Bridge construction structure stress real-time monitoring method and system

Abstract

The invention discloses a bridge construction structure stress real-time monitoring method and system, and belongs to the field of bridge construction monitoring, and the method comprises the steps: building a finite element model based on design parameters of a bridge, analyzing a bridge construction structure based on a finite element simulation result, obtaining a stress / strain distribution cloud picture in a bridge construction process, and obtaining a stress / strain distribution cloud picture; stress sensitive points existing in the construction process are analyzed; arranging sensors at different parts of the bridge construction structure according to the stress sensitive points, collecting data, processing the data through an edge gateway, and sending the processed data to a cloud; after receiving the data sent by the edge gateway, the cloud analyzes the received sensor data through a multi-physics field coupling model; and deducing the stress state of the structure on the basis of data obtained by calculation of the real-time sensor data through a multi-physics field coupling model. The stress change in the bridge construction process can be monitored in real time, and the safety of bridge construction is improved.

Description

Technical Field

[0001] The present invention relates to the field of bridge construction monitoring, and in particular to a real-time monitoring method and system for bridge construction structure stress. Background Art

[0002] During the bridge construction process, the stress state of the construction structure needs to be monitored in real time to ensure the safety and reliability of the construction. Existing technologies mainly rely on manual inspections and simple sensor arrangements, and are unable to comprehensively and in real time monitor the stress response of the bridge structure under static loads, dynamic loads, and time-varying effects during the construction process. At the same time, the lack of an effective data analysis model makes it difficult to accurately evaluate the safety and reliability of bridge construction, and there are certain safety hazards.

[0003] In order to solve the above problems, a method is needed to monitor key data such as stress, strain, displacement, etc. in real time during bridge construction, and to comprehensively evaluate the stress state of the structure through an effective data analysis model. At the same time, the method should also be able to capture static loads, dynamic loads, and time-varying effects during construction, so as to comprehensively evaluate the stress state of the bridge structure and improve the safety and reliability of bridge construction. In addition, the method also needs to be combined with actual monitoring data to verify the accuracy of the model in real time, so as to provide a scientific decision-making basis for bridge construction. By timely discovering potential safety hazards and issuing early warnings, safety accidents can be avoided to the greatest extent. Summary of the invention

[0004] One of the purposes of the present invention is to provide a real-time monitoring method for stress of a bridge construction structure, so as to solve the problem in the prior art that key data such as stress, strain, displacement, etc. during the bridge construction process cannot be monitored in real time and comprehensively.

[0005] The present invention is implemented through the following technical scheme, a real-time monitoring method for stress of a bridge construction structure, comprising the following steps: S1, based on the design parameters of the bridge, constructing a finite element model, simulating the mechanical behavior of different construction stages through the finite element model, and applying different load conditions according to the construction structure required in the actual construction process and in combination with the load changes of the construction structure, setting boundary conditions and load application, and analyzing the stress, strain, displacement and other parameters of the bridge construction structure based on the finite element simulation results, obtaining the stress / strain distribution cloud map during the bridge construction process, and analyzing the stress sensitive points existing in the construction process; S2, arranging sensors at different parts of the bridge construction structure according to the stress sensitive points, collecting data and processing the data through the edge gateway, and sending the processed data to the cloud; S3, after receiving the data sent by the edge gateway, the cloud side performs data analysis on the received sensor data through the multi-physical field coupling model; S4, based on the real-time sensor data, the data calculated by the multi-physical field coupling model is combined with the finite element model for dynamic simulation, the stress state of the structure is deduced, and an early warning is issued.

[0006] Furthermore, the stress sensitive points are divided into different sensitive areas according to their different characteristics. The sensitive areas include: a static sensitive area, a dynamic sensitive area and a construction process sensitive area.

[0007] Furthermore, the static sensitive area is obtained by the following method: using finite element analysis to calculate the static stress value of each node of the structure; The area is the node where the static stress is greater than 70% of the material yield stress. is the static stress value, is the material yield stress.

[0008] Furthermore, the dynamic sensitive area is obtained by the following method: performing modal analysis on the structure and extracting the first five modal vibration modes; identifying the node with the largest amplitude in the modal vibration mode; applying unit harmonic force on the structure and calculating the unit movement response of the structure to obtain different gain areas of the structure, and taking the high gain area as the dynamic sensitive area.

[0009] Furthermore, the sensitive areas of the construction process are obtained by the following method: Using the MCS method, the failure probability P of different construction stages is evaluated. f (t); Select P f (t)>10 -4 The critical stage is regarded as the sensitive area of ​​the construction process.

[0010] Furthermore, after the sensitive areas are identified, these areas can be further optimized to improve the performance and reliability of the entire structure. The optimized output of sensitive points can be achieved through the following formula:

[0011] ,

[0012] in, is the objective function, F is the external force vector, U is the displacement vector, is the inner product of the external force vector and the displacement vector, As constraints, To optimize the material volume of the structure, is the material volume of the original structure, It is a credit limit, usually between 30% and 50%. is a density variable, ranging between 0 and 1. =0 means that the unit has no material (hollow); when =1 indicates that the unit is completely material (solid), is the minimum density value, usually set to a very small positive number to avoid singularity problems in numerical calculations, but does not indicate the actual presence of material.

[0013] Furthermore, step S2 also includes: integrating sensors installed at different sensitive areas into a sensor cluster, configuring unique identifiers for sensors in the same sensor cluster and stamping the same timestamp, the edge gateway performs data processing and multi-source fusion processing on the received data, and through the data fusion algorithm, fuses the data from multiple sources and different types of sensors and sends them to the cloud.

[0014] Further, the following sub-steps are included: S2.1, using Z-score standardization to standardize the data so that it is in the same dimensional range; S2.2, according to the configured unique identifier, the data of different sensor areas are divided into key sensor clusters and non-key sensor clusters, and a lower weight coefficient is set for the non-key sensor cluster. For the key area sensor, the specific sensitive area is determined according to the unique identifier, and the priority of the key area sensor is dynamically adjusted through an adaptive mechanism according to the change of time; S2.3, the data from different sensor clusters are weighted linearly combined by the following formula:

[0015] , where N is the sensor cluster, i is the sensor cluster, d i (t) is the data of the sensor cluster, w i is the weight of the sensor cluster, and D(t) is the final fused data D(t).

[0016] Furthermore, dynamically adjusting the priority of the key area sensor through the adaptive mechanism includes: dynamically adjusting the priority of the key area sensor through the following formula:

[0017] ,in, is the weight coefficient of the key area sensor, is the basic priority of sensor cluster i, is the standard deviation of sensor cluster i at time t, is the coefficient used to adjust the effect of data change rate on weight, is the data gradient of sensor cluster i.

[0018] Furthermore, the multi-physics field coupling model includes: elastic statics field equations, structural dynamics field equations and construction time-varying field equations, among which the elastic statics field equations are: , where ∇ is the divergence operator, is the stress tensor, is the static body force. The structural dynamics equation is: , where u is the displacement field, is the material density, is the dynamic load. The field equation during construction is:

[0019] ,in, is the time-varying elastic tensor of the material, is the elastic property of the material in its initial state, is the time-varying function of the material, F(t) is the total load, is the time-varying load during construction.

[0020] Furthermore, the stress tensor obeys the constitutive relation: ,in, is the fourth-order elasticity tensor, is the strain tensor.

[0021] Furthermore, the strain tensor The definition is , where u is the displacement field (unit: m).

[0022] Furthermore, the material time-varying function is defined as: , where e is a natural constant, The rate constant, t is the time.

[0023] Furthermore, the static load can be expressed as follows:

[0024] ,in, is the material density (unit: kg / m³), is the gravity acceleration vector (unit: m / s²), is the kth static construction load (unit: N), is the number of statically applied loads, i.e. the total number of static construction loads involved in the formula, is the Dirac function, indicating that the load acts at position x k (Unit: m).

[0025] Furthermore, the dynamic load can be expressed as follows:

[0026] ,in, is the mth dynamic load amplitude (unit: N), is the number of dynamic loads, that is, the total number of dynamic construction loads involved in the formula, is the excitation frequency (unit: rad / s), corresponding to the main frequency of the construction machinery, is the phase angle (unit: rad), which is used to describe the phase difference of multi-source excitation. is the position where the dynamic load acts.

[0027] Furthermore, the construction time-varying load can be expressed by the following formula:

[0028] ,in, is the load at the nth construction stage (unit: N), is the Heaviside step function, defining the load application and removal time, is the starting time of the nth stage, is the end time of the nth phase. is the number of construction stages, i.e. the total number of construction stages involved in this formula, is the Heaviside step function, defining the load application and removal time, Indicates time t Greater than or equal to When , the function value of the Heaviside step function is used to define the load of the nth stage from This is the moment that starts to work. is the starting time of the nth stage, is the end time of the nth phase. Indicates time t Greater than or equal to When , the function value of the Heaviside step function is used to define the load of the nth stage from This moment stops working.

[0029] Furthermore, the early warning includes: setting safety thresholds for various monitoring data according to the design specifications of the bridge and the actual stress analysis results. When it is found that the data calculated by the coupled partial differential equation group exceeds the set safety threshold, the system automatically triggers an early warning.

[0030] On the other hand, the present invention provides a real-time monitoring system for stress of a bridge construction structure. The system includes a processor; a memory storing a computer program. When the computer program is executed by the processor, the real-time monitoring method for stress of a bridge construction structure as described above is implemented.

[0031] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0032] 1. The present invention can monitor key data such as stress, strain, displacement, etc. in real time and comprehensively during the bridge construction process, and timely discover potential safety hazards, thereby improving the safety and reliability of bridge construction.

[0033] 2. The present invention uses a multi-physical field coupling model to accurately capture the impact of static loads, dynamic loads and time-varying effects during the construction process on the structure, comprehensively evaluate the safety and reliability of bridge construction, combine actual monitoring data with model calculation results, verify the accuracy of the model in real time, and provide a scientific decision-making basis for bridge construction.

[0034] 3. The present invention adopts advanced sensor layout and edge gateway processing technology. The monitoring means are advanced and comprehensive, which can meet the monitoring needs of the entire bridge construction process. By adopting different sensor layout strategies for different sensitive areas, key parts can be monitored in a targeted manner to improve the accuracy and efficiency of monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, constitute a part of this application, and do not constitute a limitation of the embodiments of the present invention. In the drawings:

[0036] Figure 1 This is a flow chart of the method provided in Example 1 of the present invention. DETAILED DESCRIPTION

[0037] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0038] Example 1

[0039] In this embodiment, in order to solve the problems that the prior art cannot monitor the key data such as stress, strain, displacement, etc. in the bridge construction process in real time and comprehensively, it is difficult to discover potential safety hazards in time, and there are certain safety risks; lack of effective data analysis model, unable to accurately evaluate the safety and reliability of bridge construction, unable to fully capture the impact of static load, dynamic load and time-varying effects in the construction process on the structure; unable to combine with actual monitoring data to verify the accuracy of the model in real time, and difficult to provide a scientific decision-making basis for bridge construction; mainly relying on manual inspection and simple sensor arrangement, single monitoring means, and unable to meet the monitoring needs of the entire bridge construction process, a real-time monitoring method for bridge construction structure stress is disclosed. The method analyzes the collected data through the Internet of Things technology combined with a multi-physical field coupling model, which can effectively solve the various shortcomings of the prior art and realize intelligent stress monitoring of bridge construction sites.

[0040] Figure 1 The flowchart of the real-time monitoring method in this embodiment is shown. It can be seen from the figure that this embodiment includes the following steps:

[0041] Step 1: First, based on the design parameters of the bridge, a refined finite element model is constructed in the cloud. The constructed finite element model should include material properties, geometric parameters, and the dynamics of construction loads, thereby forming a three-dimensional finite element model containing multi-dimensional data to simulate the mechanical behavior of different construction stages. Different load conditions can be applied according to the construction structure required in the actual construction process and combined with the load changes of the construction structure to set boundary conditions and load application.

[0042] Based on the finite element simulation results, the stress, strain, displacement and other parameters of the bridge construction structure are analyzed. The stress / strain distribution cloud diagram during the bridge construction process is obtained, and the stress sensitive points in the construction process are analyzed.

[0043] At the same time, based on the finite element simulation results and combined with the design standards of the bridge, the safety factor, limit state, fatigue life, etc. of the bridge construction structure can be calculated.

[0044] Specifically, in this embodiment, the stress sensitive points are divided into a static sensitive area, a dynamic sensitive area and a construction process sensitive area, and the sensors arranged in different sensitive areas are also different.

[0045] 1) The static sensitive area is designed to identify the high stress areas and vulnerable parts of the structure under static loads, which can be obtained through the following analysis steps:

[0046] First, by using finite element analysis, the static stress values ​​of each node of the structure are calculated.

[0047] Then filter out The area is the node where the static stress is greater than 70% of the material yield stress. is the static stress value, is the material yield stress.

[0048] 2) Dynamic sensitive area aims to identify the vibration characteristics and sensitive parts of the structure under dynamic loads.

[0049] First, perform modal analysis on the structure and extract the first five modal vibration shapes. Then identify the node with the largest amplitude in the modal vibration shape.

[0050] Finally, by applying unit harmonic force on the structure, the unit movement response H(ω) of the structure is calculated to obtain different gain areas of the structure, and the high gain area is taken as the dynamic sensitive point.

[0051] 3) Sensitive areas during construction are designed to evaluate the time-varying mechanical behavior of the structure during construction and identify stress concentration areas during critical construction stages. This is analyzed through the following steps:

[0052] First, the failure probability P of different construction stages is evaluated by using the MCS method. f (t).

[0053] Then select P f (t)>10 -4 The critical stage is regarded as the sensitive area of ​​the construction process.

[0054] In this embodiment, after analyzing the static sensitive area, the dynamic sensitive area and the construction process sensitive area to obtain the key data such as stress, strain, displacement, etc. in the static, dynamic or construction process, a sensitive point optimization step can be included. This step can further optimize these areas after identifying the sensitive areas to improve the performance and reliability of the entire structure.

[0055] By adjusting the material layout, the high stress area can be strengthened or the stress concentration can be reduced, thereby improving the bearing capacity and reliability of the structure. By changing the modal vibration of the structure, the amplitude of the vibration-sensitive area can be reduced and the dynamic response performance of the structure can be improved, thereby ensuring stability and safety during the construction process.

[0056] In this embodiment, the sensitive point optimization output can be achieved by the following formula:

[0057] ,

[0058] in, is the objective function, which is used to represent the total elastic potential energy of the structure. F is the external force vector, which represents the external force acting on the structure. U is the displacement vector, which represents the node displacement of the structure under the action of the external force F. It is the inner product of the external force vector and the displacement vector, which represents the total elastic potential energy of the structure under the action of external force, or the compliance of the structure. The physical meaning of this item is the work done by the system. By minimizing this goal, the stiffness characteristics of the structure can be optimized.

[0059] As constraints,

[0060] is the material volume of the optimized structure. is the material volume of the original structure. It is a point fraction constraint, usually between 30% and 50%, which indicates the ratio of the material volume of the optimized structure to the original material volume. This constraint ensures that the material usage does not exceed the set ratio range. is a density variable, which indicates the material density of the structural unit, and its value range is between 0 and 1. =0 means that the unit has no material (hollow); when =1 indicates that the element is completely material (solid). is the minimum density value, usually set to a very small positive number to avoid singularity problems in numerical calculations, but does not indicate the actual presence of material.

[0061] It should be noted that the above formula can be used to optimize sensitive areas and optimize structural performance under the premise of high material utilization, thereby improving the overall efficiency and reliability of the structure. This process combines the results of the previous sensitive area analysis to ensure that the direction and focus of the optimization are targeted at areas with potential problems, ultimately resulting in a more efficient and reliable construction structure design.

[0062] Step 2: According to the division of stress sensitive points, different sensors are arranged at different locations, the data collected by the sensors are collected and processed through the edge gateway, and the processed data is sent to the cloud.

[0063] For static sensitive areas, the goal is to monitor stress, strain, displacement and other data under static loads. The types of sensors used are strain gauges, stress sensors and other sensors. These sensors should be placed in stress concentration areas or locations with high strain energy density, which are where the structure is most likely to be damaged or fail under static loads.

[0064] For the dynamic sensitive area, the goal is to monitor the vibration characteristics of the structure under dynamic loads, including frequency, mode, amplitude, etc. The selected sensor type should be accelerometer, displacement sensor, laser sensor, etc. When arranging the sensor, it should be arranged at the node position with the largest amplitude according to the main vibration point in the modal analysis, and the high gain position in the frequency response function analysis, so as to capture the dynamic response characteristics of the structure.

[0065] For sensitive areas during construction, the goal is to monitor the changes in the stress state and safety of the structure during construction, and special attention should be paid to time-varying effects and stress changes in critical time periods. It should use a combination of strain gauges, stress sensors, displacement sensors, and accelerometers for monitoring. When arranging sensors, key construction nodes and locations where large stress or displacement changes may occur during construction, such as support points and connection points, should be considered, with a focus on monitoring the structural response at specific stages during construction.

[0066] It should be noted that when arranging, it is necessary to refer to the stress / strain distribution cloud map and the division of sensitive areas. At key sensitive points, sensors should be densely covered and arranged, preferably with a spacing of ≤1m; for non-critical areas, sparse arrangements can be made, with a spacing of 3-5m. Sensors installed in different sensitive areas are integrated into one sensor cluster, while sensors arranged in non-critical areas are arranged in another sensor cluster. Sensors in the same sensor cluster are configured with unique identifiers and the same timestamps to ensure the temporal consistency of data in the same sensor cluster, so as to facilitate the distinction of data areas and priorities in subsequent calculations.

[0067] The data collected by each sensor cluster is first sent to the edge gateway, which processes the received data before sending it to the cloud. Considering the chaos at the bridge construction site, unnecessary dangers may be caused by traditional priority links. In this embodiment, the edge gateway and each sensor cluster can communicate using wireless methods such as Wi-Fi, LoRa, and NB-IoT. For key construction areas, redundant communication links can be added to avoid single point failures affecting overall data collection.

[0068] Specifically, in this embodiment, the edge gateway can perform data processing and multi-source fusion processing on the received data. Through the data fusion algorithm, the data from multiple sources and different types of sensors are fused. In this embodiment, data fusion can include the following steps:

[0069] 1) Since sensor data may come from different types of sensors, their dimensions, data ranges and units may be different. In order to ensure the effectiveness of data fusion, the data must first be standardized to be in the same dimension range. In this embodiment, Z-score standardization is used to make the output of each sensor on the same scale.

[0070] 2) Then, based on the configured unique identifier, the data from different sensor areas are divided into two categories: "critical sensor clusters" and "non-critical sensor clusters". And the sensor clusters are prioritized according to different sensitive areas.

[0071] A lower weight coefficient is set for non-critical sensor clusters. For critical area sensors, it is necessary to determine which specific sensitive area they belong to based on their unique identifiers, and dynamically adjust the priority of critical area sensors through an adaptive mechanism based on changes in time.

[0072] The priority of key area sensors can be dynamically adjusted by the following formula:

[0073] ,

[0074] in, is the weight coefficient of the key area sensor, is the basic priority (static weight) of sensor cluster i, is the standard deviation of sensor cluster i at time t, indicating the volatility of the cluster data and reflecting the reliability of the sensor. It is a coefficient used to adjust the effect of data change rate on weight. The larger the value, the greater the effect of change rate on weight. is the data gradient of sensor cluster i, which indicates the rate of data change within time t and reflects the sensitivity of the sensor to dynamic changes.

[0075] It should be noted that the above formula can be used to dynamically adjust the weights so that when the data of a sensor cluster changes dramatically, the influence of the cluster in the fusion can be enhanced, especially in critical construction stages or when emergencies occur, to help the system respond quickly.

[0076] 3) For each time t, we perform a weighted linear combination of the data from different sensor clusters. Assume there are N sensor clusters, and the data of cluster i at time t is d i (t), the weight of the cluster is w i , then the final fused data D(t) can be expressed as:

[0077] .

[0078] It should be noted that the weight coefficients of the sensors in the key areas can be dynamically updated according to the formula in step 2. At the same time, the linear algebraic weighted fusion algorithm can effectively integrate the data from different sensitive areas and different types of sensors. While ensuring the consistency of the data time series, the weights can be dynamically adjusted according to the priority and regional characteristics of the sensor cluster, thereby optimizing the utilization efficiency of the sensor data. This method not only takes into account the different needs of static, dynamic and construction sensitive areas, but also dynamically adjusts the data fusion strategy according to the real-time construction situation, thereby improving the real-time and accuracy of the system.

[0079] Step 3: After receiving the data sent by the edge gateway, the cloud analyzes the received sensor data through the multi-physics field coupling model.

[0080] In this embodiment, the multi-physics field coupling model includes the coupling of elastic statics field, structural dynamics field and construction time-varying field.

[0081] Among them, the elastic statics field is used to capture the stress concentration area under constant load / static load (corresponding to the static sensitive area), the structural dynamics field is used to analyze the vibration mode and frequency response characteristics (corresponding to the dynamic sensitive area), and the construction time-varying field is used to characterize the changes of materials and loads with the construction stage (corresponding to the sensitive area of ​​the construction process).

[0082] The coupling mechanism of the coupled equations is to use the results of the static analysis as the initial conditions for the dynamic analysis. The dynamic load affects the dynamic response of the structure, while the material changes and load adjustments during the construction process further affect the static and dynamic behaviors. In addition, sensor data feedback affects the loads and boundary conditions through weight adjustment, thus forming a closed loop. The specific effect of the coupled equations is to provide the basic stress distribution for the static equations and identify high stress areas. The dynamic equations capture the vibration characteristics and identify the resonance points and dynamically sensitive areas. The construction time-varying field equations handle the changes in the construction stage to ensure the accuracy of the model evolution over time.

[0083] Specifically, the elastostatic field equation is as follows:

[0084] ,

[0085] Among them, ∇ is the divergence operator, is the stress tensor (unit: Pa), which obeys the constitutive relation: , is the fourth-order elastic tensor (unit: Pa), which is determined by the elastic modulus E and Poisson’s ratio. is the strain tensor, which is defined in this embodiment as , u is the displacement field (unit: m). is the static body force (unit: N / m³), which is used to represent the dead load or construction static load. It should be noted that the divergence operation refers to the degree of outflow or inflow of the vector field at each point. In this formula and the structural dynamics equation, the divergence of the stress tensor is used to describe the distribution change of stress in space.

[0086] The structural dynamics equation is shown below:

[0087] ,

[0088] in, is the material density (unit: kg / m³), is a dynamic load (unit: N / m³) used to represent harmonic excitation or construction equipment vibration.

[0089] The time-varying field equation during construction is shown as follows:

[0090] ,

[0091] in, is the time-varying elastic tensor of the material (dimensionless), which may change as the model is constructed (e.g. during the curing of concrete). The elastic property of the material in its initial state, usually a constant elastic tensor at t = 0 or when it is not affected by time changes. This value can reflect the mechanical properties of the material at the initial moment, such as stiffness and tensile strength. is a material time-varying function, which is used to describe the change of material properties over time and can be defined as: (i.e. concrete hardening model), where e is a natural constant, The rate constant, t is the time, is an exponential decay term, which represents the gradual change of material properties over time. The time-varying function of materials can also be discretely assigned according to the construction stage. is the load related to the construction process (unit: N / m³), such as the load change caused by the removal of the support. F(t) is the total load, which is a time function and is used to represent the total external force acting on the system during the construction process. It includes static force, dynamic force and time-varying force related to construction. The combination of these forces determines the total load on the construction structure at different time points.

[0092] Specifically, in this embodiment, according to the static, dynamic and time-varying characteristics of bridge construction, and in combination with the principles of engineering mechanics and the monitoring requirements during the actual construction process, three types of loads are defined by the following formula:

[0093] Static load:

[0094] ,

[0095] in, is the material density (unit: kg / m³), is the gravity acceleration vector (unit: m / s²), is the number of statically applied loads, that is, the total number of static construction loads involved in this formula. is the kth static construction load (unit: N), is the Dirac function, indicating that the load acts at position x k (Unit: m).

[0096] In this formula, represents the weight of the structure itself, evenly distributed as volume force, Represents the concentrated force of construction equipment or temporary structures, applied at key nodes through delta functions.

[0097] Dynamic load:

[0098] ,

[0099] in, is the mth dynamic load amplitude (unit: N), is the number of dynamic loads, that is, the total number of dynamic construction loads involved in the formula, is the excitation frequency (unit: rad / s), corresponding to the main frequency of the construction machinery, is the phase angle (unit: rad), which is used to describe the phase difference of multi-source excitation. is the position where the dynamic load acts.

[0100] Construction time-varying loads:

[0101] ,

[0102] in, is the load at the nth construction stage (unit: N), is the number of construction stages, i.e. the total number of construction stages involved in this formula, is the Heaviside step function, defining the load application and removal time, Indicates time t Greater than or equal to When , the function value of the Heaviside step function is used to define the load of the nth stage from This is the moment that starts to work. is the starting time of the nth stage, is the end time of the nth phase. Indicates time t Greater than or equal to When , the function value of the Heaviside step function is used to define the load of the nth stage from This moment stops working.

[0103] It should be noted that the static stress can be calculated using the static load formula , thereby filtering out The high gain area can be identified by calculating the unit movement response H(ω) of the structure through the dynamic load formula excitation structure; the failure probability P at different construction stages can be evaluated by combining the time-varying characteristics of the construction time-varying load formula with the MCS method.f (t).

[0104] Step 4: Finally, the data calculated by the multi-physics field coupling model based on the real-time sensor data is combined with the finite element model for dynamic simulation to deduce the stress state of the structure and issue an early warning. At the same time, the accuracy of the model can be verified in real time by comparing it with the actual monitoring data.

[0105] Specifically, the early warning can be achieved by setting safety thresholds for various monitoring data (stress, strain, displacement, etc.) according to the design specifications of the bridge and the actual force analysis results. These thresholds can be set based on empirical data, theoretical analysis or historical monitoring data. Early warning trigger mechanism: When the data calculated by the coupled partial differential equations exceeds the set safety threshold, the system automatically triggers an early warning. The early warning level can be graded according to the severity of the deviation (such as green, yellow, and red warnings).

[0106] When the threshold is exceeded, the system should immediately notify relevant personnel through a visual interface, SMS or email, etc. The system should provide engineers with follow-up maintenance suggestions based on monitoring data, simulation results and historical data, such as reinforcement, repair or replacement of certain components.

[0107] Example 2

[0108] In this embodiment, a real-time monitoring system for stress of a bridge construction structure is disclosed.

[0109] In this embodiment, the real-time monitoring system includes a processor and a memory.

[0110] A computer program is stored in the memory, and when the computer program is executed by the processor, the real-time monitoring method for stress of a bridge construction structure as described in the embodiment can be implemented.

[0111] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A real-time monitoring method for bridge construction structure stress, characterized in that: The real-time monitoring method comprises: S1 constructs a finite element model based on the design parameters of the bridge, simulates the mechanical behavior of different construction stages through the finite element model, and applies different load conditions according to the construction structure required in the actual construction process and the load changes of the construction structure, and sets boundary conditions and load application. Based on the finite element simulation results, the stress, strain and displacement parameters of the bridge construction structure are analyzed to obtain the stress / strain distribution cloud map during the bridge construction process and analyze the stress sensitive points in the construction process; S2, arranging sensors at different parts of the bridge construction structure according to stress sensitive points, collecting data and processing the data through the edge gateway, and sending the processed data to the cloud; S3. After receiving the data sent by the edge gateway, the cloud performs data analysis on the received sensor data through the multi-physics field coupling model; S4. Based on the real-time sensor data, the data calculated by the multi-physics field coupling model is combined with the finite element model to perform dynamic simulation, deduce the stress state of the structure, and issue an early warning.

2. The real-time monitoring method for bridge construction structure stress according to claim 1 is characterized in that: According to the different characteristics of the stress sensitive points, they are divided into different sensitive areas. The sensitive areas include: static sensitive areas, dynamic sensitive areas and construction process sensitive areas.

3. The real-time monitoring method for bridge construction structure stress according to claim 2 is characterized in that: The static sensitive area is obtained by the following method: Use finite element analysis to calculate the static stress values ​​of each node of the structure; Filter out of regions, where is the static stress value, is the material yield stress; The dynamic sensitive area is obtained by the following method: Perform modal analysis on the structure and extract the first five modal vibration shapes; Identify the nodes with the largest amplitudes in the mode shapes; By applying unit harmonic force on the structure, the unit movement response of the structure is calculated to obtain different gain areas of the structure, and the high gain area is taken as the dynamic sensitive area; The sensitive area of ​​the construction process is obtained by the following method: Using the MCS method, the failure probability P at different construction stages is evaluated. f (t); Select P f (t)>10 -4 The critical stage is regarded as the sensitive area of ​​the construction process.

4. The real-time monitoring method for bridge construction structure stress according to claim 1 is characterized in that: The step S2 further includes: integrating sensors installed at different sensitive areas into a sensor cluster, configuring unique identifiers for sensors in the same sensor cluster and stamping the same timestamps on them, The edge gateway processes the received data and performs multi-source fusion processing. Through the data fusion algorithm, it fuses the data from multiple sources and different types of sensors and sends them to the cloud, including the following sub-steps: S2.

1. Use Z-score standardization to standardize the data so that they are in the same dimensional range; S2.2, divide the data of different sensor areas into key sensor clusters and non-key sensor clusters according to the configured unique identifier, Set lower weight coefficients for non-critical sensor clusters. For key area sensors, the specific sensitive area is determined based on the unique identifier, and the priority of the key area sensors is dynamically adjusted through an adaptive mechanism based on the change of time; S2.

3. Perform a weighted linear combination of the data from different sensor clusters using the following formula: ， Where N is the sensor cluster, i is the sensor cluster, d i (t) is the data of the sensor cluster, w i is the weight of the sensor cluster, and D(t) is the final fused data D(t).

5. The real-time monitoring method for bridge construction structure stress according to claim 4 is characterized in that: The method of dynamically adjusting the priority of sensors in key areas through an adaptive mechanism includes: The priority of key area sensors is dynamically adjusted by the following formula: ， in, is the weight coefficient of the key area sensor, is the basic priority of sensor cluster i, is the standard deviation of sensor cluster i at time t, is the coefficient used to adjust the effect of data change rate on weight, is the data gradient of sensor cluster i.

6. The real-time monitoring method for bridge construction structure stress according to claim 1 is characterized in that: The multi-physics field coupling model includes: elastic statics field equations, structural dynamics field equations and construction time-varying field equations, where: The field equations for elastostatics are: ， Among them, ∇ is the divergence operator, is the stress tensor, is the static body force; The structural dynamics equation is: ， Where u is the displacement field, is the material density, is the dynamic load; The field equation during construction is: ， in, is the time-varying elastic tensor of the material, is the elastic property of the material in its initial state, is the time-varying function of the material, F(t) is the total load, is the time-varying load during construction.

7. The real-time monitoring method for bridge construction structure stress according to claim 6 is characterized in that: The stress tensor obeys the constitutive relation: ， in, is the fourth-order elasticity tensor, is the strain tensor.

8. The real-time monitoring method for bridge construction structure stress according to claim 7 is characterized in that: The strain tensor is expressed by the following formula: ， Where u is the displacement field, is the gradient tensor of the displacement field, is the transposed matrix of the displacement field gradient tensor.

9. The real-time monitoring method for bridge construction structure stress according to claim 6 is characterized in that: The material time-varying function is defined as: ， Among them, e is a natural constant, which is The rate constant, t is the time.

10. A real-time monitoring system for bridge construction structure stress, characterized in that: The real-time monitoring system comprises: processor; The memory stores a computer program, and when the computer program is executed by the processor, the real-time monitoring method of bridge construction structure stress according to any one of claims 1 to 9 is implemented.

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