WO2023241472A1 - 一种岩土工程结构模态预测分析方法以及系统 - Google Patents

一种岩土工程结构模态预测分析方法以及系统 Download PDF

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WO2023241472A1
WO2023241472A1 PCT/CN2023/099354 CN2023099354W WO2023241472A1 WO 2023241472 A1 WO2023241472 A1 WO 2023241472A1 CN 2023099354 W CN2023099354 W CN 2023099354W WO 2023241472 A1 WO2023241472 A1 WO 2023241472A1
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geotechnical
status information
analysis algorithm
influencing
structure analysis
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PCT/CN2023/099354
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English (en)
French (fr)
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包小华
陈湘生
沈俊
崔宏志
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深圳大学
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/10—Geometric CAD
    • G06F30/13—Architectural design, e.g. computer-aided architectural design [CAAD] related to design of buildings, bridges, landscapes, production plants or roads
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/08—Construction
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
    • Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Definitions

  • This application relates to the technical field of health monitoring of civil engineering structures, and in particular to a geotechnical structure modal prediction analysis method and system.
  • the inventor found that there are the following shortcomings: on the one hand, manual mechanical simulation calculation and analysis is cumbersome and time-consuming; on the other hand, there is a separation phenomenon between geotechnical engineering design and construction, which is based on artificial mechanics.
  • the construction plan planning made by simulation analysis is relatively ideal.
  • the influencing factors include the construction environment, load conditions, drainage and future precipitation. Taking future precipitation as an example, the following Rainy days will have an impact on the construction of geotechnical engineering structures, and even affect changes in their stress data.
  • this application provides a Geotechnical structural modal prediction analysis methods and systems.
  • this application provides a geotechnical structure modal prediction analysis method, using the following technical solutions:
  • a geotechnical structure modal prediction analysis method including:
  • the geotechnical structure analysis algorithm corresponding to the status information of the influencing factors obtained is determined, and the corresponding geotechnical structure analysis algorithm is used to analyze the geotechnical engineering structure.
  • the structural design model performs modal prediction to calculate the stress information of the geotechnical engineering structure and the modal deformation data of the soil-structure under different stress information, and use them as training data;
  • the pre-built neural network algorithm is applied to modify the geotechnical structure analysis algorithm.
  • analyze and determine the geotechnical structure analysis algorithm corresponding to the acquired status information of influencing factors including:
  • the analysis and determination of the geotechnical structure analysis algorithm corresponding to the status information of the influencing factors with the highest similarity to the obtained status information of the influencing factors includes:
  • the analysis Based on the similarity between the status information of the influencing factors obtained through analysis and the status information of the influencing factors involved in the existing geotechnical structure analysis algorithm, and the influence proportion of the preset influencing factors on the geotechnical structure analysis algorithm, the analysis Determine the geotechnical structure analysis algorithm corresponding to the status information of the influencing factors with the highest similarity to the obtained status information of the influencing factors;
  • the geotechnical engineering structure analysis algorithm determined through analysis is used as the geotechnical engineering structure analysis algorithm for this practical application.
  • the impact proportion of different influencing factors on the geotechnical structure analysis algorithm and the obtained status information of the influencing factors and the status information of the influencing factors involved in the existing geotechnical structure analysis algorithm are fully considered.
  • the similarity can effectively analyze and determine the status information of the influencing factors that is most similar to the status information of the influencing factors obtained, so that the status information of the influencing factors with the highest similarity to the status information of the influencing factors obtained can be more accurately and effectively analyzed and determined.
  • Geotechnical structural analysis algorithm corresponding to condition information.
  • the similarity analysis between the obtained status information of influencing factors and the status information of influencing factors involved in the existing geotechnical structure analysis algorithm includes:
  • the obtained influencing factors are analyzed and determined.
  • the proportion of occurrence time of different types of conditions of different influencing factors and the proportion of occurrence time of corresponding types of conditions of the same influencing factor in the status information of influencing factors involved in different preset geotechnical structure analysis algorithms are further taken into consideration. , so that the time proportion similarity between the influencing factors involved in different geotechnical engineering structure analysis algorithms and the obtained same influencing factor can be analyzed and obtained, and the status information of the influencing factors obtained and the existing geotechnical engineering structure analysis can be used for subsequent analysis.
  • the similarity of the status information of the influencing factors involved in the algorithm lays the foundation.
  • it also includes the geotechnical engineering structure analysis algorithm that is located and analyzed and determined corresponding to the status information of the influencing factors that is most similar to the obtained status information of the influencing factors and is used as the geotechnical engineering for this practical application.
  • the steps before the structural analysis algorithm are as follows:
  • the geotechnical structure analysis algorithm determined by analysis will be used as the geotechnical structure analysis algorithm for this actual application;
  • the gap information between the status information of the influencing factor that is most similar to the obtained status information of the influencing factor and the obtained status information of the influencing factor is analyzed;
  • the geotechnical structure analysis algorithm corresponding to the status information of the influencing factor with the highest similarity to the obtained status information of the influencing factor, the corresponding relationship between the status information of a single influencing factor and the geotechnical structure analysis algorithm, and Apply the prediction formula of the preset geotechnical structure analysis algorithm, and perform predictive analysis to obtain the geotechnical structure analysis algorithm for this practical application.
  • the gap information between the situation information corresponding to the corresponding algorithm and the obtained situation information of the influencing factors will be synthesized and combined with The geotechnical engineering structure analysis algorithm corresponding to the status information of the most similar influencing factors, and the corresponding relationship between the status information of a single influencing factor and the geotechnical engineering structure analysis algorithm, to further predict and analyze the geotechnical engineering structure for this practical application. Analysis algorithm.
  • the predictive analysis of the geotechnical structural analysis algorithm actually applied this time includes:
  • each influencing factor contained in the status information of the influencing factor with the highest similarity is analyzed, and the individual gap proportion information of the same influencing factor contained in the obtained status information of the influencing factor;
  • the effective influencing factors of each influencing factor are analyzed and obtained;
  • Z is the geotechnical engineering structure analysis algorithm predicted and analyzed for this practical application
  • a is the geotechnical structure analysis algorithm corresponding to the status information of the influencing factors with the highest similarity to the obtained status information of the influencing factors;
  • q1 is the weight proportion coefficient of the geotechnical structure analysis algorithm corresponding to the status information of the influencing factors with the highest similarity among the obtained influencing factors;
  • q2 is the weight proportion coefficient of the algorithm corresponding to the individual influencing factors
  • b is the geotechnical structure analysis algorithm corresponding to the single first influencing factor
  • t 1 is the effective influencing factor of the single first influencing factor
  • c is the geotechnical structure analysis algorithm corresponding to the second influencing factor alone
  • t 2 is the effective influencing factor of the independent second influencing factor
  • d is the geotechnical structure analysis algorithm corresponding to the third influencing factor alone
  • t 3 is the effective influencing factor of the third influencing factor alone
  • e is the geotechnical engineering structure analysis algorithm corresponding to the fourth influencing factor alone;
  • t 4 is the effective influencing factor of the independent fourth influencing factor.
  • it also includes steps after obtaining the actually obtained stress information of the geotechnical engineering structure and the structural deformation data under different stress information as actual data and before correcting the geotechnical structure analysis algorithm, as follows: :
  • the subsequent steps are stopped, and based on the distribution probability of the problem that the historical difference exceeds the first preset value and at different extents, the problems are sorted from high to low according to the distribution probability and sent.
  • the distribution probability is given by Sort the problems from high to low and send them to the terminal held by the person in charge of geotechnical engineering;
  • the selection of the geotechnical person in charge is as follows:
  • the notification information sent because the difference information exceeds the first preset value will be sent to the remaining technical personnel responsible for the deep foundation pit project, and After the remaining technical staff confirm that they accept the relevant notification information, build a problem discussion group;
  • it also includes steps between sorting the problems from high to low distribution probability and sending them to the terminal held by the geotechnical engineering leader, as follows:
  • the problems ranked before the preset position are selected and marked with the warning color preferred by the person in charge.
  • the reminder method will be adjusted in time to provide a second reminder.
  • this application provides a geotechnical structure modal prediction and analysis system, which adopts the following technical solution:
  • a geotechnical structure modal prediction and analysis system including a memory, a processor, and a program stored in the memory and executable on the processor.
  • the program can be loaded and executed by the processor to implement the first aspect A geotechnical engineering structure modal prediction and analysis method.
  • the geotechnical structure analysis algorithm can be effectively analyzed and determined to adapt to the situation information of the influencing factors during the planned geotechnical structure, so that the geotechnical structure can be calculated
  • the stress information and the soil-structure modal deformation data under different stress information are also applied to the neural network algorithm to continuously revise the geotechnical engineering structure analysis algorithm based on the comparison between actual and theoretical conditions.
  • the beneficial technical effects of this application are: applying existing geotechnical engineering structure analysis algorithms to effectively predict and analyze the mechanical conditions of geotechnical engineering structures, and fully considering the theoretical values and actual values of geotechnical engineering structures. situation, and make adjustments through the neural network algorithm to make the geotechnical structure analysis algorithm more accurate.
  • Figure 1 is a schematic flow chart of a geotechnical structure modal prediction analysis method according to an embodiment of the present application.
  • Figure 2 is a schematic flowchart of a geotechnical engineering structure analysis algorithm corresponding to analyzing and determining the acquired status information of influencing factors according to another embodiment of the present application.
  • Figure 3 is a schematic flowchart of the analysis and determination process of the geotechnical engineering structure analysis algorithm corresponding to the status information of the influencing factors with the highest similarity to the obtained status information of the influencing factors according to another embodiment of the present application.
  • Figure 4 is a schematic flowchart of the similarity analysis of the obtained status information of influencing factors and the status information of influencing factors involved in the existing geotechnical structure analysis algorithm according to another embodiment of the present application.
  • Figure 5 is another embodiment of the present application, after analyzing and determining the geotechnical structure analysis algorithm corresponding to the status information of the influencing factors that is most similar to the status information of the acquired influencing factors, and as this practical application A schematic flowchart of the geotechnical structure analysis algorithm before.
  • Figure 6 is a schematic flow chart of the predictive analysis of the geotechnical engineering structure analysis algorithm that is actually applied this time in another embodiment of the present application.
  • Figure 7 is another embodiment of the present application after obtaining the actually obtained stress information of the geotechnical engineering structure and the structural deformation data under different stress information as actual data and before revising the geotechnical structure analysis algorithm. Process diagram.
  • Figure 8 is a schematic flowchart of the selection of the person in charge of geotechnical engineering according to another embodiment of the present application.
  • Figure 9 is another embodiment of the present application in which problems are sorted from high to low according to distribution probability and sent to the person in charge of geotechnical engineering. Schematic diagram of the flow between held terminals.
  • a geotechnical structure modal prediction analysis method disclosed in this application includes:
  • Step S100 Obtain the geotechnical engineering structure design model and status information of influencing factors during the planned geotechnical engineering structure.
  • the geotechnical engineering structure design model is constructed through BIM or related building model systems; influencing factors include construction environment, load conditions, drainage and future precipitation.
  • the status information of the influencing factors during the planned geotechnical engineering structure can be obtained from the prediction
  • the system is retrieved from a database that stores status information of influencing factors during the period of geotechnical engineering structures.
  • the construction environment can be queried and obtained from a preset database that stores the construction locations of the geotechnical structure design model; the load conditions can be queried from a preset database that stores the load conditions corresponding to the geotechnical structure design model.
  • Step S200 Analyze and determine the geotechnical structure analysis algorithm corresponding to the obtained status information of the influencing factors according to the corresponding relationship between the status information of the influencing factors and the geotechnical structure analysis algorithm, and use the corresponding geotechnical structure analysis algorithm to analyze the geotechnical structure analysis algorithm.
  • the geotechnical structure design model performs modal prediction to calculate the stress information of the geotechnical engineering structure and the modal deformation data of the soil-structure under different stress information, and serve as training data.
  • the geotechnical structure analysis algorithm corresponding to the status information of the influencing factors can be obtained from a preset database storing the geotechnical structure analysis algorithm.
  • the geotechnical structure analysis algorithm is formed in the following way: According to Test data are obtained through multiple tests under the constraints of relevant factors, and empirical formulas are constructed through more test data to form a geotechnical engineering structure analysis algorithm.
  • the stress information of geotechnical engineering structures and the soil-structure modal deformation data under different stress information can be used in the following ways: build a simulation system of the corresponding geotechnical engineering design model based on the geotechnical engineering structure analysis algorithm; and then The simulation system will analyze and calculate the stress information and the soil-structure modal deformation data under different stress information based on the input parameters of the influencing factors and the parameter information of some models.
  • Step S300 Obtain the actually obtained stress information of the geotechnical engineering structure and the structural deformation data under different stress information as actual data.
  • the actually obtained stress information of the geotechnical engineering structure and the structural deformation data under different stress information can be used to detect the stress information of the geotechnical engineering structure and the structural deformation under different stress information through actual settings on site.
  • the data is detected and obtained by a detection device. For example, if a geotechnical engineering structure contains columns, the settlement data of the columns can be measured and obtained by a static level.
  • the stress on the columns can be measured by using steel bars evenly arranged around the outer periphery of the columns. Detect the corresponding stress data.
  • Step S400 Based on the training data and actual data, apply the pre-built neural network algorithm to modify the geotechnical engineering structure analysis algorithm.
  • the neural network algorithm is an algorithm mathematical model that imitates the behavioral characteristics of animal neural networks and performs distributed parallel information processing.
  • the neural network algorithm mentioned in step S400 can use a feedforward neural network, which is the most common in practical applications.
  • Neural network type The first layer is input and the last layer is output.
  • the input is training data and the output is actual data.
  • the modification of the original geotechnical structure analysis algorithm can be through multiple input and output data and inserting correlation coefficients into the original algorithm to make adjustments.
  • the network algorithm can modify the geotechnical structure analysis algorithm, thereby improving the geotechnical structure analysis algorithm under corresponding influence conditions and further modifying it to facilitate the application of more accurate geotechnical structure analysis when encountering corresponding situations. algorithm.
  • step S200 of Figure 1 further consider the situation where the geotechnical structure analysis algorithm corresponding to the status information of the influencing factors that may be obtained cannot be obtained from the corresponding relationship between the status information of the influencing factors and the geotechnical structure analysis algorithm. In this case, it is necessary to further analyze the geotechnical structure analysis algorithm corresponding to the situation information of the influencing factors, which will be explained in detail with reference to the embodiment shown in Figure 2.
  • the geotechnical engineering structure analysis algorithm corresponding to the analysis and determination of the acquired status information of influencing factors mentioned in step S200 includes:
  • Step S210 According to the corresponding relationship between the status information of the influencing factors and the geotechnical structure analysis algorithm, query whether there is a geotechnical structure analysis algorithm corresponding to the obtained status information of the influencing factors. If yes, execute step S220; otherwise, execute step S230.
  • Step S220 Use the geotechnical engineering structure analysis algorithm corresponding to the obtained status information of the influencing factors as the analyzed and determined geotechnical engineering structure analysis algorithm.
  • Step S230 query and obtain the geotechnical engineering structure analysis algorithm corresponding to the status information of other influencing factors, and analyze and determine the geotechnical engineering structure corresponding to the status information of the influencing factor with the highest similarity to the obtained status information of the influencing factors.
  • the analysis algorithm is used as the geotechnical engineering structure analysis algorithm for this practical application.
  • step S230 of Figure 2 it is further considered that when determining the similarity of the situation information, it is necessary to consider the influence proportion of the influencing factors and the similarity of the situation information to make a comprehensive judgment. Therefore, it is also necessary to compare the status of the obtained influencing factors.
  • the geotechnical structure analysis algorithm corresponding to the status information of the influencing factors with the highest information similarity is used for further analysis and determination, which will be explained in detail with reference to the embodiment shown in Figure 3.
  • the analysis and determination of the geotechnical structure analysis algorithm corresponding to the status information of the influencing factors with the highest similarity to the obtained status information of the influencing factors includes:
  • Step S231 Analyze the similarity between the obtained status information of the influencing factors and the status information of the influencing factors involved in the existing geotechnical engineering structure analysis algorithm.
  • Step S232 based on the similarity between the status information of the influencing factors obtained through analysis and the status information of the influencing factors involved in the existing geotechnical engineering structure analysis algorithm, and the influence proportion of the preset influencing factors on the geotechnical engineering structure analysis algorithm. Compare and analyze to determine the Geotechnical structure analysis algorithm corresponding to the status information of the influencing factors with the highest similarity obtained.
  • the preset influence proportion of the influencing factors on the geotechnical structure analysis algorithm can be obtained from the preset database that stores the influence proportion of the influencing factors on the geotechnical structure analysis algorithm.
  • the specific influence proportion is calculated.
  • the method can be to calculate the ratio of the average change of the corresponding influencing factors to the value corresponding to the overall geotechnical structure analysis algorithm, calculate the ratio of each influencing factor to the whole, and then calculate the ratio of each influencing factor to the whole, and compare it with all
  • the ratio of the sum of the ratios of the influencing factors to the whole is used as the influence proportion of each influencing factor on the geotechnical structural analysis algorithm.
  • the three influencing factors are influencing factor A, influencing factor B, and influencing factor C, among which The influence of factor A accounts for 30%, the influence of factor B accounts for 50%, and the influence of factor C accounts for 20%.
  • the similarity between the first set of geotechnical structure analysis algorithms and the same influencing factor of influencing factor A is 80%.
  • the similarity with the same influencing factor of influencing factor A is 70%.
  • the similarity with the same influencing factor of influencing factor B is 80%.
  • the similarity between the second set of geotechnical structure analysis algorithms and the same influencing factor as influencing factor C is 60%.
  • the second set of geotechnical structural analysis algorithms is selected.
  • Step S233 use the analyzed and determined geotechnical engineering structure analysis algorithm as the geotechnical engineering structure analysis algorithm for this practical application.
  • step S232 of Figure 3 it is further considered that in the process of similarity analysis of the obtained status information of influencing factors and the status information of influencing factors involved in the existing geotechnical structure analysis algorithm, it is also necessary to consider The type and proportion of influencing factors and the proportion of time are used to better determine the similarity of the status information. Therefore, it is necessary to obtain the status information of the influencing factors and the influencing factors involved in the existing geotechnical structure analysis algorithm.
  • the similarity of the status information is further analyzed, and the details are described with reference to the embodiment shown in FIG. 4 .
  • the similarity analysis between the status information of the influencing factors obtained in step S232 and the status information of the influencing factors involved in the existing geotechnical structure analysis algorithm includes:
  • Step S232.a Analyze the occurrence time proportions of different types of conditions of different influencing factors in the obtained status information of influencing factors.
  • the occurrence time proportions of different types of conditions of different influencing factors in the acquired status information of influencing factors can be obtained from the preset storage.
  • the occurrence time proportions of different types of status of different influencing factors in the acquired status information of influencing factors Obtained from the database of the ratio,
  • Step S232.b analysis of different geotechnical engineering structures based on the proportion of occurrence times of different types of conditions and presets of different influencing factors
  • the proportion of occurrence time of corresponding types of conditions of the same influencing factor in the status information of the influencing factors involved in the algorithm is analyzed to obtain the similarity of the time proportions of the influencing factors involved in different geotechnical structure analysis algorithms and the obtained same influencing factor.
  • the time proportion similarity is 50%. If the first set of geotechnical structure analysis algorithm In the geotechnical structure analysis algorithm, the time of the same influencing factor as influencing factor A is 90 minutes, so the time proportion similarity is also 67%.
  • Step S232.c according to the similarity of the time proportions of the influencing factors involved in the different geotechnical engineering structure analysis algorithms obtained by the analysis and the obtained same influencing factors, and the occurrence time proportions of different types of conditions of the same influencing factors, analyze and determine the The similarity between the status information of the influencing factors obtained and the status information of the influencing factors involved in the existing geotechnical structure analysis algorithm.
  • type A different types of conditions with the same influencing factors can be divided into type A and type B.
  • type A accounts for 30% of the time
  • type B accounts for 70% of the time.
  • Existing geotechnical engineering structural analysis Different types of conditions of the same influencing factors in the status information of the influencing factors involved in the algorithm can also be divided into type A and type B. Among them, type A accounts for 50% of the time and type B accounts for 50% of the time.
  • step S220 of Figure 2 it is further considered that the similarity of the geotechnical engineering structure analysis algorithm corresponding to the status information of the influencing factor with the highest similarity to the status information of the obtained influencing factor does not meet the requirements, so it is also It is necessary to further analyze the status information of the influencing factors with the highest similarity after determining the geotechnical structure analysis algorithm corresponding to the status information of the influencing factors with the highest similarity to the obtained status information of the influencing factors. For details, refer to The embodiment shown in Figure 5.
  • a geotechnical structure modal prediction analysis method also includes a geotechnical structure analysis algorithm located and analyzing and determining the status information of the influencing factors that is most similar to the obtained status information of the influencing factors.
  • the steps before the geotechnical structural analysis algorithm used in this practical application are as follows:
  • Step S2a0 Obtain the similarity between the situation information corresponding to the analyzed and determined geotechnical structure analysis algorithm and the obtained situation information of the influencing factors.
  • Step S2b0 if the similarity of the situation information exceeds the first preset similarity, the analyzed and determined geotechnical engineering structure analysis algorithm is used as the geotechnical engineering structure analysis algorithm for this practical application.
  • the first preset similarity may be 70%, or may be other similarities set by the user.
  • Step S2c0 analyzes the gap information between the status information of the influencing factor that is most similar to the acquired status information of the influencing factor and the acquired status information of the influencing factor.
  • the analysis of the gap information between the status information of the influencing factor that has the highest similarity to the status information of the influencing factors obtained and the status information of the influencing factors obtained takes future precipitation as an example.
  • the gap information is the similarity obtained
  • Step S2d0 based on the gap information, the geotechnical structure analysis algorithm corresponding to the status information of the influencing factor with the highest similarity to the obtained status information of the influencing factor, the correspondence between the status information of the single influencing factor and the geotechnical structure analysis algorithm Relationship, and apply the prediction formula of the preset geotechnical structure analysis algorithm, and the prediction analysis obtains the geotechnical structure analysis algorithm actually applied this time.
  • Step S2d1 Obtain the geotechnical engineering structure analysis algorithm corresponding to the status information of the influencing factor with the highest similarity to the obtained status information of the influencing factor, and the corresponding relationship between the status information of a single influencing factor and the geotechnical structure analysis algorithm.
  • the corresponding relationship between the status information of a single influencing factor and the geotechnical structure analysis algorithm can be queried and obtained from a preset database that stores the corresponding relationship between the status information of a single influencing factor and the geotechnical structure analysis algorithm.
  • Step S2d2 Analyze, based on the gap information, each influencing factor contained in the status information of the influencing factor with the highest similarity, and the individual gap proportion information of the same influencing factor contained in the obtained status information of the influencing factor.
  • Step S2d3 Analyze and obtain the effective influencing factors of each influencing factor based on the individual gap proportion information of each influencing factor and the influence proportion of the preset influencing factors on the geotechnical structure analysis algorithm.
  • the effective influencing factor of each influencing factor is the product of the individual gap proportion information of each influencing factor and the preset influence proportion of the influencing factor on the geotechnical structure analysis algorithm.
  • Step S2d4 Apply the preset prediction formula of the geotechnical engineering structure analysis algorithm, and perform predictive analysis to obtain the geotechnical engineering structure analysis algorithm for this actual application.
  • the weight proportion coefficient of the structural analysis algorithm is the weight proportion coefficient of the algorithm corresponding to the independent influencing factor; b is the geotechnical engineering structure analysis algorithm corresponding to the independent first influencing factor; t 1 is the effective value of the independent first influencing factor.
  • Influence factor is the geotechnical engineering structure analysis algorithm corresponding to the independent second influencing factor; t 2 is the effective influencing factor of the independent second influencing factor; d is the geotechnical engineering structure analysis algorithm corresponding to the independent third influencing factor; t 3 is the effective influencing factor of the third independent influencing factor; e is the geotechnical structure analysis algorithm corresponding to the independent fourth influencing factor; t 4 is the effective influencing factor of the independent fourth influencing factor.
  • step S300 and step S400 in Figure 1 it is further considered that before correcting the geotechnical structure analysis algorithm, there is a large gap between the actual data and the training data. At this time, it is not conducive to correction through the neural network algorithm, and further steps are required. Analyze and notify the person in charge of the reasons. Please refer to the embodiment shown in Figure 7 for detailed description.
  • a geotechnical engineering structure modal prediction analysis method also includes obtaining the actually acquired stress information of the geotechnical engineering structure and the structural deformation data under different stress information, as the actual data and after correcting the rock
  • the steps before the soil engineering structure analysis algorithm are as follows:
  • Step SA00 Analyze and obtain the actually obtained stress information of the geotechnical engineering structure and the structural deformation data under different stress information, and the calculated stress information of the geotechnical engineering structure and the structure under different stress information. The difference in deformation data.
  • Step SB00 if the difference exceeds the first preset value, the subsequent steps are stopped, and based on the problem distribution probability of the historical difference exceeding the first preset value and at different excess degrees, the problem is processed from high to low according to the distribution probability. Sorted and sent to the terminal held by the geotechnical engineer.
  • the first preset value can be 10000N, or other preset force values; the problem distribution probability exceeding the first preset value and under different excess degrees can be obtained from the preset storage of the first The preset values are queried and obtained in the database of problem distribution probabilities under different degrees of excess; the terminal held by the person in charge of geotechnical engineering can be a mobile phone, a computer, or other communicable terminal equipment.
  • the problem may be a problem with the detection device used for actual detection, or it may be a major problem with the applied algorithm.
  • Step SC00 if the difference information exceeds the second preset value and is lower than the first preset value, then stop subsequent steps, and based on the historical difference value exceeds the second preset value and the problem distribution probability under different exceedance procedures, according to The problems are sorted from high to low according to the distribution probability and sent to the terminal held by the person in charge of geotechnical engineering.
  • the problem distribution probability based on the historical difference exceeding the first preset value and under different exceeding procedures can be stored from the preset storage based on the problem distribution probability based on the historical difference exceeding the first preset value and under different exceeding procedures. Query and obtain in the database.
  • Step SD00 otherwise, proceed to subsequent steps.
  • the person in charge of geotechnical engineering in the terminal held by the person in charge of geotechnical engineering mentioned in step SB00 needs to make a selection so that the notified person in charge of geotechnical engineering can effectively handle the corresponding problem.
  • the person in charge of geotechnical engineering in the terminal held by the person in charge of geotechnical engineering mentioned in step SB00 needs to make a selection so that the notified person in charge of geotechnical engineering can effectively handle the corresponding problem.
  • the geotechnical person in charge is selected as follows:
  • Step SCa0 Analyze and obtain the working years of the person in charge of geotechnical engineering.
  • the analysis and acquisition of the working years of the person in charge of geotechnical engineering can be queried and obtained from a preset database storing the working years of the person in charge of geotechnical engineering.
  • Step SCb0 if the working years of the person in charge of geotechnical engineering is less than the preset number of years, the notification information sent because the difference information exceeds the first preset value will be sent to the remaining technical personnel responsible for this deep foundation pit project. personnel, and after other technical personnel confirm to accept the relevant notification information, build a problem discussion group.
  • the preset period may be 1 year or other years.
  • Step SCc0 otherwise, no other settings are made.
  • a geotechnical engineering structure modal prediction analysis method also includes steps between sorting problems according to distribution probability from high to low and sending them to the terminal held by the person in charge of geotechnical engineering, as follows:
  • Step SC10 Analyze and obtain problems in which the distribution probability exceeds the preset probability.
  • Step SC20 Select the questions ranked before the preset position from the questions whose distribution probability exceeds the preset probability and mark them with the warning color preferred by the person in charge.
  • the preset probability can be 30%, or it can be a probability set by other users.
  • the warning color preferred by the person in charge can be red or blue, or other colors set by the user based on actual needs.
  • embodiments of the present invention provide a geotechnical structure modal prediction and analysis system, including a memory and a processor.
  • the memory stores information that can be run on the processor to implement any one of Figures 1 to 9. method procedure.

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Abstract

本申请涉及一种岩土工程结构模态预测分析方法以及系统，涉及土木工程结构健康监测技术领域，解决了施工过程中存在各种对施工方案执行影响的因素，影响因素包括施工环境、荷载条件、排水和未来降水，以未来降水为例，下雨天就会给岩土工程结构的施工带来影响，甚至影响其受力数据出现变化的问题，其方法包括：获取实际获取的岩土工程结构的受力信息以及在不同受力信息下的结构变形数据，作为实际数据；基于训练数据、实际数据，应用所构建的神经网络算法，修正岩土工程结构分析算法。

Description

一种岩土工程结构模态预测分析方法以及系统 技术领域
本申请涉及土木工程结构健康监测技术领域，尤其是涉及一种岩土工程结构模态预测分析方法以及系统。
背景技术
近十年来，城市的地下空间得到大规模开发与利用。在人口稠密和建筑物密集的城市区进行地下工程施工的工程数量直线上升，城市地下空间的开发和集约利用是城市发展新的空间，解决城市资源承载力不足的必然之选。
大型城市综合体、超大规模建筑小区、超大规模街坊项目不断涌现，与此同时，以基坑开挖为典型的地下空间岩土工程开发活动所引起的工程事故和环境问题日渐增多。鉴于城市岩土工程越来越趋向于形状复杂、开挖深度深、开挖面积大的特点，且基坑工程在城市繁华区，施工场地离已有建(构)筑物距离近，如果对复杂情况下的岩土工程施工缺乏准确的认识，将导致系列工程风险问题，例如，随着我市地下空间的大规模开发利用，经常发生的岩土工程围护体系变形过大甚至失稳、地表开裂甚至导致城市道路坍塌、地表沉降过大而导致近邻建构筑物与城市生命线受损甚至倒塌破坏等，其后果将不堪设想。
相关技术中，在岩土工程设计施工之前，相关设计人员会基于工程项目图纸和数据对结构进行人工的力学模拟计算分析和预测，以便于规划后续的结构施工方案的确定。
针对上述中的相关技术，发明人发现存在有如下缺陷：一方面人工的力学模拟计算分析较为麻烦且耗时过长，另一方面，岩土工程设计与施工存在割裂现象，基于人工所作的力学模拟分析所作的施工方案规划较为理想，而实际上，由于施工过程中存在各种对施工方案执行影响的因素，影响因素包括施工环境、荷载条件、排水和未来降水，以未来降水为例，下雨天就会给岩土工程结构的施工带来影响，甚至影响其受力数据出现变化。
发明内容
为了能够更加精准的分析岩土工程结构的预计力学情况，而且还会结合实际情况的反馈作整体算法的调整，以不断提升对岩土工程结构的预计力学情况的精准度，本申请提供一种岩土工程结构模态预测分析方法以及系统。
第一方面，本申请提供一种岩土工程结构模态预测分析方法，采用如下的技术方案：
一种岩土工程结构模态预测分析方法，包括：
获取岩土工程结构设计模型以及所规划的岩土工程结构期间的影响因素的状况信息，其中，影响因素包括施工环境、荷载条件、排水和未来降水；
根据影响因素的状况信息与岩土工程结构分析算法的对应关系，分析确定所获取影响因素的状况信息所对应的岩土工程结构分析算法，并采用相应岩土工程结构分析算法，对岩土工程结构设计模型进行模态预测，以计算得出岩土工程结构的受力信息以及在不同受力信息下的土体-结构模态变形数据，并作为训练数据；
获取实际获取的岩土工程结构的受力信息以及在不同受力信息下的结构变形数据，作为实际数据；
基于训练数据、实际数据，应用预先构建的神经网络算法，修正岩土工程结构分析算法。
通过采用上述技术方案，可以有效分析确定适配于所规划的岩土工程结构期间的影响因素的状况信息的岩土工程结构分析算法，从而可以计算出岩土工程结构的受力信息以及在不同受力信息下的土体-结构模态变形数据，而且还会根据实际和理论的前后对比，应用神经网络算法，不断修正岩土工程结构分析算法，从而不断提升对岩土工程结构的预计力学情况的精准度。
可选的，分析确定所获取影响因素的状况信息所对应的岩土工程结构分析算法包括：
根据影响因素的状况信息与岩土工程结构分析算法的对应关系，查询是否存在与获取的影响因素的状况信息相对应的岩土工程结构分析算法；
若为是，则将所获取的影响因素的状况信息相对应的岩土工程结构分析算法，作为所分析确定的岩土工程结构分析算法；
反之，则查询获取其它影响因素的状况信息所对应的岩土工程结构分析算法，并从中分析判定与所获取的影响因素的状况信息相似度最高的影响因素的状况信息所对应的岩土工程结构分析算法，作为本次实际应用的岩土工程结构分析算法。
通过采用上述技术方案，具体公开了如何采用影响因素的状况信息所对应的岩土工程结构分析算法，尤其考虑到当不存在与之对应的岩土工程结构分析算法时，会应用与所获取的影响因素的状况信息相似度最高的影响因素的状况信息所对应的岩土工程结构分析算法。
可选的，与所获取的影响因素的状况信息相似度最高的影响因素的状况信息所对应的岩土工程结构分析算法的分析判定包括：
分析所获取的影响因素的状况信息和已有的岩土工程结构分析算法所涉及的影响因素的状况信息的相似度；
基于所分析获取的影响因素的状况信息和已有的岩土工程结构分析算法所涉及的影响因素的状况信息的相似度、预设的影响因素关于岩土工程结构分析算法的影响占比，分析确定与所获取的影响因素的状况信息相似度最高的影响因素的状况信息所对应的岩土工程结构分析算法；
将所分析确定的岩土工程结构分析算法作为本次实际应用的岩土工程结构分析算法。
通过采用上述技术方案，充分考虑了不同影响因素关于岩土工程结构分析算法的影响占比以及所获取的影响因素的状况信息和已有的岩土工程结构分析算法所涉及的影响因素的状况信息的相似度，可以有效分析确定与所获取的影响因素的状况信息相似度最高的影响因素的状况信息，从而可以更加准确有效分析确定与所获取的影响因素的状况信息相似度最高的影响因素的状况信息所对应的岩土工程结构分析算法。
可选的，所获取的影响因素的状况信息和已有的岩土工程结构分析算法所涉及的影响因素的状况信息的相似度分析包括：
分析所获取的影响因素的状况信息中不同影响因素的不同种类状况的出现时间占比；
根据不同影响因素的不同种类状况出现时间占比、预设的不同岩土工程结构分析算法所涉及影响因素的状况信息中同一影响因素的相应种类状况出现时间占比，分析获取不同岩土工程结构分析算法所涉及影响因素与所获取的同一影响因素的时间占比相似度；
根据所分析获取的不同岩土工程结构分析算法所涉及影响因素与所获取的同一影响因素的时间占比相似度、同影响因素的不同种类状况出现时间占比，分析确定所获取的影响因素的状况信息和已有的岩土工程结构分析算法所涉及的影响因素的状况信息的相似度。
通过采用上述技术方案，进一步考虑到了不同影响因素的不同种类状况出现时间占比、预设的不同岩土工程结构分析算法所涉及影响因素的状况信息中同一影响因素的相应种类状况出现时间占比，从而可以分析获取不同岩土工程结构分析算法所涉及影响因素与所获取的同一影响因素的时间占比相似度，为后续分析所获取的影响因素的状况信息和已有的岩土工程结构分析算法所涉及的影响因素的状况信息的相似度奠定基础。
可选的，还包括位于并从中分析判定与所获取的影响因素的状况信息相似度最高的影响因素的状况信息所对应的岩土工程结构分析算法之后且在作为本次实际应用的岩土工程结构分析算法之前的步骤，具体如下：
获取所分析确定的岩土工程结构分析算法所对应的状况信息与所获取的影响因素的状况信息的相似度；
若状况信息相似度超过第一预设相似度，则将所分析确定的岩土工程结构分析算法作为本次实际应用的岩土工程结构分析算法；
反之，则分析与所获取的影响因素的状况信息相似度最高的影响因素的状况信息与所获取的影响因素的状况信息的差距信息；
根据差距信息、与所获取的影响因素的状况信息相似度最高的影响因素的状况信息所对应的岩土工程结构分析算法、单一影响因素的状况信息与岩土工程结构分析算法的对应关系，并应用预设的岩土工程结构分析算法的预测公式，预测分析获取本次实际应用的岩土工程结构分析算法。
通过采用上述技术方案，进一步考虑到可能会存在状况信息相似度不高的情况，在这个情况下会综合根据相应算法所对应的状况信息与所获取的影响因素的状况信息的差距信息，并结合相似度最高的影响因素的状况信息所对应的岩土工程结构分析算法、单一影响因素的状况信息与岩土工程结构分析算法的对应关系，来进一步预测分析获取本次实际应用的岩土工程结构分析算法。
可选的，本次实际应用的岩土工程结构分析算法的预测分析包括：
获取与所获取的影响因素的状况信息相似度最高的影响因素的状况信息所对应的岩土工程结构分析算法、单一影响因素的状况信息与岩土工程结构分析算法的对应关系；
根据差距信息分析相似度最高的影响因素的状况信息所包含的每个影响因素，与所获取的影响因素的状况信息所包含的同一影响因素的单独差距比例信息；
根据每个影响因素的单独差距比例信息、预设的影响因素关于岩土工程结构分析算法的影响占比，分析获取每个影响因素的有效影响因子；
应用预设的岩土工程结构分析算法的预测公式,预测分析获取本次实际应用的岩土工程结构分析算法,具体预测公式如下：
Z＝a*q1+q2*[b*(t1/t总)+c*(t2/t总)+d*(t3/t总)+e*(t4/t总)，q1+q2＝1，t总＝t1+t2+t3+t4；
其中，Z为所预测分析的本次实际应用的岩土工程结构分析算法；
a为与所获取的影响因素的状况信息相似度最高的影响因素的状况信息所对应的岩土工程结构分析算法；
q1为所获取的影响因素的状况信息相似度最高的影响因素的状况信息所对应的岩土工程结构分析算法的权重占比系数；
q2为单独影响因素所对应算法的权重占比系数；
b为单独第一影响因素所对应的岩土工程结构分析算法；
t1为单独第一影响因素的有效影响因子；
c为单独第二影响因素所对应的岩土工程结构分析算法；
t2为单独第二影响因素的有效影响因子；
d为单独第三影响因素所对应的岩土工程结构分析算法；
t3为单独第三影响因素的有效影响因子；
e为单独第四影响因素所对应的岩土工程结构分析算法；
t4为单独第四影响因素的有效影响因子。
通过采用上述技术方案，进一步考虑分析获取每个影响因素的有效影响因子，并根据每个影响因素的有效影响因子、岩土工程结构分析算法的预测公式，来预测本次实际应用的岩土工程结构分析算法。
可选的，还包括位于获取实际获取的岩土工程结构的受力信息以及在不同受力信息下的结构变形数据，作为实际数据之后且在修正岩土工程结构分析算法之前的步骤，具体如下：
分析获取实际获取的岩土工程结构的受力信息以及在不同受力信息下的结构变形数据，与计算得出的岩土工程结构的受力信息以及在不同受力信息下的结构变形数据的差值；
若差值超过第一预设值，则停止后续步骤，且根据历史差值超过第一预设值且在不同超出程度下的问题分布概率，按照分布概率由高至低对问题作排序并发送至岩土工程负责人所持终端；
若差值信息超过第二预设值且低于第一预设值，则停止后续步骤，且根据历史差值超过第二预设值且在不同超出程序下的问题分布概率，按照分布概率由高至低对问题作排序并发送至岩土工程负责人所持终端；
反之，则进行后续的步骤。
通过采用上述技术方案，进一步考虑到了在不同差值情况下所遇到的问题也有所不同，因此在发送相应负责人的时候会按照问题的概率作排序，从而方便负责人更好的解决问题。
可选的，岩土工程负责人的选择如下：
分析获取岩土工程负责人的从业工作年限；
若岩土工程负责人的从业工作年限小于预设年限，则将因差值信息超过第一预设值而发送的通知信息，一并发送至负责本次深基坑工程的其余技术人员，并在其余技术人员确认接受相关通知信息后，构建问题讨论群；
反之，则不作处理。
通过采用上述技术方案，进一步考虑到负责人由于从业年限不够而不能有效处理问题的情况， 在这个情况下会构建问题讨论群，以便于更好的解决问题。
可选的，还包括位于按照分布概率由高至低对问题作排序与发送至岩土工程负责人所持终端之间的步骤，具体如下：
分析获取其中分布概率超过预设概率的问题；
从分布概率超过预设概率的问题中，选择其中排序在预设位置之前的问题并以负责人所倾向的警示颜色作标记。
通过采用上述技术方案，进一步考虑到负责人在收到信息后未及时回复的情况，在这个情况下，会及时调整提醒方式来作二次提醒。
第二方面，本申请提供一种岩土工程结构模态预测分析系统，采用如下的技术方案：
一种岩土工程结构模态预测分析系统，包括存储器、处理器及存储在所述存储器上并可在所述处理器上运行的程序，该程序能够被处理器加载执行时实现如第一方面所述的一种岩土工程结构模态预测分析方法。
通过采用上述技术方案，通过相关程序的调取，可以有效分析确定适配于所规划的岩土工程结构期间的影响因素的状况信息的岩土工程结构分析算法，从而可以计算出岩土工程结构的受力信息以及在不同受力信息下的土体-结构模态变形数据，而且还会根据实际和理论的前后对比，应用神经网络算法，不断修正岩土工程结构分析算法。
综上所述，本申请的有益技术效果为：应用已有的岩土工程结构分析算法，使岩土工程结构的力学状况得到有效预测分析，并且充分考虑岩土工程结构的理论值和实际值的情况，并通过神经网络算法作出调整，使岩土工程结构分析算法更加精准。
附图说明
图1是本申请实施例一种岩土工程结构模态预测分析方法的流程示意图。
图2是本申请另一实施例的分析确定所获取影响因素的状况信息所对应的岩土工程结构分析算法的流程示意图。
图3是本申请另一实施例的与所获取的影响因素的状况信息相似度最高的影响因素的状况信息所对应的岩土工程结构分析算法的分析判定的流程示意图。
图4是本申请另一实施例的所获取的影响因素的状况信息和已有的岩土工程结构分析算法所涉及的影响因素的状况信息的相似度分析的流程示意图。
图5是本申请另一实施例的位于并从中分析判定与所获取的影响因素的状况信息相似度最高的影响因素的状况信息所对应的岩土工程结构分析算法之后且在作为本次实际应用的岩土工程结构分析算法之前的流程示意图。
图6是本申请另一实施例的本次实际应用的岩土工程结构分析算法的预测分析的流程示意图。
图7是本申请另一实施例的位于获取实际获取的岩土工程结构的受力信息以及在不同受力信息下的结构变形数据，作为实际数据之后且在修正岩土工程结构分析算法之前的流程示意图。
图8是本申请另一实施例的岩土工程负责人的选择的流程示意图。
图9是本申请另一实施例的位于按照分布概率由高至低对问题作排序与发送至岩土工程负责人 所持终端之间的流程示意图。
具体实施方式
以下结合附图对本申请作进一步详细说明。
参照图1，为本申请公开的一种岩土工程结构模态预测分析方法，包括：
步骤S100，获取岩土工程结构设计模型以及所规划的岩土工程结构期间的影响因素的状况信息。
其中，岩土工程结构设计模型为通过BIM或相关建筑模型系统构建的；影响因素包括施工环境、荷载条件、排水和未来降水，所规划的岩土工程结构期间的影响因素的状况信息可以从预设的存储有岩土工程结构期间的影响因素的状况信息的数据库中调取获取。
其中，施工环境可以从预设的存储有岩土工程结构设计模型所施工的位置的数据库中查询获取；荷载条件可以从预设的存储有岩土工程结构设计模型所对应荷载条件的数据库中查询获取；排水可以预设的存储有岩土工程结构设计模型所施工位置的排水条件的数据库中查询获取；未来降水可以通过爬虫技术从网络搜索相关未来天气的降水状况。
步骤S200，根据影响因素的状况信息与岩土工程结构分析算法的对应关系，分析确定所获取影响因素的状况信息所对应的岩土工程结构分析算法，并采用相应岩土工程结构分析算法，对岩土工程结构设计模型进行模态预测，以计算得出岩土工程结构的受力信息以及在不同受力信息下的土体-结构模态变形数据，并作为训练数据。
其中，影响因素的状况信息所对应的岩土工程结构分析算法可以从预设的存储有岩土工程结构分析算法的数据库中获取，具体的，岩土工程结构分析算法的形成通过如下方式：根据相关因素限定条件下通过多次试验来获取试验数据，并通过较多的试验数据来构建经验公式，形成岩土工程结构分析算法。
其中，岩土工程结构的受力信息以及在不同受力信息下的土体-结构模态变形数据可以采用如下方式：根据岩土工程结构分析算法构建相应岩土工程设计模型的仿真系统；然后仿真系统会根据所输入的影响因素的参数以及一些模型的参数信息来分析计算出其中受力信息以及在不同受力信息下的土体-结构模态变形数据。
步骤S300，获取实际获取的岩土工程结构的受力信息以及在不同受力信息下的结构变形数据，作为实际数据。
其中，实际获取的岩土工程结构的受力信息以及在不同受力信息下的结构变形数据可以通过实际设置在现场用于检测岩土工程结构受力信息以及在不同受力信息下的结构变形数据的检测装置来检测获取，例如针对岩土工程结构中会包含立柱，立柱的沉降数据可以通过静力水准仪来测量获取，立柱所受的应力可以通过通过在立柱外周边均匀布置的钢筋计来检测相应所受应力数据。
步骤S400，基于训练数据、实际数据，应用预先构建的神经网络算法，修正岩土工程结构分析算法。
其中，神经网络算法是一种模仿动物神经网络行为特征，进行分布式并行信息处理的算法数学模型，步骤S400所提及的神经网络算法可以采用前馈神经网络，这是实际应用中最常见的神经网络类型。第一层是输入,最后一层是输出。
在本申请中，输入为训练数据，输出为实际数据，其对原有岩土工程结构分析算法的修正，可以是通过多个输入输出数据，并在原有算法上插入相关系数来作调整。
本实施例的实施原理如下：
能够有效根据岩土工程结构期间的影响因素的状况信息寻找到相应影响因素下的岩土工程结构分析算法，并根据岩土工程结构分析算法分析获取训练数据，并根据实际数据与训练数据以及神经网络算法，可以对岩土工程结构分析算法作修正，从而提高对相应影响状况下的岩土工程结构分析算法作进一步修正，方便后续在遇到相应情况时可以应用更加准确的岩土工程结构分析算法。
在图1的步骤S200中，进一步考虑可能获取的影响因素的状况信息相对应的岩土工程结构分析算法无法从影响因素的状况信息与岩土工程结构分析算法的对应关系获取的情况，在这个情况下，需要对采用影响因素的状况信息所对应的岩土工程结构分析算法作进一步的分析，具体参照图2所示实施例作详细说明。
参照图2，步骤S200所提及的分析确定所获取影响因素的状况信息所对应的岩土工程结构分析算法包括：
步骤S210，根据影响因素的状况信息与岩土工程结构分析算法的对应关系，查询是否存在与获取的影响因素的状况信息相对应的岩土工程结构分析算法。若为是，则执行步骤S220；反之，则执行步骤S230。
步骤S220，将所获取的影响因素的状况信息相对应的岩土工程结构分析算法，作为所分析确定的岩土工程结构分析算法。
步骤S230，查询获取其它影响因素的状况信息所对应的岩土工程结构分析算法，并从中分析判定与所获取的影响因素的状况信息相似度最高的影响因素的状况信息所对应的岩土工程结构分析算法，作为本次实际应用的岩土工程结构分析算法。
本实施例的实施原理如下：
有效考虑到在无法通过据影响因素的状况信息与岩土工程结构分析算法的对应关系，来确定岩土工程结构分析算法时，可以寻找与所获取的影响因素的状况信息相似度最高的影响因素的状况信息所对应的岩土工程结构分析算法，来作为本次实际应用的岩土工程结构分析算法。
在图2的步骤S230中，进一步考虑到在确定状况信息相似度的时候，需要考虑影响因素的影响占比以及状况信息的相似度来综合判定，因此还需要对与所获取的影响因素的状况信息相似度最高的影响因素的状况信息所对应的岩土工程结构分析算法作进一步的分析判定，具体参照图3所示实施例作详细说明。
参照图3，与所获取的影响因素的状况信息相似度最高的影响因素的状况信息所对应的岩土工程结构分析算法的分析判定包括：
步骤S231，分析所获取的影响因素的状况信息和已有的岩土工程结构分析算法所涉及的影响因素的状况信息的相似度。
步骤S232，基于所分析获取的影响因素的状况信息和已有的岩土工程结构分析算法所涉及的影响因素的状况信息的相似度、预设的影响因素关于岩土工程结构分析算法的影响占比，分析确定与所 获取的影响因素的状况信息相似度最高的影响因素的状况信息所对应的岩土工程结构分析算法。
其中，预设的影响因素关于岩土工程结构分析算法的影响占比可以从预设的存储有影响因素关于岩土工程结构分析算法的影响占比的数据库中来获取，具体影响占比的计算方式可以是计算相应影响因素的平均变化与整体岩土工程结构分析算法所对应数值的比值，计算出每个影响因素关于整体的比值情况，然后计算出每个影响因素关于整体的比值，与所有影响因素关于整体的比值之和的比值，作为每个影响因素关于岩土工程结构分析算法的影响占比。
举例来说，本申请中的4个影响因素中，本次存在有3个影响因素，其中一个影响因素不作用，3个影响因素，分别为影响因素A、影响因素B、影响因素C，其中影响因素A的影响占比为30％，影响因素B的影响占比为50％，影响因素C的影响占比为20％。
另外存在两套岩土工程结构分析算法，其中第一套岩土工程结构分析算法中与影响因素A的同一影响因素的相似度为80％，第一套岩土工程结构分析算法中与影响因素B的同一影响因素的相似度为90％，第一套岩土工程结构分析算法中与影响因素C的同一影响因素的相似度为90％，那么第一套岩土工程结构分析算法的相似度如下：Y＝0.8*0.3+0.9*0.5+0.9*0.2＝0.87。
第二套岩土工程结构分析算法中与影响因素A的同一影响因素的相似度为70％，第二套岩土工程结构分析算法中与影响因素B的同一影响因素的相似度为80％，第二套岩土工程结构分析算法中与影响因素C的同一影响因素的相似度为60％，那么第一套岩土工程结构分析算法的相似度如下：Y＝0.7*0.3+0.8*0.5+0.6*0.2＝0.77。
综上，因此选择第二套岩土工程结构分析算法。
步骤S233，将所分析确定的岩土工程结构分析算法作为本次实际应用的岩土工程结构分析算法。
在图3的步骤S232中，进一步考虑到在对所获取的影响因素的状况信息和已有的岩土工程结构分析算法所涉及的影响因素的状况信息的相似度分析的过程中，还需要考虑影响因素的种类占比情况以及时间占比情况，来更好的确定状况信息的相似度，因此需要对所获取的影响因素的状况信息和已有的岩土工程结构分析算法所涉及的影响因素的状况信息的相似度作进一步的分析，具体参照图4所示实施例作详细说明。
参照图4，其中，步骤S232所提及的所获取的影响因素的状况信息和已有的岩土工程结构分析算法所涉及的影响因素的状况信息的相似度分析包括：
步骤S232.a,分析所获取的影响因素的状况信息中不同影响因素的不同种类状况的出现时间占比。
其中，所获取的影响因素的状况信息中不同影响因素的不同种类状况的出现时间占比可以从预设的存储有所获取的影响因素的状况信息中不同影响因素的不同种类状况的出现时间占比的数据库中获取，
其中，以未来降水这个影响因素作为案例，其中不同种类状况可划分为暴雨、大暴雨、特大暴雨，即24小时降水量为50-99.9毫米称“暴雨”、100-249.9毫米之间为“大暴雨”、250毫米以上称“特大暴雨”。
步骤S232.b,根据不同影响因素的不同种类状况出现时间占比、预设的不同岩土工程结构分析 算法所涉及影响因素的状况信息中同一影响因素的相应种类状况出现时间占比，分析获取不同岩土工程结构分析算法所涉及影响因素与所获取的同一影响因素的时间占比相似度。
举例来说，假定影响因素A的时间为1小时，第一套岩土工程结构分析算法中与影响因素A同一影响因素的时间为30分钟，那么时间占比相似度为50％，若第一套岩土工程结构分析算法中与影响因素A同一影响因素的时间为90分钟，那么时间占比相似度的占比亦为67％。
步骤S232.c,根据所分析获取的不同岩土工程结构分析算法所涉及影响因素与所获取的同一影响因素的时间占比相似度、同影响因素的不同种类状况出现时间占比，分析确定所获取的影响因素的状况信息和已有的岩土工程结构分析算法所涉及的影响因素的状况信息的相似度。
举例来说，同影响因素的不同种类状况可划分为种类甲、种类乙，其中，种类甲的时间占比为30％，种类乙的时间占比为70％；已有的岩土工程结构分析算法所涉及的影响因素的状况信息中同影响因素的不同种类状况亦可划分为种类甲、种类乙，其中，种类甲的时间占比为50％，种类乙的时间占比为50％。
假定本影响因素的时间占比相似度为50％，那么本影响因素状况信息和已有的岩土工程结构分析算法所涉及的影响因素的状况信息的相似度具体计算如下：Z＝{(30％/70％)/(50％/50％)}*50％＝21.4％。
在图2的步骤S220中，进一步考虑到与所获取的影响因素的状况信息相似度最高的影响因素的状况信息所对应的岩土工程结构分析算法其相似度打不到要求的情况，因此还需要在判定与所获取的影响因素的状况信息相似度最高的影响因素的状况信息所对应的岩土工程结构分析算法之后，对相似度最高的影响因素的状况信息作更进一步的分析，具体参照图5所示实施例。
参照图5，一种岩土工程结构模态预测分析方法还包括位于并从中分析判定与所获取的影响因素的状况信息相似度最高的影响因素的状况信息所对应的岩土工程结构分析算法之后且在作为本次实际应用的岩土工程结构分析算法之前的步骤，具体如下：
步骤S2a0，获取所分析确定的岩土工程结构分析算法所对应的状况信息与所获取的影响因素的状况信息的相似度。
步骤S2b0，若状况信息相似度超过第一预设相似度，则将所分析确定的岩土工程结构分析算法作为本次实际应用的岩土工程结构分析算法。
其中，第一预设相似度可以是70％，还可以是其他根据用户设置的相似度。
步骤S2c0，反之，则分析与所获取的影响因素的状况信息相似度最高的影响因素的状况信息与所获取的影响因素的状况信息的差距信息。
其中，与所获取的影响因素的状况信息相似度最高的影响因素的状况信息与所获取的影响因素的状况信息的差距信息的分析以未来降水为例，此时差距信息为所获取的相似度最高岩土工程结构分析算法所对应的未来降水总量与本次的未来降水总量的差值，如以排水为例，则以排水差值作为差距信息。
步骤S2d0，根据差距信息、与所获取的影响因素的状况信息相似度最高的影响因素的状况信息所对应的岩土工程结构分析算法、单一影响因素的状况信息与岩土工程结构分析算法的对应关系，并应用预设的岩土工程结构分析算法的预测公式，预测分析获取本次实际应用的岩土工程结构分析算法。
参照图6，本次实际应用的岩土工程结构分析算法的预测分析包括：
步骤S2d1，获取与所获取的影响因素的状况信息相似度最高的影响因素的状况信息所对应的岩土工程结构分析算法、单一影响因素的状况信息与岩土工程结构分析算法的对应关系。
其中，单一影响因素的状况信息与岩土工程结构分析算法的对应关系可以从预设的存储有单一影响因素的状况信息与岩土工程结构分析算法的对应关系的数据库中查询获取。
步骤S2d2，根据差距信息分析相似度最高的影响因素的状况信息所包含的每个影响因素，与所获取的影响因素的状况信息所包含的同一影响因素的单独差距比例信息。
以影响因素1为例，假定所分析算法的影响因素1的数据为10，影响因素1的数据为8，那么单独差距比例信息的计算如下：Z＝(10-8)/8＝25％。
步骤S2d3，根据每个影响因素的单独差距比例信息、预设的影响因素关于岩土工程结构分析算法的影响占比，分析获取每个影响因素的有效影响因子。
其中，每个影响因素的有效影响因子为每个影响因素的单独差距比例信息与预设的影响因素关于岩土工程结构分析算法的影响占比的乘积。
步骤S2d4，应用预设的岩土工程结构分析算法的预测公式,预测分析获取本次实际应用的岩土工程结构分析算法。
具体预测公式如下：Z＝a*q1+q2*[b*(t1/t总)+c*(t2/t总)+d*(t3/t总)+e*(t4/t总)，q1+q2＝1，t总＝t1+t2+t3+t4；其中，Z为所预测分析的本次实际应用的岩土工程结构分析算法；a为与所获取的影响因素的状况信息相似度最高的影响因素的状况信息所对应的岩土工程结构分析算法；q1为所获取的影响因素的状况信息相似度最高的影响因素的状况信息所对应的岩土工程结构分析算法的权重占比系数；q2为单独影响因素所对应算法的权重占比系数；b为单独第一影响因素所对应的岩土工程结构分析算法；t1为单独第一影响因素的有效影响因子；c为单独第二影响因素所对应的岩土工程结构分析算法；t2为单独第二影响因素的有效影响因子；d为单独第三影响因素所对应的岩土工程结构分析算法；t3为单独第三影响因素的有效影响因子；e为单独第四影响因素所对应的岩土工程结构分析算法；t4为单独第四影响因素的有效影响因子。
在图1的步骤S300和步骤S400之间，进一步考虑到在修正岩土工程结构分析算法之前，存在实际数据和训练数据差距较大的情况，此时不利于通过神经网络算法修正，需要作进一步的分析并将原因通知到到负责人，具体参照图7所示实施例作详细说明。
参照图7，一种岩土工程结构模态预测分析方法还包括位于获取实际获取的岩土工程结构的受力信息以及在不同受力信息下的结构变形数据，作为实际数据之后且在修正岩土工程结构分析算法之前的步骤，具体如下：
步骤SA00，分析获取实际获取的岩土工程结构的受力信息以及在不同受力信息下的结构变形数据，与计算得出的岩土工程结构的受力信息以及在不同受力信息下的结构变形数据的差值。
步骤SB00，若差值超过第一预设值，则停止后续步骤，且根据历史差值超过第一预设值且在不同超出程度下的问题分布概率，按照分布概率由高至低对问题作排序并发送至岩土工程负责人所持终端。
其中，第一预设值可以为10000N,也可以是其他预设的受力值；超过第一预设值且在不同超出程度下的问题分布概率的获取可以从预设的存储有过第一预设值且在不同超出程度下的问题分布概率的数据库中查询获取；岩土工程负责人所持终端可以为手机、电脑，还可以是其它可通信终端设备。
其中，问题可以是用于实际检测的检测装置出现问题，也可能是所应用的算法出现较大的问题。
步骤SC00，若差值信息超过第二预设值且低于第一预设值，则停止后续步骤，且根据历史差值超过第二预设值且在不同超出程序下的问题分布概率，按照分布概率由高至低对问题作排序并发送至岩土工程负责人所持终端。
其中，基于历史差值超过第一预设值且在不同超出程序下的问题分布概率可以从预设的存储有基于历史差值超过第一预设值且在不同超出程序下的问题分布概率的数据库中查询获取。
步骤SD00，反之，则进行后续的步骤。
其中，步骤SB00所提及的岩土工程负责人所持终端中的岩土工程负责人需要作选择，以便于所通知的岩土工程负责人员能够有效处理相应问题，具体参照图8所实施例作详细说明。
参照图8，岩土工程负责人的选择如下：
步骤SCa0,分析获取岩土工程负责人的从业工作年限。
其中，岩土工程负责人的从业工作年限的分析获取可以从预设的存储有岩土工程负责人的从业工作年限的数据库中查询获取。
步骤SCb0,若岩土工程负责人的从业工作年限小于预设年限，则将因差值信息超过第一预设值而发送的通知信息，一并发送至负责本次深基坑工程的其余技术人员，并在其余技术人员确认接受相关通知信息，构建问题讨论群。
其中，预设年限可以是1年，还可以是其他年限。
步骤SCc0,反之，则不作其他设置。
另外，考虑在通知岩土工程负责人的时候，可以对出现概率较高的问题让负责人引起重视，还需要在按照分布概率由高至低作对问题作排序与发送至岩土工程负责人所持终端之间，作进一步的分析，具体参照图9所示实施例作详细说明。
参照图9，一种岩土工程结构模态预测分析方法还包括位于按照分布概率由高至低作对问题作排序与发送至岩土工程负责人所持终端之间的步骤，具体如下：
步骤SC10，分析获取其中分布概率超过预设概率的问题。
步骤SC20，从分布概率超过预设概率的问题中，选择其中排序在预设位置之前的问题并以负责人所倾向的警示颜色作标记。
其中，预设概率可以是30％，也可以是其他用户设置的概率，负责人所倾向的警示颜色可以是红色或蓝色，还可以是用户基于实际需要设置的其他颜色。
基于同一发明构思，本发明实施例提供一种岩土工程结构模态预测分析系统，包括存储器、处理器，存储器上存储有可在所述处理器上运行实现如图1至图9任一种方法的程序。
本具体实施方式的实施例均为本申请的较佳实施例，并非依此限制本申请的保护范围，故：凡依本申请的结构、形状、原理所做的等效变化，均应涵盖于本申请的保护范围之内。

Claims (9)

  1. 一种岩土工程结构模态预测分析方法，其特征在于，包括：
    获取岩土工程结构设计模型以及所规划的岩土工程结构期间的影响因素的状况信息，其中，影响因素包括施工环境、荷载条件、排水和未来降水；
    根据影响因素的状况信息与岩土工程结构分析算法的对应关系，分析确定所获取影响因素的状况信息所对应的岩土工程结构分析算法，并采用相应岩土工程结构分析算法，对岩土工程结构设计模型进行模态预测，以计算得出岩土工程结构的受力信息以及在不同受力信息下的土体-结构模态变形数据，并作为训练数据；
    获取实际获取的岩土工程结构的受力信息以及在不同受力信息下的结构变形数据，作为实际数据；
    基于训练数据、实际数据，应用预先构建的神经网络算法，修正岩土工程结构分析算法；
    分析确定所获取影响因素的状况信息所对应的岩土工程结构分析算法包括：
    根据影响因素的状况信息与岩土工程结构分析算法的对应关系，查询是否存在与获取的影响因素的状况信息相对应的岩土工程结构分析算法；
    若为是，则将所获取的影响因素的状况信息相对应的岩土工程结构分析算法，作为所分析确定的岩土工程结构分析算法；
    反之，则查询获取其它影响因素的状况信息所对应的岩土工程结构分析算法，并从中分析判定与所获取的影响因素的状况信息相似度最高的影响因素的状况信息所对应的岩土工程结构分析算法，作为本次实际应用的岩土工程结构分析算法。
  2. 根据权利要求1所述的一种岩土工程结构模态预测分析方法，其特征在于，与所获取的影响因素的状况信息相似度最高的影响因素的状况信息所对应的岩土工程结构分析算法的分析判定包括：
    分析所获取的影响因素的状况信息和已有的岩土工程结构分析算法所涉及的影响因素的状况信息的相似度；
    基于所分析获取的影响因素的状况信息和已有的岩土工程结构分析算法所涉及的影响因素的状况信息的相似度、预设的影响因素关于岩土工程结构分析算法的影响占比，分析确定与所获取的影响因素的状况信息相似度最高的影响因素的状况信息所对应的岩土工程结构分析算法；
    将所分析确定的岩土工程结构分析算法作为本次实际应用的岩土工程结构分析算法。
  3. 根据权利要求2所述的一种岩土工程结构模态预测分析方法，其特征在于，所获取的影响因素的状况信息和已有的岩土工程结构分析算法所涉及的影响因素的状 况信息的相似度分析包括：
    分析所获取的影响因素的状况信息中不同影响因素的不同种类状况的出现时间占比；
    根据不同影响因素的不同种类状况出现时间占比、预设的不同岩土工程结构分析算法所涉及影响因素的状况信息中同一影响因素的相应种类状况出现时间占比，分析获取不同岩土工程结构分析算法所涉及影响因素与所获取的同一影响因素的时间占比相似度；
    根据所分析获取的不同岩土工程结构分析算法所涉及影响因素与所获取的同一影响因素的时间占比相似度、同影响因素的不同种类状况出现时间占比，分析确定所获取的影响因素的状况信息和已有的岩土工程结构分析算法所涉及的影响因素的状况信息的相似度。
  4. 根据权利要求1所述的一种岩土工程结构模态预测分析方法，其特征在于，还包括位于并从中分析判定与所获取的影响因素的状况信息相似度最高的影响因素的状况信息所对应的岩土工程结构分析算法之后且在作为本次实际应用的岩土工程结构分析算法之前的步骤，具体如下：
    获取所分析确定的岩土工程结构分析算法所对应的状况信息与所获取的影响因素的状况信息的相似度；
    若状况信息相似度超过第一预设相似度，则将所分析确定的岩土工程结构分析算法作为本次实际应用的岩土工程结构分析算法；
    反之，则分析与所获取的影响因素的状况信息相似度最高的影响因素的状况信息与所获取的影响因素的状况信息的差距信息；
    根据差距信息、与所获取的影响因素的状况信息相似度最高的影响因素的状况信息所对应的岩土工程结构分析算法、单一影响因素的状况信息与岩土工程结构分析算法的对应关系，并应用预设的岩土工程结构分析算法的预测公式，预测分析获取本次实际应用的岩土工程结构分析算法。
  5. 根据权利要求4所述的一种岩土工程结构模态预测分析方法，其特征在于，本次实际应用的岩土工程结构分析算法的预测分析包括：
    获取与所获取的影响因素的状况信息相似度最高的影响因素的状况信息所对应的岩土工程结构分析算法、单一影响因素的状况信息与岩土工程结构分析算法的对应关系；
    根据差距信息分析相似度最高的影响因素的状况信息所包含的每个影响因素，与所获取的影响因素的状况信息所包含的同一影响因素的单独差距比例信息；
    根据每个影响因素的单独差距比例信息、预设的影响因素关于岩土工程结构分析算法的影响占比，分析获取每个影响因素的有效影响因子；
    应用预设的岩土工程结构分析算法的预测公式,预测分析获取本次实际应用的岩土工程结构分析算法,具体预测公式如下：
    Z＝a*q1+q2*[b*(t1/t总)+c*(t2/t总)+d*(t3/t总)+e*(t4/t总)，q1+q2＝1，t
    总＝t1+t2+t3+t4；
    其中，Z为所预测分析的本次实际应用的岩土工程结构分析算法；
    a为与所获取的影响因素的状况信息相似度最高的影响因素的状况信息所对应的岩土工程结构分析算法；
    q1为所获取的影响因素的状况信息相似度最高的影响因素的状况信息所对应的岩土工程结构分析算法的权重占比系数；
    q2为单独影响因素所对应算法的权重占比系数；
    b为单独第一影响因素所对应的岩土工程结构分析算法；
    t1为单独第一影响因素的有效影响因子；
    c为单独第二影响因素所对应的岩土工程结构分析算法；
    t2为单独第二影响因素的有效影响因子；
    d为单独第三影响因素所对应的岩土工程结构分析算法；
    t3为单独第三影响因素的有效影响因子；
    e为单独第四影响因素所对应的岩土工程结构分析算法；
    t4为单独第四影响因素的有效影响因子。
  6. 根据权利要求1所述的一种岩土工程结构模态预测分析方法，其特征在于，还包括位于获取实际获取的岩土工程结构的受力信息以及在不同受力信息下的结构变形数据，作为实际数据之后且在修正岩土工程结构分析算法之前的步骤，具体如下：
    分析获取实际获取的岩土工程结构的受力信息以及在不同受力信息下的结构变形数据，与计算得出的岩土工程结构的受力信息以及在不同受力信息下的结构变形数据的差值；
    若差值超过第一预设值，则停止后续步骤，且根据历史差值超过第一预设值且在不同超出程度下的问题分布概率，按照分布概率由高至低对问题作排序并发送至岩土工程负责人所持终端；
    若差值信息超过第二预设值且低于第一预设值，则停止后续步骤，且根据历史差值超过第二预设值且在不同超出程序下的问题分布概率，按照分布概率由高至低对问题作排序并发送至岩土工程负责人所持终端；
    反之，则进行后续的步骤。
  7. 根据权利要求6所述的一种岩土工程结构模态预测分析方法，其特征在于，岩土工程负责人的选择如下：
    分析获取岩土工程负责人的从业工作年限；
    若岩土工程负责人的从业工作年限小于预设年限，则将因差值信息超过第一预设值而发送的通知信息，一并发送至负责本次深基坑工程的其余技术人员，并在其余技术人员确认接受相关通知信息后，构建问题讨论群；
    反之，则不作处理。
  8. 根据权利要求6所述的一种岩土工程结构模态预测分析方法，其特征在于，还包括位于按照分布概率由高至低对问题作排序与发送至岩土工程负责人所持终端之间的步骤，具体如下：
    分析获取其中分布概率超过预设概率的问题；
    从分布概率超过预设概率的问题中，选择其中排序在预设位置之前的问题并以负责人所倾向的警示颜色作标记。
  9. 一种岩土工程结构模态预测分析系统，其特征在于，包括存储器、处理器及存储在所述存储器上并可在所述处理器上运行的程序，该程序能够被处理器加载执行时实现如权利要求1所述的一种岩土工程结构模态预测分析方法。
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