Method and equipment for evaluating durability of lightweight concrete column beam based on twinborn simulation

Abstract

The invention discloses a lightweight concrete column beam durability evaluation method and device based on twinborn simulation, and relates to the technical field of twinborn simulation, and the method comprises the steps: collecting the degradation characteristics of a lightweight concrete column beam, and constructing a three-dimensional multi-scale structure model; executing coupling simulation to generate a twin simulation model; monitoring a column beam through a multi-source sensor, and determining a real-time performance degradation parameter; updating the twinborn simulation model by using the degradation parameters; and collecting future environment and load data, inputting the future environment and load data into the updated model, simulating a degradation evolution process, and generating a durability evaluation result. The technical problem that a prediction result and actual service performance have significant deviation due to the fact that a traditional durability evaluation method is difficult to dynamically couple a multi-physical field environment and a material microcosmic degradation mechanism is solved, and a dynamic evaluation system for virtual-real mapping is constructed through twinborn simulation. And the prediction precision of the degradation process of the lightweight concrete column beam and the dynamic deduction capability of the service life are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of twin simulation, and in particular to a method and device for evaluating the durability of lightweight concrete columns and beams based on twin simulation. Background Art

[0002] Lightweight concrete is widely used in high-rise buildings and large-span structures due to its low density and excellent thermal insulation properties. However, its porosity can easily lead to accelerated deterioration during service due to the coupling of multiple factors such as environmental erosion, repeated loads, and steel corrosion. Traditional durability assessment methods are mostly based on laboratory accelerated tests or static analysis. These methods face limitations in practical applications. It is difficult to dynamically correlate the cross-scale evolution mechanism of material microdamage and macroscopic structural performance degradation, and there is a lack of real-time mapping capabilities for the multi-physical field coupling effects of real service environments such as temperature and humidity migration, freeze-thaw cycles, and chemical erosion, resulting in significant deviations between the assessment results and engineering practice. Summary of the Invention

[0003] This application provides a lightweight concrete column-beam durability assessment method and equipment based on twin simulation, which is used to solve the technical problem that traditional durability assessment methods are difficult to dynamically couple multi-physical field environments and material micro-degradation mechanisms, resulting in significant deviations between predicted results and actual service performance.

[0004] The first aspect of the present application provides a durability assessment method for lightweight concrete columns and beams based on twin simulation, which includes: collecting degradation characteristics of lightweight concrete columns and beams from the material level to the structural level, and constructing a three-dimensional multi-scale structural model integrating microscale, mesoscale and macroscale; performing coupling simulation of a preset coupling field type with the three-dimensional multi-scale structural model to generate a twin simulation model; monitoring the lightweight concrete columns and beams through multi-source sensors to determine real-time performance degradation parameters; updating the parameters of the twin simulation model with the real-time performance degradation parameters; collecting environmental data and column and beam load data in a future time window, and inputting the updated twin simulation model to simulate the degradation evolution process to generate a durability assessment result.

[0005] The second aspect of the present application provides a lightweight concrete column and beam durability assessment device based on twin simulation, which includes: a three-dimensional model construction module: collecting degradation characteristics of lightweight concrete columns and beams from the material level to the structural level, and constructing a three-dimensional multi-scale structural model integrating microscale, mesoscale and macroscale; a coupling simulation module: performing coupling simulation of a preset coupling field type with the three-dimensional multi-scale structural model to generate a twin simulation model; a column and beam monitoring module: monitoring the lightweight concrete columns and beams through multi-source sensors to determine real-time performance degradation parameters; a parameter update module: updating the parameters of the twin simulation model with the real-time performance degradation parameters; a degradation evolution module: collecting environmental data and column and beam load data in the future time window, inputting the updated twin simulation model to simulate the degradation evolution process, and generating a durability assessment result.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages: Degradation characteristics of lightweight concrete columns and beams from the material level to the structural level are collected to construct a three-dimensional multiscale structural model integrating micro, meso, and macro scales. Coupled simulations of preset coupling field types are performed using the three-dimensional multiscale structural model to generate a twin simulation model. Multi-source sensors are used to monitor lightweight concrete columns and beams to determine real-time performance degradation parameters. The twin simulation model is updated with these real-time performance degradation parameters. Environmental data and column and beam load data from future time windows are collected and input into the updated twin simulation model to simulate the degradation evolution process and generate durability assessment results. This achieves the technical effect of building a dynamic assessment system with virtual-real mapping through twin simulation, improving the accuracy of lightweight concrete column and beam degradation prediction and the ability to dynamically simulate service life. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0008] Figure 1 A schematic flow chart of the lightweight concrete column-beam durability assessment method based on twin simulation provided in an embodiment of the present application.

[0009] Figure 2 Schematic diagram of the structure of the lightweight concrete column and beam durability assessment equipment based on twin simulation provided in an embodiment of the present application.

[0010] Explanation of the reference numerals: three-dimensional model construction module 11, coupling simulation module 12, column and beam monitoring module 13, parameter updating module 14, degradation evolution module 15. DETAILED DESCRIPTION

[0011] This application provides a lightweight concrete column-beam durability assessment method and equipment based on twin simulation, which is used to solve the technical problem that traditional durability assessment methods are difficult to dynamically couple multi-physical field environments and material micro-degradation mechanisms, resulting in significant deviations between predicted results and actual service performance.

[0012] The following will be combined with the accompanying drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making any creative work are within the scope of protection of this application.

[0013] Example 1, as Figure 1 As shown, the present application provides a lightweight concrete column-beam durability assessment method based on twin simulation, the method comprising: The degradation characteristics of lightweight concrete columns and beams from the material level to the structural level are collected to construct a three-dimensional multi-scale structural model integrating microscale, mesoscale and macroscale.

[0014] Specifically, the degradation characteristics of lightweight concrete columns and beams are first collected, including information on everything from material composition (such as the distribution of cement and aggregate) to structural performance (such as the load conditions of the columns and beams). This information helps understand the degradation characteristics of the columns and beams under different usage conditions. Subsequently, based on these degradation characteristics, a comprehensive and accurate three-dimensional multiscale structural model is constructed through uplink coupling and downlink mapping at three different scales: micro, meso, and macro. This model reflects the degradation process from the microstructure to the entire column and beam.

[0015] In one possible implementation, the macroscale includes column-beam structural geometry, reinforcement, and load conditions, the mesoscale includes aggregate distribution, pore structure, and interface transition zone, and the microscale includes cement-based structure and hydration products.

[0016] Specifically, the macro scale mainly involves the structural and bearing capacity characteristics of the entire column and beam, including the column-beam structural geometry, reinforcement and load conditions. Among them, the column-beam structural geometry refers to the overall shape and size of the column and beam, such as length, width, height, cross-sectional shape, etc. These geometric characteristics affect the force distribution and mechanical properties of the column and beam; reinforcement refers to the configuration of steel bars in concrete columns and beams, including the number, diameter, density, etc. of steel bars. Reinforcement plays an important role in the strength, bending resistance and crack resistance of concrete; load conditions include the external loads that columns and beams bear during use, such as static loads, dynamic loads, etc. The size, distribution and changes of the loads will directly affect the deformation and stress state of the column and beam, and are closely related to their durability. The mesoscopic scale involves the structural characteristics inside concrete, which mainly affect the overall performance and durability of concrete, including aggregate distribution, pore structure and interface transition zone. Among them, aggregate distribution refers to the particle size, shape and distribution of coarse aggregate (such as crushed stone) and fine aggregate (such as sand) in concrete. The arrangement of aggregate is directly related to the overall structure and stability of concrete; pore structure refers to the large and small pores in concrete. Their distribution, number and connectivity will affect the strength and durability of concrete. Excessive or uneven distribution of pores may lead to a decrease in the strength of concrete and accelerate the penetration of water or chemicals; the interface transition zone refers to the bonding area between the cement matrix and aggregate. The microstructure and physicochemical properties of this area have an important influence on the mechanical properties of concrete. A weakened interface transition zone may lead to a decrease in the tensile strength of concrete and accelerate the expansion of cracks. The microscale involves the characteristics of the cement-based structure and hydration products in concrete, which directly affect the chemical stability and physical properties of concrete. Among them, the cement-based structure is the basic solid material formed by the reaction of cement and water, which determines the initial strength and structural stability of concrete; hydration products refer to chemical products generated after the reaction of cement and water, such as calcium silicate hydrate (CSH), calcium aluminate hydrate (CAH), etc. These hydration products form the solid skeleton of concrete, and their quantity and structural density determine the strength, durability and anti-aging ability of concrete.

[0017] In one possible implementation, degradation characteristics of lightweight concrete from the material level to the structural level are collected to construct a three-dimensional multi-scale structural model integrating microscale, mesoscale, and macroscale, including: The degradation characteristics are divided into multiple scales to generate microscale characteristics, mesoscale characteristics and macroscale characteristics; uplink transfer coupling is performed based on the microscale characteristics, the mesoscale characteristics and the macroscale characteristics to generate uplink transfer equivalent parameters; downlink mapping is performed based on the microscale characteristics, the mesoscale characteristics and the macroscale characteristics to determine downlink response parameters; multiscale modeling is performed in combination with the microscale characteristics, the mesoscale characteristics, the macroscale characteristics, the uplink transfer equivalent parameters and the downlink response parameters to generate the three-dimensional multiscale structural model.

[0018] Specifically, after collecting degradation signatures containing characteristic parameters such as column-beam length, column-beam width, column-beam height, number of columns and beams, rebar diameter, column-beam external load, aggregate size, aggregate distribution, pore quantity, porosity, number of microcracks, and hydration degree, the characteristic parameters in the degradation signature are divided into microscale, mesoscale, and macroscale characteristics according to pre-defined feature classifications. Subsequently, during multiscale analysis, an upward transfer coupling approach is adopted to transfer the micro-, meso-, and macroscale characteristics to higher scales through the coupling network. For example, microscale physical behaviors (such as hydration reactions and microcrack formation in cement-based structures) and mesoscale physical behaviors (such as changes in aggregate distribution and pore structure) are converted into equivalent macroscale mechanical or physical parameters such as elastic modulus and permeability through the coupling network. These equivalent parameters transform micro- and mesoscale changes into macroscale structural responses, thereby generating upward transfer equivalent parameters that serve as a bridge between different scales. During the down-mapping process, micro-, meso-, and macro-scale characteristics will negatively affect the overall response of the structure. For example, based on the load effects of macro- and meso-scale characteristics, a structure-load-response network is established to propagate the physical field, mapping the macro-loading and meso-scale characteristics to the micro-response. This process allows the mechanical behavior of the macro-scale to be fed back to the changes in the microstructure and generates down-mapping response parameters, such as crack distribution and interface damage. These response parameters reflect the relationship between external loading and internal degradation of the material. Subsequently, through three-dimensional modeling technology, a micro-scale model is constructed based on the micro-scale characteristics to simulate the hydration reaction of cement particles, the expansion of microcracks, etc. A meso-scale model is constructed based on the meso-scale characteristics to describe the influence of aggregate and cement matrix in the degradation process. A macro-scale model is established based on the macro-scale characteristics to simulate the stress and deformation behavior of columns and beams in long-term use. The micro, meso, and macroscale models are then coupled together, enabling interaction between different scales through the upward transfer of equivalent parameters and downward response parameters. This allows changes at the micro and meso scales to influence external loading and boundary conditions at the macro scale through the upward transfer of equivalent parameters, which are then fed back to the micro and meso levels through the downward response parameters. After coupling the micro, meso, and macroscale models, a three-dimensional multiscale structural model was constructed. This model can simulate and predict the durability performance of concrete columns and beams under different environmental and loading conditions by applying preset coupling field types, providing an accurate tool for subsequent durability assessments of lightweight concrete columns and beams.

[0019] In one possible implementation, performing uplink transfer coupling based on the microscale feature, the mesoscale feature, and the macroscale feature to generate uplink transfer equivalent parameters includes: A graph neural network is used to map the microstructure and mesostructure, learn the structure-performance mapping, and construct an uplink transfer coupling network; the uplink transfer coupling network is used to perform macro-equivalent transfer mapping on the microscale features and the mesoscale features to generate the uplink transfer equivalent parameters.

[0020] Specifically, the microstructure and mesostructure are first represented as graphs, with the various elements of these structures (such as the cement matrix, aggregate distribution, and pore structure) being treated as nodes in the graph. These nodes are connected by edges, and the edge weights represent the physical relationships or interactions between the different elements. For example, in the microstructure, the connection between cement particles and the cement matrix can be an edge, while in the mesostructure, the interface transition zone between aggregate and the cement matrix can be an edge. This graph construction can be defined by physical properties (such as particle size and porosity) and spatial arrangement relationships. Subsequently, a graph neural network (GNN) is trained using the input graph data to learn the mapping between structure and performance. At this stage, the GNN captures the physical properties of the micro- and mesostructures through information about node connections and node properties (such as porosity and aggregate size). Feature aggregation and updating are performed through multiple network layers. This process allows each node (representing an element in the micro- or mesostructure) to update its representation based on information from its neighborhood, ultimately learning the complex relationship between structure and performance. During the training process, a labeled data set, that is, multiple actual or simulated microstructure samples and their corresponding mechanical responses (such as elastic modulus and permeability) are composed of input-output pairs and input into the graph neural network. The predicted structural performance values ​​are obtained through forward propagation and compared with the true values. The loss function, such as the mean square error (MSE) or relative error, is calculated. The network weights are then continuously adjusted through the backpropagation algorithm (such as the Adam optimizer). The training is iterated until the loss converges. The trained graph neural network will serve as an uplink transmission coupling network, which generates equivalent performance parameters adapted to the macroscale by mapping microscale features and mesoscale features to a higher macroscale. These equivalent parameters can comprehensively reflect the impact of micro- and mesoscale degradation on the macrostructure. Finally, the uplink transfer coupling network is used to perform macro-equivalent transfer mapping on the micro-scale characteristics and meso-scale characteristics to obtain the final uplink transfer equivalent parameters. These uplink transfer equivalent parameters represent the influence of micro- and meso-structures on the macro-structural performance. For example, information such as porosity and crack evolution at the microscale and aggregate distribution at the mesoscale will eventually be converted into macro-scale equivalent elastic modulus, permeability and other parameters. These equivalent parameters can be used for subsequent structural analysis and durability evaluation.

[0021] In one possible implementation, performing downlink mapping based on the microscale feature, the mesoscale feature, and the macroscale feature to determine a downlink response parameter includes: The microstructure and mesostructure are represented by a graph structure, physical field propagation is performed, and a structure-load-response network is established from macroscopic and mesoscopic loading to microscopic response; the mesoscopic scale characteristics and the macroscopic scale characteristics are down-mapped using the structure-load-response network to generate the down-link response parameters.

[0022] Specifically, first, the microstructure (such as cement-based particles, hydration products, microcracks) and mesostructure (such as aggregate particles, pores, and interface transition zones) of lightweight concrete are represented by a graph structure, where the nodes in the graph represent basic units (such as particles, pores, and interface regions), and the edges represent the physical or mechanical connection relationships between these units (such as the contact between adjacent particles and the stress conduction path in the interface transition zone). Each node can carry material property information, such as Young's modulus, porosity, relative position, etc., and the edge weight reflects physical field characteristics such as connection strength, thermal / moisture conductivity, or stress transfer capacity. Subsequently, loading boundary conditions from the macroscale and mesoscale are superimposed on this graph structure, including global loads (such as axial pressure and lateral shear) and local mesoscale perturbations (such as concentrated cracks in the interface region and changes in pore pressure). These loading conditions are propagated layer by layer from the high-level nodes of the graph (representing the macro / mesoscale regions) to the low-level micro nodes through a physical field propagation mechanism based on a graph neural network (such as a GNN model based on a message-passing mechanism). This simulates the conduction process of multiple physical fields such as force, heat, and moisture in the microstructure, reflecting how macro- and mesoscale loadings act step by step and trigger microscopic responses. Subsequently, combining the physical states of the nodes in the graph with the loading input, a structure-load-response network (Structure-Load-Response Network) is constructed to learn the response mechanism of the microstructure under loading conditions at different scales. This network predicts the stress, deformation, and failure characteristics of each node and simulates microscale response behaviors, such as the propagation path of microcracks, the conditions for the occurrence of interface failure, and the evolution of pore compaction. This network has a hierarchical propagation capability and can automatically establish a mechanical / physical logical chain from macroscopic loading to microscopic response. Finally, the mesoscale and macroscale characteristics are input into the constructed structure-load-response network for inference, outputting downstream response parameters such as microscopic crack distribution, local damage factor, and interface degradation index. These parameters directly reflect the possible evolutionary trends of the microstructure under macroscopic and mesoscopic loading. In summary, this process completes the downstream mapping from macroscopic loading conditions to microscopic response behaviors through the combination of graphical structure modeling, physical field propagation, and structure-load-response networks. This creates a quantifiable and trainable downstream response parameter output mechanism, supporting the multi-scale coupling accuracy and dynamic evolution capabilities of the twin simulation model.

[0023] A coupling simulation of a preset coupling field type is performed using the three-dimensional multi-scale structural model to generate a twin simulation model.

[0024] Specifically, the constructed three-dimensional multiscale structural model is first input into the simulation system. This model integrates information at the micro, meso, and macro scales, fully reflecting the structural characteristics of concrete columns and beams. Subsequently, a coupled simulation is performed based on pre-set coupling field types, including coupling of multiple physical fields, such as thermal-moisture migration coupling, mechanical-chemical coupling, and freeze-thaw-mechanical coupling. These coupling field types reflect the complex behavior of concrete columns and beams in real-world environments, such as how material properties degrade and change under the influence of humidity, temperature fluctuations, external loads, or chemical reactions. The coupled simulation process is based on historical response damage data. By learning from this historical data, a dynamic twin simulation model is generated. This model not only reflects the physical behavior of concrete columns and beams in their current state but also predicts their future performance and degradation based on factors such as environmental conditions and load changes. Ultimately, the generated twin simulation model will be used for subsequent durability assessments and structural health monitoring to provide decision support.

[0025] In one possible implementation, the preset coupling field types include thermal-moisture migration coupling, mechanical-chemical coupling, freeze-thaw-mechanical coupling, and steel corrosion expansion coupling.

[0026] Specifically, the preset coupling field types include thermal-moisture migration coupling, mechanical-chemical coupling, freeze-thaw-mechanical coupling, and steel corrosion expansion coupling. Thermal-moisture migration coupling primarily simulates the migration of water vapor within concrete and changes in pore pressure under the influence of temperature and humidity fluctuations. By introducing temperature and humidity gradients, it simulates the absorption, evaporation, and migration of water vapor. Furthermore, humidity fluctuations can cause pressure changes in the pores within concrete, thus affecting its physical properties. This coupling field type is suitable for environments with dry-wet cycles, such as where the concrete surface is in contact with air. Mechanical-chemical coupling simulates how mechanical damage accelerates the penetration of chloride ions or CO2. When a concrete structure is subjected to mechanical loads (such as compressive or tensile stress), microcracks or damage will form, increasing the permeability of the concrete, allowing chloride ions or CO2 to penetrate more easily and accelerating the corrosion process. This coupling field type is primarily used in the analysis of chloride ion erosion and carbonation corrosion, particularly in concrete structures exposed to the external environment, such as bridges and roads. Freeze-thaw-mechanical coupling simulates the effects of frost heave on concrete structures. In cold regions, the freezing and thawing of water in concrete causes the concrete's volume to expand and contract. After each freeze-thaw cycle, cracks may form within the concrete, affecting the stability and durability of the structure. This coupling field type is suitable for concrete structures serving in cold regions. Rebar corrosion-expansion coupling simulates the cracking and spalling of concrete caused by the corrosion and expansion of rebar. Rebar expands during corrosion, and the resulting expansion force exerts pressure on the surrounding concrete, causing cracking and even spalling on the concrete surface. This coupling field type is suitable for marine environments or areas where deicing salt is used. Simulating these preset coupling field types provides a more comprehensive understanding of the degradation behavior and durability of lightweight concrete columns and beams under various physical and chemical conditions under different environmental conditions, providing a scientific basis for structural maintenance and lifespan prediction.

[0027] In one possible implementation, performing a coupling simulation of a preset coupling field type with the three-dimensional multi-scale structural model to generate a twin simulation model includes: Based on the preset coupling field type, historical reaction damage data is collected to construct a multi-field reaction-damage constitutive model; coupling simulation is performed using the multi-field reaction-damage constitutive model to generate the twin simulation model.

[0028] Specifically, first, based on the preset coupling field types (such as thermal-moisture migration coupling, force-chemistry coupling, freeze-thaw-mechanical coupling, and steel corrosion expansion coupling), the historical response and damage data of lightweight concrete columns and beams under different environmental and loading conditions are collected. The historical response data include changes in multiple physical fields such as temperature, humidity, stress, and chemical corrosion, while the historical damage data reflects the crack development, material strength decay, porosity changes, etc. of the structure. These data come from experimental measurements, long-term monitoring, and numerical simulations, providing a basis for subsequent modeling. Subsequently, based on the collected historical reaction damage data, the reaction model and damage model of each preset coupling field type are constructed through data learning and constitutive relations. The reaction model describes the material response under the action of the corresponding physical field, such as the hydration reaction of cement-based materials and the corrosion of steel bars. The damage model reflects the material damage caused by multiple physical fields, mainly quantifying the damage process through crack development, porosity change, interface degradation, etc. The reaction model and the damage model are then coupled according to the constitutive relationship to obtain the reaction-damage constitutive model of each preset coupling field type. These reaction-damage constitutive models will be integrated into a unified framework, and a multi-field reaction-damage constitutive model will be established by coupling different field effects. This process uses numerical methods (such as finite element analysis) to realize the interaction and influence between different physical fields, providing accurate predictions for the damage evolution of concrete structures. The constructed multi-field response-damage constitutive model and the 3D multiscale structural model were then input into the simulation system. The 3D multiscale structural model comprehensively describes the structural properties of the lightweight concrete column-beam by integrating the microscopic, mesoscopic, and macroscopic scales. The multi-field response-damage constitutive model provides the relationship between the material's response and damage evolution under different physical fields. In this coupled process, the response model interacts with the various scale features in the 3D structural model to simulate the effects of different physical fields on the structure, while the damage model drives the evolution of material damage by calculating crack development and strength degradation caused by the response. Through coupled simulations in the simulation system, the 3D multiscale structural model and the multi-field response-damage constitutive model are combined to generate a twin simulation model. This twin simulation model, as a virtual representation of the lightweight concrete column-beam, can reflect its dynamic behavior in real time under different environments and loading conditions. It can also be adjusted in real time based on continuously updated sensor data and simulation results, further improving the accuracy of predicting damage evolution and performance degradation under actual operating conditions.

[0029] The lightweight concrete column beam is monitored by multi-source sensors to determine real-time performance degradation parameters.

[0030] Specifically, monitoring lightweight concrete columns and beams through multi-source sensors aims to obtain real-time performance data on the structure under different operating conditions. These sensors typically include fiber Bragg grating sensors, electrochemical sensors, stress sensors, humidity sensors, displacement sensors, and crack sensors. They are used to measure the different physical and mechanical effects on the columns and beams during use. For example, fiber Bragg grating sensors can monitor longitudinal and transverse strain distribution and capture local stiffness degradation caused by crack propagation. Electrochemical sensors can detect the current density of steel corrosion. Stress sensors can directly measure the stress distribution in the structure. Humidity sensors help capture changes in moisture content in the concrete, and crack sensors can detect crack formation and propagation. Through the coordinated operation of these multi-source sensors, the performance changes of lightweight concrete columns and beams in actual use can be comprehensively and accurately reflected. This real-time monitoring data will form real-time performance degradation parameters such as stress, strain, and crack width. These degradation parameters can reflect the damage process of the structure under different environmental conditions and provide a reliable basis for subsequent structural health management and durability assessment.

[0031] The parameters of the twin simulation model are updated using the real-time performance degradation parameters.

[0032] Specifically, after obtaining the real-time performance degradation parameters, the collected real-time performance degradation parameters will be applied to the twin simulation model, and the twin simulation model will be dynamically updated so that the twin simulation model can reflect the current structural status in real time, and can also predict future degradation trends and structural performance changes through continuously updated parameters.

[0033] In one possible implementation, updating the parameters of the twin simulation model using the real-time performance degradation parameter includes: The twin simulation model matches the simulation parameters corresponding to the real-time performance degradation parameters, updates the simulation parameters, and completes the update of the twin simulation model.

[0034] Specifically, in the twin simulation model, real-time performance degradation parameters (such as elastic modulus, strain, crack width, chloride ion concentration, etc.) are matched with corresponding parameters in the simulation model. Once matched, the real-time performance degradation parameters are used to replace the corresponding parameters in the simulation model. In this way, the twin simulation model can self-correct in a constantly changing environment, accurately reflecting the actual conditions of lightweight concrete columns and beams. This updating process not only improves the accuracy of the real model but also enables it to reflect the actual degradation process of the structure, facilitating effective long-term prediction and maintenance decisions.

[0035] Collect environmental data and column and beam load data in the future time window, input them into the updated twin simulation model to simulate the degradation evolution process, and generate durability assessment results.

[0036] Specifically, by first collecting historical environmental data at the locations of lightweight concrete columns and beams for trend analysis, the team obtained environmental data (such as temperature, humidity, precipitation, and wind speed) that may affect the lightweight concrete columns and beams within a future time window. The team then used the same method to obtain column and beam load data (such as load changes, vibration, and shear forces) for the future time window. The collected future environmental and load data were then input into a previously updated twin simulation model, which now includes real-time performance degradation parameters and the current state. This input process allows the model to further simulate evolution based on future environmental and load changes. The input data drives the model's degradation evolution simulation. During this process, the simulation model simulates the physical and chemical reactions that the structure may experience during this process, such as crack propagation, material corrosion, and steel corrosion, based on future environmental changes (such as temperature fluctuations and humidity changes) and the loads borne by the columns and beams (such as sustained loads and vibration), thereby predicting the performance changes of the structure over a period of time. Afterwards, by simulating the degradation evolution process, the twin simulation model will generate durability assessment results, that is, evaluating the health status, service life, performance degradation trend, etc. of the structure in the future time window. These results can include future crack development, strength attenuation, compressive capacity changes and other information to help engineers understand whether lightweight concrete columns and beams will face the risk of failure in the future.

[0037] In one possible implementation, the environmental data is predicted by collecting historical environmental data at the location of the lightweight concrete column and beam and performing trend analysis, and the column and beam load data is determined by collecting historical load data and performing load fluctuation analysis to determine the load sequence.

[0038] Specifically, historical environmental data for the locations of lightweight concrete columns and beams is collected, such as past temperature, humidity, precipitation, and wind speed. This data reflects the impact of environmental factors on the columns and beams and helps us understand the changing trends of these factors over time. This historical environmental data is then used to train a long short-term memory (LSTM) network through forward propagation, loss calculation, backpropagation, and parameter optimization to construct an environmental prediction network. Historical environmental data from a preset period closest to the current moment is then fed into the environmental prediction network for trend analysis and prediction, yielding environmental data for future time windows. Similarly, historical load data from a preset period closest to the current moment is fed into the load prediction network constructed in the same manner for load sequence prediction, yielding column and beam load data for future time windows. This predicted data helps simulate future degradation evolution, providing an important basis for durability assessment and structural health management of lightweight concrete columns and beams.

[0039] Example 2 is based on the same inventive concept as the lightweight concrete column-beam durability evaluation method based on twin simulation in the previous embodiment. Figure 2 As shown, the present application provides a lightweight concrete column-beam durability evaluation device based on twin simulation, wherein the device includes: Three-dimensional model construction module 11: collects degradation characteristics of lightweight concrete columns and beams from material level to structural level, and constructs a three-dimensional multi-scale structural model integrating microscale, mesoscale and macroscale; coupling simulation module 12: performs coupling simulation of preset coupling field type with the three-dimensional multi-scale structural model to generate a twin simulation model; column and beam monitoring module 13: monitors the lightweight concrete columns and beams through multi-source sensors to determine real-time performance degradation parameters; parameter update module 14: updates the parameters of the twin simulation model with the real-time performance degradation parameters; degradation evolution module 15: collects environmental data and column and beam load data in the future time window, inputs the updated twin simulation model to simulate the degradation evolution process, and generates durability evaluation results.

[0040] Furthermore, the three-dimensional model building module 11 further includes: The macroscopic scale includes the geometry of the column-beam structure, reinforcement and load conditions, the mesoscopic scale includes aggregate distribution, pore structure and interface transition zone, and the microscopic scale includes cement-based structure and hydration products.

[0041] Furthermore, the three-dimensional model building module 11 further includes: The degradation characteristics are divided into multiple scales to generate microscale characteristics, mesoscale characteristics and macroscale characteristics; uplink transfer coupling is performed based on the microscale characteristics, the mesoscale characteristics and the macroscale characteristics to generate uplink transfer equivalent parameters; downlink mapping is performed based on the microscale characteristics, the mesoscale characteristics and the macroscale characteristics to determine downlink response parameters; multiscale modeling is performed in combination with the microscale characteristics, the mesoscale characteristics, the macroscale characteristics, the uplink transfer equivalent parameters and the downlink response parameters to generate the three-dimensional multiscale structural model.

[0042] Furthermore, the three-dimensional model building module 11 further includes: A graph neural network is used to map the microstructure and mesostructure, learn the structure-performance mapping, and construct an uplink transfer coupling network; the uplink transfer coupling network is used to perform macro-equivalent transfer mapping on the microscale features and the mesoscale features to generate the uplink transfer equivalent parameters.

[0043] Furthermore, the three-dimensional model building module 11 further includes: The microstructure and mesostructure are represented by a graph structure, physical field propagation is performed, and a structure-load-response network is established from macroscopic and mesoscopic loading to microscopic response; the mesoscopic scale characteristics and the macroscopic scale characteristics are down-mapped using the structure-load-response network to generate the down-link response parameters.

[0044] Furthermore, the coupling simulation module 12 further includes: The preset coupling field types include thermal-moisture migration coupling, mechanical-chemical coupling, freeze-thaw-mechanical coupling and steel corrosion expansion coupling.

[0045] Furthermore, the coupling simulation module 12 further includes: Based on the preset coupling field type, historical reaction damage data is collected to construct a multi-field reaction-damage constitutive model; coupling simulation is performed using the multi-field reaction-damage constitutive model to generate the twin simulation model.

[0046] Furthermore, the parameter updating module 14 further includes: The twin simulation model matches the simulation parameters corresponding to the real-time performance degradation parameters, updates the simulation parameters, and completes the update of the twin simulation model.

[0047] Furthermore, the degradation evolution module 15 further includes: The environmental data is predicted by collecting historical environmental data of the location of the lightweight concrete column and beam and performing trend analysis. The column and beam load data is a load sequence determined by collecting historical load data and performing load fluctuation analysis.

[0048] It should be noted that the order in which the embodiments of the present application are presented is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. Furthermore, the foregoing descriptions of specific embodiments of this specification are provided. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential sequence shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0049] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should be included in the scope of protection of the present application.

[0050] This specification and drawings are merely illustrative of the present application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Obviously, those skilled in the art may make various modifications and variations to this application without departing from the scope of this application. Thus, this application is intended to include such modifications and variations as fall within the scope of this application and its equivalents.

Claims

1. A lightweight concrete column-beam durability assessment method based on twin simulation, characterized in that: include: Collect degradation characteristics of lightweight concrete columns and beams from the material level to the structural level, and construct a three-dimensional multi-scale structural model integrating microscale, mesoscale and macroscale; Performing a coupling simulation of a preset coupling field type using the three-dimensional multi-scale structural model to generate a twin simulation model; monitoring the lightweight concrete column and beam using multi-source sensors to determine real-time performance degradation parameters; updating the parameters of the twin simulation model using the real-time performance degradation parameters; Collect environmental data and column and beam load data in the future time window, input them into the updated twin simulation model to simulate the degradation evolution process, and generate durability assessment results.

2. The lightweight concrete column-beam durability assessment method based on twin simulation according to claim 1, characterized in that: The macroscopic scale includes the geometry of the column-beam structure, reinforcement and load conditions, the mesoscopic scale includes aggregate distribution, pore structure and interface transition zone, and the microscopic scale includes cement-based structure and hydration products.

3. The lightweight concrete column-beam durability assessment method based on twin simulation according to claim 2, characterized in that: The degradation characteristics of lightweight concrete from the material level to the structural level are collected to construct a three-dimensional multi-scale structural model integrating microscale, mesoscale and macroscale, including: Performing multi-scale division on the degradation features to generate micro-scale features, meso-scale features, and macro-scale features; performing uplink transfer coupling based on the microscopic scale feature, the mesoscopic scale feature, and the macroscopic scale feature to generate uplink transfer equivalent parameters; Performing downlink mapping based on the microscale features, the mesoscale features, and the macroscale features to determine downlink response parameters; Multi-scale modeling is performed by combining the micro-scale features, the meso-scale features, the macro-scale features, the uplink transfer equivalent parameters and the downlink response parameters to generate the three-dimensional multi-scale structural model.

4. The lightweight concrete column-beam durability assessment method based on twin simulation according to claim 3, characterized in that: Performing uplink transfer coupling based on the microscopic scale feature, the mesoscopic scale feature, and the macroscopic scale feature to generate uplink transfer equivalent parameters includes: Use graph neural networks to map microstructures and mesostructures, learn structure-performance mapping, and build an upstream transmission coupling network; The uplink transfer coupling network is used to perform macro-equivalent transfer mapping on the microscopic scale features and the mesoscopic scale features to generate the uplink transfer equivalent parameters.

5. The lightweight concrete column-beam durability assessment method based on twin simulation according to claim 3, characterized in that: Performing downlink mapping based on the microscopic scale feature, the mesoscopic scale feature, and the macroscopic scale feature to determine a downlink response parameter includes: Use graph structures to represent microstructures and mesostructures, conduct physical field propagation, and establish a structure-load-response network from macroscopic and mesoscopic loading to microscopic response; Downlink mapping is performed on the mesoscopic scale features and the macroscopic scale features using the structure-load-response network to generate the downlink response parameters.

6. The lightweight concrete column-beam durability assessment method based on twin simulation according to claim 1, characterized in that: The preset coupling field types include thermal-moisture migration coupling, mechanical-chemical coupling, freeze-thaw-mechanical coupling and steel corrosion expansion coupling.

7. The lightweight concrete column-beam durability assessment method based on twin simulation according to claim 6, characterized in that: Performing a coupling simulation of a preset coupling field type using the three-dimensional multi-scale structural model to generate a twin simulation model includes: Collecting historical reaction damage data based on the preset coupling field type and constructing a multi-field reaction-damage constitutive model; The multi-field reaction-damage constitutive model is used to perform coupling simulation to generate the twin simulation model.

8. The lightweight concrete column-beam durability assessment method based on twin simulation according to claim 1, characterized in that: Updating the parameters of the twin simulation model using the real-time performance degradation parameters includes: The twin simulation model matches the simulation parameters corresponding to the real-time performance degradation parameters, updates the simulation parameters, and completes the update of the twin simulation model.

9. The lightweight concrete column-beam durability assessment method based on twin simulation according to claim 1, characterized in that: The environmental data is predicted by collecting historical environmental data of the location of the lightweight concrete column and beam and performing trend analysis. The column and beam load data is a load sequence determined by collecting historical load data and performing load fluctuation analysis.

10. Lightweight concrete column and beam durability assessment equipment based on twin simulation, characterized in that: The device is used to perform the lightweight concrete column-beam durability assessment method based on twin simulation according to any one of claims 1 to 9, comprising: 3D model construction module: collects degradation characteristics of lightweight concrete columns and beams from the material level to the structural level, and constructs a 3D multi-scale structural model integrating microscale, mesoscale and macroscale; Coupling simulation module: performing coupling simulation of a preset coupling field type using the three-dimensional multi-scale structural model to generate a twin simulation model; Column and beam monitoring module: monitors the lightweight concrete columns and beams through multi-source sensors to determine real-time performance degradation parameters; Parameter updating module: updating the parameters of the twin simulation model using the real-time performance degradation parameters; Deterioration evolution module: Collects environmental data and column and beam load data in the future time window, inputs the updated twin simulation model to simulate the degradation evolution process, and generates durability assessment results.

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