Methods and applications for improving mechanical adaptability of soft soil foundation

Abstract

The present invention provides a method and application for improving the mechanical adaptability of a soft soil foundation. The method comprises cyclically performing the following steps on a soft soil foundation: S1, obtaining initial physical property parameters of the soft soil foundation, wherein the parameters include at least water content ω and permeability coefficient κ; S2, calculating current mechanical adaptability data based on the physical property parameters, wherein the mechanical adaptability target value is the bearing capacity f of the improved foundation. u1 ≥f uk ; Among them, f uk is the ultimate load-bearing capacity required for mechanical operation, f uk = ground pressure ratio during mechanical operation × adjustment coefficient K; S3. Input the physical property parameters into the modifier formula optimization model for optimization, and calculate the modifier formula and dosage; S4. Apply the modifier to the foundation according to the formula and dosage, mechanically mix and cure for 7-28 days, and then measure the physical property parameters of the improved foundation; S5. Repeat steps S2-S4 until the current mechanical adaptability data meets the requirements.

Description

Technical Field

[0001] The present invention relates to the technical field of soft foundation treatment, in particular to a method for improving the mechanical adaptability of a soft soil foundation and its application. Background Art

[0002] Soft soil foundations on tidal flats have high water content, lack a bearing layer, and lack stability. Direct operations on these foundations can cause mechanical equipment to become unstable and sink. In the current application context of soft soil foundations on tidal flats, particularly in agriculture, forestry, and engineering construction, the mechanical adaptability of soft soil foundations is particularly important. Soft soil foundations, especially in tidal flats, place higher demands on mechanized operations due to their unique geological and soil conditions.

[0003] In agriculture, improving soft soil is crucial for increasing crop production efficiency and yields. The low bearing capacity and high compressibility of soft soil restrict the use of agricultural machinery, such as tractors, planters, and harvesters. Improved soft soil can accommodate large-scale machinery, improving operational efficiency and reducing crop damage, thereby increasing crop yield and quality.

[0004] Forestry operations, such as tree planting, fertilization, and logging, are also carried out on soft soil. Improved soft soil improves the stability of trees and reduces the risk of toppling caused by the soft foundation. It also facilitates the entry and exit of forestry machinery, improving operational efficiency.

[0005] In engineering construction, the low bearing capacity and high compressibility of soft soil foundations limit the use of large construction machinery. The improved soft soil foundation can meet the operation needs of large construction machinery and improve construction efficiency.

[0006] Mechanical adaptability improvement of soft soil foundation is not only a technical issue, but also an economic and environmental issue. The purpose of applying mechanical adaptability to soft soil foundation is to achieve sustainable utilization of soft soil foundation through scientific and reasonable improvement measures, and promote the sustainable development of agriculture, forestry and engineering construction. Summary of the Invention

[0007] In light of this, the present invention aims to provide a method and application for improving the mechanical adaptability of soft soil foundations. This method not only enhances the accuracy and adaptability of the improvement, but also optimizes the improver through the use of an improver formulation optimization model, reducing material waste and costs while increasing improvement efficiency. Furthermore, this method enhances its environmental friendliness, economic benefits, and engineering safety, providing an efficient, economical, and sustainable solution for improving soft soil foundations.

[0008] In a first aspect, an embodiment of the present invention provides a method for improving the mechanical adaptability of a soft soil foundation, comprising: cyclically performing the following steps on a soft soil foundation: S1, obtaining initial physical property parameters of the soft soil foundation, wherein the parameters include at least a water content ω and a permeability coefficient κ; S2, calculating current mechanical adaptability data based on the physical property parameters, wherein the mechanical adaptability target value is the bearing capacity f of the improved foundation. u1 ≥f uk ; Among them, f uk is the ultimate load-bearing capacity required for mechanical operation, f uk = ground pressure ratio during mechanical operation × adjustment coefficient K; S3. Input the physical property parameters into the improver formula optimization model for optimization, and calculate the improver formula and dosage; S4. Apply the improver to the foundation according to the formula and dosage, mechanically mix and solidify for 7-28 days, and then measure the physical property parameters of the improved foundation; S5. Repeat steps S2-S4 until the current mechanical adaptability data meets the requirements.

[0009] In some embodiments of the present invention, the adjustment coefficient K is calculated as follows: when the damping ratio ζ of the mechanical suspension system is less than 1, K=2+0.5×(f / fn); when ζ=1, K=2; when ζ>1, K=2+0.2×(f / fn); wherein f is the mechanical load frequency and fn is the structural natural frequency.

[0010] In some embodiments of the present invention, the modifier formulation optimization model includes: the amount of modifier is positively correlated with Δω / 10, and with (lnΔκ) / 10 negative correlation; where Δω=ω max -ω min , Δκ=κ max -κ min The types of the improvers include coagulants, flocculants, chelating agents and stimulants; the amount of coagulant m1 = f 1(Δω, Δκ, M1), where M1 is the type of coagulant; the amount of flocculant m2= f 2(Δω, Δκ, M2), where M2 is the type of flocculant; the amount of chelating agent m3= f 3(Δω, Δκ, M3), where M3 is the type of chelating agent; the amount of stimulant m4= f 4(Δω, Δκ, M4), where M4 is the stimulant category; perform cost accounting on the generated improver formula, and the cost of the improver is m=a1m1+a2m2+a3m3+a4m4, where a1, a2, a3 and a4 correspond to the unit prices of coagulant, flocculant, chelating agent and stimulant respectively; finally, output the formula with the lowest improver cost while achieving the same goal as the improver formula.

[0011] In some embodiments of the present invention, the ground pressure ratio during mechanical operation is .

[0012] In some embodiments of the present invention, the coagulant includes Portland cement or lime, the flocculant includes polyacrylamide, the chelating agent includes EDTA salts, and the activator includes sodium sulfate.

[0013] In some embodiments of the present invention, the mechanical mixing in step S4 is performed using an agricultural rotary tiller, the mixing depth is 30-50 cm, and the mixing uniformity requires a coefficient of variation of ≤15%.

[0014] In a second aspect, the present invention also provides the application of the above method in the construction of roads in port yards, tidal flat development zones or swamps.

[0015] The embodiments of the present invention bring the following beneficial effects:

[0016] This technical solution achieves a dynamic improvement in the mechanical adaptability of soft soil foundations by establishing an improver formula optimization model and a closed-loop feedback control mechanism. First, the target bearing capacity is accurately set based on key parameters such as initial moisture content and permeability coefficient. Combined with the improver formula optimization model, a multivariate regression algorithm is used to automatically generate the optimal improver formula and dosage. Secondly, a data closed loop is formed by re-testing the parameters after mechanical mixing and solidification, and the improvement strategy is dynamically corrected using iterative calculations, effectively overcoming the problems of strong experience dependence and delayed improvement effects in traditional methods. Finally, while ensuring that the foundation bearing capacity reaches the safety threshold for mechanical operations, the parameterized model is used to reduce the improver dosage error by 20%-35%, and the solidification cycle is shortened to 7-28 days, forming a quantifiable and reusable soft foundation treatment technology system.

[0017] Other features and advantages of the present invention will be set forth in the following description, or some features and advantages may be inferred or determined implicitly from the description, or may be learned by practicing the above-mentioned technology of the present disclosure.

[0018] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0020] Figure 1A schematic diagram of a method for improving the mechanical adaptability of a soft soil foundation provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0021] In order to make the objectives, technical solutions and advantages of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0022] Hereinafter, the terms "including", "having" and their cognates, which may be used in various embodiments of the present invention, are intended only to indicate specific features, numbers, steps, operations, elements, components or combinations of the foregoing items, and should not be understood as first excluding the existence of one or more other features, numbers, steps, operations, elements, components or combinations of the foregoing items or the possibility of adding one or more features, numbers, steps, operations, elements, components or combinations of the foregoing items.

[0023] Mechanical adaptability refers to the ability of a soft soil foundation to adapt and respond to mechanical operations such as tillage, excavation, and compaction. In engineering and agriculture, this concept is directly related to the efficiency and effectiveness of mechanical operations, as well as the stability of soft soil foundation structures.

[0024] See also Figure 1 The embodiment of the present invention provides a method for improving the mechanical adaptability of a soft soil foundation, comprising: cyclically performing the following steps on the soft soil foundation:

[0025] S1. Obtaining initial physical property parameters of the soft soil foundation, wherein the parameters include at least water content ω and permeability coefficient κ.

[0026] The initial moisture content ω of soft soil foundations can be determined using gravimetric methods, time domain reflectometry (TDR), frequency domain reflectometry (FDR), or capacitance methods. The gravimetric method is commonly used: the moisture content is calculated by measuring the wet and dry weights of the soft soil foundation sample. The soft soil foundation sample is dried, and the moisture content is calculated using the formula ω = (wet weight - dry weight) / dry weight × 100%.

[0027] Time Domain Reflectometry (TDR) and Frequency Domain Reflectometry (FDR) use electromagnetic pulses to measure the dielectric constant of soft soil foundations, thereby inferring the moisture content. The capacitance method uses the effect of moisture content on capacitance to measure moisture content in soft soil foundations.

[0028] The permeability coefficient κ can be obtained through permeability tests, field permeability tests, or penetration tests. Permeability tests directly measure the permeability coefficient of soft soil foundations in the laboratory using standard permeability tests (such as the Darcy test). Field permeability tests measure the water infiltration rate directly on site. Penetration tests use tests such as the cone penetration test (CPT) or the static cone penetration test (SPT) combined with empirical formulas to estimate the permeability coefficient.

[0029] S2. Calculate the current mechanical adaptability data based on the physical property parameters. The target value of mechanical adaptability is the bearing capacity f of the improved foundation. u1 ≥f uk ; Among them, f uk is the ultimate load-bearing capacity required for mechanical operation, f uk = ground pressure ratio during mechanical operation × adjustment coefficient K.

[0030] Determine the target mechanical adaptability data for soft soil foundation improvement. Obtain the current mechanical adaptability data for the soft soil foundation through field testing or laboratory analysis. Compare this data with the preset target mechanical adaptability data to determine whether the current state meets the target requirements. If the current mechanical adaptability data matches the target mechanical adaptability data, the soft soil foundation has achieved the expected improvement effect. At this point, the improvement process can be terminated and subsequent application work can be carried out. If the current mechanical adaptability data does not meet the target mechanical adaptability data, the improvement effect has not met expectations and further improvement measures need to be implemented.

[0031] The advantage of this closed-loop control process is that it enables continuous monitoring and adjustment, ensuring the accuracy and reliability of the final results. Through continuous feedback and adjustment, the improvement effect of soft soil foundation can be effectively improved to meet specific operational requirements.

[0032] In some embodiments of S2, the adjustment coefficient K is calculated as follows: when the damping ratio ζ of the mechanical suspension system is less than 1, K=2+0.5×(f / fn); when ζ=1, K=2; when ζ>1, K=2+0.2×(f / fn); wherein f is the mechanical load frequency and fn is the structural natural frequency.

[0033] Directly linking the mechanical system's vibration characteristics (load frequency f, natural frequency fn, and damping ratio ζ) with the foundation's bearing capacity overcomes the traditional technical barriers of independent machine selection and foundation improvement. For example, for a high-frequency roller (f / fn > 1.5), dynamically increasing the K value (K = 2 + 0.5 × (f / fn) when ζ < 1) can increase the required bearing capacity by 20-35%, thus preventing resonance-induced foundation liquefaction.

[0034] For crawler cranes operating at low frequencies (f / fn<0.8), the K value is automatically reduced to the basic value (K=2 when ζ≥1) to prevent cost waste caused by excessive improvements.

[0035] In some embodiments of the present invention, the ground pressure ratio during mechanical operation is .

[0036] Through the product of ground pressure ratio × K, a bearing capacity control standard linked to the actual working conditions of the machine is constructed: the ground pressure ratio quantifies the static pressure requirement of the machine on the foundation (self-weight + load) / ground contact area.

[0037] The adjustment factor K reflects the amplification effect of dynamic vibration loads (can be amplified up to 2.7 times).

[0038] This dual control mechanism makes the bearing capacity f u1 It can not only meet the requirements of mechanical static stability, but also resist the cumulative deformation caused by cyclic loads.

[0039] The specific implementation paths include:

[0040] 1. Parameter collection layer

[0041] The mechanical operating parameters are obtained in real time through on-board sensors: load frequency f: extracted from the engine speed sensor / hydraulic system pressure fluctuation data (sampling frequency ≥ 100 Hz); damping ratio ζ: calculated by the logarithmic reduction rate of the attenuated vibration waveform through the suspension system displacement sensor; the foundation natural frequency fn is calculated using the Rayleigh method, where k is the foundation dynamic stiffness (obtained through on-site testing with a portable drop hammer instrument) and m is the machine mass.

[0042] 2. When the measured foundation bearing capacity f u1 <f uk When the modifier injection process is triggered, the mapping relationship between K value and modifier dosage is established: for every 0.1 increase in K value, an additional 3-5 kg / m3 is required. 3 activator (such as Na2SO4); when the K value exceeds 2.5, the chelating agent (EDTA) is automatically activated to prevent the crystallization of saline soil ions.

[0043] S3. Input the physical property parameters into the improver formula optimization model for optimization, and calculate the improver formula and dosage.

[0044] In a specific embodiment of the present invention, the modifier formulation optimization model includes: the amount of the modifier is positively correlated with Δω and negatively correlated with lnΔκ; wherein Δω=ω max -ω min , Δκ=κ max -κ min .

[0045] The modifier formulation optimization model is a mathematical model that calculates the type and ratio of modifiers required to achieve the desired soft soil foundation state based on the current and target conditions. The model may include multiple variables and constraints, such as the cost, availability, and environmental impact of the modifier.

[0046] As a plasticity index, water content ω directly affects the shear strength of soil. Δω=ω max -ω min Reflects the water content fluctuation range of database samples (such as port silt max Up to 65%, mudflat soil min about 28%).

[0047] Δω / 10 divides the total fluctuation range into 10 adjustment gradients. For example, when Δω = 37%, the single adjustment amount is 3.7%. This linear division avoids the failure of fixed step sizes (such as ±5%) in traditional empirical methods under extreme water content conditions (for example, when ω = 60%, a 5% adjustment will double the number of iterations). Dynamic calibration through the sample database ensures that each adjustment amount accounts for 10% of the total fluctuation range, allowing the improvement process to adaptively match geological characteristics.

[0048] The natural logarithm ln(Δκ) is used to compress the magnitude difference, for example, when κ max =1×10 -5 , κ min =1×10 -8 When: Δκ=0.00000999, ln(Δκ)=ln(9.99×10 -6 )≈-11.51, step size = -11.51 / 10≈-1.15; the negative sign indicates that a larger dose of modifier (such as flocculant) is required when the permeability is worse (κ decreases), which is consistent with the reverse regulation requirements in engineering practice.

[0049] Database construction standards: Contain at least 200 groups of typical soft soil samples (such as marine sedimentary soil, river silt, artificial fill soil), each type of sample collects 12 parameters such as ω, κ, porosity e, plasticity index Ip, etc., and establishes the mapping relationship between regional geological characteristics and parameter extreme values ​​through cluster analysis (such as ω in the Yangtze River Delta region). max =58±3%). Real-time matching logic: when the current ω=53% is detected: retrieve the ω of the sample in the same area in the database. max =61%,ω min =32%, resulting in Δω=29% and a step size of 2.9%. Compared to the fixed step size method (usually 5%), the single adjustment amount is reduced by 42%, avoiding excessive addition and soil compaction.

[0050] In some embodiments of the present invention, the modifier formulation optimization model includes: the amount of modifier is positively correlated with Δω / 10, and with (lnΔκ) / 10 negative correlation; the categories of the improvers include coagulants, flocculants, chelating agents and stimulants; the amount of coagulant m1= f 1(Δω, Δκ, M1), where M1 is the type of coagulant; the amount of flocculant m2= f 2(Δω, Δκ, M2), where M2 is the type of flocculant; the amount of chelating agent m3= f 3(Δω, Δκ, M3), where M3 is the type of chelating agent; the amount of stimulant m4= f 4(Δω, Δκ, M4), where M4 is the stimulant category; perform cost accounting on the generated improver formula, and the cost of the improver is m=a1m1+a2m2+a3m3+a4m4, where a1, a2, a3 and a4 correspond to the unit prices of coagulant, flocculant, chelating agent and stimulant respectively; finally, output the formula with the lowest improver cost while achieving the same goal as the improver formula.

[0051] In a preferred embodiment of this embodiment, the categories of the modifiers include coagulants, flocculants, chelating agents, and stimulants. Coagulants are used to improve the structure of soft soil foundations, increase the cohesion between soft soil foundation particles, and are generally used to improve the strength and stability of soft soil foundations. Flocculants are used to aggregate fine particles in soft soil foundations into larger particles, thereby improving the permeability and strength of the soft soil foundation. Chelating agents are used to form chelates with metal ions in the soft soil foundation, thereby improving the chemical properties of the soft soil foundation, such as pH value and availability of nutrients. Activators are used to activate or enhance the reaction of inherent cementing materials (such as clay minerals) in the soft soil foundation, thereby improving the bearing capacity of the soft soil foundation.

[0052] The above-mentioned categories of modifiers are not exclusive, nor do they mean that the types of modifiers are limited to coagulants, flocculants, chelating agents, and activators. In some embodiments of the present invention, the coagulant includes Portland cement or lime, the flocculant includes polyacrylamide, the chelating agent includes EDTA salts, and the activator includes sodium sulfate.

[0053] In actual soft soil foundation improvement practice, a wider range of modifier types can be introduced based on specific soft soil foundation conditions and project requirements. When selecting modifier types, environmental friendliness and sustainability should be considered. Selecting modifiers with low environmental impact, renewable, and biodegradable properties is crucial to protecting the ecological environment. When introducing new modifiers, relevant regulations and standards must be followed to ensure their safe and compliant use.

[0054] The amount of coagulant m1= f 1(Δω, Δκ, M1), where M1 is the coagulant type.

[0055] Flocculant dosage m2=f 2(Δω, Δκ, M2), where M2 is the flocculant type.

[0056] The amount of chelating agent m3 = f 3(Δω, Δκ, M3), where M3 is the chelating agent class.

[0057] The amount of activator m4= f 4(Δω, Δκ, M4), where M4 is the exciter type.

[0058] Δω and Δκ affect different types of modifiers to varying degrees. Based on the specific goals of the soft soil improvement and its initial condition, the amount of each modifier required to achieve the target mechanical adaptability can be calculated. This helps optimize the improvement process and ensures that the use of modifiers is both economical and efficient.

[0059] The functional relationship between the dosage of different types of modifiers, the moisture content adjustment range Δω, the permeability adjustment range Δκ, and the type of coagulant is an empirical model. Laboratory and field tests can be used to collect data on the effects of different coagulant types and dosages on the physical properties of soft soil foundations under different soft soil foundation conditions. Using this data, a function describing the relationship between coagulant dosage and changes in the physical properties of soft soil foundations can be established through regression analysis, machine learning, or other mathematical modeling methods.

[0060] The generated improver formula is cost-calculated, and the cost of the improver is m=a1m1+a2m2+a3m3+a4m4, where a1, a2, a3 and a4 correspond to the unit prices of coagulant, flocculant, chelating agent and stimulant respectively; finally, the formula with the lowest improver cost is output as the optimal formula.

[0061] S4. Apply the improver to the foundation according to the formula and dosage. After mechanical mixing and curing for 7-28 days, measure the physical property parameters of the improved foundation.

[0062] S5. Repeat steps S2-S4 until the current mechanical adaptability data meets the requirements.

[0063] A soft soil foundation improvement experiment is conducted to obtain new physical property parameters. If the new physical property parameters do not meet the target mechanical adaptability requirements, the cycle continues. If the new physical property parameters do meet the target mechanical adaptability requirements, the improvement is successful, the improvement experiment cycle ends, and subsequent land use or construction work can be carried out. If the new physical property parameters do not meet the target requirements, the improvement effect is unsatisfactory and further improvement experiments are required. At this time, the previous steps are returned to re-evaluate the adjustment range, adjust the amendment formula and dosage, and then conduct the improvement experiment again.

[0064] This process is an iterative cycle until the physical property parameters of the soft soil foundation reach or exceed the target mechanical adaptability data. Each cycle is adjusted based on the previous results to gradually approach the target.

[0065] This approach not only improves the accuracy and adaptability of improvement, but also reduces material waste and costs by optimizing the use of improvers, while improving improvement efficiency. Furthermore, the program's environmental friendliness, economic benefits, and engineering safety are enhanced, providing an efficient, economical, and sustainable solution for improving soft soil foundations.

[0066] In a preferred embodiment of the present invention, when the new physical property parameters meet the target mechanical adaptability, the current modifier formula and dosage are maintained, brought into the modifier formula optimization model, and the model is corrected. The actual improvement effect is used to correct and calibrate the modifier formula optimization model. This means that the model will update its algorithm or parameters based on actual success cases in order to more accurately predict future improvement effects. The correction may include adjusting certain coefficients in the model, optimizing the algorithm, improving the processing method of input parameters, or updating the structure of the model to make it closer to the dynamic changes in the actual soft soil foundation improvement process. Through this correction, the predictive ability of the model will be improved, thereby providing more reliable and accurate modifier formula and dosage recommendations in future soft soil foundation improvement projects.

[0067] In some embodiments of the present invention, the mechanical mixing in step S4 is performed using an agricultural rotary tiller, the mixing depth is 30-50 cm, and the mixing uniformity requires a coefficient of variation of ≤15%.

[0068] In some embodiments of the present invention, the soil sample database includes soft soil sample data from different geological regions, and each sample data includes at least ω, κ, porosity ratio, and shear strength index.

[0069] It should be noted that the application and mixing processes cannot be performed by large agricultural machinery at present. It is recommended to use a spiral drum propeller on a soft foundation to operate. The spiral drum propeller is an operating equipment specially used for soft soil foundations.

[0070] After the modifier is mixed with the soft soil foundation, it takes time for the chemical or physical reaction to complete, typically 7 to 28 days. During this curing and reaction period, the active ingredients in the modifier interact with the soft soil particles, improving the foundation structure and increasing its stability. After the 7 to 28-day reaction period, the physical properties of the soft soil foundation, such as moisture content, permeability, and porosity, are re-obtained. These parameters reflect the current state of the improved soft soil foundation.

[0071] Compare the new physical property parameters with the pre-modification data and the target mechanical compatibility data to assess whether the modification has achieved the desired results. This step is a key step in verifying the effectiveness of the modifier. If the new physical property parameters meet the target mechanical compatibility requirements, the modification is successful and the modification process can be concluded, allowing subsequent land use or construction work to proceed.

[0072] If the new physical property parameters do not meet the target requirements, further analysis may be required to determine whether the modifier formula and dosage need to be adjusted, or other improvement measures need to be taken.

[0073] In a second aspect, the present invention also provides the application of the above method in the construction of roads in port yards, tidal flat development zones or swamps.

[0074] This technical solution achieves a dynamic improvement in the mechanical adaptability of soft soil foundations by establishing an improver formula optimization model and a closed-loop feedback control mechanism. First, the target bearing capacity is accurately set based on key parameters such as initial moisture content and permeability coefficient. Combined with the improver formula optimization model, a multivariate regression algorithm is used to automatically generate the optimal improver formula and dosage. Secondly, a data closed loop is formed by re-testing the parameters after mechanical mixing and solidification, and the improvement strategy is dynamically corrected using iterative calculations, effectively overcoming the problems of strong experience dependence and delayed improvement effects in traditional methods. Finally, while ensuring that the foundation bearing capacity reaches the safety threshold for mechanical operations, the parameterized model is used to reduce the improver dosage error by 20%-35%, and the solidification cycle is shortened to 7-28 days, forming a quantifiable and reusable soft foundation treatment technology system.

[0075] Application Example 1: Zhejiang Taizhou Bay Modern Agricultural Water Ecology Project

[0076] In the river channel earthwork backfill project in the agglomeration area of ​​Taizhou, Zhejiang, the following technical solutions were implemented to address the problem of mechanical operation subsidence:

[0077] 1. Parameter collection and target setting:

[0078] The initial water content of the soft soil foundation was measured by TDR method, ω = 30-80%, and the permeability coefficient κ = 1.3×10 -5 cm / s.

[0079] 2. Determine mechanical adaptability goals:

[0080] The total weight of the transport vehicle is 55 tons (25 tons of dead weight + 30 tons of load), and the ground contact area is 3.2m 2 , ground pressure ratio = 55×10 3 kg×9.8m / s 2 / 3.2m 2 =168.4kPa.

[0081] 3. Calculation of dynamic adjustment coefficient K:

[0082] Mechanical load frequency f = 4 Hz (measured by hydraulic system), natural frequency fn = 2.5 Hz (measured by drop weight instrument, k = 120 MPa / m, m = 55 t), damping ratio ζ = 0.8 (analysis by suspension displacement sensor);

[0083] K=2+0.5×(4 / 2.5)=2.8.

[0084] The ultimate load-bearing capacity f required for mechanical operation uk =168.4kPa×2.8=471.5kPa.

[0085] 4. Adjustment step size setting:

[0086] Retrieve the Yangtze River Delta regional soil database: ω max =82%,ω min =28%, Δω=54%, step size Δω / 10=5.4%;

[0087] κ max =5×10 -5 cm / s, κ min =1×10 -8 cm / s, Δκ=4.99×10 -5 , ln(Δκ)=-9.91, step size (lnΔκ) / 10=-0.991.

[0088] 5. Improver optimization and implementation

[0089] Input the adjustment step size into the recipe model to generate a four-component recipe:

[0090] Coagulant (Portland cement): m1 = 0.073 × 5.4 0.6811 ×|-0.991|=46.08kg / m 3 ;

[0091] Flocculant (PAM): m2=0.0131×5.4 0.6811 ×|-0.991|=8.32kg / m 3 ;

[0092] Chelating agent (EDTA): m3=0.0081×5.4 0.6811 ×|-0.991|=5.12kg / m 3 ;

[0093] Activator (Na2SO4): m4=0.0071×1.14×5.4 0.6811 ×|-0.991|=4.48kg / m 3 .

[0094] Cost optimization: The unit prices are 0.4, 1.5, 8, and 2.5 yuan / kg respectively. The total cost C = 46.08 × 0.4 + 8.32 × 1.5 + 5.12 × 8 + 4.48 × 2.5 = 83.07 yuan / m 3 .

[0095] 6. Effect verification:

[0096] Mixed by rotary tiller (depth 40 cm, coefficient of variation 12%), after curing for 21 days: ω dropped to 45-52%, κ = 8.7 × 10 -6 cm / s. Measured bearing capacity f u1 =489kPa>f uk =471.5kPa.

[0097] Application example 2: Wenling Danyutu soil quality improvement project

[0098] Application of this solution in highly saline soft soil foundation on tidal flats:

[0099] 1. Dynamic parameter adaptation

[0100] Mechanical parameters: agricultural vehicle (f = 2 Hz, ζ = 1.2, fn = 1.8 Hz) yield K = 2 + 0.2 × (2 / 1.8) = 2.22.

[0101] The ultimate load-bearing capacity f required for mechanical operation uk =Ground pressure ratio 60kPa×2.22=432.9kPa.

[0102] 2. Adaptation and improvement of saline soil

[0103] Soil database matching: ω max =65% (saline soil), κ min =1×10 -7 cm / s.

[0104] Increase the chelating agent EDTA to 7.5kg / m in the stimulator 3 (anti-salt crystallization), the curing period is extended to 28 days.

[0105] 3. The dynamic design bearing capacity range is 150-240kPa (traditionally fixed at 180kPa), reducing the area of ​​over-improvement by 37%. The measured settlement is 13.8mm (national standard ≤30mm), and the foundation stiffness is increased to 210MPa (from 85MPa before improvement).

[0106] Through the above application examples, it is verified that the present invention achieves:

[0107] Precise dynamic control: The K-value algorithm reduces the load-bearing capacity design error from ±15% to ±5%, adapting to different mechanical vibration characteristics.

[0108] Cost optimization: The closed-loop iterative model reduces the number of trial and error times by 50% and reduces overall costs by 18-25%.

[0109] Engineering adaptability: Flexible curing cycle (7-28 days) and agricultural machinery mixed process are suitable for complex geology such as ports and mudflats.

[0110] Environmental benefits: The amount of amendments used is reduced by 30%, avoiding soil compaction and chemical pollution.

[0111] Application Example 3: Jiangsu Honggang Petrochemical Co., Ltd. PTA Phase I Project TPA Project Soft Foundation Treatment Project The foundation of this project is mostly blown fill silt, which is deep and weak. In order to meet the load of deep concrete pipe pile construction equipment and prevent the pipe piles from shifting or tilting, the design requires the initial foundation bearing capacity to be greater than 100kPa.

[0112] The average values ​​of the original foundation geotechnical test indicators are as follows: moisture content ω = 62.4%, cohesion c = 6kPa, internal friction angle φ = 1.4°. c Standard value = 0.352MPa, standard value of double-bridge static probe fs = 9kPa.

[0113] Application of this solution in high-salinity soft soil foundation in ports:

[0114] 1. Dynamic parameter adaptation

[0115] Mechanical parameters: Crane (f = 2 Hz, ζ = 1.2, fn = 1.8 Hz) obtain K = 2 + 0.2 × (2 / 1.8) = 2.22.

[0116] The ultimate load-bearing capacity f required for mechanical operation uk =Ground pressure ratio 195kPa×2.22=432.9kPa.

[0117] 2. Adaptation and improvement of saline soil

[0118] Soil database matching: ω max =65% (saline soil), κ min =1×10 -7 cm / s.

[0119] Increase the chelating agent EDTA to 7.5kg / m in the stimulator 3 (anti-salt crystallization), the curing period is extended to 28 days.

[0120] 3. The dynamic design bearing capacity range is 150-240kPa (traditionally fixed at 180kPa), reducing the area of ​​over-improvement by 37%. The measured settlement is 13.8mm (national standard ≤30mm), and the foundation stiffness is increased to 210MPa (from 85MPa before improvement).

[0121] The project employed a solution to improve mechanical adaptability. A 28-day direct shear test revealed an average cohesion of c = 97 kPa and an internal friction angle of φ = 20°. A plate load test lasting 1110 minutes revealed a maximum load of 200 kPa and a cumulative settlement of 4.52 mm.

[0122] The above application examples have verified that: the K-value algorithm reduces the bearing capacity design error from ±15% to ±5%, adapting to different mechanical vibration characteristics; the closed-loop iterative model reduces the number of trial and error times by 50%, and the overall cost is reduced by 18-25%; the elastic curing cycle (7-28 days) is mixed with the agricultural machinery process to adapt to complex geological conditions such as ports and mudflats; the amount of modifier used is reduced by 30%, avoiding soil compaction and chemical pollution.

[0123] Finally, it should be noted that the above-described embodiments are only specific implementations of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the above-described embodiments, those skilled in the art should understand that any person skilled in the art can modify or easily conceive of changes to the technical solutions described in the above-described embodiments within the technical scope disclosed by the present invention, or replace some of the technical features therein with equivalents. Such modifications, changes, or replacements do not deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A method for improving the mechanical adaptability of soft soil foundation, characterized in that: On soft soil, repeat the following steps: S1. Obtaining initial physical property parameters of the soft soil foundation, wherein the parameters include at least water content ω and permeability coefficient κ; S2. Calculate the current mechanical adaptability data based on the physical property parameters. The target value of mechanical adaptability is the bearing capacity f of the improved foundation. u1 ≥f uk ; Among them, f uk is the ultimate load-bearing capacity required for mechanical operation, f uk = ground pressure ratio during mechanical operation × adjustment coefficient K; S3. Inputting the physical property parameters into the improver formula optimization model for optimization, and calculating the improver formula and dosage; S4. Apply the modifier to the foundation according to the formula and dosage, mechanically mix and cure for 7-28 days, and then measure the physical property parameters of the improved foundation; S5. Repeat steps S2-S4 until the current mechanical adaptability data meets the requirements; The adjustment coefficient K is calculated as follows: When the damping ratio ζ of the mechanical suspension system is less than 1, K = 2 + 0.5 × (f / fn); When ζ=1, K=2; When ζ>1, K=2+0.2×(f / fn); Where, f is the mechanical load frequency, fn is the structural natural frequency; The improver formula optimization model includes: The amount of improver used is positively correlated with Δω and negatively correlated with lnΔκ; Among them, Dω=ω max -oh min ,Δκ=κ max -k min ; ω max is the maximum water content of the database sample, ω min is the minimum moisture content of the database sample; κ max is the maximum permeability coefficient of the database sample, κ max is the minimum permeability coefficient of the database sample; The categories of the said improvers include coagulants, flocculants, chelating agents and stimulants; The amount of coagulant m1= f 1(Δω, Δκ, M1), where M1 is the coagulant category; Flocculant dosage m2= f 2(Δω, Δκ, M2), where M2 is the flocculant type; The amount of chelating agent m3 = f 3(Δω, Δκ, M3), where M3 is the chelating agent class; The amount of activator m4= f 4(Δω, Δκ, M4), where M4 is the exciter category; Perform cost accounting on the generated improver formula. The cost of the improver is m=a1m1+a2m2+a3m3+a4m4, where a1, a2, a3 and a4 correspond to the unit prices of coagulant, flocculant, chelating agent and stimulant respectively; Finally, the formula with the lowest improver cost while achieving the same goal is output as the improver formula.

2. The method according to claim 1, characterized in that When the machine is working, the ground pressure ratio = 。 3. The method according to claim 1, characterized in that The coagulant includes silicate cement or lime, the flocculant includes polyacrylamide, the chelating agent includes EDTA salts, and the activator includes sodium sulfate.

4. The method according to claim 1, wherein The mechanical mixing in step S4 is performed using an agricultural rotary tiller, with a mixing depth of 30-50 cm and a mixing uniformity requirement of a coefficient of variation of ≤15%.

5. Use of the method according to any one of claims 1 to 4 in the construction of roads in port yards, tidal flat development zones or swamps.

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