Analysis method of deformation characteristics of deep marine mud under large-scale heap loading

Abstract

The present invention discloses a method for analyzing the deformation characteristics of deep marine silt under large-scale heap loading, which belongs to the field of geotechnical testing technology. In order to solve the problems of insufficient prediction accuracy, insufficient simulation of actual working conditions, and insufficient ability to discover and prevent safety hazards, the deformation characteristics of deep marine silt under large-scale heap loading are comprehensively analyzed by integrating monitoring data, numerical simulation and indoor experiments. Through a multi-stage loading and unloading cycle mode, the stress process of the silt layer is accurately simulated, and the additional stress transfer law and creep behavior are revealed. The creep test system studies the creep law under different working conditions, especially the changes in the secondary consolidation deformation and the secondary consolidation coefficient, improves the settlement prediction accuracy and the accuracy of consolidation degree calculation, draws the creep deformation curve, and fits the data based on the time series model to ensure data consistency. The use of comprehensive means effectively evaluates the impact of heap loading, timely discovers and prevents safety hazards, and significantly improves the safety and stability of the project.

Description

Technical Field

[0001] The invention relates to the technical field of geotechnical testing, and in particular to a method for analyzing deformation characteristics of deep marine silt under large-area loading. Background Art

[0002] When large-scale engineering construction is carried out in areas near the sea, it is difficult to control the deformation stability of the island foundation under marine silt geological conditions. After formation, the artificial island is located on deep marine soft soil, which is different from the general limited width load form. Therefore, it is necessary to consider the settlement deformation of the lower soil layer without drainage boards.

[0003] However, there are still the following problems in actual operation:

[0004] Existing technologies often find it difficult to accurately predict the long-term deformation characteristics of deep marine silt under loading, especially post-construction settlement and secondary consolidation deformation. At the same time, it is difficult to effectively simulate the complex and changeable stress process of the silt layer in actual engineering, and it is difficult to comprehensively evaluate the impact of loading on the silt layer and to timely discover potential safety hazards. Summary of the invention

[0005] The purpose of the present invention is to provide a method for analyzing deformation characteristics of deep marine silt under large-area loading, so as to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solution: A method for analyzing deformation characteristics of deep marine silt under large-area loading, comprising the following steps:

[0007] Design of on-site monitoring scheme, design of monitoring scheme for deformation and stress distribution of marine silt layer under large-area loading, including monitoring point layout, monitoring equipment selection, data collection frequency and data transmission method. Based on the actual project situation, GIS software is used to plan the monitoring point layout;

[0008] Monitoring data collection and processing: using the automatic data acquisition system to upload data regularly, combined with manual on-site verification, to continuously collect monitoring data and pre-process the data;

[0009] Additional stress transfer analysis: Based on the monitoring data, a geomechanical model is established, the monitoring data and soil parameters are input, and numerical simulation analysis is performed to analyze the additional stress transfer law and distribution characteristics of the deep marine mud layer under the action of heap loading, and to evaluate the impact of heap loading on the mud layer;

[0010] Indoor creep test design, preparation of standard soil samples, setting of different loading and unloading sequences, simulation of different loading and unloading conditions, use of creep test equipment for observation, and recording of creep deformation data;

[0011] Creep deformation and secondary consolidation coefficient analysis: Based on the creep test results, time series analysis is used to fit the data and identify parameters, extract the secondary consolidation coefficient, and analyze the changing law of the consolidation coefficient affected by different factors;

[0012] Settlement prediction and consolidation degree calculation, comprehensive analysis of monitoring data, numerical analysis results and creep test conclusions, use of soil mechanics models to predict settlement and calculate consolidation degree, and form a prediction report.

[0013] Furthermore, the on-site monitoring scheme design specifically includes the following steps:

[0014] Analyze monitoring needs, clarify the purpose, scope and key parameters of monitoring, determine the physical quantities that need to be monitored for large-scale marine mud layers under heap loading, including stress, strain, displacement, pore water pressure, and formulate a monitoring needs list;

[0015] Monitoring point layout planning: Use GIS system in combination with engineering topographic map and geological exploration report to plan monitoring point layout, import relevant layers into GIS system, use spatial analysis function to determine monitoring point location, compile monitoring point layout map, and mark monitoring point number, location, and monitoring content;

[0016] Monitoring equipment selection: selecting and installing monitoring equipment according to monitoring requirements. The monitoring equipment includes stress gauges, strain gauges, displacement gauges, and pore water pressure gauges.

[0017] Furthermore, the monitoring data collection and processing specifically includes the following steps:

[0018] Automatic data collection system configuration: configure the data collection terminal according to the interface type and communication protocol of the monitoring equipment, and set the data collection frequency to automatically collect data;

[0019] Manual on-site verification: while the automatic data collection system is working, formulate an on-site verification plan, specify the verification time, verification content and verification standards, conduct on-site measurements and records of key monitoring points, and compare manual measurement results with automatically collected data;

[0020] Data preprocessing is the preliminary processing of the collected monitoring data, which includes data cleaning, outlier removal, missing value processing and data format conversion.

[0021] Furthermore, the process of setting the data collection frequency includes:

[0022] Real-time collection of data transmission rates and interface bandwidths corresponding to different interface types;

[0023] The first coefficient parameter is obtained by using the data transmission rate corresponding to the different interface types; wherein the first coefficient parameter is obtained by the following formula:

[0024]

[0025] Among them, F 01 represents the first coefficient parameter; n represents the number of interface types; V i Indicates the standard data transmission rate corresponding to the i-th interface type; V b Indicates the standard deviation of the standard data transmission rate corresponding to n interface types; V mi Indicates the maximum data transmission rate that can be achieved by the i-th interface type; V mb Indicates the standard deviation of the maximum data transmission rate that can be achieved for n interface types;

[0026] The second coefficient parameter is obtained by using the interface bandwidth corresponding to the different interface types; wherein the second coefficient parameter is obtained by the following formula:

[0027]

[0028] Among them, F 02 Indicates the second coefficient parameter; n indicates the number of interface types; B i Indicates the interface bandwidth corresponding to the i-th interface type; B h Indicates the interface bandwidth and value corresponding to n interface types; B c Indicates the preset interface bandwidth reference value; B max Indicates the maximum interface bandwidth corresponding to n interface types; V max Indicates the data transmission rate of the interface type corresponding to the maximum value of the interface bandwidth; V h Indicates the data transmission rate and value corresponding to n interface types; V c Indicates the theoretical maximum permissible data transmission rate corresponding to the preset interface bandwidth reference value; V mi Indicates the maximum data transmission rate that can be achieved by the i-th interface type; B b Indicates the standard deviation of the interface bandwidth corresponding to n interface types;

[0029] The data acquisition frequency is determined by using the first coefficient parameter and the second coefficient parameter in combination with the setting parameters of the communication protocol.

[0030] Further, the data acquisition frequency is determined by using the first coefficient parameter and the second coefficient parameter in combination with the setting parameters of the communication protocol, including:

[0031] Extracting the data transmission efficiency corresponding to the communication protocol;

[0032] Extracting the size of the data packet in the data transmission process corresponding to the setting of the communication protocol;

[0033] Obtaining a third coefficient parameter using the data transmission efficiency corresponding to the communication protocol and the size of a data packet in a data transmission process set corresponding to the communication protocol;

[0034] The third coefficient parameter is obtained by the following formula:

[0035]

[0036] Among them, F 03 represents the third coefficient parameter; m represents the number of types of communication protocols; P mi represents the maximum data transmission efficiency corresponding to the i-th communication protocol; C i Indicates the maximum amount of data packets in the data transmission process corresponding to the i-th communication protocol;

[0037] Retrieving the first coefficient parameter and the second coefficient parameter;

[0038] Obtaining a data acquisition frequency using the first coefficient parameter and the second coefficient parameter in combination with a third coefficient parameter;

[0039] The data collection frequency is obtained by the following formula:

[0040]

[0041] Where, F represents the data collection frequency; F0 represents the preset initial data collection frequency value; F 01 Represents the first coefficient parameter; F 02 Represents the second coefficient parameter; F 03 Represents the third coefficient parameter.

[0042] Furthermore, the additional stress transfer analysis specifically includes the following steps:

[0043] Geomechanical model construction: According to the characteristics of the silt layer and the loading conditions, a three-dimensional geomechanical model is selected, a three-dimensional geomechanical model is established, geological stratification, boundary conditions, initial stress state parameters are input, and the model is meshed;

[0044] Data input model verification: input the stress and deformation monitoring data monitored on site into the geomechanics model as boundary conditions or initial conditions, run the model and observe the degree of consistency between the simulation results and the monitoring data, compare the simulation results with the monitoring data, verify the model and adjust the model parameters;

[0045] The additional stress transfer law analysis is based on the model output results to analyze the distribution and change of the internal stress field of the silt layer under the action of heap loading, as well as the transmission path and range of the additional stress.

[0046] Furthermore, the indoor creep test design specifically includes the following steps:

[0047] Soil sample preparation and standardization: collect marine silt soil samples from the project site or areas with similar geological conditions, remove impurities, air-dry, crush and sieve the collected soil samples, reshape the treated soil samples into standard-sized samples through static pressure, place the reshaped samples in a saturation device, and saturate them through back pressure to make them fully saturated;

[0048] Loading and unloading sequence design: different loading and unloading sequences are designed according to the actual project conditions and monitoring data. Each sequence contains multiple loading and unloading stages for simulating the stress process in the actual project, and the loading rate, loading amount, holding time and unloading method parameters of each loading and unloading stage are set;

[0049] The creep test is carried out by calibrating and preheating the creep instrument. The prepared standard specimens are installed on the test equipment. The specimens are loaded according to the designed loading and unloading sequence. At the same time, the start time, load amount, holding time and corresponding creep deformation data of each loading and unloading stage are recorded during the loading process.

[0050] Furthermore, the loading and unloading sequence design specifically includes the following steps:

[0051] Select the multi-stage loading and unloading cycle mode as the basic loading and unloading mode;

[0052] Set the loading stage parameters, determine the loading rate based on the loading rate range in the actual project, and determine the loading amount based on the strength characteristics of the soil sample and the actual project conditions. The loading amount of each level increases step by step, and the holding time is determined based on the creep characteristics of the soil sample.

[0053] Design the parameters of the unloading phase, select graded unloading as the unloading method, and determine the unloading rate;

[0054] Set the number and interval of loading and unloading cycles. Determine the number of loading and unloading cycles based on the test purpose and the creep characteristics of the soil sample. Determine the cycle interval based on the creep recovery characteristics of the soil sample and the test efficiency.

[0055] Furthermore, the creep deformation and secondary consolidation coefficient analysis specifically includes the following steps:

[0056] Creep deformation data collation and analysis, based on the data obtained from indoor creep test design, a curve chart of creep deformation changing with time is drawn;

[0057] Fit the creep deformation data based on the time series model and determine the best fitting model and parameters;

[0058] Based on the results of the fitted model, the secondary consolidation coefficient is extracted.

[0059] Furthermore, the settlement prediction and consolidation degree calculation further includes the following steps:

[0060] Integrate the field monitoring data, numerical simulation analysis results and indoor creep test data, and verify the integrated data;

[0061] According to the monitoring data and soil parameters, the parameters of the consolidation model are set, the monitoring data, soil parameters and model parameters are input into the consolidation model, settlement prediction calculation is performed, and the consolidation degree of the silt layer is calculated based on the settlement prediction results and the consolidation model;

[0062] The calculated degree of consolidation is evaluated and a settlement prediction and consolidation calculation report is generated.

[0063] Compared with the prior art, the present invention has the following beneficial effects:

[0064] 1. The numerical simulation analysis based on monitoring data in the present invention can clearly reveal the additional stress transfer law and distribution characteristics of the deep marine mud layer under the action of heap loading. Through indoor multiple groups of soil creep tests under complex loading processes, the creep law of soil under different loading and unloading conditions can be systematically studied, especially the overload weakening of the secondary consolidation deformation of marine soft soil, the change law of the secondary consolidation coefficient under different recent loads under the same maximum overload, etc., which is helpful to predict post-construction settlement and improve the prediction accuracy, and provides a scientific basis for the reasonable selection of the secondary consolidation coefficient under different working conditions, and enhances the accuracy of settlement prediction and consolidation degree calculation. Through a comprehensive analysis of the deformation characteristics of deep marine mud under large-area heap loading, the settlement trend and consolidation process can be scientifically predicted. By comprehensively using on-site monitoring, numerical simulation and indoor tests, etc., the impact of heap loading on the mud layer can be comprehensively evaluated, potential safety hazards can be discovered in time, and effective measures can be taken to prevent them, which is helpful to improve the safety and stability of the project.

[0065] 2. The selection of the multi-stage loading and unloading cycle mode of the present invention can better simulate the stress process of the sludge layer in actual engineering and reflect its complex creep behavior. By designing different loading and unloading sequences, the creep characteristics and recovery ability of the sludge under different stress conditions can be systematically studied, providing an important basis for theoretical research and engineering practice. The results of the creep test can directly reflect the deformation characteristics of the sludge layer under the action of loading, and provide a scientific basis for engineers to formulate reasonable loading plans, loading rates and vacuum pumping time during the design and construction process, thereby ensuring the safety and stability of the project.

[0066] 3. The present invention can intuitively reflect the deformation trend and law of sludge under long-term loading by drawing a curve graph of creep deformation changing with time, and provide an intuitive basis for subsequent data analysis and model fitting. The creep deformation data is fitted based on the time series model, and the best fitting model and parameters can be determined, thereby improving the prediction accuracy of sludge creep behavior, which helps to more accurately grasp the long-term deformation characteristics of sludge. The field monitoring data, numerical simulation analysis results and indoor creep test data are integrated and verified to ensure the accuracy and consistency of the data, which helps to improve the prediction accuracy and reliability. The consolidation model is parameterized according to the monitoring data and soil parameters, and the data is input into the consolidation model for settlement prediction calculation, which can obtain relatively accurate settlement prediction results, which helps engineers to formulate reasonable construction plans and monitoring plans during the design and construction process. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 The figure is a schematic diagram of the analytical method of the present invention. DETAILED DESCRIPTION

[0068] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0069] In order to solve the technical problems that existing technologies often have difficulty in accurately predicting the long-term deformation characteristics of deep marine silt under heap loading, especially post-construction settlement and secondary consolidation deformation, and at the same time, it is difficult to effectively simulate the complex and changeable stress process of the silt layer in actual engineering, it is difficult to comprehensively evaluate the impact of heap loading on the silt layer, and it is difficult to timely discover potential safety hazards, please refer to Figure 1 , the present invention provides the following technical solutions:

[0070] The method for analyzing deformation characteristics of deep marine silt under large-area loading includes the following steps:

[0071] Design of on-site monitoring scheme, design of monitoring scheme for deformation and stress distribution of marine silt layer under large-area loading, including monitoring point layout, monitoring equipment selection, data collection frequency and data transmission method. Based on the actual project situation, GIS software is used to plan the monitoring point layout;

[0072] Monitoring data collection and processing: using the automatic data acquisition system to upload data regularly, combined with manual on-site verification, to continuously collect monitoring data and pre-process the data;

[0073] Additional stress transfer analysis: Based on the monitoring data, a geomechanical model is established, the monitoring data and soil parameters are input, and numerical simulation analysis is performed to analyze the additional stress transfer law and distribution characteristics of the deep marine mud layer under the action of heap loading, and to evaluate the impact of heap loading on the mud layer;

[0074] Indoor creep test design, preparation of standard soil samples, setting of different loading and unloading sequences, simulation of different loading and unloading conditions, use of creep test equipment for observation, and recording of creep deformation data;

[0075] Creep deformation and secondary consolidation coefficient analysis: Based on the creep test results, time series analysis is used to fit the data and identify parameters, extract the secondary consolidation coefficient, and analyze the changing law of the consolidation coefficient affected by different factors;

[0076] Settlement prediction and consolidation degree calculation, comprehensive analysis of monitoring data, numerical analysis results and creep test conclusions, use of soil mechanics models to predict settlement and calculate consolidation degree, and form a prediction report.

[0077] In the above embodiment, the comprehensiveness and accuracy of the monitoring data are ensured by carefully designing the on-site monitoring plan and combining the GIS software to plan the monitoring point layout. At the same time, the combination of the automatic data acquisition system and manual on-site verification improves the efficiency and reliability of data collection, providing a solid data foundation for subsequent analysis.

[0078] In the above embodiment, the numerical simulation analysis based on the monitoring data can clearly reveal the additional stress transfer law and distribution characteristics of the deep marine mud layer under the action of heap loading, help engineers to deeply understand the stress condition of the mud layer, and provide a scientific basis for engineering design. Through multiple sets of soil creep tests under complex loading processes indoors, the soil creep law under different loading and unloading conditions can be systematically studied, especially the overload-reduced secondary consolidation deformation of marine soft soil, the change law of the secondary consolidation coefficient under different recent loads under the same maximum overload, etc., which is helpful to predict post-construction settlement and improve prediction accuracy.

[0079] In the above embodiment, the application of the time series analysis method makes it possible to extract the secondary consolidation coefficient from the creep test data, and reveals the changing law of the secondary consolidation coefficient under the influence of different factors, which provides a scientific basis for the reasonable selection of the secondary consolidation coefficient under different working conditions, enhances the accuracy of settlement prediction and consolidation degree calculation, and through a comprehensive analysis of the deformation characteristics of deep marine silt under large-area loading, can scientifically predict the settlement trend and consolidation process, thereby providing strong support for the determination of the vacuum pumping stop time, helping to optimize the engineering construction plan and improve engineering benefits.

[0080] In the above embodiments, the comprehensive use of on-site monitoring, numerical simulation and indoor testing can comprehensively evaluate the impact of loading on the silt layer, timely discover potential safety hazards, and take effective measures to prevent them, which helps to improve the safety and stability of the project.

[0081] The design of the on-site monitoring plan also includes the following steps:

[0082] Analyze monitoring needs, clarify the purpose, scope and key parameters of monitoring, determine the physical quantities that need to be monitored for large-scale marine mud layers under heap loading, including stress, strain, displacement, pore water pressure, and formulate a monitoring needs list;

[0083] Monitoring point layout planning: Use GIS system in combination with engineering topographic map and geological exploration report to plan monitoring point layout, import relevant layers into GIS system, use spatial analysis function to determine monitoring point location, compile monitoring point layout map, and mark monitoring point number, location, and monitoring content;

[0084] Monitoring equipment selection: selecting and installing monitoring equipment according to monitoring requirements. The monitoring equipment includes stress gauges, strain gauges, displacement gauges, and pore water pressure gauges.

[0085] In the above embodiment, the purpose, scope and key parameters of monitoring are clarified in the monitoring needs analysis stage, which helps to ensure the pertinence and effectiveness of subsequent work. By determining the physical quantities that need to be monitored, the deformation characteristics of the marine silt layer under the action of loading can be fully understood, providing accurate data support for subsequent analysis and prediction. The monitoring point layout planning is carried out using the GIS system in combination with engineering topographic maps and geological exploration reports to ensure the scientificity and rationality of the monitoring point layout. The spatial analysis function of the GIS system can comprehensively consider factors such as terrain, geological conditions and loading distribution, optimize the location of monitoring points, and improve the representativeness and reliability of monitoring data. At the same time, compiling a monitoring point layout map and marking relevant information is helpful for on-site implementation and subsequent data analysis.

[0086] In the above embodiments, appropriate monitoring equipment is selected according to the monitoring requirements during the monitoring equipment selection phase. These devices can accurately measure the required physical quantities to ensure the accuracy and precision of the monitoring data. At the same time, the correct installation and maintenance of the equipment is also an important guarantee for obtaining high-quality monitoring data. The design of the overall on-site monitoring plan focuses on improving monitoring efficiency and reliability. Through scientific and reasonable layout of monitoring points and selected monitoring equipment, redundant monitoring points can be reduced and monitoring efficiency can be improved; at the same time, the continuity and stability of the monitoring data can be ensured, and the reliability of the monitoring results can be improved.

[0087] Monitoring data collection and processing also includes the following steps:

[0088] Automatic data collection system configuration: configure the data collection terminal according to the interface type and communication protocol of the monitoring equipment, and set the data collection frequency to automatically collect data;

[0089] Manual on-site verification: while the automatic data collection system is working, formulate an on-site verification plan, specify the verification time, verification content and verification standards, conduct on-site measurements and records of key monitoring points, and compare manual measurement results with automatically collected data;

[0090] Data preprocessing is the preliminary processing of the collected monitoring data, which includes data cleaning, outlier removal, missing value processing and data format conversion.

[0091] In the above embodiment, by configuring the data acquisition terminal according to the interface type and communication protocol of the monitoring equipment and setting a reasonable data acquisition frequency, automatic and continuous acquisition of monitoring data is achieved, the efficiency of data acquisition is improved, and human intervention is reduced, thereby reducing the risk of data errors and omissions and improving data accuracy. While the automatic data acquisition system is working, an on-site verification plan is formulated and implemented, key monitoring points are measured and recorded on the spot, and the manual measurement results are compared with the automatically collected data, thereby ensuring the reliability and accuracy of the automatically collected data and improving the credibility of subsequent analysis.

[0092] In the above embodiment, preliminary processing of the collected monitoring data helps to remove noise and errors in the data, fill in data gaps, unify the data format, and provide high-quality input data for subsequent data analysis and modeling. The preprocessed high-quality data can more accurately reflect the deformation characteristics and additional stress transfer laws of the marine mud layer under loading.

[0093] Specifically, the process of setting the data collection frequency includes:

[0094] Real-time collection of data transmission rates and interface bandwidths corresponding to different interface types;

[0095] The first coefficient parameter is obtained by using the data transmission rate corresponding to the different interface types; wherein the first coefficient parameter is obtained by the following formula:

[0096]

[0097] Among them, F 01 represents the first coefficient parameter; n represents the number of interface types; V i Indicates the standard data transmission rate corresponding to the i-th interface type; V b Indicates the standard deviation of the standard data transmission rate corresponding to n interface types; V mi Indicates the maximum data transmission rate that can be achieved by the i-th interface type; Vmb Indicates the standard deviation of the maximum data transmission rate that can be achieved for n interface types;

[0098] The second coefficient parameter is obtained by using the interface bandwidth corresponding to the different interface types; wherein the second coefficient parameter is obtained by the following formula:

[0099]

[0100] Among them, F 02 Indicates the second coefficient parameter; n indicates the number of interface types; B i Indicates the interface bandwidth corresponding to the i-th interface type; B h Indicates the interface bandwidth and value corresponding to n interface types; B c Indicates the preset interface bandwidth reference value; B max Indicates the maximum interface bandwidth corresponding to n interface types; V max Indicates the data transmission rate of the interface type corresponding to the maximum value of the interface bandwidth; V h Indicates the data transmission rate and value corresponding to n interface types; V c Indicates the theoretical maximum permissible data transmission rate corresponding to the preset interface bandwidth reference value; V mi Indicates the maximum data transmission rate that can be achieved by the i-th interface type; B b Indicates the standard deviation of the interface bandwidth corresponding to n interface types;

[0101] The data acquisition frequency is determined by using the first coefficient parameter and the second coefficient parameter in combination with the setting parameters of the communication protocol.

[0102] The technical effect of the above technical solution is: the solution can accurately understand the performance characteristics of each interface by real-time acquisition of data transmission rates and interface bandwidths of different interface types. The first coefficient parameter (F01) and the second coefficient parameter (F02) calculated using these data can comprehensively consider the data processing capability (i.e., data transmission rate) and physical limitations (i.e., interface bandwidth) of the interface, thereby more accurately determining the data acquisition frequency. Since the solution is calculated based on real-time acquired data, it can adapt to changes in interface performance. For example, when the interface type is upgraded or the network conditions are improved, the data acquisition frequency can be adjusted accordingly to maintain the best data acquisition efficiency. By reasonably setting the data acquisition frequency, it is possible to avoid resource waste (such as excessive use of CPU, memory, and network bandwidth) caused by too frequent acquisition, and it is also possible to avoid the loss of important data due to too low acquisition frequency. Accurate data acquisition frequency setting helps reduce delays and errors in data transmission, thereby improving the stability and reliability of the entire system. In addition, the solution also takes into account factors such as the maximum value and standard deviation of the interface bandwidth, which helps to maintain the stability and continuity of data acquisition when the interface performance fluctuates. The formulas and parameters in the solution can be adjusted and optimized according to the actual application scenario to adapt to different data acquisition requirements and interface types. As technology continues to develop, the solution can be easily expanded and upgraded to adapt to new interface types and higher data transmission requirements.

[0103] In summary, this technical solution achieves accurate setting and optimization of data acquisition frequency by comprehensively considering the data transmission rate of the interface, interface bandwidth, and setting parameters of the communication protocol, improves the efficiency, stability, and reliability of data acquisition, and has good flexibility and scalability.

[0104] Specifically, the data acquisition frequency is determined by using the first coefficient parameter and the second coefficient parameter in combination with the setting parameters of the communication protocol, including:

[0105] Extracting the data transmission efficiency corresponding to the communication protocol;

[0106] Extracting the size of the data packet in the data transmission process corresponding to the setting of the communication protocol;

[0107] Obtaining a third coefficient parameter using the data transmission efficiency corresponding to the communication protocol and the size of a data packet in a data transmission process set corresponding to the communication protocol;

[0108] The third coefficient parameter is obtained by the following formula:

[0109]

[0110] Among them, F 03 represents the third coefficient parameter; m represents the number of types of communication protocols; Pmi represents the maximum data transmission efficiency corresponding to the i-th communication protocol; C i Indicates the maximum amount of data packets in the data transmission process corresponding to the i-th communication protocol;

[0111] Retrieving the first coefficient parameter and the second coefficient parameter;

[0112] Obtaining a data acquisition frequency using the first coefficient parameter and the second coefficient parameter in combination with a third coefficient parameter;

[0113] The data collection frequency is obtained by the following formula:

[0114]

[0115] Where, F represents the data collection frequency; F0 represents the preset initial data collection frequency value; F 01 Represents the first coefficient parameter; F 02 Represents the second coefficient parameter; F 03 Represents the third coefficient parameter.

[0116] The technical effect of the above technical solution is that the solution not only considers the impact of interface type and bandwidth on data acquisition frequency (reflected by the first coefficient parameter and the second coefficient parameter), but also further introduces the factor of communication protocol (reflected by the third coefficient parameter). This includes the data transmission efficiency and data packet size of the communication protocol, which are important factors affecting data acquisition efficiency.

[0117] By comprehensively considering these factors, the solution can more accurately determine the data collection frequency, thereby optimizing resource utilization and reducing data transmission delays while ensuring data collection integrity.

[0118] Different communication protocols may have different data transmission characteristics and requirements. This solution can flexibly adapt to the needs of different communication protocols by extracting the maximum data transmission efficiency and packet size of each communication protocol and calculating the third coefficient parameter accordingly, ensuring that the data acquisition frequency matches the performance of the communication protocol. The formulas and parameters in this solution can be adjusted and optimized according to the actual application scenarios to adapt to different data acquisition requirements and communication protocol types. With the continuous development of technology, this solution can be easily expanded and upgraded to adapt to new communication protocols and higher data acquisition requirements. By accurately determining the data acquisition frequency, this solution can avoid the waste of resources (such as excessive use of CPU, memory and network bandwidth) caused by too frequent acquisition, and also avoid the loss of important data due to too low acquisition frequency. This helps to optimize system performance and improve the efficiency and stability of data transmission. Accurate data acquisition frequency setting helps to reduce delays and errors in data transmission, thereby improving the reliability and stability of the entire system. By comprehensively considering multiple factors, this solution can maintain the stability and continuity of data acquisition under different conditions and ensure the accuracy and integrity of the data.

[0119] In summary, this technical solution achieves accurate setting and optimization of data acquisition frequency by comprehensively considering multiple factors such as interface type, bandwidth, communication protocol, etc., improves the efficiency, flexibility and reliability of data acquisition, and optimizes resource utilization and system performance.

[0120] Additional stress transfer analysis also includes the following steps:

[0121] Geomechanical model construction: According to the characteristics of the silt layer and the loading conditions, a three-dimensional geomechanical model is selected, a three-dimensional geomechanical model is established, geological stratification, boundary conditions, initial stress state parameters are input, and the model is meshed;

[0122] Data input model verification: input the stress and deformation monitoring data monitored on site into the geomechanics model as boundary conditions or initial conditions, run the model and observe the degree of consistency between the simulation results and the monitoring data, compare the simulation results with the monitoring data, verify the model and adjust the model parameters;

[0123] The additional stress transfer law analysis is based on the model output results to analyze the distribution and change of the internal stress field of the silt layer under the action of heap loading, as well as the transmission path and range of the additional stress.

[0124] In the above embodiment, the three-dimensional geomechanical model can more realistically reflect the complexity and nonlinear characteristics of the silt layer, improve the accuracy and reliability of the simulation results, analyze the deformation and stress transfer of the silt layer from multiple angles and levels, and provide engineers with more comprehensive data support. Through data input and model verification, it is ensured that the geomechanical model can accurately reflect the actual situation and provide a reliable basis for subsequent analysis. The verified model can more accurately predict the deformation and stress transfer of the silt layer under the action of loading, and provide strong support for engineering design and construction.

[0125] In the above embodiment, by analyzing the additional stress transfer law, the deformation mechanism and stress transfer characteristics of the silt layer under the action of loading can be deeply understood, providing a scientific basis for engineering design and construction. Based on the analysis results, key parameters such as loading rate and vacuum pumping stop time in the engineering plan can be optimized to reduce engineering risks and improve engineering benefits. Understanding the transfer law and impact range of additional stress is helpful to guide the maintenance and monitoring work of the later engineering and ensure the safety and stability of the engineering.

[0126] The indoor creep test design also includes the following steps:

[0127] Soil sample preparation and standardization: collect marine silt soil samples from the project site or areas with similar geological conditions, remove impurities, air-dry, crush and sieve the collected soil samples, reshape the treated soil samples into standard-sized samples through static pressure, place the reshaped samples in a saturation device, and saturate them through back pressure to make them fully saturated;

[0128] Loading and unloading sequence design: different loading and unloading sequences are designed according to the actual project conditions and monitoring data. Each sequence contains multiple loading and unloading stages for simulating the stress process in the actual project, and the loading rate, loading amount, holding time and unloading method parameters of each loading and unloading stage are set;

[0129] The creep test is carried out by calibrating and preheating the creep instrument. The prepared standard specimens are installed on the test equipment. The specimens are loaded according to the designed loading and unloading sequence. At the same time, the start time, load amount, holding time and corresponding creep deformation data of each loading and unloading stage are recorded during the loading process.

[0130] The loading and unloading sequence design specifically includes the following steps:

[0131] Select the multi-stage loading and unloading cycle mode as the basic loading and unloading mode;

[0132] Set the loading stage parameters, determine the loading rate based on the loading rate range in the actual project, and determine the loading amount based on the strength characteristics of the soil sample and the actual project conditions. The loading amount of each level increases step by step, and the holding time is determined based on the creep characteristics of the soil sample.

[0133] Design the parameters of the unloading phase, select graded unloading as the unloading method, and determine the unloading rate;

[0134] Set the number and interval of loading and unloading cycles. Determine the number of loading and unloading cycles based on the test purpose and the creep characteristics of the soil sample. Determine the cycle interval based on the creep recovery characteristics of the soil sample and the test efficiency.

[0135] In the above embodiment, the soil samples collected from the engineering site or the area with similar geological conditions can truly reflect the physical and mechanical properties of the sludge. The uniformity and representativeness of the samples are ensured by removing impurities, air drying, crushing, screening and reshaping into standard size samples, thereby improving the accuracy of the test results. Through static pressure reshaping and saturation treatment, all samples are made to reach a fully saturated state, reducing the differences between samples and enhancing the comparability of the test results.

[0136] In the above embodiment, the selection of multi-stage loading and unloading cycle mode can better simulate the stress process of the sludge layer in the actual project and reflect its complex creep behavior. The gradual increase of the loading amount at each stage and the reasonable setting of the holding time make the test results closer to the actual situation. By designing different loading and unloading sequences, the creep characteristics and recovery ability of the sludge under different stress conditions can be systematically studied, which provides an important basis for theoretical research and engineering practice. The reasonable setting of the number and interval of loading and unloading cycles not only ensures the comprehensiveness and depth of the test results, but also takes into account the test efficiency, avoiding unnecessary waste of time and resources.

[0137] In the above embodiment, during the creep test, the start time, load amount, holding time and corresponding creep deformation data of each loading and unloading stage are recorded, providing rich and accurate information for subsequent data processing and analysis. By comparing and analyzing the test results with the theoretical model, the accuracy and applicability of the theoretical model can be verified, providing guidance for the improvement and optimization of the model. The results of the creep test can directly reflect the deformation characteristics of the sludge layer under loading, and provide a scientific basis for engineers to formulate reasonable loading plans, loading rates and vacuum pumping stop times during the design and construction process, thereby ensuring the safety and stability of the project.

[0138] Creep deformation and secondary consolidation coefficient analysis also includes the following steps:

[0139] Creep deformation data collation and analysis, based on the data obtained from indoor creep test design, a curve chart of creep deformation changing with time is drawn;

[0140] Fit the creep deformation data based on the time series model and determine the best fitting model and parameters;

[0141] Based on the results of the fitted model, the secondary consolidation coefficient is extracted.

[0142] Settlement prediction and consolidation degree calculation also include the following steps:

[0143] Integrate the field monitoring data, numerical simulation analysis results and indoor creep test data, and verify the integrated data;

[0144] According to the monitoring data and soil parameters, the parameters of the consolidation model are set, the monitoring data, soil parameters and model parameters are input into the consolidation model, settlement prediction calculation is performed, and the consolidation degree of the silt layer is calculated based on the settlement prediction results and the consolidation model;

[0145] The calculated degree of consolidation is evaluated and a settlement prediction and consolidation calculation report is generated.

[0146] In the above embodiment, by drawing a curve graph of creep deformation changing with time, the deformation trend and law of sludge under long-term loading can be intuitively reflected, providing an intuitive basis for subsequent data analysis and model fitting. By fitting the creep deformation data based on the time series model, the best fitting model and parameters can be determined, thereby improving the prediction accuracy of sludge creep behavior, helping to more accurately grasp the long-term deformation characteristics of sludge, and providing a scientific basis for engineering design and construction. Extracting the secondary consolidation coefficient from the fitting model helps to understand the sludge consolidation characteristics and helps to evaluate foundation stability and post-construction settlement.

[0147] In the above embodiment, the field monitoring data, numerical simulation analysis results and indoor creep test data are integrated and verified, which can ensure the accuracy and consistency of the data, help to improve the prediction accuracy and reliability, and set the parameters of the consolidation model according to the monitoring data and soil parameters, and input them into the consolidation model for settlement prediction calculation, which can produce more accurate settlement prediction results, which helps engineers to formulate reasonable construction plans and monitoring plans during the design and construction process to ensure the safety and stability of the project.

[0148] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A method for analyzing deformation characteristics of deep marine silt under large-area loading, characterized in that: The following steps are involved: Design of on-site monitoring scheme, design of monitoring scheme for deformation and stress distribution of marine silt layer under large-area loading, including monitoring point layout, monitoring equipment selection, data collection frequency and data transmission method. Based on the actual project situation, GIS software is used to plan the monitoring point layout; Monitoring data collection and processing: using the automatic data acquisition system to upload data regularly, combined with manual on-site verification, to continuously collect monitoring data and pre-process the data; Additional stress transfer analysis: Based on the monitoring data, a geomechanical model is established, the monitoring data and soil parameters are input, and numerical simulation analysis is performed to analyze the additional stress transfer law and distribution characteristics of the deep marine mud layer under the action of heap loading, and to evaluate the impact of heap loading on the mud layer; Indoor creep test design, preparation of standard soil samples, setting of different loading and unloading sequences, simulation of different loading and unloading conditions, use of creep test equipment for observation, and recording of creep deformation data; Creep deformation and secondary consolidation coefficient analysis: Based on the creep test results, time series analysis is used to fit the data and identify parameters, extract the secondary consolidation coefficient, and analyze the changing law of the consolidation coefficient affected by different factors; Settlement prediction and consolidation calculation: Comprehensively analyze monitoring data, numerical analysis results and creep test conclusions, use soil mechanics models to predict settlement and calculate consolidation, and form a prediction report; The monitoring data collection and processing specifically further includes the following steps: Automatic data collection system configuration: configure the data collection terminal according to the interface type and communication protocol of the monitoring equipment, and set the data collection frequency to automatically collect data; Manual on-site verification: while the automatic data collection system is working, formulate an on-site verification plan, specify the verification time, verification content and verification standards, conduct on-site measurements and records of key monitoring points, and compare manual measurement results with automatically collected data; Data preprocessing, which is to perform preliminary processing on the collected monitoring data, including data cleaning, outlier removal, missing value processing and data format conversion; The process of setting the data collection frequency includes: Real-time collection of data transmission rates and interface bandwidths corresponding to different interface types; The first coefficient parameter is obtained by using the data transmission rate corresponding to the different interface types; wherein the first coefficient parameter is obtained by the following formula: ； Among them, F 01 represents the first coefficient parameter; n represents the number of interface types; V i Indicates the standard data transmission rate corresponding to the i-th interface type; V b Indicates the standard deviation of the standard data transmission rate corresponding to n interface types; V mi Indicates the maximum data transmission rate that can be achieved by the i-th interface type; V mb Indicates the standard deviation of the maximum data transmission rate that can be achieved for n interface types; The second coefficient parameter is obtained by using the interface bandwidth corresponding to the different interface types; wherein the second coefficient parameter is obtained by the following formula: ； Among them, F 02 Represents the second coefficient parameter; B i Indicates the interface bandwidth corresponding to the i-th interface type; B h Indicates the interface bandwidth and value corresponding to n interface types; B c Indicates the preset interface bandwidth reference value; B max Indicates the maximum interface bandwidth corresponding to n interface types; V max Indicates the data transmission rate of the interface type corresponding to the maximum value of the interface bandwidth; V h Indicates the data transmission rate and value corresponding to n interface types; V c Indicates the theoretical maximum permissible data transmission rate corresponding to the preset interface bandwidth reference value; B b Indicates the standard deviation of the interface bandwidth corresponding to n interface types; The data acquisition frequency is determined by using the first coefficient parameter and the second coefficient parameter in combination with the setting parameters of the communication protocol.

2. The method for analyzing deformation characteristics of deep marine mud under large-area loading as claimed in claim 1, characterized in that: The on-site monitoring scheme design specifically includes the following steps: Analyze monitoring needs, clarify the purpose, scope and key parameters of monitoring, determine the physical quantities that need to be monitored for large-scale marine mud layers under heap loading, including stress, strain, displacement, pore water pressure, and formulate a monitoring needs list; Monitoring point layout planning: Use GIS system in combination with engineering topographic map and geological exploration report to plan monitoring point layout, import relevant layers into GIS system, use spatial analysis function to determine monitoring point location, compile monitoring point layout map, and mark monitoring point number, location, and monitoring content; Monitoring equipment selection: selecting and installing monitoring equipment according to monitoring requirements. The monitoring equipment includes stress gauges, strain gauges, displacement gauges, and pore water pressure gauges.

3. The method for analyzing deformation characteristics of deep marine mud under large-area loading as claimed in claim 1, characterized in that: Determining the data acquisition frequency by using the first coefficient parameter and the second coefficient parameter in combination with the setting parameters of the communication protocol includes: Extracting the data transmission efficiency corresponding to the communication protocol; Extracting the size of the data packet in the data transmission process corresponding to the setting of the communication protocol; Obtaining a third coefficient parameter using the data transmission efficiency corresponding to the communication protocol and the size of a data packet in a data transmission process set corresponding to the communication protocol; The third coefficient parameter is obtained by the following formula: ； Among them, F 03 represents the third coefficient parameter; m represents the number of types of communication protocols; P mi represents the maximum data transmission efficiency corresponding to the i-th communication protocol; C i Indicates the maximum amount of data packets in the data transmission process corresponding to the i-th communication protocol; Retrieving the first coefficient parameter and the second coefficient parameter; Obtaining a data acquisition frequency using the first coefficient parameter and the second coefficient parameter in combination with a third coefficient parameter; The data collection frequency is obtained by the following formula: ； Where, F represents the data collection frequency; F0 represents the preset initial data collection frequency value; F 01 Represents the first coefficient parameter; F 02 Represents the second coefficient parameter; F 03 Represents the third coefficient parameter.

4. The method for analyzing deformation characteristics of deep marine mud under large-area loading as claimed in claim 1, characterized in that: The additional stress transfer analysis specifically further includes the following steps: Geomechanical model construction: According to the characteristics of the silt layer and the loading conditions, a three-dimensional geomechanical model is selected, a three-dimensional geomechanical model is established, geological stratification, boundary conditions, initial stress state parameters are input, and the model is meshed; Data input model verification: input the stress and deformation monitoring data monitored on site into the geomechanics model as boundary conditions or initial conditions, run the model and observe the degree of consistency between the simulation results and the monitoring data, compare the simulation results with the monitoring data, verify the model and adjust the model parameters; The additional stress transfer law analysis is based on the model output results to analyze the distribution and change of the internal stress field of the silt layer under the action of heap loading, as well as the transmission path and range of the additional stress.

5. The method for analyzing deformation characteristics of deep marine mud under large-area loading as claimed in claim 1, characterized in that: The indoor creep test design specifically includes the following steps: Soil sample preparation and standardization: collect marine silt soil samples from the project site or areas with similar geological conditions, remove impurities, air-dry, crush and sieve the collected soil samples, reshape the treated soil samples into standard-sized samples through static pressure, place the reshaped samples in a saturation device, and saturate them through back pressure to make them fully saturated; Loading and unloading sequence design: different loading and unloading sequences are designed according to the actual project conditions and monitoring data. Each sequence contains multiple loading and unloading stages for simulating the stress process in the actual project, and the loading rate, loading amount, holding time and unloading method parameters of each loading and unloading stage are set; The creep test is carried out by calibrating and preheating the creep instrument. The prepared standard specimens are installed on the test equipment. The specimens are loaded according to the designed loading and unloading sequence. At the same time, the start time, load amount, holding time and corresponding creep deformation data of each loading and unloading stage are recorded during the loading process.

6. The method for analyzing deformation characteristics of deep marine mud under large-area loading as claimed in claim 5, characterized in that: The loading and unloading sequence design specifically includes the following steps: Select the multi-stage loading and unloading cycle mode as the basic loading and unloading mode; Set the loading stage parameters, determine the loading rate based on the loading rate range in the actual project, and determine the loading amount based on the strength characteristics of the soil sample and the actual project conditions. The loading amount of each level increases step by step, and the holding time is determined based on the creep characteristics of the soil sample. Design the parameters of the unloading phase, select graded unloading as the unloading method, and determine the unloading rate; Set the number and interval of loading and unloading cycles. Determine the number of loading and unloading cycles based on the test purpose and the creep characteristics of the soil sample. Determine the cycle interval based on the creep recovery characteristics of the soil sample and the test efficiency.

7. The method for analyzing deformation characteristics of deep marine mud under large-area loading as claimed in claim 1, characterized in that: The creep deformation and secondary consolidation coefficient analysis specifically further includes the following steps: Creep deformation data collation and analysis, based on the data obtained from indoor creep test design, a curve chart of creep deformation changing with time is drawn; Fit the creep deformation data based on the time series model and determine the best fitting model and parameters; Based on the results of the fitted model, the secondary consolidation coefficient is extracted.

8. The method for analyzing deformation characteristics of deep marine mud under large-area loading as claimed in claim 1, characterized in that: The settlement prediction and consolidation degree calculation specifically further includes the following steps: Integrate the field monitoring data, numerical simulation analysis results and indoor creep test data, and verify the integrated data; According to the monitoring data and soil parameters, the parameters of the consolidation model are set, the monitoring data, soil parameters and model parameters are input into the consolidation model, settlement prediction calculation is performed, and the consolidation degree of the silt layer is calculated based on the settlement prediction results and the consolidation model; The calculated degree of consolidation is evaluated and a settlement prediction and consolidation calculation report is generated.

Images