CN-115423167-B - Subway deep foundation pit construction safety early warning and auxiliary decision making method and system

CN115423167BCN 115423167 BCN115423167 BCN 115423167BCN-115423167-B

Abstract

The invention relates to a subway deep foundation pit construction safety early warning and auxiliary decision-making method and system, comprising the steps of obtaining physical data of a subway deep foundation pit; the physical data comprises foundation pit entity information, foundation pit state information and foundation pit environment information, a clustering algorithm is applied to partition a subway deep foundation pit based on the physical data, a CSA-BPNN neural network is applied to each partition, geological parameter inversion is conducted by combining simulation data output by a digital twin model of the subway deep foundation pit, geological parameter predicted values, foundation pit entity simulation information and foundation pit environment simulation information obtained based on the digital twin model of the subway deep foundation pit are input into a foundation pit state predicted model, foundation pit state predicted values of each partition are obtained, and construction safety pre-warning and auxiliary decision are conducted. Based on refinement subregion is carried out to the foundation ditch to can carry out the state prediction to each subregion, improve the accuracy of state prediction and then more accurate safety precaution and the auxiliary decision of carrying out.

Inventors

WANG GUOGUANG
PENG DA
FANG QIAN
GAO XIUQIANG
WANG JUN
ZHAO GUANYUAN
ZHOU MOZHEN

Assignees

中国电建集团华东勘测设计研究院有限公司

Dates

Publication Date: 20260505
Application Date: 20220829

Claims (6)

1. A subway deep foundation pit construction safety early warning and auxiliary decision-making method is characterized by comprising the following steps: The method comprises the steps of obtaining physical data of a deep foundation pit of a subway, wherein the physical data comprise foundation pit entity information, foundation pit state information and foundation pit environment information, the foundation pit entity information is foundation pit size data, and the foundation pit state information is foundation pit stress and deformation data; partitioning the subway deep foundation pit by applying a K-Means algorithm based on the physical data; For each partition, training a first CSA-BPNN neural network by taking the foundation pit entity information, the foundation pit environment information and the corresponding foundation pit state information as inputs and taking geological parameters as labels to obtain a geological parameter prediction model, wherein the geological parameters comprise elastic modulus and internal friction angle; Inputting the physical data into a digital twin model of the subway deep foundation pit for simulation prediction, outputting simulation data, and inputting the simulation data into the geological parameter prediction model to obtain a geological parameter prediction value; taking the geological parameter predicted value, the foundation pit entity information and the foundation pit environment information as inputs, and training a second CSA-BPNN neural network by taking the foundation pit state information as a label to obtain a foundation pit state predicted model; Inputting the geological parameter predicted value, the foundation pit entity simulation information and the foundation pit environment simulation information which are obtained based on the subway deep foundation pit digital twin model into the foundation pit state predicted model to obtain a foundation pit state predicted value of each subarea; carrying out construction safety early warning and auxiliary decision making according to the foundation pit state predicted value of each partition; The method for partitioning the subway deep foundation pit based on the physical data by using the K-Means algorithm further comprises the following steps: establishing foundation pit digital models of different support types; Carrying out multi-factor horizontal tests on the foundation pit digital models of different support types to obtain test data; Combining the test data with the physical data, wherein the combined data is used for carrying out the training of the first CSA-BPNN neural network and the second CSA-BPNN neural network, and the subway deep foundation pit is partitioned; the method for partitioning the subway deep foundation pit based on the physical data by applying a K-Means algorithm specifically comprises the following steps: Preprocessing the merged data by adopting a principal component analysis method, wherein the preprocessing comprises dimension reduction, and removing and recombining redundant data; And extracting data containing preset percentage information from the preprocessed data, and partitioning the subway deep foundation pit by applying a K-Means algorithm.
2. The method of claim 1, wherein for each partition, training a first CSA-BPNN neural network with the foundation pit entity information and the foundation pit environment information and the corresponding state information as inputs and with a geological parameter as a tag to obtain a geological parameter prediction model, specifically comprises: for each partition, determining key foundation pit entity information and key foundation pit environment information in the physical data by using a parameter sensitivity analysis method; and training a first CSA-BPNN neural network by taking the key foundation pit entity information, the key foundation pit environment information and the corresponding foundation pit state information as inputs and the geological parameters as labels to obtain the geological parameter prediction model.
3. The method of claim 1, wherein the performing construction safety pre-warning and auxiliary decision-making according to the foundation pit state prediction value of each partition specifically comprises: setting a foundation pit state early warning value for each subarea based on a physical model of the subway deep foundation pit; And comparing the foundation pit state predicted value of each partition with the corresponding foundation pit state early-warning value, and carrying out construction safety early warning and auxiliary decision.
4. A subway deep foundation pit construction safety precaution and auxiliary decision making system based on the subway deep foundation pit construction safety precaution and auxiliary decision making method as claimed in any one of claims 1 to 3, characterized by comprising: The system comprises a foundation pit physical data acquisition module, a foundation pit physical data acquisition module and a foundation pit analysis module, wherein the foundation pit physical data acquisition module is used for acquiring physical data of a deep foundation pit of a subway, the physical data comprises foundation pit entity information, foundation pit state information and foundation pit environment information, the foundation pit entity information is foundation pit size data, and the foundation pit state information is foundation pit stress and deformation data; The partitioning module is used for partitioning the subway deep foundation pit by applying a K-Means algorithm based on the physical data; the geological parameter prediction model construction module is used for training a first CSA-BPNN neural network by taking the foundation pit entity information, the foundation pit environment information and the corresponding state information as input and taking geological parameters as labels for each partition to obtain a geological parameter prediction model, wherein the geological parameters comprise elastic modulus and internal friction angle; The geological parameter prediction module is used for inputting the physical data into a digital twin model of the subway deep foundation pit for simulation prediction, outputting simulation data, and inputting the simulation data into the geological parameter prediction model to obtain a geological parameter prediction value; the foundation pit state prediction model construction module is used for taking the geological parameter predicted value, the foundation pit entity information and the foundation pit environment information as inputs, and the foundation pit state information is a label training second CSA-BPNN neural network to obtain a foundation pit state prediction model; The foundation pit state prediction module is used for inputting the geological parameter predicted value, the foundation pit entity simulation information and the foundation pit environment simulation information which are obtained based on the subway deep foundation pit digital twin model into the foundation pit state prediction model to obtain a foundation pit state predicted value of each subarea; the early warning and decision module is used for carrying out construction safety early warning and auxiliary decision according to the foundation pit state predicted value of each partition; The system also comprises a data expansion module, wherein the data expansion module comprises: The foundation pit digital model construction unit is used for building foundation pit digital models of different support types; the test data acquisition unit is used for carrying out multi-factor horizontal tests on the foundation pit digital models of different support types to obtain test data; the data merging unit is used for merging the test data and the physical data, and the merged data is used for carrying out the training of the first CSA-BPNN neural network and the second CSA-BPNN neural network and the subway deep foundation pit; The partition module specifically comprises: the data preprocessing unit is used for preprocessing the merged data by adopting a principal component analysis method, wherein the preprocessing comprises dimension reduction, and redundant data are removed and recombined; The partitioning unit is used for extracting the data containing the preset percentage information from the preprocessed data and partitioning the subway deep foundation pit by applying a K-Means algorithm.
5. The system of claim 4, wherein the geologic parameter prediction model building module specifically comprises: The key parameter acquisition unit is used for determining key foundation pit entity information and key foundation pit environment information in the physical data by using a parameter sensitivity analysis method for each partition; the training unit is used for taking the key foundation pit entity information, the key foundation pit environment information and the corresponding state information as input, and taking the geological parameters as labels to train the first CSA-BPNN neural network so as to obtain the geological parameter prediction model.
6. The system of claim 4, wherein the early warning and decision module specifically comprises: the state early warning threshold setting unit is used for setting a foundation pit state early warning value for each subarea based on a physical model of the subway deep foundation pit; And the early warning and decision unit is used for comparing the predicted value of the foundation pit state of each partition with the corresponding early warning value of the foundation pit state to perform construction safety early warning and auxiliary decision.

Description

Subway deep foundation pit construction safety early warning and auxiliary decision making method and system Technical Field The invention relates to the technical field of subway deep foundation pit monitoring, in particular to a subway deep foundation pit construction safety early warning and auxiliary decision making method and system. Background The foundation pit engineering is an engineering project with high time consumption, cost and danger coefficient in urban construction projects. In the engineering, the excavation depth of the foundation pit is more than or equal to 5m, and the subway station is built by adopting an open cut method. The engineering foundation pit monitoring is mostly responsible for a third party monitoring unit, and the foundation pit safety early warning value is referred to GB50497-2009 "technical Specification for monitoring engineering of building foundation pit", wherein the provision of the foundation pit safety early warning value is related to the type of foundation pit enclosure and the depth of the foundation pit. At present, space-time effect is not considered in detection data analysis and early warning threshold value determination of foundation pit engineering, traditional foundation pit engineering safety early warning is regulated according to specifications, state control threshold values of foundation pit areas are the same, firstly, frequent warning of local areas is caused by too low threshold values in the engineering, and secondly, the local areas are in dangerous states caused by too high threshold values. The existing data of the foundation pit construction process is less in excavation, and the future state of the foundation pit cannot be predicted by fully utilizing the historical data. The foundation pit safety is difficult to provide timely strategic guidance and improvement suggestion on measures, the implementation effect analysis and visualization of emergency control measures are insufficient, the optimal remedying opportunity is easily missed or the engineering is in a dangerous state due to poor control effect of the remedying measures. Therefore, a method and a system for pre-warning and assisting decision-making of subway deep foundation pit construction considering the space-time effect of the foundation pit are needed. Disclosure of Invention The invention aims to provide a subway deep foundation pit construction safety early warning and auxiliary decision-making method and system, which are used for partitioning a foundation pit space, respectively carrying out geological parameter inversion and state prediction on each partition, greatly improving the accuracy of foundation pit state prediction, setting a state early warning threshold value for each partition, and accurately carrying out construction safety early warning and further carrying out auxiliary decision-making. In order to achieve the above object, the present invention provides the following solutions: A subway deep foundation pit construction safety early warning and auxiliary decision-making method comprises the following steps: The method comprises the steps of obtaining physical data of a deep foundation pit of a subway, wherein the physical data comprise foundation pit entity information, foundation pit state information and foundation pit environment information, the foundation pit entity information is foundation pit size data, and the foundation pit state information is foundation pit stress and deformation data; partitioning the subway deep foundation pit by applying a K-Means algorithm based on the physical data; For each partition, training a first CSA-BPNN neural network by taking the foundation pit entity information, the foundation pit environment information and the corresponding foundation pit state information as inputs and taking geological parameters as labels to obtain a geological parameter prediction model, wherein the geological parameters comprise elastic modulus and internal friction angle; Inputting the physical data into a digital twin model of the subway deep foundation pit for simulation prediction, outputting simulation data, and inputting the simulation data into the geological parameter prediction model to obtain a geological parameter prediction value; Taking the geological parameter predicted value, the foundation pit entity information and the foundation pit environment information as inputs, and taking the foundation pit state information as a label to train a second CSA-BPNN neural network to obtain a foundation pit state predicted model; Inputting the geological parameter predicted value, the foundation pit entity simulation information and the foundation pit environment simulation information which are obtained based on the subway deep foundation pit digital twin model into the foundation pit state predicted model to obtain a foundation pit state predicted value of each subarea; And carrying out construction safety early warning and auxil