Multi-factor coupled CFG pile full-life failure probability prediction and optimization method

Abstract

The invention relates to the technical field of geotechnical engineering structures, in particular to a multi-factor coupling CFG pile full-life failure probability prediction and optimization method. Comprising the following steps; the method comprises the following steps: S1, integrating resistance degradation, pile-soil interaction, geological evolution and load time-varying characteristics to construct a limit state model; s2, on the basis of the limit state model constructed in the step S1, Monte Carlo simulation circulation is used, the failure probability is calculated, and a CFG pile system failure probability change graph is obtained, S3, parameters in the limit state model are optimized, after each parameter is optimized, the steps S1 to S2 are carried out again, and the optimized CFG pile system failure probability change graph is obtained; according to the method, the accuracy and the adaptability of full-life prediction and optimization of the CFG pile are improved.

Description

Technical Field

[0001] This invention relates to the field of geotechnical engineering structural technology, and in particular to a method for predicting and optimizing the failure probability of CFG piles throughout their lifespan based on multi-factor coupling. Background Technology

[0002] CFG piles are cement-fly ash-gravel piles, which are high-bond-strength piles formed by mixing cement, fly ash, gravel, stone chips, or sand with water. CFG piles are a type of low-strength concrete pile that can fully utilize the bearing capacity of the soil between the piles and can transfer loads to deep foundations, exhibiting good technical performance and economic benefits. Therefore, the full life-cycle prediction and optimization of CFG piles are crucial for the reliability of foundation treatment and geotechnical engineering structures.

[0003] However, existing methods for predicting and optimizing the entire lifespan of CFG piles only consider a single factor, leading to a large deviation between the predicted results and the actual service performance. They do not consider the dynamic interaction of various factors over time, making it difficult to accurately reflect the performance evolution of CFG piles throughout their entire lifespan. Most existing optimization methods focus on adjusting a single parameter and do not quantitatively analyze the optimization effect of optimization measures on the failure probability. At the same time, they have poor adaptability to complex geological conditions and environmental disturbances, and cannot meet the needs of differentiated engineering projects.

[0004] Therefore, there is an urgent need to provide a multi-factor coupled method for predicting and optimizing the failure probability of CFG piles throughout their lifespan, which can improve the accuracy and adaptability of CFG pile lifespan prediction and optimization compared to existing technologies. Summary of the Invention

[0005] This invention addresses the technical problems existing in the prior art and provides a method for predicting and optimizing the failure probability of CFG piles throughout their lifespan based on multi-factor coupling.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows: A multi-factor coupled method for predicting and optimizing the failure probability of CFG piles throughout their lifespan includes the following steps; S1. Integrate resistance degradation, pile-soil interaction, geological evolution, and time-varying load characteristics to construct a limit state model; S2. Based on the limit state model constructed in step S1, the failure probability is calculated using a Monte Carlo simulation cycle to obtain the failure probability variation diagram of the CFG pile system. Specifically, it is calculated using the following formula: ; In the above formula, Indicates the probability of failure. Let represent the limit state function for the i-th iteration. Indicates the number of simulated samples; Represents an exponential function; S3. Optimize the parameters in the limit state model. After optimizing each parameter, repeat steps S1-S2 to obtain the optimized failure probability change diagram of the CFG pile system.

[0007] Furthermore, the limit state model in step S1 is specifically expressed by the following equation: ; In the above formula, Represents the limit state function; This represents the resistance degradation model, specifically the CFG pile resistance attenuation function; This represents the pile-soil interaction capacity function. This represents the geological evolution influence function. This represents the total applied load function.

[0008] Furthermore, Specifically, it is expressed by the following formula: ; In the above formula, This indicates the initial bearing capacity of the CFG pile. The value represents the rate of degradation of resistance, and t represents time; Represents the material degradation function. This represents the reduction factor for construction defects.

[0009] Furthermore, Specifically, it is calculated using the following formula: ; In the above formula, Indicates material degradation parameters, Indicates shape parameters.

[0010] Furthermore, Specifically, it is expressed by the following formula: ; In the above formula, This represents the initial pile-soil synergy coefficient. Indicates the degradation coefficient. Indicates the initial pile-soil coupling strength. This indicates the rate of degradation of the pile-soil contact. Indicates the amplitude of the disturbance. Indicates the frequency of the disturbance. Indicates the phase angle. It represents the standard deviation of random disturbance.

[0011] Furthermore, Specifically, it is expressed by the following formula: ; In the above formula, Indicates the geological sensitivity coefficient. Indicates the rate of geological change.

[0012] Furthermore, Specifically, it is expressed by the following formula: ; In the above formula, Indicates the static load term. This indicates the dynamic load term.

[0013] Furthermore, , The following formula can be used to calculate: ; ; In the above formula, Indicates the initial static load. Represents the random fluctuation term. This represents the average value of the dynamic load. This indicates the event triggered by the Poisson process.

[0014] Furthermore, in step S3, the three parameters in the limit state model—initial bearing capacity, resistance degradation rate, and construction defect reduction factor—are optimized.

[0015] Furthermore, in step S3, the specific method for optimizing the initial bearing capacity, resistance degradation rate, and construction defect reduction factor is as follows: each optimization of the initial bearing capacity increases it by 500KN, each optimization of the resistance degradation rate decreases it by 0.005, and each optimization of the construction defect reduction factor decreases it by 0.025.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention significantly improves the reliability assessment and optimization level of CFG piles throughout their entire life cycle through dynamic coupling modeling, probabilistic quantitative analysis, and intelligent optimization decision-making. By considering model dynamism and parameter randomness, it solves the problem of insufficient long-term reliability caused by traditional methods that only consider static bearing capacity and fail to account for time-varying load growth (such as changes in building use), material strength degradation (such as concrete carbonation), and environmental coupling effects. Through dynamic limit state functions, it addresses the problem that existing failure probability analysis methods rely on deterministic models, ignore parameter randomness (such as soil resistance fluctuations), and are difficult to quantify dynamic risks. By prioritizing importance and quantifying the effects of optimization measures, it solves the problem that traditional methods lack quantitative ranking of key influencing factors and rely on experience to select optimization measures, resulting in resource waste. Attached Figure Description

[0017] Figure 1 This is a flowchart of the present invention.

[0018] Figure 2 This is a graph showing the change in failure probability of the CFG pile system before optimization according to the present invention.

[0019] Figure 3 This is a comparison chart of the failure probability changes of the CFG pile system under the three optimized schemes of this invention. Detailed Implementation

[0020] The technical solution of the present invention will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are not all embodiments of the present invention. All other embodiments obtained by those skilled in the art without creative effort are within the protection scope of the present invention. It should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," etc., indicating the orientation or positional relationship are based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description. They do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention.

[0021] like Figure 1 As shown, this invention provides a method for predicting and optimizing the failure probability of CFG piles throughout their lifespan based on multiple coupled factors, including the following steps: S1. Construct a limit state model, integrating multi-physics coupling mechanisms such as resistance degradation, pile-soil interaction, geological evolution, and time-varying load characteristics. Define the failure criterion as the load exceeding the pile's bearing capacity, specifically calculated using the following formula: ; In the above formula, Represents the limit state function; This represents the resistance degradation model, specifically the CFG pile resistance attenuation function; This represents the pile-soil interaction capacity function. This represents the geological evolution influence function. This represents the total applied load function.

[0022] (1) This describes the attenuation of the bearing capacity of CFG piles during long-term use. Its construction process combines materials science, soil mechanics, and probability and statistics theories, specifically expressed as follows: ; In the above formula, This indicates the initial bearing capacity of the CFG pile, representing the initial bearing capacity of the pile foundation upon completion. It is usually determined through static load tests or design specifications. This indicates the rate of resistance degradation, reflecting the decrease in resistance caused by factors such as material aging and crack propagation; t represents time. It represents the material degradation function, indicating the gradual degradation of material properties over time, such as concrete carbonization and steel corrosion. It follows a Weibull distribution and is suitable for describing the failure time distribution of brittle materials. This represents the construction defect reduction factor, which reflects the reduction of initial resistance due to construction quality defects, and is preferably 0.05.

[0023] Specifically, it is calculated using the following formula: ; In the above formula, This represents a material degradation parameter used to control the cracking rate; the larger the value, the faster the degradation. This represents shape parameters and is applicable to random, independent damage.

[0024] (2) The time-varying bearing capacity contribution of CFG piles in interaction with the surrounding soil is used to quantify the effect of the interaction between the piles and the surrounding soil. Its construction requires comprehensive consideration of multiple factors, including interfacial friction degradation, soil stiffness reduction, environmental disturbance, and construction defects. This is specifically expressed by the following formula: ; In the above formula, This represents the pile-soil synergy coefficient function. Represents the soil support stiffness function. Represents the environmental disturbance function. This represents the pile-soil contact defect coefficient, which is preferably 0.1.

[0025] Specifically, This can be expressed by the following formula: ; In the above formula, This represents the initial pile-soil synergy coefficient, indicating the initial interaction efficiency between the pile and the soil. This indicates that the pile and soil are perfectly coordinated under ideal conditions. The degradation coefficient is determined by historical data from long-term pull-out tests or numerical simulations, and is preferably 0.005 / year.

[0026] This can be expressed by the following formula: ; In the above formula, This represents the initial pile-soil coupling strength, and the initial side resistance and end resistance provided by the soil around the pile, preferably 500 kN; This indicates the rate of pile-soil contact degradation, the decrease in bearing capacity caused by soil creep or liquefaction, preferably 0.01 / year.

[0027] This can be expressed by the following formula: ; In the above formula, This represents the disturbance amplitude, indicating the intensity of environmental fluctuations (such as changes in groundwater level), and is preferably 0.01. This indicates the frequency of the disturbance, or the frequency of periodic changes (such as seasonal effects). ; This represents the phase angle, and this represents the initial phase offset. The standard deviation of the random disturbance represents the intensity of random fluctuations in the environment, and is preferably 0.02.

[0028] (3) This method is used to quantify the negative impact of long-term geological activities (such as soil compression, karst development, and groundwater level changes) on the bearing capacity of CFG piles. Its construction requires combining geotechnical mechanics theory, long-term monitoring data, and probabilistic statistical methods, and is specifically expressed by the following formula: ; In the above formula, The geological sensitivity coefficient, measured in kN, reflects the intensity of the influence of geological conditions on pile foundation performance. It quantifies the relationship between soil settlement and pile foundation bearing capacity loss through three methods: centrifuge test, numerical simulation, and analysis of historical engineering data. This represents the geological change rate ( / year), reflecting the rate of long-term geological subsidence or karst development. Data is obtained by long-term observation of soil deformation rate or by consulting karst development rate in engineering geological reports. For soft soil areas, the preferred value is 0.01-0.05 / year, and for karst areas, the preferred value is 0.02-0.1 / year.

[0029] (4) This formula describes the time-varying characteristics of static and dynamic loads borne by CFG piles during their service life. Its construction must comprehensively consider load type, time effect, randomness, and actual engineering requirements, and is specifically expressed by the following formula: ; ; ; In the above formula, Indicates the static load term. Indicates the dynamic load term; This indicates the initial static load, used to reflect the building's dead load; This represents the static load growth rate, which indicates the rate at which the service load increases over time, preferably 0.001-0.005 / year; This represents a random fluctuation term, simulating the uncertainty of static load changes; This represents the mean value of dynamic loads, indicating a low-probability, high-load effect (such as heavy vehicle overloading, earthquakes, etc.). Vehicle loads can be determined based on axle load statistics, or seismic loads can be determined using the response spectrum method. This represents a Poisson process triggering event, indicating that the dynamic load (such as a vehicle collision or earthquake) is a discrete random event with an occurrence rate of [missing information]. Based on traffic volume statistics and regional seismic hazard analysis data, generally .

[0030] S2. Based on the limit state model constructed in step S1, the failure probability is calculated using a Monte Carlo simulation cycle to obtain the failure probability variation diagram of the CFG pile system. Specifically, it is calculated using the following formula: ; In the above formula, Indicates the probability of failure. Let represent the limit state function for the i-th iteration. Indicates the number of simulated samples; This represents an exponential function, where 1 is recorded if the event occurs and 0 is recorded if the event does not occur.

[0031] In this embodiment, the preferred settings for each parameter are shown in Table 1: Table 1

[0032] Based on the specific values ​​in Table 1, the resulting graph showing the change in the failure probability of the CFG pile system is as follows. Figure 2 As shown in the figure, the failure probability variation diagram of the CFG pile system is analyzed, mainly divided into three key stages: Initial low-risk stable period (approximately 0-15 years): Phenomenon: The broken line starts from the origin, and for the first 15 years or so, the failure probability P(t) is extremely low, almost remaining near 0. Explanation: This represents the system's "effective service period" or "latency period." During this stage, the CFG pile system is in good working condition, and its structural performance is stable and reliable. The risk of failure is extremely low, indicating that the initial structural strength, design, construction quality, and material properties all meet the requirements, and the effects of environmental degradation factors (such as corrosion and material fatigue) have not yet manifested or have accumulated sufficiently.

[0033] A period of sharp increase in medium-term risks (approximately 15-25 years): Phenomenon: After a stabilization period of approximately 15 years, the failure probability begins to increase rapidly and significantly. The slope of the broken line is very steep, with the failure probability rising rapidly from near 0 to near 1 within just about 10 years (15 to 25 years). Explanation: This indicates that the system performance has entered a stage of accelerated degradation or a period of manifest cumulative damage. Possible causes include: a. Material aging and deterioration: For example, the carbonation depth of concrete reaches the protective layer of the reinforcing steel, causing corrosion; the integrity of the pile body is affected by factors such as erosion, freeze-thaw cycles, and chemical corrosion, leading to a significant decrease in strength. b. Cumulative load effect: Long-term cyclic loads (traffic loads, seismic waves, etc.) lead to the accumulation of fatigue damage, and the frictional resistance at the pile-soil interface gradually decreases. c. Critical performance thresholds are breached: Certain degradation factors (such as corrosion rate, microcrack propagation, and changes in foundation soil properties) may have reached a critical point, leading to a sharp decline in bearing capacity and stability.

[0034] The high failure risk stabilization period in the later stages (approximately 25 years later): Phenomenon: After the failure probability reaches close to 1 (approximately 25 years), the broken line becomes a horizontal straight line (P(t)=1) and remains so for up to 50 years. Explanation: This indicates that the system has entered the failure stage. A failure probability close to 1 means that under the simulation conditions of this model, system failure is considered almost inevitable. The broken line remaining horizontal indicates that the system performance has been severely compromised, it no longer possesses reliable load-bearing capacity, and this state will continue without significant change (i.e., the "failed" state persists). At this point, the system function is completely lost, requiring reinforcement or reconstruction.

[0035] S3, Regarding initial load-bearing capacity Resistance degradation rate Construction defect reduction factor Optimize the initial load-bearing capacity each time. Each optimization increases it by 500KN, and the rate of degradation of resistance is increased each time. Each optimization reduces it by 0.005, and each time the construction defect reduction factor is adjusted. The optimizations all involve reducing the initial load-bearing capacity by 0.025. Each optimization targets one parameter, resulting in three optimization schemes. The first optimization scheme involves reducing the initial load-bearing capacity by 0.025. The second optimization scheme, which increases the resistance by 500 kN, is designed to counteract the rate of force degradation. The third optimization scheme reduces the construction defect reduction factor by 0.005. Reduce by 0.025. Repeat steps S1-S2 for each optimization scheme to obtain the failure probability variation diagrams of the CFG pile system corresponding to the three optimization schemes, as shown below. Figure 3 As shown, adjusting the three parameters can effectively optimize the failure probability of CFG piles throughout their lifespan.

[0036] This invention significantly improves the reliability assessment and optimization level of CFG piles throughout their entire life cycle through dynamic coupling modeling, probabilistic quantitative analysis, and intelligent optimization decision-making. By considering model dynamism and parameter randomness, it solves the problem of insufficient long-term reliability caused by traditional methods that only consider static bearing capacity and fail to account for time-varying load growth (such as changes in building use), material strength degradation (such as concrete carbonation), and environmental coupling effects. Through dynamic limit state functions, it addresses the problem that existing failure probability analysis methods rely on deterministic models, ignore parameter randomness (such as soil resistance fluctuations), and are difficult to quantify dynamic risks. By prioritizing importance and quantifying the effects of optimization measures, it solves the problem that traditional methods lack quantitative ranking of key influencing factors and rely on experience to select optimization measures, resulting in resource waste.

[0037] Finally, it should be noted that the above content is only used to illustrate the technical solution of the present invention, and is not intended to limit the scope of protection of the present invention. Simple modifications or equivalent substitutions made by those skilled in the art to the technical solution of the present invention do not depart from the essence and scope of the technical solution of the present invention.

Claims

1. A multi-factor coupled method for predicting and optimizing the failure probability of CFG piles throughout their lifespan, characterized in that, Includes the following steps; S1. Integrate resistance degradation, pile-soil interaction, geological evolution, and time-varying load characteristics to construct a limit state model; S2. Based on the limit state model constructed in step S1, the failure probability is calculated using a Monte Carlo simulation cycle to obtain the failure probability variation diagram of the CFG pile system. Specifically, it is calculated using the following formula: ； In the above formula, Indicates the probability of failure. Let represent the limit state function for the i-th iteration. Indicates the number of simulated samples; Represents an exponential function; S3. Optimize the parameters in the limit state model. After optimizing each parameter, repeat steps S1-S2 to obtain the optimized failure probability change diagram of the CFG pile system.

2. The multi-factor coupled method for predicting and optimizing the failure probability of CFG piles throughout their lifespan, as described in claim 1, is characterized in that... The limit state model in step S1 is specifically represented by the following formula: ； In the above formula, Describes the limit state function; This represents the resistance degradation model, specifically the CFG pile resistance attenuation function; The pile-soil interaction capacity function is represented by the following: Represents the influence function of geological evolution. This represents the total applied load function.

3. The multi-factor coupled method for predicting and optimizing the failure probability of CFG piles throughout their lifespan, as described in claim 2, is characterized in that... Specifically, it is expressed by the following formula: ； In the above formula, This indicates the initial bearing capacity of the CFG pile. The value represents the rate of degradation of resistance, and t represents time; Represents the material degradation function. This represents the reduction factor for construction defects.

4. The multi-factor coupled method for predicting and optimizing the failure probability of CFG piles throughout their lifespan, as described in claim 3, is characterized in that... Specifically, it is calculated using the following formula: ； In the above formula, Indicates material degradation parameters, Indicates shape parameters.

5. The multi-factor coupled method for predicting and optimizing the failure probability of CFG piles throughout their lifespan, as described in claim 2, is characterized in that... Specifically, it is expressed by the following formula: ； In the above formula, This represents the initial pile-soil synergy coefficient. Indicates the degradation coefficient. Indicates the initial pile-soil coupling strength. This indicates the rate of degradation of the pile-soil contact. Indicates the amplitude of the disturbance. Indicates the frequency of the disturbance. Indicates the phase angle. It represents the standard deviation of random disturbance.

6. The multi-factor coupled method for predicting and optimizing the failure probability of CFG piles throughout their lifespan, as described in claim 2, is characterized in that... Specifically, it is expressed by the following formula: ； In the above formula, Indicates the geological sensitivity coefficient. Indicates the rate of geological change.

7. The multi-factor coupled method for predicting and optimizing the failure probability of CFG piles throughout their lifespan, as described in claim 2, is characterized in that... Specifically, it is expressed by the following formula: ； In the above formula, Indicates the static load term. This indicates the dynamic load term.

8. The multi-factor coupled method for predicting and optimizing the failure probability of CFG piles throughout their lifespan, as described in claim 7, is characterized in that... , The following formula can be used to calculate: ； ； In the above formula, Indicates the initial static load. Represents the random fluctuation term. This represents the average value of the dynamic load. This indicates the event triggered by the Poisson process.

9. The multi-factor coupled method for predicting and optimizing the failure probability of CFG piles throughout their lifespan, as described in claim 1, is characterized in that... In step S3, the three parameters in the limit state model—initial bearing capacity, resistance degradation rate, and construction defect reduction factor—are optimized.

10. The multi-factor coupled method for predicting and optimizing the failure probability of CFG piles throughout their lifespan, as described in claim 9, is characterized in that... In step S3, the specific method for optimizing the initial bearing capacity, resistance degradation rate, and construction defect reduction coefficient is as follows: each optimization of the initial bearing capacity increases it by 500KN, each optimization of the resistance degradation rate decreases it by 0.005, and each optimization of the construction defect reduction coefficient decreases it by 0.025.

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