Retaining wall deformation control measure decision-making method and system based on accident tree analysis

Abstract

The invention relates to a retaining wall deformation control measure decision-making method and system based on accident tree analysis, and the method comprises the following steps: taking foundation pit monitoring data as an input parameter of a retaining wall maximum deformation prediction model, and obtaining a retaining wall maximum deformation prediction value through the retaining wall maximum deformation prediction model; and obtaining the deformation of the retaining wall in real time through the monitoring data of the foundation pit, and when the deformation of the retaining wall exceeds the maximum deformation predicted value of the retaining wall, making a deformation control scheme decision through the retaining wall deformation control measure decision model according to the monitoring data of the foundation pit to obtain a retaining wall deformation control measure. Compared with the prior art, the method has the advantages that the deformation of the retaining wall is predicted, the deformation control measures of the retaining wall are decided, and the construction efficiency is improved.

Description

Technical Field

[0001] The invention relates to the field of foundation pit engineering, and in particular to a retaining wall deformation control measure solution method and system based on accident tree analysis. Background Art

[0002] The purpose of foundation pit support is to provide a safe operating space for the construction of underground projects. Once the foundation pit has a large deformation or even collapses, it will not only have a great impact on the adjacent structures, but also have a great negative social effect. No matter what kind of support is used for the foundation pit, the retaining wall will inevitably deform into the pit, resulting in the displacement of the surrounding soil, which will eventually have a certain impact on the adjacent structures. At the same time, since the foundation pit projects in urban areas are often adjacent to densely packed buildings, this places high demands on the control of foundation pit deformation. If you are not careful, it may even have a huge impact on the structure itself and the surrounding environment. In recent years, the scale and depth of foundation pit excavation have become larger and larger, and the impact on the surrounding environment has also increased. Existing studies mostly use overall numerical methods to analyze the impact of foundation pit excavation on the surrounding environment. This method is relatively complex and time-consuming. Often, only when the monitoring data is close to or exceeds the warning value will the design be re-verified, and the redesign will consume a lot of time and manpower, delaying the construction period. Summary of the invention

[0003] The purpose of the present invention is to overcome the defects of the above-mentioned prior art and to provide a retaining wall deformation control measure decision-making method and system based on fault tree analysis, so as to realize the prediction of retaining wall deformation and the decision of retaining wall deformation control measures and improve construction efficiency.

[0004] The purpose of the present invention can be achieved by the following technical solutions:

[0005] A method for decision-making on retaining wall deformation control measures based on accident tree analysis comprises the following steps:

[0006] Using the foundation pit monitoring data as input parameters of a retaining wall maximum deformation prediction model, and obtaining a retaining wall maximum deformation prediction value through the retaining wall maximum deformation prediction model;

[0007] The deformation of the retaining wall is obtained in real time through the foundation pit monitoring data. When the deformation of the retaining wall exceeds the predicted maximum deformation value of the retaining wall, a deformation control plan decision is made through a retaining wall deformation control measure decision model according to the foundation pit monitoring data to obtain a retaining wall deformation control measure;

[0008] Among them, the steps of constructing the decision model for the deformation control measures of the retaining wall include: obtaining the cause of the accident and the severity of the deformation based on the accident case of excessive deformation of the foundation pit retaining wall, obtaining the probability of each event at the bottom of the accident tree in the accident of excessive deformation of the foundation pit, obtaining the retaining wall deformation control measures corresponding to the event at the bottom of the accident tree of the foundation pit retaining wall deformation according to the accident tree of the foundation pit retaining wall deformation, establishing the corresponding relationship between the deformation control measures and the foundation pit monitoring data, and obtaining the decision model for the deformation control measures of the retaining wall.

[0009] Furthermore, the foundation pit monitoring data includes foundation pit soil parameters, foundation pit geometric parameters and retaining structure parameters.

[0010] Furthermore, the foundation pit soil parameters include soil layer thickness, soil layer density, cohesion, internal friction angle, triaxial loading modulus, one-dimensional compression modulus, unloading-reloading modulus, shear strain corresponding to 70% stiffness and small strain shear modulus.

[0011] Furthermore, the foundation pit geometric parameters include foundation pit excavation depth, foundation pit excavation width, retaining wall length, retaining structure bending stiffness and groundwater depth.

[0012] Furthermore, the enclosure structure parameters include support axial force, support lateral spacing, support axial stiffness, support bending stiffness and support distance from the ground.

[0013] Furthermore, the deformation accident tree of the foundation pit retaining wall includes top events, intermediate events and bottom events. The top event is excessive deformation of the foundation pit retaining wall. The intermediate events include multiple events such as retaining wall damage, internal support damage, damage caused by increased groundwater level, damage caused by failure to effectively stop water, and damage caused by soil pressure. The bottom event is the basic event that causes excessive deformation of the foundation pit retaining wall.

[0014] Furthermore, the basic events include multiple ones of too small embedding depth, large amount of edge loading and over-excavation of foundation pit.

[0015] Furthermore, the maximum deformation prediction model of the retaining wall is a BP neural network, the activation function of the BP neural network adopts a linear rectification function, the loss function adopts a mean square error, and the optimization function adopts an adaptive moment estimation.

[0016] Furthermore, the deformation control measures include applying a water-stop curtain, dewatering outside the pit, reinforcing the bottom of the foundation pit, unloading soil outside the pit and backfilling inside the pit, increasing support stiffness, adjusting support axial force, adjusting support spacing, increasing retaining wall bending stiffness, early bottom slab pouring and load removal.

[0017] According to another aspect of the present invention, a retaining wall deformation control measure decision system based on fault tree analysis is provided, comprising:

[0018] A retaining wall maximum deformation prediction module is used to use the foundation pit monitoring data as the input of the retaining wall maximum deformation prediction model, and obtain the retaining wall maximum deformation prediction value through the retaining wall maximum deformation prediction model;

[0019] A retaining wall deformation control measure decision module is used to obtain the retaining wall deformation in real time through the foundation pit monitoring data. When the retaining wall deformation exceeds the maximum deformation prediction value of the retaining wall, a deformation control plan decision is made through the retaining wall deformation control measure decision model according to the foundation pit monitoring data to obtain the retaining wall deformation control measure;

[0020] Among them, the steps of constructing the decision model for the deformation control measures of the retaining wall include: summarizing the causes of accidents and the severity of deformation according to the accident cases of excessive deformation of the foundation pit retaining wall, obtaining the probability of each event at the bottom of the accident tree in the accident of excessive deformation of the foundation pit, compiling the deformation control measures of the retaining wall corresponding to the events at the bottom of the accident tree of the foundation pit retaining wall deformation according to the accident tree of the deformation of the foundation pit retaining wall, establishing the corresponding relationship between the deformation control measures and the input parameters of the maximum deformation prediction model of the retaining wall, and obtaining the decision model for the deformation control measures of the retaining wall.

[0021] Compared with the prior art, the present invention has the following beneficial effects:

[0022] 1. The present invention summarizes and statistics the deformation degree of the causes of the accidents based on the cases of excessive deformation of the retaining walls of foundation pits, obtains the probability of each event at the bottom of the accident tree in the accident of excessive deformation of the foundation pit, compiles the retaining wall deformation control measures corresponding to the events at the bottom of the accident tree according to the deformation accident tree of the retaining wall of the foundation pit, establishes the corresponding relationship between the deformation control measures and the input parameters of the maximum deformation prediction model of the retaining wall, obtains the retaining wall deformation control measure decision model, and provides control measures for the deformation of the retaining wall according to the foundation pit monitoring data through the retaining wall deformation control measure decision model, thereby improving the construction efficiency.

[0023] 2. The present invention adopts a maximum deformation prediction model for retaining walls and uses foundation pit monitoring data as the input for model prediction to obtain a predicted value of the maximum deformation of the retaining wall. It comprehensively considers the influence of foundation pit soil parameters, foundation pit geometric parameters and retaining structure parameters on the deformation of the retaining wall, provides a reference value for retaining wall deformation accidents, avoids the probability of accidents and improves construction safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 A flow chart of a method for making decisions on retaining wall deformation control measures based on fault tree analysis proposed by the present invention;

[0025] Figure 2 It is the structural schematic diagram of the maximum deformation prediction model of retaining wall;

[0026] Figure 3 This is a schematic diagram of the fault tree for excessive deformation of the foundation pit retaining wall;

[0027] Figure 4 It is the plan layout of foundation pit;

[0028] Figure 5 This is the cross-section diagram of foundation pit support;

[0029] Figure 6 This is the deformation diagram of the retaining wall after deformation control measures. DETAILED DESCRIPTION

[0030] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. This embodiment is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process, but the protection scope of the present invention is not limited to the following embodiments.

[0031] Example 1

[0032] This embodiment provides a method for making a decision on the deformation control measures of retaining walls based on an accident tree analysis. Figure 1 As shown, the following steps are included:

[0033] S1. Use the foundation pit monitoring data as the input parameter of the retaining wall maximum deformation prediction model, and obtain the maximum deformation prediction value of the retaining wall through the retaining wall maximum deformation prediction model.

[0034] The maximum deformation prediction model of retaining wall is established, and the automatic modeling program is compiled based on the Python language interface provided by PLAXIS to realize the batch automatic calculation of two-dimensional modeling of foundation pit. The program has the following features: automatically generate soil and structure and assign material parameter characteristics, automatically set groundwater level conditions, automatically divide grids, automatically set excavation conditions and perform calculations, and automatically write the maximum horizontal deformation of retaining wall and the above material parameters obtained each time into the database; the parameters of the sample database include input parameters, model intermediate parameters, and output parameters, among which the input parameters are foundation pit monitoring data, including foundation pit soil parameters, foundation pit geometric parameters and retaining structure parameters. The foundation pit soil parameters considering the HSS constitutive model include soil layer thickness, soil layer weight, cohesion, internal friction angle, triaxial loading modulus, one-dimensional compression modulus, unloading-reloading modulus, shear strain corresponding to 70% stiffness and small strain shear modulus. The soil layer is taken as 9 layers, and the soil parameters of each layer include the above 9 parameters, and there are 81 input parameters. The geometric parameters of the foundation pit include excavation depth, excavation width, retaining wall length, retaining structure bending stiffness and groundwater depth, with 5 input parameters. The retaining structure parameters include support axial force, support lateral spacing, support axial stiffness, support bending stiffness and support distance from the ground. There are four support retaining structure parameters, with 16 input parameters. In total, there are 102 input parameters, and the output parameter is 1 parameter, the maximum deformation value of the retaining wall.

[0035] The neural network used in the maximum deformation prediction model of the retaining wall is BP neural network, in which the activation function uses the linear rectification function ReLU and the loss function uses the mean square error The optimization function uses adaptive moment estimation, and the parameters are set to LR = 0.01, β1 = 0.9, β2 = 0.99; the internal structure diagram of the BP neural network is shown in Figure 2 As shown in the figure, the internal structure of the BP neural network during calculation has five hidden layers, and the number of neurons in each layer is 102, 73, 58, 48, and 22, respectively. That is, the neural network structure is 102-102-73-58-48-22-1, of which the input layer parameters have a total of 102 parameters, and the output parameter is one parameter of the maximum horizontal deformation of the foundation pit retaining wall. The BP neural network training ends after 15349 iterations.

[0036] By predicting 50 sets of examples randomly generated by finite element, the basic parameters of the predicted data are shown in Table 1:

[0037] Table 1 Basic parameters of prediction data

[0038]

[0039] The prediction results are shown in Table 2:

[0040] Table 2 Prediction results

[0041]

[0042] The error percentage of the prediction results is mostly within 10%, and the absolute value of most prediction errors is less than 2 mm. Therefore, based on the training of the finite element database, the prediction accuracy of the model can meet the requirements for various excavation conditions of the foundation pit generated by the finite element calculation example.

[0043] S2. Obtain the deformation of the retaining wall in real time through the foundation pit monitoring data. When the deformation of the retaining wall exceeds the predicted maximum deformation value of the retaining wall, make a deformation control plan decision through the retaining wall deformation control measure decision model based on the foundation pit monitoring data to obtain the retaining wall deformation control measures.

[0044] The accident tree of the pit retaining wall deformation is drawn in a top-to-bottom order. The top event is the excessive deformation of the pit retaining wall. The intermediate events are analyzed layer by layer from the retaining structure, soil pressure problems and water leakage problems. They are divided into retaining wall damage, internal support damage, damage caused by increased groundwater level, damage caused by failure to effectively stop water, damage caused by soil pressure, etc. The logic gate symbol is "OR gate". According to the characteristics of the pit retaining wall support structure system and its damage and the principle of the accident tree, the accident tree of excessive deformation of the pit retaining wall is drawn step by step, such as Figure 3As shown, A1, A2, and A3 correspond to the intermediate events of water leakage, retaining structure problem, and soil pressure problem respectively; the intermediate events B1, B2, B3, B4, B5, and B6 are the design factors, construction factors, and environmental factors caused by them, B1 is the water leakage problem caused by the surrounding environmental factors, B2 is the water leakage problem caused by the construction factors, B3 is the retaining structure problem caused by the construction factors, B4 is the retaining structure problem caused by the design factors, B5 is the soil pressure problem caused by the design factors, and B6 is the soil pressure problem caused by the construction factors; C1 is the climate reason, C2 is the municipal reason, C3 is the water leakage on the side of the foundation pit, C4 is the water leakage at the bottom of the pit, C5 is the retaining wall construction reason, C6 is the support construction reason, C7 is the retaining wall design reason, C8 is the support design reason, C9 is the lateral soil pressure reason, and C10 is the soil pressure reason at the bottom of the pit; D1 is the water-stop curtain problem, D2 is the dewatering well problem, and X1-X15 are the basic events of the accident causes, such as small embedding depth, large amount of side load, over-excavation of the foundation pit, etc. According to the above fault tree structure, the Boolean algebra expression of the fault tree is obtained through logical operation:

[0045] T=A1+A2+A3=(B1+B2)+(B3+B4)+(B5+B6)=(C1+C2)+(C3+C4)+(C5+C6)+(C7+C8)+(C9+C10)+X12=X1+X2+(D1+D2)+ X5+X6+(X7+X8)+X9+(X10+X11)+X12+X13+(X14+X15)=X1+X2+X3+X4+X5+X6+X7+X8+X9+X10+X11+X12+X13+X14+X15

[0046] From the above formula, we can see that there are 15 minimum cut sets, namely {X1}, {X2}, {X3}, {X4}, {X5}, {X6}, {X7}, {X8}, {X9}, {X 10}, {X 11}, {X 12}, {X 13}, {X 14}, {X 15 Each minimum cut set represents a mode that leads to an accident, indicating that there are 15 potential failure modes that lead to excessive deformation of the foundation pit retaining wall, and each minimum cut set contains only one bottom event, which means that in the foundation pit retaining wall support structure system, even a small unsafe factor (event) may cause the entire system to fail.

[0047] By means of literature research, we collected and statistically collected accident cases of excessive deformation of foundation pit retaining walls in recent years, and summarized and counted the causes of the accidents and the severity of the deformation. The probability of each bottom event in the accident of excessive deformation of the foundation pit is shown in Table 3:

[0048] Table 3 Probability of bottom events in accidents of excessive pit deformation

[0049]

[0050]

[0051] A control measure decision model is established. The steps of constructing the retaining wall deformation control measure decision model include: summarizing the causes of accidents based on cases of excessive deformation of foundation pit retaining walls, obtaining the probability of each event at the bottom of the accident tree in the accident of excessive deformation of the foundation pit, compiling retaining wall deformation control measures corresponding to the events at the bottom of the accident tree of the foundation pit retaining wall deformation according to the accident tree of the foundation pit retaining wall deformation, establishing a corresponding relationship between the deformation control measures and the input parameters of the maximum deformation prediction model of the retaining wall, and obtaining the retaining wall deformation control measure decision model.

[0052] Based on the deformation accident tree of the foundation pit retaining wall, deformation control measures corresponding to the bottom events of the accident tree are compiled; the corresponding relationship between the deformation control measures and the neural network input parameters is established. The control measures for excessive deformation of the retaining wall are compiled based on the maximum deformation prediction model of the retaining wall. Each retaining wall deformation control measure is closely related to the neural network input parameters. The relationship between the retaining wall control measures and the neural network input parameters is shown in Table 4:

[0053] Table 4 Relationship between retaining wall control measures and foundation pit monitoring data

[0054]

[0055] A decision model for retaining wall deformation control measures based on accident tree analysis is established. Based on the accident tree analysis of retaining wall deformation and comprehensive consideration of real-time monitoring data at the construction site, a decision model for retaining wall deformation control measures is constructed. The decision model parameters are shown in Table 5:

[0056] Table 5 Decision model parameters

[0057]

[0058]

[0059] The first column of Table 5 is the input parameters of the decision model; the second column of the table is the parameter variation range of the input parameters; the third column of the table is the warning value of each parameter index, which is 70% of the maximum value of the variation range; the fourth column of the table is the model output parameter, which is the deformation control measure to be adopted first when the warning value is reached. The specific control measures have been simplified by digital numbers. The deformation control measures and digital numbers are shown in Table 6:

[0060] Table 6 Deformation control measures and digital numbers

[0061]

[0062] The decision tree algorithm is used to realize the input parameters and output parameters of the decision model for retaining wall deformation control measures.

[0063] Based on the monitoring data of foundation pit engineering projects and the retaining wall deformation accident tree, a retaining wall deformation control measure decision model database is generated. Part of the database data is shown in Table 7:

[0064] Table 7 Database

[0065]

[0066]

[0067] The first column of Table 7 is the decision model input parameter number, each of the columns 2-10 of Table 7 corresponds to a piece of data, and the last row of the table is the decision model output parameter.

[0068] In another preferred embodiment, the steps are further included:

[0069] S3. Predict the maximum horizontal deformation of the retaining wall after adopting control measures through the maximum deformation prediction model of the retaining wall

[0070] The planned land area for a certain project is about 13372.8m 2 , with a construction area of ​​approximately 96,200 m 2 , of which the above-ground building area is about 53,500 m 2 The underground building area is about 42,700 m 2 The plan layout of the foundation pit is as follows: Figure 4 As shown in Figure 2, the foundation pit support profile is as follows: Figure 5 As shown. The monitoring point CX17 detected that the actual value of the maximum deformation of the retaining wall was 29.09mm, where the predicted maximum deformation of the retaining wall was 28.91mm, and the deformation control value was 28.00mm. When the excavation depth reached 16.75m, the maximum horizontal deformation of the retaining wall under this condition reached 29.09mm, and the deformation control value was assumed to be 28mm. Therefore, in order to effectively control the maximum horizontal deformation of the retaining wall, based on the foundation pit monitoring data, the retaining wall deformation control measure decision model was used to make a deformation control plan decision. The input parameters of the decision model under this condition are shown in Table 8:

[0071] Table 8 Decision model input parameters

[0072]

[0073] The decision model outputs solution number 2, and the specific measure is dewatering outside the pit. The maximum deformation prediction model of the retaining wall is used to predict the maximum horizontal deformation of the retaining wall after the control measures are adopted. The deformation control measure of dewatering outside the pit by 60cm is adopted, and the corresponding neural network input parameter groundwater level is reduced by 0.6m. After updating the neural network model input parameters, the maximum horizontal deformation of the retaining wall is predicted. The prediction results are as follows Figure 6 As shown in the figure, the maximum horizontal deformation of the retaining wall is 27.9 mm, which meets the deformation control value required by the specification.

[0074] Example 2

[0075] This embodiment provides a retaining wall deformation control measure decision system based on fault tree analysis, including:

[0076] The maximum deformation prediction module of the retaining wall is used to use the foundation pit monitoring data as the input of the maximum deformation prediction model of the retaining wall, and obtain the maximum deformation prediction value of the retaining wall through the maximum deformation prediction model of the retaining wall;

[0077] The retaining wall deformation control measure decision module is used to make a deformation control plan decision through the retaining wall deformation control measure decision model according to the foundation pit monitoring data when the retaining wall deformation exceeds the maximum deformation prediction value of the retaining wall, and obtain the retaining wall deformation control measures;

[0078] Among them, the steps of constructing the decision-making model for retaining wall deformation control measures include: summarizing and statistically analyzing the causes of accidents and the severity of deformation according to the accident cases of excessive deformation of foundation pit retaining walls, obtaining the probability of each event at the bottom of the accident tree in the accident of excessive deformation of foundation pits, compiling retaining wall deformation control measures corresponding to the events at the bottom of the accident tree of foundation pit retaining wall deformation according to the accident tree of foundation pit retaining wall deformation, establishing the corresponding relationship between the deformation control measures and the input parameters of the maximum deformation prediction model of retaining walls, and obtaining the decision-making model for retaining wall deformation control measures.

[0079] The rest is the same as in Example 1.

[0080] The preferred specific embodiments of the present invention are described in detail above. It should be understood that a person skilled in the art can make many modifications and changes based on the concept of the present invention without creative work. Therefore, any technical solution that can be obtained by a person skilled in the art through logical analysis, reasoning or limited experiments based on the concept of the present invention on the basis of the prior art should be within the scope of protection determined by the claims.

Claims

1. A decision-making method for retaining wall deformation control measures based on accident tree analysis, characterized in that: The following steps are involved: Using the foundation pit monitoring data as input parameters of a retaining wall maximum deformation prediction model, and obtaining a retaining wall maximum deformation prediction value through the retaining wall maximum deformation prediction model; The deformation of the retaining wall is obtained in real time through the foundation pit monitoring data. When the deformation of the retaining wall exceeds the predicted maximum deformation value of the retaining wall, a deformation control plan decision is made through a retaining wall deformation control measure decision model according to the foundation pit monitoring data to obtain a retaining wall deformation control measure; Among them, the steps of constructing the decision model for the deformation control measures of the retaining wall include: obtaining the cause of the accident and the severity of the deformation based on the accident case of excessive deformation of the foundation pit retaining wall, obtaining the probability of each event at the bottom of the accident tree in the accident of excessive deformation of the foundation pit, obtaining the retaining wall deformation control measures corresponding to the event at the bottom of the accident tree of the foundation pit retaining wall deformation according to the accident tree of the foundation pit retaining wall deformation, establishing the corresponding relationship between the deformation control measures and the foundation pit monitoring data, and obtaining the decision model for the deformation control measures of the retaining wall.

2. The method for decision-making on retaining wall deformation control measures based on fault tree analysis according to claim 1 is characterized in that: The foundation pit monitoring data includes foundation pit soil parameters, foundation pit geometric parameters and surrounding structure parameters.

3. The method for decision-making on retaining wall deformation control measures based on fault tree analysis according to claim 2 is characterized in that: The foundation pit soil parameters include soil layer thickness, soil layer weight, cohesion, internal friction angle, triaxial loading modulus, one-dimensional compression modulus, unloading-reloading modulus, shear strain corresponding to 70% stiffness and small strain shear modulus.

4. The method for decision-making on retaining wall deformation control measures based on fault tree analysis according to claim 2 is characterized in that: The foundation pit geometric parameters include foundation pit excavation depth, foundation pit excavation width, retaining wall length, bending stiffness of the retaining structure and groundwater depth.

5. The method for decision-making on retaining wall deformation control measures based on fault tree analysis according to claim 2 is characterized in that: The enclosure structure parameters include support axial force, support lateral spacing, support axial stiffness, support bending stiffness and support distance from the ground.

6. The method for decision-making on retaining wall deformation control measures based on fault tree analysis according to claim 1 is characterized in that: The deformation accident tree of the foundation pit retaining wall includes top events, intermediate events and bottom events. The top event is that the deformation of the foundation pit retaining wall is too large. The intermediate events include multiple events such as retaining wall damage, internal support damage, damage caused by increased groundwater level, damage caused by failure to effectively stop water, and damage caused by soil pressure. The bottom event is the basic event that causes excessive deformation of the foundation pit retaining wall.

7. The method for decision-making on retaining wall deformation control measures based on fault tree analysis according to claim 6 is characterized in that: The basic events include multiple ones of insufficient embedding depth, large amount of edge loading and over-excavation of foundation pit.

8. The method for decision-making on retaining wall deformation control measures based on fault tree analysis according to claim 1 is characterized in that: The maximum deformation prediction model of the retaining wall is a BP neural network, the activation function of the BP neural network adopts a linear rectification function, the loss function adopts a mean square error, and the optimization function adopts an adaptive moment estimation.

9. The method for decision-making on retaining wall deformation control measures based on fault tree analysis according to claim 1 is characterized in that: The deformation control measures include applying a water-stop curtain, dewatering outside the pit, reinforcing the bottom of the foundation pit, unloading soil outside the pit and backfilling inside the pit, increasing support stiffness, adjusting support axial force, adjusting support spacing, increasing retaining wall bending stiffness, early bottom slab pouring and load removal.

10. A retaining wall deformation control measure decision system based on accident tree analysis, characterized in that: include: A retaining wall maximum deformation prediction module is used to use the foundation pit monitoring data as the input of the retaining wall maximum deformation prediction model, and obtain the retaining wall maximum deformation prediction value through the retaining wall maximum deformation prediction model; A retaining wall deformation control measure decision module is used to obtain the retaining wall deformation in real time through the foundation pit monitoring data. When the retaining wall deformation exceeds the maximum deformation prediction value of the retaining wall, a deformation control plan decision is made through the retaining wall deformation control measure decision model according to the foundation pit monitoring data to obtain the retaining wall deformation control measure; Among them, the steps of constructing the decision model for the deformation control measures of the retaining wall include: summarizing the causes of accidents and the severity of deformation according to the accident cases of excessive deformation of the foundation pit retaining wall, obtaining the probability of each event at the bottom of the accident tree in the accident of excessive deformation of the foundation pit, compiling the deformation control measures of the retaining wall corresponding to the events at the bottom of the accident tree of the foundation pit retaining wall deformation according to the accident tree of the deformation of the foundation pit retaining wall, establishing the corresponding relationship between the deformation control measures and the input parameters of the maximum deformation prediction model of the retaining wall, and obtaining the decision model for the deformation control measures of the retaining wall.

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