In this regard, in one embodiment, the random field of site soil mechanical parameters in step S3 is obtained according to the following formula:

[0045] where λ f is a normal distribution with a mean of 0 and a standard deviation of σ f , and λ q is a normal distribution with a mean of 0 and a standard deviation of σ s ​For the normal distribution, N in step S3 should be greater than or equal to 100. Considering the stable mathematical properties of the normal distribution, it is convenient to generate a large number of random samples N≥100, which meets the requirements of Monte Carlo simulation. By controlling the parameter distribution through the mean and standard deviation, it matches the characteristics of loose soil or undisturbed clay in the fill area. By specifying N≥100, this ensures the statistical stability of the Monte Carlo simulation. A sufficient number of samples can cover the possible distribution range of parameters, reduce sampling errors, make the final statistical results more representative and reliable, and meet the accuracy requirements in engineering.

[0046] In the alternating strata of the fill area and undisturbed soil, there are errors in the linear superposition assumption of the pile tip resistance and side friction resistance by the empirical formula method. The main reason is that there are significant differences in the physical and mechanical properties of the fill soil and undisturbed soil. The fill soil is usually loose, has a high water content, and strong compressibility, while the undisturbed soil may be harder, have a lower water content, and weak compressibility. These differences will cause the side friction resistance and pile tip resistance of the pile in different soil layers to behave differently, making the linear superposition assumption no longer applicable. In addition, the degree of consolidation and compaction of the fill soil is also inconsistent, which further affects the exertion of the pile tip resistance and side friction resistance. Therefore, in these complex strata, more refined mechanical models and numerical analysis methods need to be used to accurately evaluate the bearing capacity of the pile. Therefore, in one embodiment, in step S4, the vertical bearing capacity of the pipe pile is obtained according to the following formula: where q z is the tip resistance corresponding to the depth position of the soil layer where the pile tip is located. The above bearing capacity calculation formula combines the side wall friction resistance and end resistance, which is the core of traditional bearing capacity calculation. However, in this method, each parameter comes from data generated by a random field, thus upgrading the deterministic formula to probabilistic analysis. This design enables each calculation to reflect the random variation of soil parameters and finally obtain the distribution of bearing capacity values, rather than a single determined value, which is more in line with the uncertainty in engineering practice. The formula clarifies the contribution weights of the pile tip resistance and side friction resistance, avoiding the failure of the traditional superposition principle in composite strata. The value corresponding to the pile tip depth can reflect the change in the position of the bearing layer caused by the excavation project.

[0047] In the probabilistic analysis of the vertical bearing capacity of pipe piles considering the non-uniformity characteristics of soil by the in-situ test method, only discrete data points can usually be obtained. This is because the in-situ conditions are complex and variable, and the non-linearity of the soil layer causes different geological conditions to affect each test point. These discrete data points are affected by many random factors, such as the variation of soil layer thickness, the non-uniformity of soil quality, and the influence of groundwater. Therefore, it is impossible to form a sufficiently dense data set for statistical analysis. A large amount of representative data is required for a statistically meaningful safety assessment to ensure the accuracy of the assessment results. However, the limited discrete data points often fail to meet this requirement, resulting in an inability to provide a reliable safety assessment with statistical significance. Based on this, in one embodiment, the statistical distribution characteristics of the vertical bearing capacity of the pipe pile in step S5 include the mean value of the vertical bearing capacity of the pipe pile, the 5% quantile value, and the 95% quantile value. The statistical distribution characteristics include the mean value and the quantile values, which provide key probabilistic indicators for engineering design. For example, the 5% and 95% quantiles represent the lower and upper limits of the bearing capacity respectively. In actual operation, appropriate values can be selected according to the required safety level, so as to achieve a balance between safety and economy and improve the scientificity and flexibility of the design.

[0048] The working principle and process of the present invention:

[0049] Quantify the influence of soil non-uniformity on the bearing capacity of pipe piles through the random field theory. Its working process is divided into five major stages: data collection, statistical modeling, random field generation, bearing capacity calculation, and probabilistic analysis. First, obtain the geometric parameters of the pipe pile (pile tip area A, pile length L, perimeter u) and the mechanical parameters of the site soil (side wall friction resistance and tip resistance at each soil layer depth). These parameters are extracted through on-site exploration and design reports. For example, the layered data at the interface between the filled area and the original soil can accurately capture the mechanical mutation characteristics. Based on the soil parameter data set, calculate the statistical parameters (such as the standard deviation of side friction resistance σ f , the standard deviation of tip resistance σ s ) and the spatial weight matrix. Among them, the standard deviation quantifies the randomness of the parameters, reflecting the parameter fluctuations of loose soil in the filled area or disturbed soil in the excavated area; the spatial weight matrix describes the spatial correlation between soil layers through an exponential function, and the parameter correlation decays exponentially with depth, simulating the natural law of geological deposition. Use the normal distribution to generate N groups (N≥100) of random fields of soil mechanical parameters. The side friction resistance and tip resistance respectively follow the normal distribution with a mean of 0 and standard deviations of σ f and σ s . Through Monte Carlo simulation, cover the possible distribution range of soil parameters to ensure that the random field not only conforms to the statistical law but also reflects the parameter variation caused by filling / excavation in actual engineering.

[0050] For each group of random fields, combined with the pipe pile size parameters, the vertical bearing capacity is calculated group by group using the bearing capacity formula. Among them, the side friction resistance and the tip resistance are taken from the random field data, and the pile tip resistance is dynamically adjusted according to the soil layer where the pile tip is located. For example, when the pile tip penetrates the loose fill and enters the dense sand layer, the pile tip resistance can suddenly increase by 2-3 times, avoiding the failure of the traditional superposition principle in the composite stratum. By statistically analyzing 100 groups of calculation results, the mean value of the bearing capacity, the 5% quantile (lower limit), and the 95% quantile (upper limit) are output. For example, in a certain project, the mean value of the bearing capacity is 16009.7 kN, the 5% quantile is 15703.3 kN (representing the safety threshold at a 95% confidence level), and the 95% quantile is 16209.6 kN (reflecting the optimal working condition). This process transforms the traditional deterministic result into a probability distribution, providing a basis for the reliability of engineering design. For example, in a tidal environment, designing according to the 5% quantile value can cover the bearing capacity fluctuations caused by water level changes, significantly reducing the safety risks of underwater structures.

[0051] The above is only a preferred embodiment of the present invention, and it does not impose any form of limitation on the present invention. Although the present invention has been disclosed above with the preferred embodiment, it is not intended to limit the present invention. Any person skilled in the art can make some changes or modifications to the equivalent embodiments with equivalent changes within the scope of the technical solution of the present invention. However, as long as it does not depart from the content of the technical solution of the present invention, any brief modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention still fall within the scope of the technical solution of the present invention.

Claims

1. A probabilistic analysis method for the vertical bearing capacity of pipe piles considering the characteristics of soil inhomogeneity, characterized in that, It includes the following steps: S1: Obtain the pipe pile size parameters and the dataset of the mechanical parameters of the site soil; S2. Calculate the statistical parameters and the spatial weight matrix of the dataset of the mechanical parameters of the site soil; S3: Based on the statistical parameters and the spatial weight matrix of the dataset of the mechanical parameters of the site soil, construct N groups of random fields of the mechanical parameters of the site soil; S4: According to the pipe pile size parameters and the N groups of constructed random fields of the mechanical parameters of the site soil, calculate the vertical bearing capacity of the pipe pile corresponding to each group of random fields of the mechanical parameters of the site soil one by one; S5: Calculate the statistical distribution characteristics of the vertical bearing capacity of the pipe pile.

2. The probability analysis method for the vertical bearing capacity of pipe piles considering the inhomogeneity characteristics of soil mass according to claim 1, wherein The pipe pile size parameters in step S1 include the pile end area A, the pile body length L, and the pile body perimeter u, and the mechanical parameters of the site soil include the side wall friction resistance and the tip resistance at each soil layer depth.

3. The probability analysis method for the vertical bearing capacity of pipe piles considering the non-uniformity characteristics of soil mass according to claim 2, characterized in that The sidewall friction resistance f s and the tip resistance q s at each soil layer depth h s are expressed as follows: [h s ,f s ,q s = [(h s,0 ,f s,0 ,q s,0 ), (h s,1 ,f s,1 ,q s,1 ), (h s,2 ,f s,2 ,q s,2 ), …, (h s,n ,f s,n ,q s,n )]。 4. The probability analysis method for the vertical bearing capacity of pipe piles considering the non-uniformity characteristics of soil mass according to claim 1, wherein In the step S2, the statistical parameters of the soil mechanical parameters of the site include the standard deviation σ of the sidewall friction resistance f , and the standard deviation σ of the tip resistance s .

5. The probability analysis method for the vertical bearing capacity of pipe piles considering the non-uniformity characteristics of soil mass according to claim 3, characterized in that The spatial weight matrix in the step S2 is obtained according to the following formula: W = [w ij n×n , w ij = exp(-|h s,i - h s,j | / d), where d represents the correlation distance.​ 6. The probabilistic analysis method for the vertical bearing capacity of pipe piles considering the non-uniformity characteristics of soil mass according to claim 5, characterized in that The random field of the mechanical parameters of the site soil in step S3 is obtained according to the following formula: where λ f is a normal distribution with a mean of 0 and a standard deviation of σ f , and λ q is a normal distribution with a mean of 0 and a standard deviation of σ s .

7. The probability analysis method for the vertical bearing capacity of pipe piles considering the non-uniformity characteristics of soil mass according to claim 1, characterized in that N in step S3 should be greater than or equal to 100.

8. The probability analysis method for the vertical bearing capacity of pipe piles considering the characteristics of soil inhomogeneity according to claim 6, characterized in that, In step S4, the vertical bearing capacity of the pipe pile is obtained according to the following formula: where q z is the tip resistance corresponding to the depth position of the soil layer where the pile tip is located.

9. The probability analysis method for the vertical bearing capacity of pipe piles considering the characteristics of soil inhomogeneity according to claim 1, characterized in that, The statistical distribution characteristics of the vertical bearing capacity of the pipe pile in step S5 include the mean value of the vertical bearing capacity of the pipe pile, the 5% percentile value, and the 95% percentile value.

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