Self-weight collapsible loess quantification and optimization method based on multiple factors
By constructing a model of self-weight compression and collapsibility potential, and combining the influence of multiple factors, the problem of quantification and optimization of self-weight collapsible loess settlement was solved. This enabled accurate simulation of the loess settlement process and the proposal of optimization measures, which are applicable to risk assessment and design of large-scale projects.
Patent Information
- Application Number
- CN202511688372.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-18
- Publication Date
- 2026-02-27
AI Technical Summary
Existing technologies cannot effectively quantify and optimize the settlement process of collapsible loess under its own weight, especially under multi-factor environments, and cannot accurately predict and control the uncertainty of foundation settlement.
A self-weight compression model and a collapsibility potential and environmental impact model were constructed and integrated with the influence of multiple factors. A stochastic simulation method was used to simulate loess settlement and optimization measures such as dynamic compaction, lime-soil compaction pile method and pre-soaking method were proposed.
It enables precise characterization of loess settlement process and quantitative expression of long-term evolution process, supports multi-scheme comparison and decision-making, and is suitable for risk assessment and optimization design of large-scale projects.
Smart Images
Figure CN121580480A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of geotechnical engineering and foundation treatment technology, and in particular to a method for quantifying and optimizing self-weight collapsible loess based on multiple factors. Background Technology
[0002] A significant characteristic of collapsible loess is that it undergoes structural collapse when exposed to water under its own weight or with light loads, leading to foundation settlement and severely impacting the safety and durability of buildings. Current technologies typically employ traditional settlement prediction methods to assess settlement, such as the layered summation method. However, this approach overlooks the influence of numerous uncertain environmental factors on loess collapsibility, such as rainfall, groundwater levels, and evaporation. Furthermore, it is affected by changes in the soil's structural parameters, making it impossible to quantify the uncertainties in the settlement process and the effectiveness of optimization measures.
[0003] Therefore, there is an urgent need to provide a multi-factor-based method for quantifying and optimizing the settlement of self-weight collapsible loess, which can quantify the settlement of self-weight collapsible loess compared with existing technologies. Summary of the Invention
[0004] This invention addresses the technical problems existing in the prior art and provides a method for quantifying and optimizing self-weight collapsible loess based on multiple factors.
[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows: A multi-factor-based method for quantifying and optimizing self-weight collapsible loess includes the following steps: S1. Construct a settlement model for self-weight collapsible loess, specifically by constructing a self-weight compression model and a collapsibility potential and environmental impact model, and then integrating the self-weight compression model and the collapsibility potential and environmental impact model. S2. The cumulative settlement of the self-weight collapsible loess constructed in step S1 is simulated to obtain the change of the cumulative settlement of loess over time, and the result is analyzed. S3. Based on the simulation results of step S2, propose optimization measures, including dynamic compaction, lime-soil compaction pile method, and pre-soaking method.
[0006] 2. The method for quantifying and optimizing self-weight collapsible loess based on multiple factors according to claim 1, characterized in that S1 includes the following steps: S11. Construct a self-weight compression model ; S12. Constructing a model for collapsibility potential and environmental impact. ; S13. Integrate the self-weight compression model constructed in step S11 and the collapsibility potential and environmental impact model constructed in step S12 to obtain the settlement model of self-weight collapsible loess, expressed as: ; In the above formula, This represents the total settlement of collapsible loess due to its own weight.
[0007] Furthermore, the self-weight compression model in step S11 is expressed as: ; In the above formula, Indicates the initial compression coefficient. Indicates the wet weight of soil. Indicates the initial void ratio. represents the compression rate constant, and t represents time.
[0008] Furthermore, the collapse potential and environmental impact model in step S12 is expressed as follows: ; In the above formula, Indicates the collapsibility coefficient. Indicates the environmental impact item. This represents the disturbance factor. This indicates the trapping trigger function.
[0009] Furthermore, the collapsibility coefficient in step S12 is calculated using the following formula: .
[0010] Furthermore, the environmental impact item in step S12 is expressed by the following formula: ; ; In the above formula, gw represents the depth of the groundwater level, and f() is the sigmoid function, which simulates the gradual process of groundwater from strong influence to no influence. This represents the natural moisture content of the undisturbed soil. Indicates annual rainfall. This represents the annual evaporation rate; This represents the groundwater level factor function.
[0011] Furthermore, the disturbance factor in step S12 is calculated using the following formula: ; In the above formula, This indicates the generation of a random number that is uniformly distributed within the interval [0,1).
[0012] Furthermore, the trapping trigger function in step S12 is calculated using the following formula: ; ; In the above formula, This indicates the generation of uniformly distributed random numbers. This represents the collapsibility probability function. This represents the natural exponential function.
[0013] Furthermore, in step S13, the dynamic compaction method is... , , , The parameters are adjusted; the compaction pile method is... , , , Parameters are adjusted; the pre-soaking method is... , , , Adjust the parameters.
[0014] Furthermore, in step S2, the cumulative settlement is simulated using MATLAB software.
[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: (1) This invention accurately depicts the long-term evolution process of loess subsidence. It adopts a stochastic simulation method to model the physical properties of loess and environmental change factors (rainfall, evaporation, groundwater level) as random variables with statistical distribution. This method can quantitatively express the long-term uncertainty of loess subsidence and replace the traditional static estimation method.
[0016] (2) This invention can simulate and evaluate various typical foundation reinforcement methods, clarify the quantitative settlement reduction effect of each measure, and allow engineering designers to select the optimal solution that balances technical feasibility and settlement reduction effect according to engineering requirements, supporting multi-solution comparison and decision-making.
[0017] (3) It is applicable to risk assessment and foundation design optimization of large-scale engineering areas. It can simulate the settlement trend of loess in different areas in the next 50 years in batches and be used for risk identification in the early stage of large projects such as urban planning, roads, railways, and pipe corridors. Attached Figure Description
[0018] Figure 1 This is a flowchart of the present invention.
[0019] Figure 2 This is a graph showing the change of cumulative loess settlement over time according to the present invention.
[0020] Figure 3 This is a line graph showing the effect of the optimization measures of this invention.
[0021] Figure 4 This is a bar chart showing the effectiveness of the optimization measures of this invention. Detailed Implementation
[0022] 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.
[0023] like Figure 1 As shown, this invention provides a method for quantifying and optimizing self-weight collapsible loess based on multiple factors, including the following steps: S1. Construct a settlement model for self-weight collapsible loess. Specifically, this is achieved by constructing a self-weight compression model and a collapsibility potential and environmental impact model, and then integrating these models. The specific steps include: S11. Construct a self-weight compression model, represented as: ; In the above formula, This indicates the initial compressibility factor, i.e., the compressibility. The graph indicates the wet weight. Indicates the initial void ratio. represents the compression rate constant, and t represents time.
[0024] in, This represents the unit ultimate settlement under initial self-weight. The looser and heavier the soil, the greater the settlement. This represents the evolutionary form of settlement that gradually approaches the ultimate settlement value over time.
[0025] S12. Construct a collapsibility potential and environmental impact model. Loess exhibits strong structural integrity when its original structure is intact and moisture content is low. However, once exposed to water or disturbed, its structure rapidly disintegrates, causing significant "collapse settlement." Collapse potential is not only related to the soil type itself but also possesses temporal evolution and significant nonlinear characteristics. Therefore, the collapsibility potential and environmental impact model is expressed as follows: ; In the above formula, Indicates the collapsibility coefficient. Indicates the environmental impact item. This represents the disturbance factor. This indicates the trapping trigger function.
[0026] The collapsibility coefficient is the specific settlement of loess under a certain stress state after structural failure triggered by water action. It determines the potential settlement and deformation capacity per unit thickness of the soil layer during collapsibility; specifically, it is expressed by the following formula: ; In the above formula, The collapsibility coefficient represents the initial collapsibility coefficient. When t=0, the collapsibility coefficient reaches its maximum value of 0.05, indicating that the initial structure is loose and the collapsibility potential is the greatest. When t approaches infinity, the collapsibility coefficient converges to 0.03, indicating that after the soil structure gradually becomes more compact, the collapsibility potential tends to a stable residual value.
[0027] The environmental impact item is calculated using the following formula: ; In the above formula, This represents the natural moisture content of the undisturbed soil, ranging from 12% to 30%. Indicates annual rainfall. This represents the annual evaporation rate; Represents the groundwater level factor function; ; In the above formula, gw represents the groundwater level depth, and f() is the sigmoid function, which simulates the gradual process of groundwater influence from strong to no influence, and has the characteristics described in Table 1: Table 1
[0028] This can be expressed by the following formula: ; In the above formula, This indicates the generation of a uniformly distributed random number within the interval [0,1). Its purpose is to introduce random disturbances, making the settlement simulation results more closely reflect the influence of random factors in actual engineering.
[0029] Trapping Trigger Function Used to determine whether sinkhole has been triggered, as shown below: ; This indicates the generation of uniformly distributed random numbers. This represents the probability function of sinkhole.
[0030] The collapse probability function is calculated using the following formula: ; In the above formula, This represents the natural exponential function.
[0031] S13. Integrate the self-weight compression model constructed in step S11 and the collapsibility potential and environmental impact model constructed in step S12 to obtain the settlement model of self-weight collapsible loess, expressed as: ; In the above formula, This represents the total settlement of collapsible loess due to its own weight.
[0032] Substituting the formulas for the self-weight compression model in step S11 and the collapsibility potential and environmental impact model in step S12 into the above equation, we obtain: .
[0033] In constructing the settlement model for self-weight collapsible loess, a series of key assumptions are made, as shown in Table 2. Table 2
[0034] In this step, It follows a normal distribution ~N(0.15, 0.02). It follows a normal distribution ~N(0.85, 0.05). It follows a normal distribution ~N(18,2). It follows a normal distribution ~N(16.5, 0.5). It follows a normal distribution ~N(500,100). It follows a normal distribution ~N(1200,150). It follows a normal distribution ~N(5,1).
[0035] S2. The settlement model of the self-weight collapsible loess constructed in step S1 is used to simulate the cumulative settlement. Specifically, based on the data sampling simulation principle, MATLAB programming is used to simulate the degradation of the cumulative settlement of the self-weight collapsible loess over time. After running the MATLAB software, the graph showing the change of the cumulative settlement of the loess over time is obtained, as shown below. Figure 2 As shown, the following conclusions are drawn from the analysis of the degradation diagram: (1) Trend: The settlement gradually increases over time, and the curve shows a non-linear increasing trend, which is consistent with the long-term accumulation characteristics of loess collapsing and compression.
[0036] (2) Total settlement: The average settlement reaches about 50 mm in 50 years, indicating that if no optimization measures are taken, the loess layer will sink significantly in the long term.
[0037] (3) Greater uncertainty: As time goes by, the 90% confidence interval widens, indicating that the later simulation results are more dispersed (increased volatility). This is due to the increased uncertainty of collapsibility as it evolves with the environment and time.
[0038] (4) Project impact: If precipitation and groundwater level are not controlled or reinforcement measures are not taken, the foundation structure will be at risk of damage due to uneven settlement in the long term.
[0039] S3. Based on the simulation results of step S2, propose and verify optimization measures, including dynamic compaction, lime-soil compaction pile method, and pre-soaking method.
[0040] Dynamic compaction is a method that uses a heavy hammer to impact the soil in free fall, disrupting its original structure, reducing the void ratio, and increasing its density. Specifically, the parameters are adjusted as follows: , , , Adjusted to: , , , .
[0041] The compaction pile method involves drilling holes in the soil and filling them with lime-soil. The piles absorb water, expand, and compact the surrounding soil, forming a composite foundation. Specific parameters are adjusted as follows: , , , Adjustments have been made as follows: , , , .
[0042] Pre-soaking is a method of artificially soaking the soil with water before construction to induce soil collapse and eliminate most of the potential settlement. Specifically, the parameters are adjusted as follows: , , , Adjusted to: , , , .
[0043] The parameters were adjusted according to the three optimization measures mentioned above and input into the settlement model of self-weight collapsible loess. The simulation in step S2 was then performed, resulting in a comparison chart of the settlement curves for the three optimization measures and the original settlement curve, as shown below. Figure 3 , Figure 4 As shown, the engineering recommendations are as follows: Table 3
[0044] The present invention has the following advantages: 1. To accurately depict the long-term evolution of loess subsidence, a stochastic simulation method is used to model the physical properties of loess and environmental change factors (rainfall, evaporation, groundwater level) as random variables with statistical distribution. This method can quantitatively express the long-term uncertainty of loess subsidence and replace the traditional static estimation method.
[0045] 2. This invention can simulate and evaluate various typical foundation reinforcement methods, clarify the quantitative settlement reduction effect of each measure, and allow engineering designers to select the optimal solution that balances technical feasibility and settlement reduction effect according to engineering requirements, supporting multi-solution comparison and decision-making.
[0046] 3. Applicable to risk assessment and foundation design optimization for large-scale engineering areas, it can simulate the settlement trend of loess in different regions over the next 50 years in batches, and can be used for early risk identification of large-scale projects such as urban planning, roads, railways, and utility tunnels.
[0047] 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 method for quantifying and optimizing self-weight collapsible loess based on multiple factors, characterized in that, Includes the following steps: S1. Construct a settlement model for self-weight collapsible loess, specifically by constructing a self-weight compression model and a collapsibility potential and environmental impact model, and then integrating the self-weight compression model and the collapsibility potential and environmental impact model. S2. The cumulative settlement of the self-weight collapsible loess constructed in step S1 is simulated to obtain the change of the cumulative settlement of loess over time, and the result is analyzed. S3. Based on the simulation results of step S2, propose optimization measures, including dynamic compaction, lime-soil compaction pile method, and pre-soaking method.
2. The method for quantifying and optimizing self-weight collapsible loess based on multiple factors according to claim 1, characterized in that, S1 includes the following steps: S11. Construct a self-weight compression model ; S12. Constructing a model for collapsibility potential and environmental impact. ; S13. Integrate the self-weight compression model constructed in step S11 and the collapsibility potential and environmental impact model constructed in step S12 to obtain the settlement model of self-weight collapsible loess, expressed as: In the above formula, This represents the total settlement of collapsible loess due to its own weight.
3. The method for quantifying and optimizing self-weight collapsible loess based on multiple factors according to claim 2, characterized in that, The self-weight compression model in step S11 is expressed as follows: In the above formula, Indicates the initial compression coefficient. Indicates the wet weight of soil. Indicates the initial void ratio. represents the compression rate constant, and t represents time.
4. The method for quantifying and optimizing self-weight collapsible loess based on multiple factors according to claim 3, characterized in that, The collapsibility potential and environmental impact model in step S12 is expressed as follows: In the above formula, Indicates the collapsibility coefficient. Indicates the environmental impact item. This represents the disturbance factor. This indicates the trapping trigger function.
5. The method for quantifying and optimizing self-weight collapsible loess based on multiple factors according to claim 4, characterized in that, The collapsibility coefficient in step S12 is calculated using the following formula: 。 6. The method for quantifying and optimizing self-weight collapsible loess based on multiple factors according to claim 5, characterized in that, The environmental impact item in step S12 is expressed by the following formula: In the above formula, gw represents the depth of the groundwater level, and f() is the sigmoid function, which simulates the gradual process of groundwater from strong influence to no influence. This represents the natural moisture content of the undisturbed soil. Indicates annual rainfall. This represents the annual evaporation rate; This represents the groundwater level factor function.
7. The method for quantifying and optimizing self-weight collapsible loess based on multiple factors according to claim 6, characterized in that, The perturbation factor in step S12 is calculated using the following formula: In the above formula, This indicates the generation of a random number that is uniformly distributed within the interval [0,1).
8. The method for quantifying and optimizing self-weight collapsible loess based on multiple factors according to claim 7, characterized in that, The trapping trigger function in step S12 is calculated using the following formula: In the above formula, This indicates the generation of uniformly distributed random numbers. This represents the collapsibility probability function. This represents the natural exponential function.
9. The method for quantifying and optimizing self-weight collapsible loess based on multiple factors according to claim 8, characterized in that, In step S13, the dynamic compaction method is... , , , The parameters are adjusted; the compaction pile method is... , , , Parameters are adjusted; the pre-soaking method is... , , , Adjust the parameters.
10. The method for quantifying and optimizing self-weight collapsible loess based on multiple factors according to claim 1, characterized in that, In step S2, the cumulative settlement is simulated using MATLAB software.