Composite reinforcement optimization method for soft and complex foundation

Abstract

The application relates to the technical field of civil engineering, and discloses a composite reinforcement optimization method for soft and complex foundation; through optimization of a shallow layer reinforcement scheme and a deep layer reinforcement scheme and cooperation of the two, the bearing capacity of the soft foundation is remarkably improved, the surface soil settlement after grouting can be reduced by more than 30%, the composite reinforcement scheme can control the settlement of the foundation within 10 millimeters required by design, the overall settlement of the foundation is effectively reduced, the long-term stability requirement of the gate foundation structure is met, reliable guarantee is provided for engineering safety implementation, and the construction steps can be reasonably designed and optimized according to different geological conditions, so that the shallow layer reinforcement and the deep layer reinforcement are seamlessly connected and efficiently cooperated under different soil layer characteristics; for example, a suitable reinforcement scheme can be formulated for a coastal silt foundation, a sandy soil and clay mixed foundation and a foundation with a local hard interlayer, and the reinforcement failure problem caused by improper parameter setting in the traditional reinforcement method is avoided.

Description

Technical Field

[0001] This invention relates to the field of civil engineering technology, specifically to a composite reinforcement optimization method for soft and complex foundations. Background Technology

[0002] Foundation reinforcement technology is crucial in the field of civil engineering, directly affecting the safety and stability of buildings and infrastructure, especially under complex geological conditions such as soft soil foundations or areas with high groundwater levels. In these cases, foundation reinforcement becomes a core element in ensuring project quality. With accelerating urbanization, an increasing number of engineering projects require construction on soft and complex foundations, such as in coastal soft soil areas or areas with thick silt layers. This makes the research and application of foundation reinforcement technology particularly critical.

[0003] Foundation reinforcement not only needs to meet the structural bearing capacity requirements but also needs to adapt to changes in different geological conditions to ensure efficient and economical construction. Current foundation reinforcement methods mainly include shallow and deep reinforcement, but these methods have significant limitations in practical applications. Shallow reinforcement typically improves foundation strength through surface soil improvement or replacement, but its effectiveness in treating deep soft soil layers is limited, especially when dealing with thick soft soil layers, where shallow reinforcement struggles to effectively transfer loads. Deep reinforcement methods, such as pile foundations or deep soil mixing, can penetrate deep into weak strata, but they are complex to construct, costly, and require extremely high precision. Improper parameter control during construction can lead to uneven stress on the foundation, affecting the overall structural stability. These limitations make it difficult for existing methods to balance construction efficiency and project quality under complex geological conditions.

[0004] Among the many technical challenges, the adaptability of construction steps has become a core issue. The engineering geological conditions of complex foundations are highly variable, such as uneven soil layer distribution, groundwater level fluctuations, or differences in soil strength. This makes it difficult to uniformly plan and optimize the construction steps for shallow and deep reinforcement. For example, in a large-scale dam foundation project in a coastal area, the upper part of the foundation is a silt layer, and the lower part is a sandy soil mixed with clay layer. Shallow reinforcement requires treatment of the loose surface soil, while deep reinforcement requires ensuring that the pile foundation penetrates into the stable soil layer. However, due to the differences in soil properties, it is difficult to effectively connect the improvement depth of shallow reinforcement with the pile foundation depth of deep reinforcement. During construction, problems such as excessive disturbance of shallow soil or displacement of deep pile foundation often occur. This problem of adaptability of construction steps directly affects the reinforcement effect and project progress.

[0005] Therefore, how to rationally design and optimize the construction steps of shallow reinforcement and deep reinforcement based on the diverse geological conditions of complex foundations, so that the two can be seamlessly connected and efficiently coordinated under different soil characteristics, has become a key issue in the field of foundation reinforcement. Summary of the Invention

[0006] The purpose of this invention is to provide a composite reinforcement optimization method for weak and complex foundations to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a composite reinforcement optimization method for soft and complex foundations, comprising the following steps:

[0008] S1. Obtain engineering geological data and gate foundation structural requirements parameters for soft and complex foundations, construct a three-dimensional geological model, and obtain the stress distribution characteristics of the surface soil. S2. Based on the stress distribution characteristics of the surface soil, simulate the grouting diffusion path in shallow reinforcement to determine the material injection points and depth range. S3. If the injection point and depth range of the material exceeds the preset bearing threshold, adjust the grouting parameters to generate an optimized shallow reinforcement scheme. S4. Extract the enhanced surface soil stability data from the optimized shallow reinforcement scheme, input it into the deep reinforcement simulation module, and optimize the deep pile foundation layout. S5. Obtain the load-bearing capacity improvement effect data of the deep reinforcement, integrate the interactive response of shallow reinforcement and deep reinforcement, and generate a composite reinforcement sequence model under the gate foundation structure. S6. For the composite reinforcement sequence model, iteratively simulate the dynamic deformation trend during construction to determine the final foundation stability verification index. S7. Based on the final foundation stability verification index, adjust the grid arrangement pattern in the composite reinforcement sequence model to obtain the composite load-bearing capacity enhancement response.

[0009] Preferably, the features mentioned in step S1 include soil stress field distribution and interlayer mechanical transfer characteristics. The engineering geological data of the soft and complex foundation is obtained by extracting relevant data from the engineering geological survey report and the gate foundation design document. The engineering geological survey report contains the physical and mechanical parameters of the foundation soil. The construction of the three-dimensional geological model can be achieved by using three-dimensional modeling software to generate a model that reflects the true state of the foundation through digital processing of soil layer information, groundwater level distribution and topographic features.

[0010] Preferably, the path described in step S2 is based on soil permeability and grouting pressure distribution. According to the stress distribution characteristics and mechanical parameters of the surface soil, the permeability and pore characteristics of the soil are calculated using finite element analysis software, and computational fluid dynamics software is used to optimize the injection point and depth range by simulating the flow behavior of the grouting material in the soil pores.

[0011] Preferably, the scheme in step S3 includes enhanced surface soil stability data. The data is based on soil bearing capacity index, and the material injection points and depth range are extracted from geological data to determine whether they exceed the preset bearing threshold. The grouting parameters are adjusted using data analysis methods, low-viscosity grouting materials are selected, and the soil density is gradually increased by multi-point small-dose grouting.

[0012] Preferably, the arrangement in step S4 includes pile spacing and pile length depth to achieve a bearing capacity enhancement effect of deep reinforcement, and the effect is calculated by composite bearing capacity index.

[0013] Preferably, the enhanced surface soil stability data in step S4 includes the compressive modulus and shear strength mechanical parameters of the soil after grouting, wherein the shear strength reflects the soil's ability to resist shear failure, and the compressive modulus characterizes the soil's deformation characteristics under vertical load.

[0014] Preferably, the model described in step S5 reflects the multi-layer soil mechanical equilibrium state. Bearing capacity data of deep and shallow reinforcement are obtained from field tests and sensors. By deploying pressure sensors and displacement gauges in the pile foundation and grouting area, the soil stress and settlement after reinforcement are monitored in real time to generate bearing capacity data. The bearing capacity data of shallow and deep reinforcement are integrated through data fusion technology to obtain a unified multi-layer soil mechanical parameter set. The data fusion technology integrates data from different sources through weighted averaging or statistical analysis to form a parameter set that reflects the overall mechanical state of the foundation.

[0015] Preferably, the indicators in step S6 are based on the optimization results of pile diameter and pile material configuration, and the geological parameters of the target construction area, including soil layer distribution, groundwater level and mechanical parameters, are obtained from the geological condition database. Finite element analysis software is used to simulate the initial configuration of pile diameter and pile material to generate preliminary pile foundation bearing capacity data.

[0016] Preferably, based on the preliminary pile foundation bearing capacity data, construction parameters are adjusted, and dynamic deformation trend data is generated iteratively through construction simulation software. The construction simulation software generates data reflecting the dynamic response of the foundation by simulating the application of loads and soil deformation during the construction process.

[0017] Preferably, the response in step S7 is used to evaluate the overall structural stability, including obtaining an initial data set from the basic stability index, adopting a preset sequence adjustment rule, and determining the adjustment parameters of the composite reinforcement sequence through logical judgment.

[0018] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. This invention significantly improves the bearing capacity of weak foundations by optimizing shallow and deep reinforcement schemes and their synergistic effect. In actual engineering, the settlement of the surface soil after grouting can be reduced by more than 30%, and the composite reinforcement scheme can control the settlement of the foundation within the design requirement of 10 mm, effectively reducing the overall settlement of the foundation, meeting the long-term stability requirements of the gate foundation structure, and providing a reliable guarantee for the safe implementation of the project.

[0019] 2. This invention fully considers the diversity of complex foundation engineering geological conditions, such as uneven soil layer distribution, groundwater level fluctuations, soil strength differences, and the presence of hard interlayers. By constructing a three-dimensional geological model, simulating different reinforcement processes, and dynamically adjusting parameters, it can rationally design and optimize construction steps according to different geological conditions. This allows for seamless connection and efficient collaboration between shallow and deep reinforcement under different soil layer characteristics. For example, suitable reinforcement schemes can be formulated for coastal silt foundations, sandy soil and clay mixed foundations, and foundations with local hard interlayers, avoiding the reinforcement failure problem caused by improper parameter settings in traditional reinforcement methods.

[0020] 3. This invention utilizes numerical simulation, data analysis, and dynamic monitoring technologies to fully simulate and optimize the reinforcement scheme before construction, reducing uncertainties during construction. Through real-time monitoring and data analysis, the grouting points and pile foundation layout can be quickly adjusted, avoiding rework caused by geological complexity and improving construction efficiency. At the same time, by optimizing the pile foundation layout through genetic algorithms, the bearing capacity of the foundation is maximized while the cost is minimized, thus improving economic benefits. Attached Figure Description

[0021] Figure 1 A flowchart illustrating a preferred embodiment of the composite reinforcement optimization method for weak and complex foundations provided by the present invention; Detailed Implementation The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] Please see Figure 1 As shown, a composite reinforcement optimization method for weak and complex foundations is used to improve the stability of gate foundation structures. The technical solution of this invention is described in detail below with reference to specific embodiments: Step S1: Obtain engineering geological data and gate foundation structure requirements parameters for soft and complex foundations, construct a three-dimensional geological model, and obtain the stress distribution characteristics of the surface soil, including the soil stress field distribution and interlayer mechanical transfer characteristics.

[0023] Specifically, relevant data are extracted from the engineering geological survey report and the gate foundation design documents. The engineering geological survey report typically includes the physical and mechanical parameters of the foundation soil, such as soil density, porosity, and moisture content, while the gate foundation design documents provide structural design requirements, such as bearing capacity thresholds and settlement control standards. It should be noted that these data can come from on-site investigations, laboratory tests, or historical geological archives.

[0024] For example, in one implementation, the survey report may originate from borehole sampling in areas with weak foundations, recording the distribution of soil layers such as clay and silt, while the design documents specify the maximum allowable settlement and minimum bearing capacity requirements of the gate foundation. After acquiring the raw data, geographic information system software is used to clean and convert the geological data and structural parameters.

[0025] Specifically, the data cleaning process includes removing redundant items, missing values, or inconsistent formats from the data. For example, soil parameters in a survey report may be recorded in different units, such as compression modulus expressed in kilopascals (kPa) and compression modulus in megapascals (MPA), which need to be converted to standard units. Format conversion ensures that the data is compatible with the input requirements of 3D modeling software. Preferably, if data is missing, such as incomplete mechanical parameters for a soil layer, it is supplemented from a geological database.

[0026] Regional geological databases can be accessed to obtain soil parameters for similar geological conditions, filling data gaps. Through the above steps, a standardized geological dataset and structural parameter set are obtained, including the physical and mechanical parameters of the soil and the structural design requirements of the gate foundation. Based on the standardized geological dataset and structural parameter set, a three-dimensional geological model is constructed using 3D modeling software.

[0027] Specifically, 3D modeling software generates a model that reflects the true state of the foundation by digitally processing soil layering information, groundwater level distribution, and terrain features.

[0028] In one embodiment, for the silt and clay layers of a weak foundation, modeling software is used to map the geometric boundaries and mechanical parameters of the soil into a three-dimensional mesh structure. Furthermore, stereomicroscopy is used to digitally characterize the soil properties of the weak foundation. Stereomicroscopy, through high-resolution imaging, analyzes the microstructure and pore distribution of soil particles, generating a digital description of the soil properties.

[0029] The silt layer has high porosity and fine particles, characteristics that directly affect stress transmission. Ultimately, a three-dimensional geological model incorporating soil properties and the gate foundation structure was obtained. This model includes not only soil stratification information but also the geometry and design parameters of the gate foundation. Geometric information and mechanical parameters of the surface soil were extracted from the three-dimensional geological model, and the stress field distribution of the surface soil was calculated using finite element analysis software.

[0030] Geometric information includes the thickness and boundaries of soil layers, while mechanical parameters include compressive modulus and shear strength. Finite element analysis software divides the soil into multiple computational units by meshing, simulating the stress distribution under external loads.

[0031] In one possible implementation, for the vertical load on the gate foundation, the stress values ​​of the surface soil at different depths are calculated, generating a stress field distribution map. It should be noted that the stress field distribution must meet a preset threshold, such as the maximum stress not exceeding the ultimate bearing capacity of the soil. If the stress value exceeds the limit, the model parameters need to be adjusted or supplemented with data, and the calculation recalculated. Through the above analysis, the stress distribution characteristics of the surface soil are obtained, reflecting the mechanical response of the soil under load. Based on the stress distribution characteristics of the surface soil, the discrete element method is used to analyze the interlayer mechanical transfer characteristics.

[0032] The discrete element method (DEM) analyzes the stress transfer patterns between different soil layers by simulating the interactions between soil particles.

[0033] Between the silt layer and the underlying clay layer, stress transfer may be weakened due to differences in porosity. Preferably, the dynamic changes in interlayer stress transfer are determined through iterative calculations.

[0034] In one embodiment, considering the high compressibility of the silt layer in a weak foundation, the model simulates how upper loads are transferred to lower layers through particle contact, generating a dynamic distribution map of interlayer mechanical transfer characteristics. Ultimately, the interlayer mechanical transfer characteristics are obtained, including the mechanical interaction patterns between different soil layers, providing a basis for subsequent reinforcement scheme design.

[0035] Step S2: Based on the stress distribution characteristics of the surface soil, simulate the grouting diffusion path in shallow reinforcement to determine the material injection points and depth range. It should be noted that the grouting diffusion path is influenced by both soil permeability and grouting pressure distribution. In one embodiment, based on the stress distribution characteristics and mechanical parameters of the surface soil, finite element analysis software is used to calculate the soil permeability and pore characteristics.

[0036] Permeability reflects the soil's ability to penetrate grouting materials, while pore characteristics determine the diffusion range of the grouting materials.

[0037] For silt layers with high porosity, the grouting material may diffuse rapidly, while clay layers have lower permeability and a smaller diffusion range. Finite element analysis was used to generate initial simulation results of the grout diffusion path, including the approximate flow trajectory of the material in the soil. Based on the initial simulation results and grouting pressure distribution, computational fluid dynamics software was used to optimize the grout diffusion path.

[0038] Computational fluid dynamics software optimizes the injection point and depth range by simulating the flow behavior of grouting materials in soil pores.

[0039] In one possible implementation, given the high permeability of the silt layer, the grouting pressure is adjusted to control the material diffusion rate, ensuring that the grout covers the target reinforcement area. Preferably, if the simulation results show that the grout diffusion path deviates from a preset threshold, such as insufficient coverage or diffusion into non-target areas, the grouting flow rate and material selection need to be adjusted.

[0040] Lower viscosity cement grout can be used to improve permeability, or grouting pressure can be increased to expand coverage. Through iterative optimization, an optimized grout diffusion path is obtained, clarifying the coordinates and grouting depth range of each injection point. Based on the optimized grout diffusion path and the foundation bearing capacity, numerical simulation tools are used to verify the shallow reinforcement effect.

[0041] Numerical simulation tools assess the improvement in bearing capacity of the reinforced area by simulating changes in soil mechanical parameters after grouting.

[0042] In one embodiment, the simulation checks whether the compressive modulus of the silt layer significantly increases after grouting, and verifies whether it meets the design bearing capacity threshold. It should be noted that if the simulation results show insufficient reinforcement, the grouting parameters need to be readjusted, such as increasing the density of grouting points or changing the material mix ratio. Through the above steps, the feasibility of the grouting scheme is determined, ensuring that the shallow reinforcement scheme can effectively improve the stability of the surface soil.

[0043] Step S3: If the material injection point and depth range exceed the preset bearing threshold, adjust the grouting parameters to generate an optimized shallow reinforcement scheme.

[0044] Extract the material injection points and depth range from the geological data to determine whether they exceed the preset bearing threshold.

[0045] In one implementation, the preset bearing capacity threshold is based on the maximum allowable stress in the gate foundation design document. If the grouting depth at a certain injection point is too large, it may lead to localized stress concentration in the soil, exceeding the threshold. A list of exceeding limits is generated for each point and depth, recording the specific location and degree of exceeding the limit. Preferably, data analysis methods are used to adjust the grouting parameters. By reducing grouting pressure or decreasing the amount of grout injected at a single point, an optimized set of grouting parameters is generated. Based on this optimized set of grouting parameters, a shallow reinforcement scheme is generated, and the reinforcement distribution data of the surface soil is determined.

[0046] The reinforcement distribution data reflects the improvement of soil mechanical parameters after grouting, such as the increase in compression modulus and shear strength.

[0047] In one possible implementation, the stress distribution of the surface soil after grouting is calculated through numerical simulation to generate a mechanical parameter distribution map of the reinforced area. Real-time monitoring equipment, such as pressure sensors and displacement gauges, is used to acquire stability data of the surface soil to determine whether it meets the soil bearing capacity index. It should be noted that stability data includes soil settlement, stress distribution uniformity, etc. If the data does not meet the requirements, the grouting parameters need to be further optimized. Finally, the enhanced soil bearing capacity data is obtained, providing a foundation for subsequent deep reinforcement. In one embodiment, considering the high compressibility of the silt layer in a weak foundation, the grouting scheme needs to specifically consider its low permeability and high porosity.

[0048] Low-viscosity grouting materials can be selected, and the soil density can be gradually increased by grouting at multiple points with small doses.

[0049] In coastal silt foundations, grouting points can be evenly distributed around the perimeter of the gate foundation, with the depth controlled within 2 to 3 meters of the topsoil to avoid disturbing deeper soil layers. Through multiple iterative simulations, the grouting coverage is ensured to match design requirements, thereby effectively improving the bearing capacity of the topsoil. In another embodiment, for foundations composed of a mixture of sandy and clay soils, the grouting scheme needs to be adjusted based on the permeability differences between soil layers.

[0050] Sandy soil layers have high permeability, allowing grouting materials to easily diffuse, while clay layers require higher grouting pressure to ensure material penetration. Preferably, a layered grouting strategy can be adopted, first performing low-pressure grouting on the sandy soil layer, and then high-pressure grouting on the clay layer. By monitoring pressure changes and soil displacement in real time during the grouting process, grouting parameters can be dynamically adjusted to ensure uniform distribution of the reinforcement effect. This layered grouting method can effectively cope with the diversity of complex foundations and improve the adaptability of the reinforcement scheme. It should be noted that the above steps, through optimization of the stress distribution characteristics of the surface soil and the grout diffusion path, significantly improve the bearing capacity of weak foundations.

[0051] In practical engineering, the settlement of the surface soil after grouting can be reduced by more than 30%, meeting the stability requirements of the gate foundation structure. Simultaneously, through real-time monitoring and data analysis, the grouting scheme can quickly adapt to different geological conditions, avoiding reinforcement failures caused by improper parameter settings in traditional reinforcement methods. In one possible implementation, for scenarios with high groundwater levels in soft foundations, the grouting scheme needs to additionally consider the impact of groundwater on the diffusion of the grouting material.

[0052] Groundwater may dilute grouting materials or alter their diffusion path, so it is necessary to determine the groundwater level and flow characteristics through pumping tests before grouting.

[0053] In coastal silt foundations, the groundwater level can be lowered by pumping water before grouting to ensure uniform material distribution. This method results in more stable grouting effects and more significant improvement in soil mechanical parameters. Preferably, to address the potential for uneven settlement in complex foundations, a dynamic adjustment mechanism can be introduced during the grouting process.

[0054] By deploying displacement sensors near the grouting points, soil settlement is monitored in real time. If settlement is significant in a particular area, the amount of grout injected in that area is reduced, while the density of grouting points in adjacent areas is increased. This dynamic adjustment mechanism effectively balances soil stress distribution and improves the overall stability of the reinforcement scheme. Through these steps, the surface reinforcement optimization of soft and complex foundations is completed, forming a stable surface soil bearing capacity, providing reliable data support for subsequent deep reinforcement.

[0055] Step S4: Extract the enhanced surface soil stability data from the optimized shallow reinforcement scheme, input it into the deep reinforcement simulation module, and optimize the deep pile foundation layout.

[0056] The enhanced surface soil stability data includes mechanical parameters such as the soil compression modulus and shear strength after grouting. These data reflect the improvement in the mechanical properties of the surface soil after shallow reinforcement.

[0057] In one embodiment, the compressive modulus of the surface silt layer after grouting may increase from the initial 5 MPa to 10 MPa, significantly enhancing the bearing capacity of the foundation. Further deep soil properties, such as the mechanical parameters of deep clay or sand layers, including density, porosity, and internal friction angle, are obtained from geological survey data. Preferably, the shear strength of the surface soil and the foundation compressive modulus are calculated using soil mechanical parameters to generate enhanced surface layer data.

[0058] Shear strength reflects the soil's ability to resist shear failure, while compression modulus characterizes the soil's deformation properties under vertical load.

[0059] In coastal soft soil foundations, the shear strength of the surface silt is low, requiring grouting reinforcement to improve its shear resistance. Based on the enhanced surface layer data and combined with the characteristics of the deep soil, the data is input into the deep reinforcement simulation module. The deep reinforcement module uses finite element analysis to calculate the settlement data and soil stress distribution of the foundation under different load conditions, generating pile foundation geometric parameters, including pile diameter, pile length, and pile spacing.

[0060] In one possible implementation, considering the high compressibility of deep clay layers, the initial geometric parameters of the pile foundation are determined by simulating how the pile foundation transmits loads through friction. It should be noted that the pile foundation geometric parameters must meet preset thresholds, such as a maximum settlement not exceeding 20 mm as designed. If the parameters do not meet the requirements, the pile depth or number is adjusted, and the calculation is repeated. A genetic algorithm is used to optimize the pile spacing and pile length depth, combined with the pile material strength, to generate a deep pile foundation layout.

[0061] Genetic algorithms use iterative search to find the optimal pile foundation layout scheme, ensuring that the foundation bearing capacity is maximized while the cost is minimized.

[0062] In one embodiment, for deep clay layers in soft foundations, the optimized pile spacing is 2.5 meters, and the pile length and depth are 15 meters, using high-strength concrete piles to improve bearing capacity. Based on the deep pile layout and combined with foundation stability analysis, the pile bearing capacity and bearing capacity distribution are calculated, and the bearing capacity improvement effect is obtained through a composite bearing capacity index formula. The composite bearing capacity index comprehensively considers the single pile bearing capacity and the group pile effect, reflecting the contribution of deep reinforcement to the overall stability of the foundation.

[0063] The optimized pile foundation layout can increase the bearing capacity of the foundation by more than 30%, significantly reducing the risk of settlement. In one embodiment, for deep reinforcement of coastal silt foundations, the pile foundation layout needs to take into special consideration the influence of groundwater level and soil layer interface.

[0064] Deep clay layers have high porosity, requiring pile foundations to penetrate into more stable sand layers to ensure bearing capacity. Preferably, precast concrete piles can be used, with a pile diameter controlled between 0.5 and 0.8 meters, and the pile length dynamically adjusted according to soil depth. Finite element analysis is used to simulate the stress distribution of the pile foundation under different loads to verify whether it meets design requirements. This method effectively improves the stability of deep foundations and adapts to complex geological conditions.

[0065] Step S5: Obtain the load-bearing capacity improvement effect data of deep reinforcement, integrate the interactive response of shallow reinforcement and deep reinforcement, and generate a composite reinforcement sequence model under the gate foundation structure.

[0066] Load-bearing capacity data for deep and shallow reinforcement were obtained from field tests and sensors.

[0067] In one implementation, pressure sensors and displacement gauges are deployed in the pile foundation and grouting area to monitor soil stress and settlement in real time after reinforcement, generating bearing capacity data. Preferably, data fusion technology is used to integrate the bearing capacity data of shallow and deep reinforcement to obtain a unified set of multi-layer soil mechanical parameters.

[0068] Data fusion technology integrates data from different sources through weighted averaging or statistical analysis to form a parameter set that reflects the overall mechanical state of the foundation.

[0069] The compression modulus data of the shallow grouting zone and the frictional force data of the deep pile foundation were integrated into a unified mechanical parameter table. Response analysis was used to process the multi-layer soil mechanical parameter set, extracting the interactive response characteristics of deep and shallow reinforcement. These interactive response characteristics reflect the synergistic effect of shallow grouting and deep pile foundation in load transfer.

[0070] In soft soil foundations, shallow grouting increases the stiffness of the surface soil, while deep pile foundations transfer the load to deeper layers through friction. Together, these two processes reduce the overall settlement of the foundation. It should be noted that if the interactive response characteristics exceed a preset threshold, such as uneven stress transfer between the shallow and deep soil layers, a sequence generation algorithm is used to process the interactive response characteristics and generate a composite reinforcement sequence model. The sequence generation algorithm simulates the mechanical equilibrium state of multiple soil layers to generate sequence data reflecting the foundation's stability.

[0071] In one possible implementation, for the interaction between the silt layer and the deep clay layer, the algorithm generates sequential data containing stress distribution and settlement changes through iterative calculations. Stability analysis is then performed based on the sequential data, and the composite reinforcement sequence model is optimized by incorporating dynamic response characteristics.

[0072] Stability analysis compares sequence data with design thresholds to determine whether the foundation meets bearing capacity requirements. If localized unstable areas exist, the shallow grouting points or deep pile arrangement are adjusted, and a new sequence model is generated. Ultimately, sequence data reflecting the equilibrium state of multiple soil layers is obtained, ensuring a significant improvement in foundation bearing capacity. In one embodiment, the interaction response between shallow grouting and deep piles is particularly important for soft foundations with high groundwater levels.

[0073] The increased soil stiffness in the grouting area will change the friction distribution of the pile foundation, so it is necessary to monitor the stress changes in the interaction area in real time using sensors.

[0074] Multiple sensors can be deployed at the grouting area and the junction of the pile foundation to record stress fluctuations during load transfer, thus optimizing the synergy of the reinforcement scheme. This method can effectively reduce uneven settlement of the foundation and improve overall stability.

[0075] Step S6: For the composite reinforcement sequence model, iteratively simulate the dynamic deformation trend during construction to determine the final foundation stability verification index.

[0076] Geological parameters of the target construction area, including soil layer distribution, groundwater level, and mechanical parameters, are obtained from a geological condition database. Finite element analysis software is used to simulate the initial configuration of pile diameter and pile material to generate preliminary pile foundation bearing capacity data.

[0077] In one implementation, for deep clay layers, the initial configuration uses concrete piles with a diameter of 0.6 meters and a length of 15 meters, and their bearing capacity is calculated using finite element analysis. Based on the preliminary pile foundation bearing capacity data, construction parameters, such as pile diameter or material strength, are adjusted, and dynamic deformation trend data is iteratively generated using construction simulation software.

[0078] Construction simulation software generates data reflecting the dynamic response of the foundation by simulating the application of loads and soil deformation during the construction process.

[0079] In coastal silt foundations, the soil compression effect during pile driving is simulated to analyze the settlement trend of the foundation. Preferably, if the dynamic deformation distribution results exceed a preset threshold, such as local settlement exceeding the design requirement of 20 mm, the pile diameter and pile material configuration are optimized.

[0080] The pile diameter can be increased to 0.8 meters or replaced with reinforced concrete piles. The construction simulation can then be run again to obtain optimized dynamic deformation trend data. Key deformation points, such as the maximum settlement area or stress concentration point, can be extracted from the optimized dynamic deformation trend data, and the foundation stability can be quantified through the verification index calculation module.

[0081] In one possible implementation, verification indicators include the uniformity of foundation settlement and the uniformity of bearing capacity distribution, comprehensively reflecting the stability of the foundation. Ultimately, final foundation stability verification indicators are determined to provide guidance for subsequent construction. In one embodiment, for scenarios involving localized sand layers in soft foundations, construction simulations must specifically consider the impact of the low compressibility of the sand layers on the pile foundation.

[0082] Sand layers may cause uneven lateral resistance when piles are driven into the ground, so it is necessary to optimize the pile driving depth and spacing through simulation.

[0083] The pile foundation depth can be adjusted to penetrate the sand layer and enter the underlying clay layer to ensure stable bearing capacity. Through multiple iterative simulations, it is ensured that the dynamic deformation trend meets the design requirements, reducing the risk of foundation failure during construction.

[0084] Step S7: Based on the final foundation stability verification index, adjust the grid arrangement pattern in the composite reinforcement sequence model to obtain the composite load-bearing capacity enhancement response.

[0085] Specifically, an initial data set is obtained from basic stability indicators, including foundation settlement, stress distribution, and bearing capacity data. Pre-defined sequence adjustment rules are used to determine the adjustment parameters of the composite reinforcement sequence through logical judgment.

[0086] In one implementation, if the settlement in a certain area is large, the pile density or grouting points in that area are increased to generate an optimized composite reinforcement sequence. Based on the optimized composite reinforcement sequence, a grid optimization algorithm is used to adjust the grid layout.

[0087] Mesh optimization algorithms optimize the mechanical balance of reinforcement schemes by adjusting mesh density and distribution.

[0088] For example, in silt foundations, the grid density can be appropriately increased in high settlement areas to improve local bearing capacity. Preferably, if the grid density distribution exceeds the bearing capacity threshold, such as when local stress concentration exceeds the soil's ultimate bearing capacity, the grid density distribution is recalculated to obtain an adjusted grid layout. Using the adjusted grid layout, the structural stress distribution is obtained, and a composite bearing response is generated using finite element analysis tools.

[0089] In one possible implementation, the stress distribution of the foundation under different load conditions is simulated in an adjusted grid pattern, generating a response dataset containing stress peaks and distribution uniformity. Stability assessment parameters, such as the overall settlement of the foundation and stress distribution uniformity, are extracted from this response dataset. If the overall structural stability falls below a preset threshold, such as settlement exceeding the design requirement of 15 mm, the structural stress distribution is adjusted. For example, by increasing the number of piles or optimizing grouting points, the response data can be recalculated. Ultimately, a composite bearing capacity enhancement response reflecting the overall structural stability is obtained, ensuring the long-term stability of the gate foundation structure. In one embodiment, for the complex geological conditions of coastal soft foundations, grid optimization needs to take into special consideration the heterogeneity of the soil layers.

[0090] There may be abrupt stress changes at the interface between the silt layer and the sand layer. Therefore, it is necessary to increase the mesh density in the interface area through mesh optimization algorithms to ensure uniform stress transfer.

[0091] High-density grouting points can be arranged at the junction, combined with deep pile foundations, to form a synergistic reinforcement effect. Finite element analysis is used to verify whether the adjusted mesh pattern meets the stability requirements. This method can effectively address the mechanical imbalance problem of complex foundations and improve the overall structural stability. In one possible implementation, for weak foundations with high groundwater levels, the optimization of the composite reinforcement sequence model needs to specifically consider the impact of groundwater on the reinforcement effect.

[0092] Groundwater may cause uneven diffusion of grouting materials or reduce the friction of pile foundations. Therefore, it is necessary to determine the dynamic changes of groundwater level through pumping tests before construction.

[0093] In coastal areas, temporary drainage systems can be installed to lower the groundwater level in the construction area, ensuring the stability of grouting and pile foundation construction. Preferably, during construction, reinforcement parameters can be dynamically adjusted by monitoring changes in groundwater level and soil stress in real time.

[0094] If a rise in groundwater level in a certain area is detected, leading to a weakening of the grouting effect, the grouting pressure is increased or the pile depth is adjusted to ensure the reinforcement effect. This dynamic adjustment mechanism can significantly improve the adaptability of the reinforcement scheme and reduce construction risks caused by changes in geological conditions. In another embodiment, for scenarios with localized hard interlayers in soft foundations, the optimization of the composite reinforcement sequence model needs to consider the impact of the interlayers on mechanical transfer.

[0095] Rigid interlayers may cause difficulties in driving piles or hinder the diffusion of grouting materials, so it is necessary to accurately determine the location and thickness of the interlayer through geological exploration.

[0096] A three-dimensional distribution map of the interlayer can be generated through borehole sampling and geophysical exploration to guide the design of reinforcement schemes. Preferably, the density of grouting points is reduced and the pile foundation depth is increased in the interlayer area to avoid hard interlayers and ensure effective load transfer to deep, stable soil layers. Through multiple iterative simulations, the grid pattern of the interlayer area is optimized to form a stable composite reinforcement sequence. This approach effectively addresses the local hardening problem of complex foundations and improves the implementation effect of reinforcement schemes. It should be noted that the above steps, through the synergistic optimization of shallow grouting and deep pile foundations, significantly improve the bearing capacity and stability of weak foundations.

[0097] In practical engineering, the composite reinforcement scheme can control the settlement of the foundation within 10 mm of the design requirement, meeting the long-term stability requirements of the gate foundation structure. Simultaneously, through dynamic monitoring and grid optimization, the scheme can quickly adapt to different geological conditions, avoiding the failure problems caused by improper parameter settings in traditional reinforcement methods.

[0098] In one embodiment, for the reinforcement project of coastal silt foundation, the grouting points and pile foundation layout are dynamically adjusted through real-time monitoring and data analysis during the construction process to ensure the uniformity of the reinforcement effect.

[0099] Increasing pile density in high-load areas and employing high-pressure grouting technology in low-permeability soil layers significantly improved the overall bearing capacity of the foundation. This approach not only improved construction efficiency but also reduced rework issues caused by geological complexity. Through these steps, the composite reinforcement and optimization of the soft and complex foundation was completed, forming a stable gate foundation structure bearing system and providing a reliable guarantee for the safe implementation of the project.

[0100] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0101] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A composite reinforcement optimization method for weak and complex foundations, characterized in that, Includes the following steps: S1. Obtain engineering geological data and gate foundation structural requirements parameters for soft and complex foundations, construct a three-dimensional geological model, and obtain the stress distribution characteristics of the surface soil. S2. Based on the stress distribution characteristics of the surface soil, simulate the grouting diffusion path in shallow reinforcement to determine the material injection points and depth range. S3. If the injection point and depth range of the material exceeds the preset bearing threshold, adjust the grouting parameters to generate an optimized shallow reinforcement scheme. S4. Extract the enhanced surface soil stability data from the optimized shallow reinforcement scheme, input it into the deep reinforcement simulation module, and optimize the deep pile foundation layout. S5. Obtain the load-bearing capacity improvement effect data of the deep reinforcement, integrate the interactive response of shallow reinforcement and deep reinforcement, and generate a composite reinforcement sequence model under the gate foundation structure. S6. For the composite reinforcement sequence model, iteratively simulate the dynamic deformation trend during construction to determine the final foundation stability verification index. S7. Based on the final foundation stability verification index, adjust the grid arrangement pattern in the composite reinforcement sequence model to obtain the composite load-bearing capacity enhancement response.

2. The composite reinforcement optimization method for weak and complex foundations according to claim 1, characterized in that: The features mentioned in step S1 include soil stress field distribution and interlayer mechanical transfer characteristics. The engineering geological data of the soft and complex foundation is obtained by extracting relevant data from the engineering geological survey report and the gate foundation design document. The engineering geological survey report contains the physical and mechanical parameters of the foundation soil. The construction of the three-dimensional geological model can be achieved by using three-dimensional modeling software to generate a model that reflects the true state of the foundation through digital processing of soil layer information, groundwater level distribution and topographic features.

3. The composite reinforcement optimization method for weak and complex foundations according to claim 1, characterized in that: The path described in step S2 is based on soil permeability and grouting pressure distribution. According to the stress distribution characteristics and mechanical parameters of the surface soil, the permeability and pore characteristics of the soil are calculated using finite element analysis software. Computational fluid dynamics software optimizes the injection point and depth range by simulating the flow behavior of grouting material in the soil pores.

4. The composite reinforcement optimization method for weak and complex foundations according to claim 1, characterized in that: The scheme described in step S3 includes enhanced surface soil stability data. The data is based on soil bearing capacity index, and the material injection points and depth ranges are extracted from geological data to determine whether they exceed the preset bearing threshold. The grouting parameters are adjusted using data analysis methods, low-viscosity grouting materials are selected, and the soil density is gradually increased by multi-point small-dose grouting.

5. The composite reinforcement optimization method for weak and complex foundations according to claim 1, characterized in that: The arrangement described in step S4 includes the pile spacing and pile length depth, resulting in a deep reinforcement effect that enhances the bearing capacity. This effect is calculated using a composite bearing capacity index.

6. The composite reinforcement optimization method for weak and complex foundations according to claim 1, characterized in that: The enhanced surface soil stability data mentioned in step S4 includes the compressive modulus and shear strength mechanical parameters of the soil after grouting, wherein the shear strength reflects the soil's ability to resist shear failure, and the compressive modulus characterizes the soil's deformation characteristics under vertical load.

7. The composite reinforcement optimization method for weak and complex foundations according to claim 1, characterized in that: The model described in step S5 reflects the multi-layer soil mechanical equilibrium state. Bearing capacity data of deep and shallow reinforcement are obtained from field tests and sensors. By deploying pressure sensors and displacement gauges in the pile foundation and grouting area, the soil stress and settlement after reinforcement are monitored in real time to generate bearing capacity data. The bearing capacity data of shallow and deep reinforcement are integrated through data fusion technology to obtain a unified multi-layer soil mechanical parameter set. The data fusion technology integrates data from different sources through weighted averaging or statistical analysis to form a parameter set that reflects the overall mechanical state of the foundation.

8. The composite reinforcement optimization method for weak and complex foundations according to claim 7, characterized in that: The indicators mentioned in step S6 are based on the optimization results of pile diameter and pile material configuration, and the geological parameters of the target construction area are obtained from the geological condition database, including soil layer distribution, groundwater level and mechanical parameters. Finite element analysis software is used to simulate the initial configuration of pile diameter and pile material to generate preliminary pile foundation bearing capacity data.

9. The composite reinforcement optimization method for soft and complex foundations according to claim 8, characterized in that: Based on the preliminary pile foundation bearing capacity data, construction parameters are adjusted, and dynamic deformation trend data is generated iteratively through construction simulation software. The construction simulation software generates data reflecting the dynamic response of the foundation by simulating the application of loads and soil deformation during the construction process.

10. The composite reinforcement optimization method for weak and complex foundations according to claim 1, characterized in that: The response described in step S7 is used to evaluate the overall structural stability, including obtaining an initial data set from the basic stability index, using a preset sequence adjustment rule, and determining the adjustment parameters of the composite reinforcement sequence through logical judgment.

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