A prestress guarantee rate acquisition method and system considering prestress fluctuation

Abstract

The application provides a prestress guarantee rate obtaining method and system considering prestress fluctuation, comprising the following steps: S1: establishing a physical mapping relationship between temperature change and prestress fluctuation of the prestressed reinforcement; S2: constructing a double-branch physical information neural network, and predicting the temperature change distribution of the prestressed reinforcement at different depths in the bonded prestressed concrete structure based on historical meteorological data of the location of the bonded prestressed concrete structure; S3: determining the probability distribution characteristics of the prestress fluctuation of the prestressed reinforcement according to the physical mapping relationship and the temperature change distribution; S4: constructing a prestress loss reference curve, and coupling the probability distribution characteristics with the prestress loss reference curve to obtain the probability distribution of the actual prestress at any time; and S5: setting a prestress safety threshold, calculating the time proportion of the actual prestress value being higher than the prestress safety threshold within a target evaluation period based on the probability distribution, and defining the time proportion as the prestress guarantee rate considering the prestress fluctuation.

Description

Technical Field

[0001] This invention relates to the technical field of civil engineering, specifically to a method and system for obtaining the prestress guarantee rate considering prestress fluctuations. Background Technology

[0002] As large prestressed concrete structures (such as nuclear power plant containment structures, long-span bridges, and storage tanks) develop towards longer service life and higher reliability, higher requirements are placed on the assessment of the prestress state of these structures during complex service periods. Prestressing is a core element in ensuring the safety and durability of these engineering structures, and accurately predicting its evolution in real natural environments is a key foundation for structural safety early warning, crack resistance calculation, and full life cycle assessment.

[0003] Currently, methods for predicting prestress loss mainly rely on the long-term macroscopic degradation mechanism of materials, typically employing a superposition calculation by introducing a classic concrete shrinkage and creep model and a steel strand relaxation model. These conventional methods can effectively reflect the long-term macroscopic attenuation trend of structural prestress over service time. However, existing prediction models generally output a smooth prestress loss curve, severely neglecting the dynamic coupling effect of unavoidable time-varying temperature patterns in the natural environment on the prestress state of the structure. In reality, due to the inherent difference in the coefficients of thermal expansion between steel strands and concrete, when prestressed concrete structures are exposed to real environmental temperature fields with significant periodic fluctuations (e.g., monthly, daily, and hourly scales), the two cannot synchronously undergo thermal expansion and contraction deformation. This incoordination in deformation directly leads to temperature-driven additional stress fluctuations within the steel strands.

[0004] This temperature coupling effect causes the actual prestress evolution curve to not decay smoothly, but rather exhibit frequent oscillations around a smooth baseline. Under the traditional structural safety assessment framework, for a given prestress safety threshold... If the predicted smooth loss curve consistently exceeds this threshold, the structure is generally considered to be in a safe state. However, after incorporating stress fluctuations caused by temperature, the actual effective prestress of the structure may momentarily drop during certain temperature drop periods, thereby breaching the safety threshold. This poses hidden and highly destructive security risks. Traditional deterministic assessment methods based on smooth curves mask the extreme risks brought about by such dynamic fluctuations.

[0005] Therefore, it is necessary to develop a prestress calculation method that considers real-world environmental fluctuations. This method should not only accurately quantify these additional fluctuations through physical mechanisms but also overcome the limitations of relying on single extreme value judgments. It should also quantify the percentage of time within a specific period where the actual prestress exceeds the safety threshold. This has significant engineering application value for improving the structural mechanical response analysis under multi-physics conditions and providing a scientific "prestress guarantee rate" indicator. Summary of the Invention

[0006] This invention is made to solve the above-mentioned problems, and aims to provide a method and system for obtaining the prestress guarantee rate considering prestress fluctuation.

[0007] This invention provides a method for obtaining the prestress guarantee rate considering prestress fluctuation, characterized by the following steps: S1: Physical mapping relationship establishment step, based on the bond strain coordination mechanism between prestressing tendons and concrete in bonded prestressed concrete structures, establishing a physical mapping relationship between the temperature change of prestressing tendons and prestress fluctuation; S2: Temperature change distribution prediction step, constructing a two-branch physical information neural network, and based on historical meteorological data of the location of the bonded prestressed concrete structure, predicting the temperature change distribution of prestressing tendons at different depths in the bonded prestressed concrete structure; S3: Probability distribution feature determination step, based on the physical mapping relationship and temperature change... S4: Obtaining the actual prestress probability distribution step. Based on the initial tensioning temperature, initial tensioning stress, arrangement of the prestressing tendons, and the preset concrete shrinkage and creep model and prestressing tendon relaxation model, a prestress loss baseline curve is constructed, and the probability distribution characteristics are coupled with the prestress loss baseline curve to obtain the probability distribution of the actual prestress at any time. S5: Calculating the prestress guarantee rate step. A prestress safety threshold is set, and based on the probability distribution, the proportion of time during which the actual prestress value is higher than the prestress safety threshold within the target evaluation period is calculated. The proportion of time is defined as the prestress guarantee rate considering prestress fluctuation.

[0008] The method for obtaining the prestress guarantee rate considering prestress fluctuation provided by this invention may also have the following feature: wherein the physical mapping relationship in S1 is expressed as:

[0009]

[0010] In the formula, For prestress fluctuation, The elastic modulus of prestressed tendons, This is the coefficient of thermal expansion of concrete. The coefficient of thermal expansion of the prestressing tendon is... This represents the temperature change at the location of the prestressing tendons.

[0011] The method for obtaining the prestress guarantee rate considering prestress fluctuation provided by the present invention may also have the following features: wherein, the dual-branch physical information neural network in S2 includes: a fully connected backbone feedforward neural network for reconstructing the global spatiotemporal temperature field, and an auxiliary feedforward neural network for inferring the time-varying boundary heat transfer coefficient.

[0012] The method for obtaining the prestress guarantee rate considering prestress fluctuation provided by the present invention may also have the following features: wherein the training of the dual-branch physical information neural network in S2 adopts a composite loss function, which includes: data fidelity loss, physical equation residual loss and energy conservation boundary residual loss.

[0013] The method for obtaining the prestress guarantee rate considering prestress fluctuations provided by this invention may also have the following feature: wherein the temperature change distribution in S2 follows a Gaussian mixture distribution, and the probability density function of the temperature change distribution is... for:

[0014]

[0015] In the formula, The number of sub-distributions in a Gaussian mixture distribution. As weight, These are the mean and variance of the corresponding normal distribution, respectively.

[0016] The method for obtaining the prestress guarantee rate considering prestress fluctuation provided by this invention may also have the following feature: wherein the probability distribution characteristic of prestress fluctuation in S3 is: the prestress fluctuation follows a Gaussian mixture distribution, and the probability density function of the prestress fluctuation is:

[0017]

[0018] In the formula, The number of sub-distributions in a Gaussian mixture distribution. As weight, The mean is variance is It follows a normal distribution.

[0019] The method for obtaining the prestress guarantee rate considering prestress fluctuations provided by this invention may also have the following feature: wherein the method for obtaining the probability distribution of the actual prestress at any time in S4 is as follows:

[0020] Based on the initial tensioning temperature Prestress fluctuation after temperature change It follows a Gaussian mixture distribution, expressed as:

[0021]

[0022] In the formula, The mean is variance is It follows a normal distribution.

[0023] Based on the initial tensile stress, the arrangement of prestressing tendons, and the pre-set concrete shrinkage and creep model and prestressing tendon relaxation model, a prestress loss baseline curve is constructed. ,

[0024] Prestress loss baseline curve With prestress fluctuation By superimposing the values, the actual value of the prestress at any given time can be obtained. Thus, the probability distribution of the actual prestress at any given time is obtained, expressed as:

[0025] .

[0026] The method for obtaining the prestress guarantee rate considering prestress fluctuation provided by the present invention may also have the following feature: wherein, in S5, the prestress guarantee rate The calculation formula is:

[0027]

[0028] In the formula, The set prestressed safety threshold, The actual value of prestress at any given time. The temperature at the location of the prestressing tendon; For time.

[0029] This invention also provides a prestress guarantee rate acquisition system considering prestress fluctuations, characterized by: a physical mapping relationship establishment module, which establishes a physical mapping relationship between the temperature change of the prestressing tendons and the prestress fluctuations based on the bond strain coordination mechanism between the prestressing tendons and concrete in bonded prestressed concrete structures; a temperature change distribution prediction module, which constructs a two-branch physical information neural network and predicts the temperature change distribution of the prestressing tendons at different depths in the bonded prestressed concrete structure based on historical meteorological data of the location of the bonded prestressed concrete structure; and a probability distribution feature determination module, which determines the probability distribution feature based on the physical mapping relationship and the temperature change distribution. The module determines the probability distribution characteristics of prestress fluctuation in prestressed tendons; the actual prestress probability distribution acquisition module constructs a prestress loss baseline curve based on the initial tensioning temperature, initial tensioning stress, arrangement of prestressed tendons, and preset concrete shrinkage and creep models and prestressed tendon relaxation models, and couples the probability distribution characteristics with the prestress loss baseline curve to obtain the probability distribution of actual prestress at any time; the prestress guarantee rate calculation module sets a prestress safety threshold, and calculates the proportion of time during which the actual prestress value is higher than the prestress safety threshold within the target evaluation period based on the probability distribution, defining the time proportion as the prestress guarantee rate considering prestress fluctuation.

[0030] Compared with the prior art, the present invention has the following advantages:

[0031] This invention incorporates the prestress fluctuations caused by temperature changes in bonded prestressed concrete structures into the prestress guarantee rate evaluation system. By establishing a physical mapping between temperature and prestress fluctuations, accurately predicting the temperature change distribution, using a Gaussian mixture distribution to describe the probability distribution characteristics, and defining a prestress guarantee rate index, it achieves a comprehensive quantitative assessment of structural safety from both probabilistic and temporal dimensions. This invention overcomes the shortcomings of traditional methods, such as only outputting smooth loss curves, ignoring temperature-coupled fluctuations, and masking instantaneous risks. It comprehensively reflects the impact of various factors, including climatic conditions, construction temperature, and design parameters, on the long-term safety of bonded prestressed concrete structures, providing a reliable scientific basis for the crack-resistant design, life-cycle assessment, and construction optimization of such structures. Attached Figure Description

[0032] Figure 1 This is a schematic diagram illustrating the principle of the coupling effect between the temperature change of the prestressing tendon and the prestress fluctuation in an embodiment of the present invention.

[0033] Figure 2 This is a flowchart of a method for obtaining the prestress guarantee rate considering prestress fluctuations in an embodiment of the present invention.

[0034] Figure 3 This is a comparison chart of the calculated and measured values ​​of the prestress fluctuation process of the steel strand at different time periods in an embodiment of the present invention.

[0035] Figure 4 This is a schematic diagram of the structure of a dual-branch physical information neural network in an embodiment of the present invention.

[0036] Figure 5 This is a schematic diagram of the first training result of the dual-branch physical information neural network in an embodiment of the present invention.

[0037] Figure 6 This is a schematic diagram of the second training result of the dual-branch physical information neural network in an embodiment of the present invention.

[0038] Figure 7 This is a schematic diagram of the third training result of the dual-branch physical information neural network in an embodiment of the present invention.

[0039] Figure 8 This is a schematic diagram of the fourth training result of the dual-branch physical information neural network in an embodiment of the present invention.

[0040] Figure 9 This is a schematic diagram of the fifth training result of the dual-branch physical information neural network in an embodiment of the present invention.

[0041] Figure 10 This is a temperature distribution diagram of steel strands at different depths in the test site, as shown in an embodiment of the present invention.

[0042] Figure 11 This is a temperature variation distribution diagram of steel strands at different geographical locations at the same depth (120mm) in an embodiment of the present invention.

[0043] Figure 12 This is a graph showing the change of prestress guarantee rate over time in different geographical locations according to an embodiment of the present invention.

[0044] Figure 13 This is a graph showing the change in prestress guarantee rate over time for different initial tensioning temperatures in an embodiment of the present invention.

[0045] Figure 14 This is a graph showing the change in prestress guarantee rate over time for different burial depths in an embodiment of the present invention.

[0046] Figure 15 This is a graph showing the change of prestress guarantee rate over time for different initial tension stresses in embodiments of the present invention. Detailed Implementation

[0047] To make the technical means, creative features, objectives and effects of the present invention easy to understand, the following embodiments, in conjunction with the accompanying drawings, specifically illustrate the method and system for obtaining the prestress guarantee rate considering prestress fluctuations.

[0048] Figure 1This is a schematic diagram illustrating the coupling effect between temperature change and prestress fluctuation in prestressing tendons in an embodiment of the present invention. In the diagram, TFOT represents the time-varying operating temperature of the prestressing tendon, TC represents the constant temperature change trend of the prestressing tendon, and the theoretical prestress loss curve PLC (i.e., the smoothed prestress loss curve predicted by traditional methods) only reflects the long-term macroscopic attenuation trend, ignoring the additional stress fluctuations caused by periodic changes in ambient temperature. The actual prestress curve is the result of superimposing temperature fluctuations onto the theoretical loss curve. This is the time required for a bonded prestressed concrete structure to reach a set prestress safety threshold, without considering temperature changes. This refers to the time it takes for a bonded prestressed concrete structure to reach a set prestress safety threshold, taking temperature variations into account.

[0049] First, the principle of this embodiment will be explained. For example... Figure 1 As shown, during the service life of prestressed tendons, temperature changes cause the actual prestress to oscillate at high frequencies around the theoretical loss curve, thereby causing bonded prestressed concrete structures to reach the set prestress safety threshold earlier. < ).

[0050] Therefore, accurately quantifying the impact of temperature changes on prestress changes is key to scientifically assessing the long-term safety of bonded two-way prestressed concrete structures.

[0051] This embodiment takes a bonded biaxial prestressed concrete structure located at a test site in Chengdu, Sichuan Province as an example. Thermocouples are embedded at different depths of the structure to simultaneously monitor various factors such as ambient temperature, solar irradiance, and prestress in the prestressing tendons of the concrete structure. This example illustrates the practical application of the prestress guarantee rate acquisition method considering prestress fluctuations provided in this embodiment, including the following steps:

[0052] Figure 2 This is a flowchart of a method for obtaining the prestress guarantee rate considering prestress fluctuations in an embodiment of the present invention.

[0053] like Figure 2 As shown, step S1 is the physical mapping relationship establishment step. Based on the bond strain coordination mechanism between prestressed tendons and concrete in bonded prestressed concrete structures, and according to the temperature stress calculation formula, the prestress fluctuation calculation formula for temperature changes of prestressed tendons (in this embodiment, prestressed tendons are steel strands) is derived, and the physical mapping relationship between the temperature change of prestressed tendons and prestress fluctuation is established, specifically as follows:

[0054] Based on bonded prestressed concrete structures, assuming the coefficient of thermal expansion of concrete is... The coefficient of thermal expansion of steel strand is The temperature change at the location of the steel strand is The elastic modulus of the steel strand is Temperature strain of concrete Temperature strain of steel strand They are respectively:

[0055]

[0056]

[0057] The coefficient of thermal expansion of steel strand is typically greater than that of concrete, leading to... Due to the bond between the steel strand and the concrete, the additional deformation of the steel strand is constrained, and the actual strain of the steel strand equals the strain of the concrete. Therefore, the actual stress change of the steel strand, i.e., the prestress fluctuation, is:

[0058]

[0059] This established a physical mapping relationship between the temperature change of the steel strand and the prestress fluctuation.

[0060] Figure 3 This is a comparison chart of the calculated and measured values ​​of the prestress fluctuation process of the steel strand at different time periods in an embodiment of the present invention.

[0061] In this embodiment, the prestress fluctuation is calculated according to Equation 1.3 using the measured temperature change process of the steel strand. Wherein, the coefficient of thermal expansion of concrete... 1.02×10 -5 / ℃, coefficient of thermal expansion of steel strand 1.25×10 -5 The temperature was / ℃, and the elastic modulus of the steel strand was 20.2 GPa. The prestress fluctuation process of different steel strands at different time periods was calculated and compared with the measured prestress fluctuation process. The comparison results are as follows: Figure 3 As shown, the calculated value of the formula agrees well with the measured value, indicating the effectiveness of the formula.

[0062] Step S2 is the temperature change distribution prediction step. A high-precision dual-branch physical information neural network is constructed, and based on historical meteorological data of the location of the bonded prestressed concrete structure, the temperature change distribution of the prestressing tendons at different depths in the bonded prestressed concrete structure is accurately predicted. Specifically:

[0063] Figure 4 This is a schematic diagram of the structure of a dual-branch physical information neural network in an embodiment of the present invention.

[0064] like Figure 4As shown, a high-precision dual-branch-Periodic-PINN neural network is constructed. This architecture consists of a fully connected feedforward neural network with a backbone for reconstructing the global spatiotemporal temperature field. A feedforward neural network for inferring time-varying boundary heat transfer coefficients constitute. Composed of four hidden layers and one output layer, to avoid the "spectral bias" problem that occurs when the multilayer perceptron learns high-frequency signals, when the input includes measured temperature data at different depths of bonded prestressed concrete structures, ambient temperature, and solar irradiance data, the high-frequency features (daily cycle) and low-frequency features (annual cycle) of these variables with respect to time are first extracted. Each hidden layer contains 64 neurons, with fully connected interlayer connections, and the hyperbolic tangent function (tanh) is used as the activation function. It consists of two hidden layers and one output layer, with 16 neurons in each hidden layer, and tanh is used as the activation function.

[0065] The input parameters for training include time information. Depth information Solar irradiance Ambient temperature Internal temperature Setting the data fidelity loss Physical equation residual loss and energy conservation boundary residual loss (loss due to upper / outer surface boundary) and lower surface / inner surface boundary loss The composite loss function consists of three parts. The calculation process is as follows:

[0066]

[0067]

[0068]

[0069]

[0070]

[0071]

[0072]

[0073] In the formula, , These are the weights of the corresponding loss function. These represent the number of spatiotemporal points in the corresponding backbone feedforward neural network. Represent A dual-branch physical information neural network is used to predict and measure temperature. Let be the thermal diffusivity of concrete, and k be the thermal conductivity. The number of boundary points. , They are respectively The thermal convection density of the upper and lower surfaces of the concrete slab at any given time. , These are the heat transfer coefficients of the upper and lower surfaces, respectively, output by the auxiliary feedforward neural network. To determine the nighttime long-wave radiation heat dissipation intensity of bonded prestressed concrete structures, denoted as the solar radiation absorption coefficient, and L is the thickness of the bonded prestressed concrete structure.

[0074] The training epochs for Dual-Branch-Periodic-PINN are configured as follows: a total of 20,000 training epochs. The first 2,000 epochs are purely data-driven, without considering the weights of the physics equations. From epoch 2,000 to 5,000, the weights of the physics equations are slowly and linearly increased to 10.0, allowing the physical laws to begin exerting their constraint effect. The training data is randomly sampled and divided into training and test sets with a size ratio of 8:2.

[0075] Based on the aforementioned Dual-Branch-Periodic-PINN, the network was trained using measured ambient temperature, solar irradiance, and thermocouple measurements at different depths in bonded prestressed concrete structures. After training, if the coefficients of determination... A value greater than 0.95 indicates high model accuracy; otherwise, the training epochs need to be readjusted or the network structure (including the number of hidden layers and nodes) needs to be optimized. The model outputs an evaluation report, showing the changes of each loss function over the training epochs and its prediction performance on the test set.

[0076] Figure 5 This is a schematic diagram of the first training result of the dual-branch physical information neural network in an embodiment of the present invention. Figure 6 This is a schematic diagram of the second training result of the dual-branch physical information neural network in an embodiment of the present invention. Figure 7 This is a schematic diagram of the third training result of the dual-branch physical information neural network in an embodiment of the present invention. Figure 8This is a schematic diagram of the fourth training result of the dual-branch physical information neural network in an embodiment of the present invention. Figure 9 This is a schematic diagram of the fifth training result of the dual-branch physical information neural network in an embodiment of the present invention.

[0077] The training results of this embodiment are as follows: Figures 5-9 As shown, the changes of different loss functions during the training convergence process are illustrated. Each loss function exhibits a stable decreasing trend with increasing training epochs, indicating that the model can fit the data well, learn physical laws, and satisfy boundary constraints. The validation set mean squared error (Val MSE) decreases synchronously with the training loss and has similar values, proving that the model has good generalization ability and there is no overfitting phenomenon. Table 1 shows the temperature prediction effect and evaluation index of Dual-Branch-Periodic-PINN at different depths in bonded prestressed concrete structures.

[0078]

[0079] Table 1 shows the temperature prediction performance and evaluation metrics of Dual-Branch-Periodic-PINN at different depths in bonded prestressed concrete structures. The evaluation metrics include root mean square error (RMSE), mean absolute error (MAE), coefficient of determination (R²), and 95% error. At depths of 40 mm and 400 mm, R² is greater than 0.95, indicating that the model's prediction accuracy meets the requirements for high precision. At depths of 80 mm and 300 mm, R² is 0.9428 and 0.9414, respectively, close to 0.95. Overall, the model performs better at shallow (40 mm) and deep (400 mm) depths, while accuracy slightly decreases at intermediate depths (80 mm, 300 mm). Adjusting the network structure (e.g., adding hidden layers or neurons), increasing the number of training iterations, or optimizing the physical constraint weights can further improve the prediction accuracy at intermediate depths.

[0080] Based on the obtained high-precision Dual-Branch-Periodic-PINN, and using historical meteorological data (geographical accuracy of meteorological data <0.1°) of the location of the bonded prestressed concrete structure, the temperature variation distribution of steel strands at different depths in the bonded prestressed concrete structure is predicted.

[0081] Theoretically, the predicted temperature The variation distribution should follow a Gaussian mixture distribution, that is:

[0082]

[0083] In the formula, The number of sub-distributions in a Gaussian mixture distribution. As weight, The mean is variance is It follows a normal distribution.

[0084] The probability density function of the temperature change distribution is:

[0085]

[0086] Figure 10 This is a temperature distribution diagram of steel strands at different depths in the test site, as shown in an embodiment of the present invention. Figure 11 This is a temperature variation distribution diagram of steel strands at different geographical locations at the same depth (120mm) in an embodiment of the present invention.

[0087] In this embodiment, the temperature of steel strands at different depths (120mm, 205mm, 295mm) at the location of the bonded prestressed concrete structure (30.6°N, 103.8°E) was predicted. The predicted meteorological data spanned from January 1, 2001 to January 1, 2026, a total of 25 years. Historical climate data for the same 25 years were also selected from the locations of nuclear power unit A (21.9°N), which has a subtropical humid climate but a lower latitude, and nuclear power unit B (38.9°N), which has a temperate continental climate but a higher latitude. The high-precision Dual-Branch-Periodic-PINN was used to predict the temperature of the steel strands in the above situations, and statistical analysis was performed based on the Gaussian mixture distribution to obtain the temperature distribution of steel strands at different depths at the test site, as shown in Table 2. The temperature distribution of steel strands at the same depth (120mm) but different geographical locations was calculated in the same way, and the results are shown in Table 3. The temperature distribution of the steel strands under all operating conditions shown in Tables 2 and 3 follows a second-order Gaussian mixture distribution. These are the means of the first and second normal distributions that make up this Gaussian mixture distribution, respectively. Let be the variances of the first and second normal distributions that make up this Gaussian mixture distribution, respectively. These are the weights of the first and second normal distributions that make up the Gaussian mixture distribution, as shown in Table 2. and These represent the probabilities of the temperature exceeding the maximum value and falling below the minimum value, respectively.

[0088] Table 2 shows the temperature change distribution. Figure 10 As shown in Table 3, the temperature variation distribution is as follows: Figure 11 As shown.

[0089]

[0090]

[0091] Step S3 is the probability distribution characteristic determination step. Based on the physical mapping relationship and temperature change distribution, the probability distribution characteristics of the prestress fluctuation of the prestressing tendon are determined, specifically as follows:

[0092] Based on the formula for prestress fluctuation with temperature obtained from S1, since prestress fluctuation is a linear transformation of temperature change, it can be deduced that prestress fluctuation also follows a Gaussian mixture distribution, and the probability density function of prestress fluctuation is:

[0093]

[0094] In the formula, The mean is variance is It follows a normal distribution.

[0095] Step S4 is the step of obtaining the actual prestress probability distribution. Based on the initial tensioning temperature, initial tensioning stress, and arrangement of the prestressing tendons, as well as the preset concrete shrinkage and creep model and prestressing tendon relaxation model, a prestress loss baseline curve is constructed. The probability distribution characteristics are then coupled with the prestress loss baseline curve to obtain the probability distribution of the actual prestress at any given time. Specifically:

[0096] Based on the initial tensioning temperature Prestress fluctuation after temperature change It follows a Gaussian mixture distribution, expressed as:

[0097]

[0098] In the formula, The mean is variance is It follows a normal distribution.

[0099] Based on the initial tensile stress, the arrangement of the steel strands, and the pre-set concrete shrinkage and creep model and prestressing tendon relaxation model (the concrete shrinkage and creep model and the prestressing tendon relaxation model can be appropriately selected according to the actual structural conditions), a prestress loss baseline curve is constructed. .

[0100] Prestress loss baseline curve With prestress fluctuation By superimposing the values, the actual value of the prestress at any given time can be obtained. Thus, the probability distribution of the actual prestress at any given time is obtained, expressed as:

[0101]

[0102] Step S5 is the prestress guarantee rate calculation step. A prestress safety threshold is set. Based on probability distribution, the percentage of time within the target evaluation period where the actual prestress value is higher than the prestress safety threshold is calculated. This percentage is defined as the prestress guarantee rate considering prestress fluctuations. Specifically:

[0103] because It also applies to the temperature of the steel strand. The linear transformation of the prestress also results in the actual value of the prestress following a Gaussian mixture distribution. When the prestress safety threshold of a bonded prestressed concrete structure is taken as MRV, the prestress guarantee rate considering prestress fluctuations is then calculated. The calculation method is as follows:

[0104]

[0105] In the formula, The set prestressed safety threshold, The actual value of prestress at any given time. The temperature at the location of the steel strand; For time.

[0106] The value is related to the initial tensioning temperature of the steel strand. Initial tensile stress It is related to multiple factors such as temperature distribution. In this embodiment, specific examples are used to demonstrate the application effect of this indicator in different engineering scenarios.

[0107] Figure 12 This is a graph showing the change of prestress guarantee rate over time in different geographical locations according to an embodiment of the present invention.

[0108] (1) Assess the safety of bonded prestressed concrete structures in different climatic environments: Set the MRV to 0.82 The prestress guarantee rate varies over time for three different geographical locations: (820MPa), initial tensile stress of 1000MPa, and initial tensile temperature of 15℃. Figure 12As shown in the figure, without considering the impact of prestress fluctuations, the actual prestress value falls below the set MRV value in year 62.7, indicating an unsafe situation. When considering prestress fluctuations caused by temperature changes, the prestress guarantee rate begins to decline earlier in all three geographical locations, indicating that prestress fluctuations due to temperature changes cause the actual prestress value to fall below the MRV value earlier. Among them, nuclear power unit B, with the highest latitude and the largest annual temperature variation, experiences more significant prestress fluctuations due to temperature changes, thus its prestress guarantee rate begins to decline as early as year 38.2. In contrast, nuclear power unit A, with a lower latitude and smaller annual temperature difference, still maintains a prestress guarantee rate of 58.1% after 80 years of service. This indicator can quantitatively assess the impact of different climatic environments on the long-term safety of bonded prestressed concrete structures, providing a basis for the site selection and differentiated maintenance of bonded prestressed concrete structures.

[0109] Figure 13 This is a graph showing the change in prestress guarantee rate over time for different initial tensioning temperatures in an embodiment of the present invention.

[0110] (2) Guiding the selection of construction temperature: For steel strands buried at a test site depth of 120 mm, under the conditions of initial tensioning temperatures of 25℃, 15℃, and 5℃, the MRV was set to 0.82 (820 MPa), the initial tensioning stress was 1000 MPa, and the prestress guarantee rate changed with time as follows: Figure 13 As shown in the figure, when the initial tensioning temperature is 25℃, the prestress guarantee rate begins to decrease at 32.5 years, reaching a remaining guarantee rate of 1% at the 80-year service life; when the initial tensioning temperature is 5℃, the prestress guarantee rate begins to decrease at 48.4 years, reaching a remaining guarantee rate of 55.9% at the 80-year service life. These results indicate that the lower the initial tensioning temperature, the later the decline in the prestress guarantee rate begins, and the higher the remaining guarantee rate at the end of the service life. This index can quantify the impact of construction temperature on structural safety and provide a reference for determining a reasonable initial tensioning temperature.

[0111] Figure 14 This is a graph showing the change in prestress guarantee rate over time for different burial depths in an embodiment of the present invention.

[0112] (3) Verification of robustness of indicators at different burial depths: For the steel strands at the test site, under the conditions of burial depths of 120 mm, 205 mm, and 295 mm, the initial tensioning temperature was set to 15℃, the MRV to be 0.82 (820 MPa), and the initial tensioning stress to be 1000 MPa. The relationship between the burial depth of the steel strands and the prestress guarantee rate is as follows: Figure 14As shown, the prestress guarantee rate exhibits very similar variation patterns at the three depths, all beginning to decline between the 39th and 40th years, with the remaining prestress guarantee rate remaining at 20%–22% after the 80th year. These results indicate that within the investigated burial depth range, the burial depth has no significant impact on the variation pattern of the prestress guarantee rate, demonstrating good robustness of this index.

[0113] Figure 15 This is a graph showing the change of prestress guarantee rate over time for different initial tension stresses in embodiments of the present invention.

[0114] (4) Optimization of initial tension stress design: For steel strands buried at a test site depth of 120 mm, under initial tension stresses of 800 MPa, 1000 MPa, 1200 MPa, and 1400 MPa, the initial tensioning temperature was set at 15℃, and the MRV was 0.82 (820 MPa). The effect of initial tension stress on the guarantee rate is as follows: Figure 15 As shown, the effect of initial tension stress on the guarantee rate is non-linear: when the initial tension stress is low (800 MPa), the prestressing guarantee rate begins to decrease at age 20.2 and reaches 0 around age 50; when the initial tension stress is high (1400 MPa), the prestressing guarantee rate begins to decrease at age 36.6 and reaches 0 around age 65; when the initial tension stress is appropriately selected (1000 MPa, 1200 MPa), the time for the prestressing guarantee rate to begin to decrease can be extended by nearly 30 years, and a guarantee rate of over 47% can still be maintained at the end of the 80-year service life. These results indicate that there exists an optimal range of initial tension stress within which the prestressing guarantee rate decays most slowly, and bonded prestressed concrete structures maintain higher reliability over a long service life. This indicator can guide the optimized design of the initial tension stress of prestressing tendons, balancing construction costs and long-term safety.

[0115] This embodiment also provides a prestress guarantee rate acquisition system that takes into account prestress fluctuations, including:

[0116] The physical mapping relationship establishment module is used to implement step S1, namely: based on the bond strain coordination mechanism between prestressing tendons and concrete in bonded prestressed concrete structures, to establish the physical mapping relationship between the temperature change of prestressing tendons and the prestress fluctuation.

[0117] The temperature change distribution prediction module is used to implement step S2, namely: constructing a two-branch physical information neural network and predicting the temperature change distribution of prestressed tendons at different depths in the bonded prestressed concrete structure based on historical meteorological data of the location of the bonded prestressed concrete structure.

[0118] The probability distribution characteristic determination module is used to implement step S3, namely: determining the probability distribution characteristics of prestress fluctuation of prestressed tendons based on physical mapping relationship and temperature change distribution.

[0119] The actual prestress probability distribution acquisition module is used to implement step S4, namely: based on the initial tensioning temperature, initial tensioning stress, arrangement of the prestressing tendons, and the preset concrete shrinkage and creep model and prestressing tendon relaxation model, a prestress loss reference curve is constructed, and the probability distribution characteristics are coupled with the prestress loss reference curve to obtain the probability distribution of actual prestress at any time.

[0120] The prestress guarantee rate calculation module is used to implement step S5, namely: setting a prestress safety threshold, calculating the proportion of time during which the actual prestress value is higher than the prestress safety threshold within the target evaluation period based on probability distribution, and defining the proportion of time as the prestress guarantee rate considering prestress fluctuation.

[0121] The role and effect of the embodiments

[0122] The method and system for obtaining the prestress guarantee rate considering prestress fluctuation according to the present invention have the following beneficial effects:

[0123] In step S1 of this invention, based on the bond strain coordination mechanism between prestressing tendons and concrete in bonded prestressed concrete structures, a physical mapping relationship between temperature changes of prestressing tendons and prestress fluctuations is established. This can accurately quantify the instantaneous changes in prestress caused by temperature changes, overcoming the problem that traditional prestress loss prediction methods only output smooth prestress loss curves, ignore the additional stress fluctuations caused by periodic changes in natural environmental temperature, and mask extreme risks.

[0124] In step S2 of this invention, a dual-branch physical information neural network is constructed. By fusing the physical equations of heat conduction with measured data, it can accurately predict the temperature variation distribution of prestressed tendons at different depths in bonded prestressed concrete structures. This network employs a composite loss function (data fidelity, physical equation residuals, and boundary residuals) and a two-stage training strategy to ensure that the prediction results conform to physical laws and have high fitting accuracy.

[0125] In steps S3-S5 of this invention, a Gaussian mixture distribution is used to describe the probability distribution characteristics of prestress fluctuations, and this distribution is coupled with a long-term prestress loss benchmark curve to obtain the probability distribution of actual prestress at any given time. Based on this, the prestress guarantee rate is defined as the percentage of time within the target evaluation period where the actual prestress exceeds the safety threshold. This index comprehensively evaluates structural safety from both probabilistic and temporal dimensions, quantifying the impact of multiple factors such as initial tensioning temperature, initial tensioning stress, prestressing tendon arrangement, and climatic conditions on the long-term safety of bonded prestressed concrete structures. It is more scientific and comprehensive than traditional single extreme value judgments, providing a reliable scientific basis for the full life-cycle evaluation of bonded prestressed concrete structures.

[0126] This invention is applicable to various types of bonded prestressed concrete structures and can be implemented by combining historical meteorological data of the structure's location. It has strong versatility and practical engineering value.

[0127] Those skilled in the art should understand that this invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to this invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. A method for obtaining the prestress guarantee rate considering prestress fluctuation, characterized in that, Includes the following steps: S1: Physical mapping relationship establishment steps: Based on the bond strain coordination mechanism between prestressed tendons and concrete in bonded prestressed concrete structures, establish the physical mapping relationship between temperature changes of prestressed tendons and prestress fluctuations. S2: Temperature change distribution prediction step, construct a two-branch physical information neural network, and based on the historical meteorological data of the location of the bonded prestressed concrete structure, predict the temperature change distribution of the prestressing tendons at different depths in the bonded prestressed concrete structure. S3: Probability distribution characteristic determination step: Based on the physical mapping relationship and the temperature change distribution, determine the probability distribution characteristics of the prestress fluctuation of the prestressing tendon; S4: Step for obtaining the actual prestress probability distribution: Based on the initial tensioning temperature, initial tensioning stress, arrangement of the prestressing tendons, and the preset concrete shrinkage and creep model and prestressing tendon relaxation model, a prestress loss reference curve is constructed, and the probability distribution characteristics are coupled with the prestress loss reference curve to obtain the probability distribution of the actual prestress at any time. S5: Prestress guarantee rate calculation steps: Set a prestress safety threshold, and based on the probability distribution, calculate the proportion of time during which the actual prestress value is higher than the prestress safety threshold within the target evaluation period, and define the proportion of time as the prestress guarantee rate considering the prestress fluctuation.

2. The method for obtaining the prestress guarantee rate considering prestress fluctuation according to claim 1, characterized in that: in, The physical mapping relationship in S1 is expressed as follows: ， In the formula, For prestress fluctuation, The elastic modulus of prestressed tendons, This is the coefficient of thermal expansion of concrete. The coefficient of thermal expansion of the prestressing tendon is... This represents the temperature change at the location of the prestressing tendons.

3. The method for obtaining the prestress guarantee rate considering prestress fluctuation according to claim 1, characterized in that: in, The dual-branch physical information neural network in S2 includes: a fully connected backbone feedforward neural network for reconstructing the global spatiotemporal temperature field, and an auxiliary feedforward neural network for inferring the time-varying boundary heat transfer coefficient.

4. The method for obtaining the prestress guarantee rate considering prestress fluctuation according to claim 1, characterized in that: in, The training of the dual-branch physical information neural network in S2 adopts a composite loss function, which includes: data fidelity loss, physical equation residual loss and energy conservation boundary residual loss.

5. The method for obtaining the prestress guarantee rate considering prestress fluctuation according to claim 1, characterized in that: in, The temperature change distribution in S2 follows a Gaussian mixture distribution, and the probability density function of the temperature change distribution is: ， In the formula, The number of sub-distributions in a Gaussian mixture distribution. As weight, These are the mean and variance of the corresponding normal distribution, respectively.

6. The method for obtaining the prestress guarantee rate considering prestress fluctuation according to claim 1, characterized in that: in, The probability distribution characteristics of the prestress fluctuation in S3 are as follows: the prestress fluctuation follows a Gaussian mixture distribution, and the probability density function of the prestress fluctuation is: ， In the formula, The number of sub-distributions in a Gaussian mixture distribution. As weight, The mean is variance is It follows a normal distribution.

7. The method for obtaining the prestress guarantee rate considering prestress fluctuation according to claim 6, characterized in that: in, The method for obtaining the probability distribution of the actual prestress at any time in S4 is as follows: Based on the initial tensioning temperature Prestress fluctuation after temperature change It follows a Gaussian mixture distribution, expressed as: ， In the formula, The mean is variance is The normal distribution Based on the initial tensile stress, the arrangement of prestressing tendons, and the pre-set concrete shrinkage and creep model and prestressing tendon relaxation model, a prestress loss baseline curve is constructed. , Prestress loss baseline curve With prestress fluctuation By superimposing the values, the actual value of the prestress at any given time can be obtained. Thus, the probability distribution of the actual prestress at any given time is obtained, expressed as: 。 8. The method for obtaining the prestress guarantee rate considering prestress fluctuation according to claim 1, characterized in that: in, The prestress guarantee rate in S5 The calculation formula is: ， In the formula, The set prestressed safety threshold, The actual value of prestress at any given time. The temperature at the location of the prestressing tendon; For time.

9. A system for obtaining the prestress guarantee rate considering prestress fluctuation, characterized in that, include: The physical mapping relationship establishment module establishes a physical mapping relationship between the temperature change of the prestressing tendon and the prestress fluctuation based on the bond strain coordination mechanism between the prestressing tendon and the concrete in the bonded prestressed concrete structure. The temperature change distribution prediction module constructs a two-branch physical information neural network and, based on historical meteorological data of the location of the bonded prestressed concrete structure, predicts the temperature change distribution of the prestressing tendons at different depths in the bonded prestressed concrete structure. The probability distribution feature determination module determines the probability distribution feature of the prestress fluctuation of the prestressing tendon based on the physical mapping relationship and the temperature change distribution. The actual prestress probability distribution acquisition module constructs a prestress loss reference curve based on the initial tensioning temperature, initial tensioning stress, arrangement of the prestressing tendons, and preset concrete shrinkage and creep models and prestressing tendon relaxation models. It then couples the probability distribution characteristics with the prestress loss reference curve to obtain the probability distribution of actual prestress at any time. The prestress guarantee rate calculation module sets a prestress safety threshold and, based on the probability distribution, calculates the proportion of time during which the actual prestress value is higher than the prestress safety threshold within the target evaluation period. The proportion of time is defined as the prestress guarantee rate considering the prestress fluctuation.

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