CN115492082B - Composite foundation treatment method, equipment and application for deep soft foundation - Google Patents
Composite foundation treatment method, equipment and application for deep soft foundation Download PDFInfo
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Abstract
The invention belongs to the technical field of foundation engineering treatment, and discloses a composite foundation treatment method, equipment and application for a deep soft foundation. The composite foundation treatment method for the deep soft foundation comprises the following steps of: pumping the light cement soil pile to a long spiral drilling device for hole forming through a pipeline; pouring light cement soil in the process of lifting the drill, and completing the construction of the light cement soil pile; and then arranging a mattress layer on the pile top to finally form the light cement soil pile composite foundation. The light cement soil suspension pile treatment technology combines a pile type composite foundation method (process) and a light embankment method (concept), and has certain strength and the density of about 1g/cm 3 The lightweight pile is used for quickly reinforcing the soft soil foundation, and as the pile body density is less than the silt density, the lightweight pile is in a suspended state in the silt to counteract part of embankment load, and meanwhile, the lightweight pile is used for replacing the soft foundation to greatly reduce the dead weight stress of the foundation and effectively unload the lower negative soft soil.
Description
Technical Field
The invention belongs to the technical field of foundation engineering treatment, and particularly relates to a composite foundation treatment method, equipment and application for a deep soft foundation.
Background
In soft foundation settlement deformation, the effective stress principle is as follows: under the action of additional stress, the excess pore water pressure is continuously dissipated, and the effective stress is continuously increased; as the superpore pressure dissipates, free water in the soft soil is continuously discharged, whereby the soft soil volume compresses, manifesting as the occurrence of sedimentation. Among the factors affecting sedimentation deformation are: additional stress (determining the final settlement of the soft soil layer), consolidation degree (representing the proportion of the settlement completed at the current moment to the total settlement) and post-construction settlement (determining the final effect of roadbed treatment). Furthermore, two methods of controlling post-construction settlement deformation in the prior art are as follows:
first kind: reducing the additional stress of the soft soil layer; the common implementation method comprises the following steps: pile type composite foundation method (setting stirring pile, CFG pile or prestressed pipe pile with certain density, transferring most of the overburden load to pile body, reducing load and additional stress born by soft soil), light embankment method (adopting light embankment such as fly ash embankment and foam light soil embankment, reducing load of embankment and additional stress of soft soil layer).
Second kind: the consolidation degree during completion is improved, and sedimentation after completion is controlled; the common implementation method is that the drainage pre-pressing method (through the vertical drainage body and vacuum pre-pressing (preloading) to quicken the consolidation rate of the soft soil layer, improve the consolidation degree when completing the construction and reduce the sedimentation after the construction.
Through the above analysis, the problems and defects existing in the prior art are as follows:
(1) The prior art does not combine a pile type composite foundation method (process) and a lightweight embankment method (concept), so that the self-weight stress of the foundation can not be effectively reduced, and the downward negative soft soil can not be effectively unloaded.
(2) The prior art construction process is tedious, the cost is high, and the foundation obtained by the prior art can not achieve the effects of drainage, buffering and collaborative deformation with the roadbed.
Disclosure of Invention
In order to overcome the problems in the related art, the disclosed embodiments of the present invention provide a composite foundation treatment method, apparatus and application for deep soft foundation.
The technical scheme is as follows: the composite foundation treatment method for the deep soft foundation comprises the following steps of:
s1, pumping the light cement soil pile to a long spiral drilling device for hole forming through a pipeline;
s2, pouring light cement soil in the process of lifting the drill, and completing construction of the light cement soil pile;
and S3, arranging a mattress layer on the pile top to finally form the light cement soil pile composite foundation.
In one embodiment, in step S1, the lightweight soil cement pile is made of foamed lightweight soil cement, and the lightweight material formed after introducing a large amount of bubbles into the concrete by adding bubbles, a foaming agent or an air entraining agent.
In one embodiment, the foamed lightweight soil cement has a slurry bulk weight of 1g/cm 3 The mass volume ratio of the foaming agent or the air entraining agent to the foamed lightweight cement soil is 0.01v/m-0.05v/m, and the gas of the bubbles is nitrogen or inert gas.
In one embodiment, the method for preparing the foamed lightweight cement soil comprises the following steps:
i) The method comprises the steps of using foam lightweight cement mixing equipment, using a first flow sensor to measure first flow L1 of foam cement slurry at an outlet of the foam lightweight cement mixing equipment, and using the first flow sensor to measure second flow L2 of cement slurry entering the foam lightweight cement mixing equipment;
ii) presetting a target value of the target foamed cement slurry density ρ1, wherein the density regulating device regulates the density ρ1 of the foamed cement slurry by regulating the second flow rate L2 by the following formula ρ1=ρ2×l2/L1.
In one embodiment, the method for adjusting the density ρ1 of the foamed cement slurry by adjusting the second flow rate L2 by the density adjusting apparatus comprises:
firstly, constructing a foam cement slurry flow prediction model based on time and flow rate;
step two, determining a density regulation prediction model framework structure based on a multi-layer perceptron;
thirdly, foam cement slurry flow speed modeling based on multi-layer perceptron integrated learning is carried out, and a prediction result is obtained through a density regulation prediction model based on the multi-layer perceptron.
In one embodiment, the construction of the foam cement slurry flow prediction model based on time and flow rate comprises the following steps:
based on the flow rate dependence modeling of the convolutional neural network, the convolutional neural network CNNS modeling is provided; extracting flow velocity information between foam cement slurries through a convolutional neural network, and adopting a residual neural network;
the residual neural network adopts a ResNet50 network, and the level parameters are described as follows:
zero block: filling matrix (3, 3) is zero filling of 3 rows and 3 columns, namely the original input foam cement slurry matrix is (2, 2) in size, the filled foam cement slurry matrix is (5, 5) in size, and the rest parts are all zero;
CONV block: the 64 convolution kernels have the size of (7, 7), and the convolution step length is the two-dimensional convolution of (2, 2);
BatchNorm block: batch normalization;
ReLU block: the Relu activation function, the formula is defined as follows:
MAXPOOL block, AVGPOOL block: maximum pooling layer, size (3, 3), step size (2, 2); average pooling, size (2, 2), step size (1, 1);
CONVBLOCK block: performing matrix addition operation on the results of the previous layers of x of the previous layers through a short 'path', wherein the size of the foamed cement paste is scaled to the range of [ -1,1] through a two-dimensional convolution operation and batch normalization operation, and finally outputting through a Relu activation function;
IDBLOCK x n block: x directly performs matrix addition operation on the output result of the previous layer through a short path; n represents a plurality of identical IDBLOCK blocks linked together;
the flat block: flattening the input into a one-dimensional foamed cement paste; the size is (M, -1), M represents the sample number, and-1 represents the input sample matrix foam cement slurry synthesis;
FC block: the size of the full-connection layer is (H, N), H represents the input dimension of the upper layer, and N represents the output dimension of the foam cement slurry to be predicted;
BigRNN is a deep neural network, and Layer1 and Layer2 are additionally added residual block networks, and the assumption that Layer2 does not go through the output of the activation function is not z l+2 Output a l+2 The formula of (c) is defined as follows:
a l+2 ＝g(z l+2 +a l )；
wherein g represents a Relu activation function; the extended formula becomes:
a l+2 ＝g(w l+2 x+b l+2 +a l )；
wherein w is l+2 、b l+2 Weights and paraphrases for Layer2 layers; if w l+2 =0, at the same time b l+2 =0, then a l+2 Will be equal to a l The performance of the network is not changed after the residual block is added;
assuming that the output in the neural network that is not batch normalized is z i Where i=1, 2, 3..where n represents n number of samples, z i The output result after batch normalization is represented, and the calculation formula is defined as follows:
wherein epsilon represents a minimum number not smaller than zero, and eta and beta are parameters learned by a neural network; let the input of the neural network be x t The selected batch size is gamma, wherein gamma is more than 0 and less than or equal to m, and m isTotal number of samples, number of batch normalization
In one embodiment, the η, β calculation process is performed with a minimum batch gradient descent algorithm as follows:
For t＝1，2，3……n；
at all x t Forward propagation is performed on the sample, and the z is obtained by using a batch normalization technology l L represents the neural network layer l, each gradient is calculated using a back propagation technique: dw (dw) l ,dη l ,dβ l ；
Updating parameters: w (w) l ＝w l -αdw l ，η l ＝η l -αdη l ，β l ＝β l -αdβ l Wherein α represents a learning rate; and obtaining a final required prediction result.
In one embodiment, in step S3, a mattress pad is provided on the pile top for drainage, cushioning and deformation in cooperation with the roadbed.
The invention further aims to provide an application of the composite foundation treatment method for the deep soft foundation in cross-sea bridge construction and soft soil layer traffic engineering pile pier construction.
Another object of the present invention is to provide a composite foundation treatment apparatus for a deep soft foundation, which implements the composite foundation treatment method for a deep soft foundation.
By combining all the technical schemes, the invention has the advantages and positive effects that:
firstly, aiming at the technical problems in the prior art and the difficulty of solving the problems, the technical problems solved by the technical proposal of the invention, the results in the research and development process, the foam cement slurry and the like are closely combined, the technical problems solved by the technical proposal of the invention are analyzed in detail and deeply, and some technical effects brought after the problems are solved are provided with creativity: the foamed light soil (foamed concrete) is a light material formed by introducing a large amount of bubbles into the concrete by adding bubbles, a foaming agent or an air entraining agent and the like. The material has the advantages of maintaining the advantages of concrete and reducing the volume weight, so that the foam lightweight soil is often used as road embankment filler. Foam light soil used in road construction at present is often prepared in a cast-in-situ method and a physical and chemical foaming combination mode. But have certain technical drawbacks. The invention also solves the problems of the complete construction process and the quality control technology of the light cement soil pile in the prior art. The introduction of new materials has great influence on construction process and control measures, and complete technology for site construction and quality control is needed to be formed, but the prior art is not perfect.
Secondly, the technical proposal is regarded as a whole or from the perspective of products, and the technical proposal to be protected has the technical effects and advantages as follows: the invention provides a light cement soil suspension pile treatment technology theory by analyzing the combination of a pile type composite foundation method and a light embankment method. The light cement soil suspension pile treatment technology combines a pile type composite foundation method (process) and a light embankment method (concept), and has certain strength and density of about 1g/cm 3 The lightweight pile is used for quickly reinforcing the soft soil foundation, and as the pile body density is less than the silt density, the lightweight pile is in a suspended state in the silt to counteract part of embankment load, and meanwhile, the lightweight pile is used for replacing the soft foundation to greatly reduce the dead weight stress of the foundation and effectively unload the lower negative soft soil. The composite foundation treatment method consists of combining soil between long spiral filling piles of light cement soil and crushed stone mattress layers, wherein the light cement soil piles are formed by combining light cement soil slurry (volume weight 1 g/cm) 3 ) And (5) pumping the light cement soil into long spiral drilling equipment to form holes through a pipeline, and pouring the light cement soil in the drilling process to finish the construction of the light cement soil pile. And then, a mattress layer is arranged on the pile top to play roles in draining, buffering and collaborative deformation with the roadbed, and finally, the light cement soil pile composite foundation is formed. Slurry volume weight of the foam lightweight cement soil is 1g/cm 3 The method comprises the steps of carrying out a first treatment on the surface of the The mass volume ratio of the foaming agent or the air entraining agent to the foamed lightweight cement soil is 0.01v/m-0.05v/m; the gas of the bubbles is nitrogen or inert gas. Through the technical scheme, the gas capable of effectively controlling bubbles is nitrogen or inert gasThe addition amount of the body can effectively control the slurry volume weight of the foam lightweight cement soil to be 1g/cm 3 Left and right, the intelligent degree is high.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the disclosure and together with the description, serve to explain the principles of the disclosure.
Fig. 1 is a flowchart of a composite foundation treatment method for a deep soft foundation according to an embodiment of the present invention.
Detailed Description
In order that the above objects, features and advantages of the invention will be readily understood, a more particular description of the invention will be rendered by reference to the appended drawings. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. The invention may be embodied in many other forms than described herein and similarly modified by those skilled in the art without departing from the spirit or scope of the invention, which is therefore not limited to the specific embodiments disclosed below.
1. Explanation of the examples:
examples
According to the embodiment of the invention, the technical theory of light cement-soil suspension pile treatment is provided by analyzing the combination of a pile-type composite foundation method and a light embankment method.
The light cement soil suspension pile treatment technology combines a pile type composite foundation method (process) and a light embankment method (concept), and has certain strength and density of about 1g/cm 3 The lightweight pile is used for quickly reinforcing the soft soil foundation, and as the pile body density is less than the silt density, the lightweight pile is in a suspended state in the silt to counteract part of embankment load, and meanwhile, the lightweight pile is used for replacing the soft foundation to greatly reduce the dead weight stress of the foundation and effectively unload the lower negative soft soil.
As shown in fig. 1, the composite foundation treatment method for a deep soft foundation provided by the embodiment of the invention comprises construction of soil and a broken stone bedding layer between long spiral bored concrete piles of light weight cement soil and combination piles, and specifically comprises the following steps:
s101, light cementThe soil pile is prepared by mixing lightweight cement soil slurry (volume weight 1g/cm 3 ) Pumped through tubing to a long auger apparatus to form a hole.
S102, pouring light cement soil in the drilling process, and completing the construction of the light cement soil pile.
And S103, arranging a mattress layer on the pile top to play roles of draining, buffering and collaborative deformation with the roadbed, and finally forming the light cement soil pile composite foundation.
In a preferred embodiment, in step S101, the lightweight cement pile material is made of foamed lightweight cement soil, and the lightweight material is formed by introducing a large amount of bubbles into concrete by adding bubbles, adding a foaming agent, or adding an air entraining agent.
Slurry volume weight of the foam lightweight cement soil is 1g/cm 3 The method comprises the steps of carrying out a first treatment on the surface of the The mass volume ratio of the foaming agent or the air entraining agent to the foamed lightweight cement soil is 0.01v/m-0.05v/m; the gas of the bubbles is nitrogen or inert gas.
In a preferred embodiment, the method for preparing the foamed lightweight cement soil comprises the following steps:
i) The foam lightweight cement soil mixing equipment is utilized for carrying out:
measuring a first flow L1 of foam cement slurry at an outlet of the foam lightweight cement mixing equipment by using a first flow sensor, and measuring a second flow L2 of cement slurry entering the foam lightweight cement mixing equipment by using the first flow sensor;
ii) presetting a target value of the density ρ1 of the target foamed cement slurry, the density regulating apparatus regulating the density ρ1 of the foamed cement slurry by regulating the second flow rate L2 by the following formula;
ρ1＝ρ2*L2/L1。
in a preferred embodiment, the method for adjusting the density ρ1 of the foamed cement slurry by adjusting the second flow rate L2 by the density adjusting apparatus comprises:
firstly, constructing a foam cement slurry flow prediction model based on time and flow rate;
step two, determining a density regulation prediction model framework structure based on a multi-layer perceptron;
thirdly, modeling the flow speed of the foam cement slurry based on the integrated learning of the multi-layer perceptron, and obtaining a prediction result through a density regulation prediction model based on the multi-layer perceptron;
the construction of the foam cement slurry flow prediction model based on time and flow rate comprises the following steps:
convolutional neural network-based flow velocity dependence modeling
A convolutional neural network CNNS modeling is proposed; extracting flow velocity information between foam cement slurries through a convolutional neural network, and adopting a residual neural network;
classical ResNet50 networks were adopted, whose level parameters were described as follows:
zero block: filling the matrix (3, 3), zero filling of 3 rows and 3 columns; the original input foam cement slurry matrix is (2, 2) in size, and the filled foam cement slurry matrix is (5, 5); the excessive part is all zero;
CONV block: the 64 convolution kernels have the size of (7, 7), and the convolution step length is the two-dimensional convolution of (2, 2);
BatchNorm block: batch normalization;
ReLU block: the Relu activation function, the formula is defined as follows:
MAXPOOL block, AVGPOOL block: maximum pooling layer, size (3, 3), step size (2, 2); average pooling, size (2, 2), step size (1, 1);
CONVBLOCK block: performing matrix addition operation on the results of the previous layers of x of the previous layers through a short 'path', wherein the size of the foamed cement paste is scaled to the range of [ -1,1] through a two-dimensional convolution operation and batch normalization operation, and finally outputting through a Relu activation function;
IDBLOCK x n block: x directly performs matrix addition operation on the output result of the previous layer through a short path; n represents a plurality of identical IDBLOCK blocks linked together;
the flat block: flattening the input into a one-dimensional foamed cement paste; the size is (M, -1), M represents the sample number, and-1 represents the input sample matrix foam cement slurry synthesis;
FC block: the size of the full-connection layer is (H, N), H represents the input dimension of the upper layer, and N represents the output dimension of the foam cement slurry to be predicted;
the residual neural network ResNet is used for solving the 'jump link' technology which occurs in the deep neural network;
the reasons for the good performance of the ResNet network are summarized below: assuming a deeper neural network with an input of x and an output of a l The method comprises the steps of carrying out a first treatment on the surface of the Adding a residual block structure, wherein the activation functions in the network are Relu activation functions, namely all the outputs are greater than or equal to zero;
BigRNN is a deep neural network, and Layer1 and Layer2 are additionally added residual block networks, and the assumption that Layer2 does not go through the output of the activation function is not z l+2 Output a l+2 The formula of (c) is defined as follows:
a l+2 ＝g(z l+2 +a l )；
wherein g represents a Relu activation function; the extended formula becomes:
a l+2 ＝g(w l+2 x+b l+2 +a l )；
wherein w is l+2 、b l+2 Weights and paraphrases for Layer2 layers; if w l+2 =0, at the same time b l+2 =0, then a l+2 Will be equal to a l The performance of the network is not changed after the residual block is added;
assuming that the output in the neural network that is not batch normalized is z i Wherein i=1, 2,3. N represents that there are n numbers of samples,the output result after batch normalization is represented, and the calculation formula is defined as follows:
wherein epsilon represents a minimum number not smaller than zero, and eta and beta are parameters learned by a neural network; let the input of the neural network be x t The selected batch size is gamma, wherein gamma is more than 0 and less than or equal to m, and m is the total number of samples, and the number of batches is normalized
In one embodiment, using the minimum batch gradient descent algorithm as an example, η, β is calculated as follows:
For t＝1，2，3……n；
at all x t Forward propagation is performed on the upper part;
obtained using batch normalization techniquesl represents the neural network layer I;
the respective gradients were calculated using a back propagation technique: dw (dw) l ,dη l ,dβ l ；
Updating parameters: w (w) l ＝w l -αdw l ，η l ＝η l -αdη l ，β l ＝β l -αdβ l Wherein α represents a learning rate;
and obtaining a final required prediction result.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and in part, not described or illustrated in any particular embodiment, reference is made to the related descriptions of other embodiments.
2. Application examples:
application example
The great bridge of the Xianghai is planned to be positioned in the Shanghai Zhongzhou district, the Jinwan district, the Siemens district and the Tanzhou town of Zhongshan, and the full length is 17.344 km. After the fragrant sea bridge is built for traffic, the fragrant sea bridge can be a second main road connected with the west region of the urban area, becomes an important venation for getting through the high-speed development of the economy of the Chinese and the western, brings more convenient life for citizens in the surrounding area, and can relieve urban traffic pressure and treat blockage and relieve blockage. The route starts from the vicinity of a shellfish road in the Zhuga city and the fragrance continent area, is connected with fragrance sea bridge branch lines, and sequentially intersects with the Guangao high-speed, zhongshan Gushen highway (planning) and Jiang Zhu high-speed in the Zhongshan city, the Zhongshan mountain Tanzhou mountain Zhongmen area and the Jinwan area, and finally reaches the S272 lake core road, the full length of the route is 20.235km, and the origin mileage is reached: k1+ 194.855-K21+ 429.822, wherein the length of the independent bridge section is 17.548km (including main bridge, east approach and west approach), and the length of the connecting line is 2.890km.
The poor soft soil foundation widely distributed in the bead sea area has great influence on the construction speed and quality of road and bridge engineering. The soil foundation of the fragrant sea bridge engineering has three sections of soft soil foundations with the total length of 1561.49m, wherein the sections K17+ 668.33-K18+260 are 591.67m long, and the maximum soil filling height is 5.82m; k19+520 to K20+100 sections, with a length of 580m and a maximum soil filling height of 5.15m; k21+040 to K21+429.82 sections, which are 389.82m long and have a maximum fill height of 1.86m.
The soft soil mainly comprises 2-2 layers of silt and 2-3 layers of silt clay: (1) 2-2 layers of sludge (Q) 4 mc ): distributed in K3+150-K21+ 429.82 alluvium areas; dark gray, gray black, flowing plastic, containing a small amount of coarse sand and shell fragments locally, and having high compressibility; the layer thickness is 5.80-24.80 m, and the average thickness is 15.57m; basic allowable value of foundation soil bearing capacity [ f ao ]=40 kPa; standard value q of pile side soil friction resistance of bored pile ik =5 kPa. (2) 2-3 layers of silt clay (Q4) mc ): distributed in K1+200-K1+550, K2+080-K2+150, K5+900-K21+ 429.82; gray, gray black, flowing plastic state, local containing sapwood and a small amount of fine sand, high compressibility; the layer thickness is 0.40-39.80 m, and the average thickness is 14.67m; basic allowable value of foundation soil bearing capacity [ f ao ]=60 kPa; standard value q of pile side soil friction resistance of bored pile ik ＝8kPa。
On the whole, the soft pearl sea soil has the characteristics of high water content, large pore ratio, high compressibility, low strength and low permeability. Due to the special properties of soft soil, the foundation containing soft soil has the characteristics of low bearing capacity, large settlement amount, long settlement consolidation stabilization time and the like, the construction difficulty is increased, and the foundation is a key ring in the quality control of roadbed engineering.
At present, in the design of a TJ4 standard segment of a fragrant sea bridge, in order to ensure the stability of filling soil and meet the requirements of bearing capacity and post-construction settlement, a main line adopts vacuum preloading and vertical drainage bodies and combines light embankment treatment, and a traditional cement mixing pile and foam light soil combination mode is adopted for foundation treatment in a splicing stage.
The foam lightweight soil replaces the traditional compacted roadbed soil, has the functions of relieving the overburden load and improving the rigidity of the foundation, ensures that the settlement of the highway and the bridge head section meets the standard requirements, but the existing three-point problem is not solved yet: 1) The loss of water in the construction process can lead to the drying and cracking of foam light soil, and the performance of the light roadbed is affected; 2) Long-term performance degradation under the action of dry and wet circulation of traffic dynamic load and the atmosphere environment; 3) The final sedimentation prediction is difficult, resulting in defects in the design method. The cement soil stirring method is used as a mature foundation treatment method, and has larger limitation in the use process: 1) The burial depth of the soft soil bottom surface is not more than 15m, and the pile end of the vertical bearing stirring pile in the deep soft soil is difficult to enter a relatively hard layer; 2) The treated soft soil plasticity index is generally not preferably greater than 22.
The light technology is applied to the foundation, so that the influence of traffic dynamic load and atmospheric effect can be avoided, the light technology is combined with the traditional foundation treatment method, the self-weight stress of the foundation is greatly reduced, the lower negative deep soft soil is effectively unloaded, and the cement soil pile can be subjected to suspension design, so that the light technology is a new thought for foundation treatment. The light-weight cement soil is porous light-weight solidified soil formed by mixing cement (solidified material), water, soil and additives in a certain proportion to form slurry, mixing and stirring the slurry with a certain amount of stable bubbles, and finally hardening the slurry. Compared with the traditional cement soil, the lightweight cement soil has smaller volume weight, can effectively reduce the dead weight load of the foundation, increases the bearing capacity of the composite foundation by phase change, reduces the settlement and deformation of the foundation, and especially has great potential advantages when being applied to the treatment of deep soft soil foundations (more than 20 m) due to the fact that the lower part of a reinforced area cannot be effectively treated with soft soil. However, at present, the application of the lightweight cement soil pile has no engineering practice for reference, and the construction process of the lightweight cement soil is yet to be analyzed.
Foam light soil is used as roadbed filler, and many engineering practices exist in China, but the foam light soil has an unsatisfactory effect on engineering construction application of a Xianghai bridge.
The treatment method provided by the embodiment of the invention has the key points of complete construction technology, is applied to construction of the Shanghai bridge, and has good effect.
While the invention has been described with respect to what is presently considered to be the most practical and preferred embodiments, it is to be understood that the invention is not limited to the disclosed embodiments, but on the contrary, is intended to cover various modifications, equivalents, and alternatives falling within the spirit and scope of the invention.
Claims (5)
1. A composite foundation treatment method for a deep soft foundation, the method comprising the steps of:
s1, pumping the light cement soil pile to a long spiral drilling device for hole forming through a pipeline;
s2, pouring light cement soil in the process of lifting the drill, and completing construction of the light cement soil pile;
s3, arranging a mattress layer on the pile top to finally form a light cement soil pile composite foundation;
in the step S1, the lightweight cement soil pile is made of foam lightweight cement soil, and a large amount of bubbles are introduced into the concrete to form lightweight materials by adding the bubbles, the foaming agent or the air entraining agent;
slurry volume weight of the foam lightweight cement soil is 1g/cm 3 The mass volume ratio of the foaming agent or the air entraining agent to the foamed lightweight cement soil is 0.01-0.05 mL/g, and the gas of the bubbles is nitrogen or inert gas;
the preparation method of the foam lightweight cement soil comprises the following steps:
i) The method comprises the steps of using foam lightweight cement mixing equipment, using a first flow sensor to measure first flow L1 of foam cement slurry at an outlet of the foam lightweight cement mixing equipment, and using a second flow sensor to measure second flow L2 of cement slurry entering the foam lightweight cement mixing equipment;
ii) presetting a target value of a target foamed cement slurry density ρ2, wherein the density regulating device regulates the density ρ1 of the foamed cement slurry by regulating the second flow rate L2 by the following formula, ρ1=ρ2×l2/L1;
the method for adjusting the density ρ1, ρ1=ρ2×l2/L1 of the foamed cement slurry by adjusting the second flow rate L2 by the density adjusting apparatus includes:
firstly, constructing a foam cement slurry flow prediction model based on time and flow rate;
step two, determining a density regulation prediction model framework structure based on a multi-layer perceptron;
thirdly, modeling the flow speed of the foam cement slurry based on the integrated learning of the multi-layer perceptron, and obtaining a prediction result through a density regulation prediction model based on the multi-layer perceptron;
in the second step, the density regulation prediction model is ρ1=ρ2l2/L1;
the construction of the foam cement slurry flow prediction model based on time and flow rate comprises the following steps:
based on the flow rate dependence modeling of the convolutional neural network, the convolutional neural network CNNS modeling is provided; extracting flow velocity information between foam cement slurries through a convolutional neural network, and adopting a residual neural network;
the residual neural network adopts a ResNet50 network, and the level parameters are described as follows:
zero block: filling matrixes (3, 3) are zero filling of 3 rows and 3 columns, namely the original input foam cement slurry flow matrixes are (2, 2) in size, the filled foam cement slurry flow matrixes are (5, 5) in size, and the rest parts are all zero;
CONV block: the 64 convolution kernels have the size of (7, 7), and the convolution step length is the two-dimensional convolution of (2, 2);
BatchNorm block: batch normalization;
ReLU block: the Relu activation function, the formula is defined as follows:
MAXPOOL block, AVGPOOL block: maximum pooling layer, size (3, 3), step size (2, 2); average pooling, size (2, 2), step size (1, 1);
CONVBLOCK block: performing matrix addition operation on the results of the previous layers of x of the previous layers through a short 'path', wherein the flow of the foam cement paste is scaled to the range of [ -1,1] through two-dimensional convolution operation and batch normalization operation, and finally, the flow is output through a Relu activation function;
IDBLOCKxn block: x directly performs matrix addition operation on the output result of the previous layer through a short path; n represents a plurality of identical IDBLOCK blocks linked together;
the flat block: flattening the input into a one-dimensional foamed cement paste; the size is (M, -1), M represents the sample number, and-1 represents the flow synthesis of the foam cement slurry of the input sample matrix;
FC block: the size of the full-connection layer is (H, N), H represents the input dimension of the upper layer, and N represents the output dimension of the flow of the foam cement slurry to be predicted;
BigRNN is a deep neural network, and Layer1 and Layer2 are additionally added residual block networks, and the assumption that Layer2 does not go through the output of the activation function is not z l+2 Output a l+2 The formula of (c) is defined as follows:
a l+2 ＝g(z l+2 +a l )；
wherein g represents a Relu activation function; the extended formula becomes:
a l+2 ＝g(w l+2 x+b l+2 +a l )；
wherein w is l+2 、b l+2 Weights and paraphrases for Layer2 layers; if w l+2 =0, at the same time b l+2 =0, then a l+2 Will be equal to a l AddingThe performance of the network is not changed after the residual block is entered;
assuming that the output in the neural network that is not batch normalized is z i Wherein i=1, 2,3. N represents that there are n numbers of samples,the output result after batch normalization is represented, and the calculation formula is defined as follows:
wherein epsilon represents a minimum number not smaller than zero, and eta and beta are parameters learned by a neural network; let the input of the neural network be x t The selected batch size is gamma, wherein gamma is more than 0 and less than or equal to m, and m is the total number of samples, and the number of batches is normalized
2. The composite foundation treatment method for deep soft foundation according to claim 1, wherein the η, β calculation process is performed with a minimum batch gradient descent algorithm as follows:
Fort＝1，2，3……n；
at all x t Forward propagation onObtaining z-ultra using batch normalization techniques l L represents the neural network layer l, each gradient is calculated using a back propagation technique: dw (dw) l ,dη l ,dβ l ；
Updating parameters: w (w) l ＝w l -αdw l ，η l ＝η l -αdη l ，β l ＝β l -αdβ l Wherein α represents a learning rate; and obtaining a final required prediction result.
3. The composite foundation treatment method for deep soft foundation according to claim 1, wherein in step S3, a mattress layer is provided on the pile top for drainage, buffering and deformation in cooperation with the roadbed.
4. Use of the composite foundation treatment method for deep soft foundation according to any one of claims 1 to 3 in cross-sea bridge construction and soft soil layer traffic engineering pier construction.
5. A composite foundation treatment apparatus for a deep soft foundation, which implements the composite foundation treatment method for a deep soft foundation according to any one of claims 1 to 3.
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| CN105200879A (en) * | 2015-10-16 | 2015-12-30 | 北京科技大学 | Water-permeable foam concrete pile compound foundation |
| CN105780764A (en) * | 2016-03-30 | 2016-07-20 | 东南大学 | Light cemented soil composite mixing pile |
| CN111364453A (en) * | 2020-03-24 | 2020-07-03 | 中交一公局第七工程有限公司 | Method for reinforcing deep soft soil foundation by light cement soil long spiral cast-in-place pile |
| JP2021169757A (en) * | 2020-04-14 | 2021-10-28 | 太平洋セメント株式会社 | Quality prediction method of compacting material for strengthening ground |
| CN114036821A (en) * | 2021-10-21 | 2022-02-11 | 北京科技大学 | Thickener control method and device based on non-deterministic hidden space model |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105200879A (en) * | 2015-10-16 | 2015-12-30 | 北京科技大学 | Water-permeable foam concrete pile compound foundation |
| CN105780764A (en) * | 2016-03-30 | 2016-07-20 | 东南大学 | Light cemented soil composite mixing pile |
| CN111364453A (en) * | 2020-03-24 | 2020-07-03 | 中交一公局第七工程有限公司 | Method for reinforcing deep soft soil foundation by light cement soil long spiral cast-in-place pile |
| JP2021169757A (en) * | 2020-04-14 | 2021-10-28 | 太平洋セメント株式会社 | Quality prediction method of compacting material for strengthening ground |
| CN114036821A (en) * | 2021-10-21 | 2022-02-11 | 北京科技大学 | Thickener control method and device based on non-deterministic hidden space model |
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