Method and system for predicting vertical temperature gradient of concrete-filled steel tube considering diameter parameter

Abstract

The present disclosure provides a steel pipe concrete vertical temperature gradient prediction method and system considering diameter parameters, relating to the technical field of bridge structure health monitoring, comprising: obtaining internal temperature data and environmental data of steel pipe concrete components of different diameters, and preprocessing the same; constructing a symmetric absolute value power function temperature gradient model, introducing a dimensionless diameter parameter to establish a functional relationship between the key parameters of the temperature gradient model and the diameter of the component; constructing a two-dimensional transient heat conduction finite element model, inputting the environmental data as the boundary, extracting the daily maximum temperature difference sequence, screening the extreme samples and completing the fitting based on the generalized Pareto distribution, and calculating the maximum temperature difference of the 100-year return period; inputting the maximum temperature difference of the 100-year return period into the temperature gradient model, combining the diameter parameter function to generate the vertical temperature distribution curve, and outputting a format file that can be directly imported into the structure analysis software. The present disclosure solves the problems of traditional model segmentation discontinuity and poor diameter adaptability.

Description

Technical Field

[0001] This disclosure relates to the field of bridge structural health monitoring technology, specifically to a method and system for predicting the vertical temperature gradient of steel-concrete composite pipes considering diameter parameters. Background Technology

[0002] The statements in this section are merely background information relating to this disclosure and do not necessarily constitute prior art.

[0003] Concrete-filled steel tube structures are widely used in engineering structures such as arch bridges due to their high load-bearing capacity, good ductility, and ease of construction. During service, factors such as solar radiation, ambient temperature changes, and wind speed can cause a significant non-uniform temperature field to form inside the concrete-filled steel tube section, resulting in temperature loads that have a significant impact on structural safety.

[0004] Existing research indicates that temperature effects can cause debonding at the interface between the steel pipe and the core concrete, stress redistribution in the structure, and degradation of load-bearing capacity. Therefore, temperature gradient distribution is typically used as the basis for temperature load calculations in bridge design.

[0005] Existing solutions mostly focus on the temperature gradient distribution characteristics of concrete-filled steel tube members of specific diameters. The proposed distribution function forms are mainly applicable to the diameter conditions of the test members, and they still have the following technical limitations: (1) The differences in component diameters were not considered, resulting in insufficient model universality; (2) The function form depends on empirical selection and is difficult to express uniformly; (3) The location of the lowest temperature point is fixed, which does not conform to the actual change pattern; (4) It lacks the ability to predict extreme temperature differences and cannot reflect risks on a century-scale. Summary of the Invention

[0006] To address the aforementioned issues, this disclosure proposes a method and system for predicting the vertical temperature gradient of steel-concrete composite tubes, taking into account the diameter parameter. By simulating solar radiation tests and long-term monitoring to obtain temperature data of components with different diameters, a unified temperature gradient function model incorporating the diameter parameter is established. Based on the generalized Pareto extreme value theory, the maximum vertical temperature difference within the return period is predicted, thereby achieving a unified expression of the temperature distribution of components with different diameters and reliable prediction of extreme temperature differences.

[0007] According to some embodiments, the present disclosure adopts the following technical solutions: Methods for predicting the vertical temperature gradient of concrete-filled steel tubes considering diameter parameters include: Acquire internal temperature data and environmental data of concrete-filled steel tubes of different diameters, and preprocess them; A symmetric absolute value power function temperature gradient model is constructed, and a dimensionless diameter parameter is introduced to establish the functional relationship between the key parameters of the temperature gradient model and the component diameter. A two-dimensional transient heat conduction finite element model was constructed, with environmental data as the boundary input. The daily maximum vertical temperature difference sequence was extracted, extreme samples were screened, and the model was fitted based on the generalized Pareto distribution. The maximum temperature difference over a 100-year return period was calculated. The maximum temperature difference over a 100-year return period is input into the temperature gradient model, and a vertical temperature distribution curve is generated by combining it with the diameter parameter function. The output is a format file that can be directly imported into structural analysis software.

[0008] As one embodiment, the acquisition and preprocessing of internal temperature data and environmental data of steel-concrete composite members of different diameters includes: Multi-source temperature data is collected, and the internal temperature data of the bridge section is continuously collected through distributed temperature sensors. Simulated solar radiation test data of components with different diameters and long-term monitoring data under actual bridge conditions are obtained, including environmental parameters such as solar radiation, ambient temperature, wind speed, and humidity. The monitoring period is a set period. Data preprocessing involves removing outliers and smoothing the collected data to form a standardized temperature difference analysis sequence.

[0009] As one embodiment, the construction of the symmetric absolute value power function temperature gradient model includes: To standardize the expression of temperature gradients for components of different diameters, the vertical position is made dimensionless. A temperature gradient model is established using a continuous symmetric absolute power function:

[0010] in, T ( x ) is the location x Temperature difference at the location; T D This represents the maximum vertical temperature difference, i.e., the temperature difference between the radiated surface and the lowest temperature point. b The shape parameter determines the location of the lowest point of the temperature gradient curve; e The shape parameter determines the curvature and flatness of the middle section of the temperature gradient curve.

[0011] As one embodiment, the construction of the two-dimensional transient heat conduction finite element model includes: A two-dimensional axisymmetric heat conduction model is established, and its governing equations are as follows:

[0012] in, T The transient temperature of the object. t The time taken for the process is expressed in seconds (s).λ The thermal conductivity of the material ρ For the density of the material, c The specific heat capacity of the material; Taking into account the boundary conditions of solar shortwave radiation, natural air convection, and longwave radiation heat transfer, and using measured environmental data as boundary input, we conduct hourly transient analysis of typical high-temperature weather in summer.

[0013] As one embodiment, the step of introducing a dimensionless diameter parameter to establish a functional relationship between key parameters of the temperature gradient model and the component diameter includes: By introducing a dimensionless diameter parameter and combining finite element simulation with measured data fitting, a monotonic functional relationship between the position parameter, shape parameter, and diameter is established: , .

[0014] As one embodiment, the step of extracting the daily maximum vertical temperature difference sequence, screening extreme samples, fitting the data based on a generalized Pareto distribution, and calculating the maximum temperature difference over a 100-year return period includes: Extreme value statistical analysis was performed on the daily maximum vertical temperature difference. First, the over-threshold method was used to screen extreme samples, and then the optimal analysis threshold was determined by combining the mean residual function plot, Hill plot, and parameter stability plot. Subsequently, the generalized Pareto distribution was used to perform probability fitting on the out-of-limit samples, and the scale and shape parameters were estimated by the maximum likelihood method to calculate the maximum temperature difference value of the 100-year return period.

[0015] According to some embodiments, the present disclosure adopts the following technical solutions: A system for predicting the vertical temperature gradient of concrete-filled steel tubes, considering diameter parameters, includes: The data acquisition module is used to acquire internal temperature data and environmental data of steel-concrete composite members of different diameters; The data preprocessing module is used to preprocess the collected data; The temperature gradient modeling module is used to construct a symmetric absolute value power function temperature gradient model. It introduces a dimensionless diameter parameter to establish the functional relationship between the key parameters of the temperature gradient model and the component diameter. The finite element analysis module is used to construct a two-dimensional transient heat conduction finite element model, taking environmental data as boundary input. The extreme value analysis module is used to extract the daily maximum vertical temperature difference sequence, screen extreme samples and complete the fitting based on the generalized Pareto distribution, and calculate the maximum temperature difference in the 100-year return period. The prediction and calculation module is used to input the maximum temperature difference over a 100-year return period into the temperature gradient model and generate a vertical temperature distribution curve by combining it with the diameter parameter function. The results output module is used to output formatted files that can be directly imported into structural analysis software.

[0016] According to some embodiments, the present disclosure adopts the following technical solutions: A computer program product includes a computer program that, when executed by a processor, implements the method for predicting the vertical temperature gradient of steel-concrete composite tubes considering diameter parameters.

[0017] According to some embodiments, the present disclosure adopts the following technical solutions: A non-transitory computer-readable storage medium is provided for storing computer instructions, which, when executed by a processor, implement the method for predicting the vertical temperature gradient of steel-concrete composite tubes considering diameter parameters.

[0018] According to some embodiments, the present disclosure adopts the following technical solutions: An electronic device includes a processor, a memory, and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory to enable the electronic device to perform the method for predicting the vertical temperature gradient of steel-concrete composite considering diameter parameters.

[0019] Compared with the prior art, the beneficial effects of this disclosure are as follows: This disclosure presents a method for predicting the vertical temperature gradient of steel-concrete composite tubes considering diameter parameters, which achieves a unified expression of the temperature gradient of components with different diameters and improves the applicability of the model. By processing the vertical position dimensionlessly and constructing a symmetric absolute value power function model, the temperature distribution of the entire cross section is continuously described. The introduction of the diameter parameter function accurately reflects the influence of component size on temperature distribution, solving the problems of discontinuous segmentation and poor diameter adaptability of traditional models.

[0020] This disclosure presents a method for predicting the vertical temperature gradient of steel-concrete composite tubes considering diameter parameters. It integrates finite element analysis and extreme value statistics to improve the reliability of extreme temperature difference prediction. The method determines the functional form of the shape function based on a two-dimensional transient heat conduction finite element model and uses a generalized Pareto distribution to model the extreme values ​​of long-term monitoring data, thereby achieving reliable extrapolation of the maximum temperature difference over a 100-year return period. This method overcomes the shortcomings of traditional methods that rely on short-term data and underestimate temperature loads.

[0021] The disclosed method for predicting the vertical temperature gradient of steel-concrete composite tubes considering diameter parameters forms a complete prediction system with outstanding engineering application value: it constructs a complete technical system from data acquisition, preprocessing, modeling, finite element analysis, extreme value calculation to result output. The output curve can be directly used for bridge temperature effect analysis and safety assessment, and is suitable for all scenarios of design and operation and maintenance of large-span steel-concrete composite tube bridges, with significant engineering promotion value. Attached Figure Description

[0022] The accompanying drawings, which form part of this disclosure, are used to provide a further understanding of this disclosure. The illustrative embodiments of this disclosure and their descriptions are used to explain this disclosure and do not constitute an undue limitation of this disclosure.

[0023] Figure 1 This is a schematic diagram of the vertical temperature gradient distribution pattern of steel-concrete composite tubes according to an embodiment of this disclosure. in, Figure 1 (a) in the figure represents a three-segment temperature gradient. Figure 1 (b) in the figure represents a two-segment temperature gradient. Figure 1 (c) in the figure represents the gauge temperature gradient; Figure 2 This is a comparison chart of the temperature gradient model and measured data from the embodiments of this disclosure; in, Figure 2 (a) in the text represents the G1 model; Figure 2 (b) in the figure represents the G2 model; Figure 2 (c) in the text represents the G3 model; Figure 3 The figure shows the fitting result of the maximum temperature difference extreme value during the 100-year return period of this disclosure embodiment (G3 as an example); in, Figure 3 (a) in the graph is the probability density function. Figure 3 (b) in the image is a QQ image; Figure 4 This is a comparison chart of the prediction results of the embodiments of this disclosure with the standard model; Figure 5 This is a flowchart of the temperature gradient prediction method according to an embodiment of the present disclosure; Figure 6 This is a block diagram of the temperature gradient prediction system according to an embodiment of the present disclosure. Detailed Implementation

[0024] The present disclosure will be further described below with reference to the accompanying drawings and embodiments.

[0025] It should be noted that the following detailed descriptions are illustrative and intended to provide further explanation of this disclosure. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains.

[0026] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this disclosure. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms “comprising” and / or “including” are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0027] Example 1 One embodiment of this disclosure provides a method for predicting the vertical temperature gradient of steel-concrete composite tubes considering diameter parameters. The method includes the following steps: Step 1: Obtain internal temperature data and environmental data of concrete-filled steel tube components of different diameters, and preprocess them; Step 2: Construct a symmetric absolute value power function temperature gradient model, and introduce a dimensionless diameter parameter to establish the functional relationship between the key parameters of the temperature gradient model and the component diameter; Step 3: Construct a two-dimensional transient heat conduction finite element model, using environmental data as boundary input, extract the daily maximum vertical temperature difference sequence, screen extreme samples and complete the fitting based on the generalized Pareto distribution, and calculate the maximum temperature difference over a 100-year return period; Step 4: Input the maximum temperature difference over the 100-year return period into the temperature gradient model, combine it with the diameter parameter function to generate the vertical temperature distribution curve, and output a format file that can be directly imported into structural analysis software.

[0028] As one embodiment, this disclosure presents a method for predicting the vertical temperature gradient of concrete-filled steel tubular (CFST) considering the diameter parameter, using the main arch rib structure of a large-span CFST arch bridge as an engineering example. The temperature gradient prediction method proposed in this embodiment includes multiple steps: data acquisition, data preprocessing, temperature gradient modeling, finite element extended analysis, extreme value sample selection, probability distribution fitting, calculation of the maximum temperature difference over a 100-year return period, and temperature gradient output. The specific implementation process is as follows: Step 1: Obtain temperature data and synchronous environmental parameters for concrete-filled steel pipe components of different diameters, and complete data preprocessing.

[0029] To study the temperature distribution of steel-concrete composite members of different diameters under solar radiation, temperature sensors were installed at the top, 1 / 4, middle, 3 / 4 and bottom of the steel pipe cross-section at the bridge site. At the same time, a small weather station was set up near the bridge site to collect environmental parameters such as solar radiation intensity, ambient temperature, wind speed and air humidity. The data sampling interval was 30 minutes and the continuous monitoring period was 12 months.

[0030] Monitoring results show that the internal temperature distribution of steel-concrete composite members of different diameters varies significantly, with larger diameter members exhibiting more pronounced thermal hysteresis and a large vertical temperature difference between the top and the lowest internal temperature point. Under extreme summer heat, the maximum temperature difference between the top and the lowest internal temperature point of member G3 reached 21.6 ℃, significantly higher than the recommended value in current standards. This indicates that traditional standards are insufficient to accurately reflect the true temperature gradient distribution within large-diameter steel-concrete composite structures.

[0031] As one embodiment, this disclosure first conducts indoor simulated solar radiation tests on steel-concrete composite members of different diameters, and establishes a temperature database in conjunction with long-term monitoring data of actual bridges.

[0032] Indoor tests used a metal halide lamp array to simulate solar radiation, conducting continuous irradiation tests on steel-concrete composite specimens with diameters of 430 mm, 650 mm, and 1000 mm, respectively. The irradiation intensity was adjusted to simulate summer solar radiation conditions. Temperature measuring points were arranged vertically inside the specimens to record temperature changes in real time.

[0033] Meanwhile, a long-term monitoring system will be established at the actual bridge site to continuously collect internal temperature data of the bridge cross-section using distributed temperature sensors, and simultaneously acquire environmental parameters such as solar radiation, ambient temperature, wind speed, and air humidity. The monitoring period will be no less than one year.

[0034] Secondly, due to issues such as equipment noise, abnormal fluctuations, and short-term data gaps in long-term monitoring data, the collected data undergoes preprocessing, including: First, outliers were removed using the 3σ criterion, and the original data was smoothed using a moving average algorithm. Then, the difference between the daily highest and lowest temperatures was used as the daily maximum temperature difference index to form a standardized temperature difference analysis sequence.

[0035] Step 2: Construct a symmetric absolute value power function temperature gradient model, and introduce a dimensionless diameter parameter to establish the functional relationship between the key parameters of the temperature gradient model and the component diameter.

[0036] First, to standardize the expression of temperature gradients for components with different diameters, this embodiment renders the height position along the vertical direction (up and down direction) of the component dimensionless. The ratio of any height position along the vertical direction to the component diameter is defined as a normalized coordinate, with a value range limited to 0 to 1. The expression is as follows: x = D i / D in, D i It represents the vertical distance from the surface receiving solar radiation. D The diameter of the component, after normalization x ∈[0,1].

[0037] like Figure 1 As shown, traditional standards typically use two- or three-stage temperature gradient expressions, resulting in significant abrupt changes in temperature distribution that fail to accurately reflect the continuous heat transfer within the concrete-filled steel tube section. This disclosure employs a continuous symmetric absolute value power function to establish a temperature gradient model:

[0038] in, T(x) The temperature difference at location x (the difference relative to the lowest temperature point of the component); T D The maximum vertical temperature difference is the temperature difference between the radiated surface (upper surface) and the lowest temperature point. b The shape parameter determines the location of the lowest point (center of symmetry) of the temperature gradient curve; e The shape parameter determines the curvature and mid-section flatness of the temperature gradient curve. This model satisfies the boundary conditions. T(0) = T D , and when x=b hour T(b)=0 .

[0039] The model disclosed herein can achieve continuous temperature gradient changes and can automatically adjust the position of the lowest temperature point and the curve shape, which is more consistent with the actual heat transfer law inside steel-concrete composite.

[0040] Furthermore, steel-concrete composite specimens with inner diameters of 430 mm (G4 component), 650 mm (G3 component), and 1000 mm (G1 component) were fabricated for simulated solar radiation tests and long-term monitoring in the actual bridge environment. Nonlinear regression analysis was performed on the monitoring data. Based on the results of the linear regression analysis, it was found that: (1) As the diameter increases, the lowest temperature point gradually shifts towards the interior of the cross section; (2) The temperature gradient curve gradually changes from steep to gentle; (3) The traditional fixed segmentation model cannot reflect this change pattern.

[0041] Therefore, this disclosure introduces a dimensionless diameter parameter: D d = D / 1000 Furthermore, a functional relationship between the parameters and the diameter is established:

[0042]

[0043] The coefficients were determined by least squares fitting: m= 0.15867, n=1.93053, p=2.32697, q=1.37536 The functional relationship between parameters and diameter is a necessary prerequisite for subsequent extreme temperature difference prediction and temperature gradient output. It can ensure the accuracy of the temperature gradient curve shape under any diameter, provide a reliable basis for the prediction of the maximum vertical temperature difference in a century, and the final output can be directly used for the vertical temperature distribution in engineering design.

[0044] like Figure 2As shown, the temperature gradient model established in this disclosure agrees well with the measured results of components with different diameters, and can accurately reflect the temperature change patterns of the high-temperature zone at the top, the gradually changing zone in the middle, and the low-temperature zone at the bottom. The overall coefficient of determination of the model, R0, is [value missing]. 2 The value is greater than 0.99, which verifies the applicability of the model to concrete-filled steel tubular members of different scales.

[0045] Step 3: Construct a two-dimensional transient heat conduction finite element model and determine the functional form of the shape function.

[0046] A shape function is constructed to quantify the influence of diameter on the temperature gradient morphology, enabling the model to adapt to different diameters. This provides a reliable foundation for extreme temperature difference prediction, ensuring that the maximum vertical temperature difference over a century obtained based on the generalized Pareto distribution can accurately drive the temperature gradient model and output a realistic and reliable vertical temperature distribution.

[0047] Specifically, a two-dimensional axisymmetric heat conduction model is established, and its governing equations are as follows:

[0048] in, T The transient temperature of an object, expressed in °C. t The time taken for the process is expressed in seconds (s). λ Ω represents the thermal conductivity of the material, expressed in W / (m·℃). -1 , ρ The density of the material is expressed in g·m³. -3 , c This represents the specific heat capacity of the material, expressed in J / (kg·℃). -1 .

[0049] Furthermore, the model comprehensively considers the boundary conditions of solar shortwave radiation, natural air convection, and longwave radiation heat transfer, and uses measured meteorological data as boundary input to conduct hourly transient analysis of typical summer high-temperature weather. The boundary expression, comprehensively considering the boundary conditions of solar radiation, convective heat transfer, and longwave radiation, is as follows:

[0050] in, q n The total heat flux experienced by the steel-concrete composite tube. q s The heat flow generated by solar radiation. q c The heat flow generated by convective heat transfer q a The heat flux generated by long-wave radiation is expressed in W / m³. 2 .

[0051] Furthermore, , , .

[0052] The maximum error between the finite element simulation results and the measured results is less than 5%, verifying the reliability of the model. Based on this, batch simulations were carried out on multiple typical diameters ranging from 300 mm to 1500 mm to establish a complete temperature gradient parameter database, enabling rapid prediction at different engineering scales.

[0053] Step 4: Extract the maximum temperature difference sequence, screen extreme samples and complete the probability distribution fitting, and calculate the maximum temperature difference in the 100-year return period.

[0054] After obtaining long-term monitoring data, extreme value statistical analysis was performed on the daily maximum vertical temperature difference, including: First, an over-threshold method is used to screen extreme temperature difference samples: a temperature threshold is set, and only data exceeding this threshold in the daily maximum vertical temperature difference are retained as extreme samples, while regular small temperature difference data are removed to focus on extreme temperature events. Based on this, the optimal threshold is determined by combining three statistical graphs: (1) Mean residual function graph: Determine whether the sample mean tends to be linearly stable as the threshold increases; the starting point of the linear interval corresponds to the candidate threshold. (2) Parameter stability plot: Observe whether the shape parameter and scale parameter of the generalized Pareto distribution tend to be stable as the threshold changes. The threshold corresponding to the parameter stability interval is the effective candidate value. (3) Hill plot: Based on the stable segment of the tail index estimate as a function of the threshold, determine the threshold interval where the tail asymptoticity is most significant.

[0055] By combining the stable intervals of the three graphs and taking the overlapping intersection, a unique and reliable optimal analysis threshold is finally determined, ensuring that extreme samples have both statistical significance and can truly reflect extreme temperature conditions.

[0056] Subsequently, a generalized Pareto distribution was used to perform probability fitting on the out-of-limit samples, and its probability density function is:

[0057] in, x This indicates the maximum vertical temperature difference, expressed in degrees Celsius (°C). σ The scaling parameter is used to measure the degree of dispersion of the data. ξ The shape parameter (extreme index) determines the thickness of the tail of the distribution. μ The location parameter (threshold) is the critical temperature value for selecting extreme data, in °C.

[0058] The scale and shape parameters are estimated using the maximum likelihood method. Based on the requirements of highway bridge design specifications, the maximum vertical temperature difference that may occur within a 100-year return period is calculated. The calculation formula is as follows:

[0059] in, x T This is the predicted maximum vertical temperature difference that may occur within 100 years, in °C. μ The distribution threshold (°C) is used. σ For scale parameters, ξ For shape parameters, p This represents the probability of exceeding an extreme temperature range that occurs only once every 100 years. p=1 / (365× 100) .

[0060] like Figure 3 As shown, the samples exceeding the threshold can follow the generalized Pareto distribution well, and the sample points in the QQ plot are generally distributed near the reference line, indicating that the extreme value prediction model established in this disclosure has good statistical reliability and can be used to predict the maximum temperature difference in the 100-year return period.

[0061] Based on the 100-year design reference period requirement for bridges, the following calculations were performed: The maximum temperature difference during the 100-year return period of component G1 is 16.8 ℃; The maximum temperature difference during the 100-year return period of component G2 is 19.7 ℃; The maximum temperature difference during the 100-year return period of the G3 component is 23.4 ℃.

[0062] Analysis results indicate that large-diameter steel-concrete composite structures may experience temperature differences far exceeding the recommended values ​​in the specifications under extreme high-temperature conditions.

[0063] Step 5: Couple the temperature gradient model with the diameter parameter function. Map the component diameter to the shape parameter required by the temperature gradient model using a pre-established parameter function. Then, substitute the shape parameter and the extreme temperature difference amplitude into the unified temperature gradient model to generate a complete temperature distribution curve point by point along the vertical height. This coupling process achieves integrated calculation of diameter, shape, and temperature difference amplitude, ensuring that the model can output a physically reasonable, continuous, smooth, and engineering-usable vertical temperature distribution curve under extreme conditions.

[0064] The maximum temperature difference over a 100-year return period is substituted into the established temperature gradient model. The model parameters are automatically matched with the diameter of the target component to generate a complete vertical temperature distribution curve. The output is a format file that can be directly imported into structural analysis software for temperature stress calculation and structural safety assessment.

[0065] The final output displays the temperature gradient curve and the maximum temperature difference value, and can export data in TXT, Excel, and CAD compatible formats, supporting integration with bridge design software.

[0066] like Figure 4As shown, compared with the current standard temperature gradient model, the prediction results of this publication can more accurately reflect the gradual temperature change characteristics and the shift law of the lowest temperature point inside the steel tube concrete section. In particular, the prediction accuracy is higher in large-diameter components, which can effectively reduce the calculation error of temperature load.

[0067] Taking component G3 as an example, structural analysis using the temperature gradient predicted by this invention revealed the following: (1) The local additional stress of the arch rib is about 12% higher than the result calculated by the standard; (2) Temperature stress concentration is more pronounced in the anchorage zone of the hanger; (3) The identification of local fatigue-sensitive areas of the structure is more accurate.

[0068] Engineering verification shows that this disclosure can effectively improve the accuracy of temperature effect analysis for steel-concrete composite bridges.

[0069] In summary, the vertical temperature gradient prediction method and system for steel-concrete composite structures considering diameter parameters proposed in this embodiment can achieve a unified expression of temperature gradients for components of different diameters and accurately reflect the internal thermal hysteresis effect and the shift law of the lowest temperature point in large-diameter steel-concrete composite structures. Compared with the traditional segmented temperature gradient model in the standard, the continuous temperature gradient function established in this disclosure can effectively reduce the temperature distribution dispersion error and improve the accuracy of temperature load calculation. Simultaneously, this disclosure combines finite element heat conduction analysis with generalized Pareto extreme value theory to achieve prediction of the maximum temperature difference over a 100-year return period, effectively avoiding the problem of traditional short-term monitoring methods underestimating the risk of extreme temperature effects. Engineering application results show that this disclosure can more accurately assess the temperature stress distribution and local fatigue-sensitive areas of steel-concrete composite bridges, and has significant engineering application value for the design, construction monitoring, and long-term operational safety assessment of large-span steel-concrete composite bridges.

[0070] Example 2 One embodiment of this disclosure provides a vertical temperature gradient prediction system for concrete-filled steel tubes considering diameter parameters, comprising: The data acquisition module is used to acquire internal temperature data and environmental data of steel-concrete composite members of different diameters; The data preprocessing module is used to preprocess the collected data; The temperature gradient modeling module is used to construct a symmetric absolute value power function temperature gradient model. It introduces a dimensionless diameter parameter to establish the functional relationship between the key parameters of the temperature gradient model and the component diameter. The finite element analysis module is used to construct a two-dimensional transient heat conduction finite element model, taking environmental data as boundary input. The extreme value analysis module is used to extract the daily maximum vertical temperature difference sequence, screen extreme samples and complete the fitting based on the generalized Pareto distribution, and calculate the maximum temperature difference in the 100-year return period. The prediction and calculation module is used to input the maximum temperature difference over a 100-year return period into the temperature gradient model and generate a vertical temperature distribution curve by combining it with the diameter parameter function. The results output module is used to output formatted files that can be directly imported into structural analysis software.

[0071] Example 3 One embodiment of this disclosure provides a computer program product, including a computer program that, when executed by a processor, implements the method for predicting the vertical temperature gradient of steel-concrete composite tubes considering diameter parameters.

[0072] Example 4 One embodiment of this disclosure provides a non-transitory computer-readable storage medium for storing computer instructions, which, when executed by a processor, implement the method for predicting the vertical temperature gradient of steel-concrete composite tubes considering diameter parameters.

[0073] Example 5 One embodiment of this disclosure provides an electronic device, including a processor, a memory, and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory to enable the electronic device to perform the method for predicting the vertical temperature gradient of steel-concrete composite considering diameter parameters.

[0074] This disclosure is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0075] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0076] While the specific embodiments of this disclosure have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of this disclosure. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of this disclosure are still within the scope of protection of this disclosure.

Claims

1. A method for predicting the vertical temperature gradient of concrete-filled steel tubing considering diameter parameters, characterized in that, include: Acquire internal temperature data and environmental data of concrete-filled steel tubes of different diameters, and preprocess them; A symmetric absolute value power function temperature gradient model is constructed, and a dimensionless diameter parameter is introduced to establish the functional relationship between the key parameters of the temperature gradient model and the component diameter. A two-dimensional transient heat conduction finite element model was constructed, with environmental data as the boundary input. The daily maximum vertical temperature difference sequence was extracted, extreme samples were screened, and the model was fitted based on the generalized Pareto distribution. The maximum temperature difference over a 100-year return period was calculated. The maximum temperature difference over a 100-year return period is input into the temperature gradient model, and a vertical temperature distribution curve is generated by combining it with the diameter parameter function. The output is a format file that can be directly imported into structural analysis software.

2. The method for predicting the vertical temperature gradient of concrete-filled steel tubing considering diameter parameters as described in claim 1, characterized in that, The acquisition and preprocessing of internal temperature and environmental data of steel-concrete composite members of different diameters includes: Multi-source temperature data is collected, and the internal temperature data of the bridge section is continuously collected through distributed temperature sensors. Simulated solar radiation test data of components with different diameters and long-term monitoring data under actual bridge conditions are obtained, including environmental parameters such as solar radiation, ambient temperature, wind speed, and humidity. The monitoring period is a set period. Data preprocessing involves removing outliers and smoothing the collected data to form a standardized temperature difference analysis sequence.

3. The method for predicting the vertical temperature gradient of concrete-filled steel tubing considering diameter parameters as described in claim 1, characterized in that, The construction of the symmetric absolute power function temperature gradient model includes: To standardize the expression of temperature gradients for components of different diameters, the vertical position is made dimensionless. A temperature gradient model is established using a continuous symmetric absolute power function: in, T ( x ) is the location x Temperature difference at the location; T D This represents the maximum vertical temperature difference, i.e., the temperature difference between the radiated surface and the lowest temperature point. b The shape parameter determines the location of the lowest point of the temperature gradient curve; c The shape parameter determines the curvature and flatness of the middle section of the temperature gradient curve.

4. The method for predicting the vertical temperature gradient of concrete-filled steel tubing considering diameter parameters as described in claim 1, characterized in that, The construction of the two-dimensional transient heat conduction finite element model includes: A two-dimensional axisymmetric heat conduction model is established, and its governing equations are as follows: in, T The transient temperature of the object. t The time taken for the process is expressed in seconds (s). λ The thermal conductivity of the material ρ For the density of the material, c The specific heat capacity of the material; Taking into account the boundary conditions of solar shortwave radiation, natural air convection, and longwave radiation heat transfer, and using measured environmental data as boundary input, we conduct hourly transient analysis of typical high-temperature weather in summer.

5. The method for predicting the vertical temperature gradient of concrete-filled steel tubing considering diameter parameters as described in claim 1, characterized in that, The introduction of dimensionless diameter parameters to establish the functional relationship between key parameters of the temperature gradient model and the component diameter includes: By introducing a dimensionless diameter parameter and combining finite element simulation with measured data fitting, a monotonic functional relationship between the position parameter, shape parameter, and diameter is established: , .

6. The method for predicting the vertical temperature gradient of concrete-filled steel tubing considering diameter parameters as described in claim 1, characterized in that, The process of extracting the daily maximum vertical temperature difference sequence, screening extreme samples, fitting the data based on a generalized Pareto distribution, and calculating the maximum temperature difference over a 100-year return period includes: Extreme value statistical analysis was performed on the daily maximum vertical temperature difference. First, the over-threshold method was used to screen extreme samples, and then the optimal analysis threshold was determined by combining the mean residual function plot, Hill plot, and parameter stability plot. Subsequently, the generalized Pareto distribution was used to perform probability fitting on the out-of-limit samples, and the scale and shape parameters were estimated by the maximum likelihood method to calculate the maximum temperature difference value of the 100-year return period.

7. A vertical temperature gradient prediction system for concrete-filled steel tubes considering diameter parameters, characterized in that, include: The data acquisition module is used to acquire internal temperature data and environmental data of steel-concrete composite members of different diameters; The data preprocessing module is used to preprocess the collected data; The temperature gradient modeling module is used to construct a symmetric absolute value power function temperature gradient model. It introduces a dimensionless diameter parameter to establish the functional relationship between the key parameters of the temperature gradient model and the component diameter. The finite element analysis module is used to construct a two-dimensional transient heat conduction finite element model, taking environmental data as boundary input. The extreme value analysis module is used to extract the daily maximum vertical temperature difference sequence, screen extreme samples and complete the fitting based on the generalized Pareto distribution, and calculate the maximum temperature difference in the 100-year return period. The prediction and calculation module is used to input the maximum temperature difference over a 100-year return period into the temperature gradient model and generate a vertical temperature distribution curve by combining it with the diameter parameter function. The results output module is used to output formatted files that can be directly imported into structural analysis software.

8. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the method for predicting the vertical temperature gradient of steel-concrete composite tubes considering diameter parameters as described in any one of claims 1-6.

9. A non-transitory computer-readable storage medium, characterized in that, The non-transitory computer-readable storage medium is used to store computer instructions, which, when executed by a processor, implement the method for predicting the vertical temperature gradient of steel-concrete composite considering diameter parameters as described in any one of claims 1-6.

10. An electronic device, characterized in that, include: The device includes a processor, a memory, and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory to enable the electronic device to perform the method for predicting the vertical temperature gradient of steel-concrete composite considering diameter parameters as described in any one of claims 1-6.

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