Metal roof stress corrosion early warning system using acoustic emission technology

Abstract

The embodiment of the present disclosure relates to a metal roof stress corrosion early warning system adopting acoustic emission technology. Wherein, an acoustic emission sensor array is configured to collect acoustic emission signals; a server is configured to locate a potential crack area in the metal roof and an associated corrosion area of the potential crack area based on the acoustic emission signals; construct a finite element model of a target area including the potential crack area and the associated corrosion area to determine stress distribution data of the target area; drive a cellular automaton model of the target area by the stress distribution data to determine corrosion trend data of the target area; determine whether to send an early warning instruction to the early warning end based on the corrosion trend data; and an early warning end is configured to output early warning information in response to the early warning instruction. Thus, the accuracy of metal roof stress corrosion early warning can be improved.

Description

Technical Field

[0001] This disclosure relates to the fields of building engineering and non-destructive testing technology, and in particular to a stress corrosion early warning system for metal roofs using acoustic emission technology. Background Technology

[0002] Acoustic emission technology refers to a non-destructive testing method that analyzes the properties or structural integrity of materials by receiving and analyzing their acoustic emission signals.

[0003] Metal roofing refers to a type of roofing that uses metal sheets as the roofing material, combining the structural layer and the waterproofing layer into one.

[0004] In related technologies, metallic materials, under specific alloy material environments, can experience delayed cracking or delayed fracture due to stress (generally or primarily tensile or compressive stress) or the influence of specific corrosive media, resulting in a corrosive damage phenomenon. This generally occurs because the metal or alloy loses its original electronic properties, leading to a decrease in overall strength. Under stress, it can exhibit no signs of deformation even at levels far below its yield strength. This is also known as low-stress brittle fracture or stress corrosion. This phenomenon accounts for a high proportion of corrosion damage in petrochemical equipment. Chemical equipment is mostly made of metals and their alloys. High-temperature or high-pressure conditions and multiple media are also common in chemical equipment. Furthermore, chemical equipment is prone to stress conditions during manufacturing, construction, installation, and use. Stress corrosion is caused by the combined effects of stress and corrosion.

[0005] In some applications of metal roofing, stress corrosion testing is required. Examples include metal roofing in chemical workshops and warehouses, metal roofing in coastal or high-humidity areas, metal roofing with large spans or heavy loads (such as stadiums and airports), and metal roofing in scenarios with large temperature differences or frequent thermal expansion and contraction.

[0006] In practice, when acoustic emission detection technology is applied to stress corrosion detection of metal roofs, it can monitor the acoustic emission signals generated by the roof during stress or corrosion in real time. This allows for the determination of the initiation and propagation locations of stress corrosion cracks, and the analysis of signal characteristics can assess the degree of corrosion and defect activity. Furthermore, this method can achieve long-term online monitoring without damaging the roof structure, providing a basis for roof maintenance. However, acoustic emission signals are susceptible to environmental noise interference, which can lead to misjudgments or missed detections in noisy environments. This results in relatively low accuracy for stress corrosion early warning of metal roofs using acoustic emission technology. Summary of the Invention

[0007] In view of this, in order to solve some or all of the above-mentioned technical problems, this disclosure provides a metal roof stress corrosion early warning system using acoustic emission technology.

[0008] In a first aspect, embodiments of this disclosure provide a stress corrosion early warning system for metal roofs employing acoustic emission technology. The system includes an acoustic emission sensor array, a server, and an early warning terminal. Each acoustic emission sensor in the acoustic emission sensor array is distributed and disposed on the metal roof. The server is communicatively connected to each acoustic emission sensor in the acoustic emission sensor array and the early warning terminal. Wherein:

[0009] The acoustic emission sensor array is configured to: acquire acoustic emission signals; and transmit the acoustic emission signals to the server.

[0010] The server is configured to: locate potential crack areas and associated corrosion areas in the metal roof based on the acoustic emission signal; construct a finite element model of the target area using the potential crack areas and the associated corrosion areas as target areas to determine stress distribution data of the target areas; drive a cellular automaton model of the target areas using the stress distribution data to determine corrosion trend data of the target areas; and determine whether to send an early warning command to the early warning terminal based on the corrosion trend data.

[0011] The warning terminal is configured to output warning information in response to the warning command.

[0012] In some possible implementations, the server includes a first server and a second server, which are communicatively connected; and

[0013] The first server is configured to: construct a finite element model of the target region to determine stress distribution data of the target region; determine the timestamp corresponding to the stress distribution data; and send the stress distribution data and the timestamp to the second server.

[0014] The second server is configured to: drive a cellular automaton model of the target area using the stress distribution data to determine corrosion trend data of the target area; and determine whether to send an early warning command to the early warning terminal based on the corrosion trend data and the timestamp.

[0015] In some possible implementations, the first server is specifically configured to:

[0016] Determine the crack geometry, material properties, and environmental loads in the target region;

[0017] Based on the crack geometry, the material properties, and the environmental load, a three-dimensional model of the target region is constructed. The three-dimensional model includes a first mesh element and a second mesh element. The crack tip radius corresponding to the first mesh element is smaller than the crack tip radius corresponding to the second mesh element, and the size of the first mesh element is smaller than the size of the second mesh element.

[0018] A finite element model of the target region is constructed by determining displacement boundary conditions that match the support type of the metal roof and mapping the environmental loads to nodal forces or surface pressures of the three-dimensional model.

[0019] In some possible implementations, the second server is specifically configured to:

[0020] Establish the spatial correspondence between the nodes of the finite element model and the cells of the cellular automata model to be constructed;

[0021] Based on the spatial correspondence, the stress distribution data, and the property parameters of the metal roof, the initial state parameters of each cell in the cellular automata model are determined, wherein the initial state parameters include: stress state parameters, corrosion depth, damage variables, and crack propagation probability.

[0022] According to the preset update rules, the initial state parameters are iteratively calculated to obtain multiple updated state parameters;

[0023] Based on the initial state parameters and the updated multiple state parameters, the corrosion trend data of the target area is determined.

[0024] In some possible implementations,

[0025] The second server is also configured to transmit the corrosion depth, damage variables, and crack propagation probability from the updated multiple state parameters as corrosion damage results to the first server.

[0026] The first server is further configured to: update the finite element model based on the corrosion damage results; calculate new stress distribution data based on the updated finite element model; and transmit the new stress distribution data to the second server.

[0027] Secondly, embodiments of this disclosure provide a method for early warning of stress corrosion on metal roofs using acoustic emission technology. The method is applied to a server, which is communicatively connected to each acoustic emission sensor in an acoustic emission sensor array and an early warning terminal. The acoustic emission sensors in the acoustic emission sensor array are distributed on the metal roof. The method includes:

[0028] Acquire the acoustic emission signals collected by the acoustic emission sensor array;

[0029] Based on the acoustic emission signal, the potential crack area in the metal roof and the associated corrosion area of ​​the potential crack area are located.

[0030] Using the potential crack region and the associated corrosion region as target regions, a finite element model of the target regions is constructed to determine the stress distribution data of the target regions.

[0031] The stress distribution data is used to drive a cellular automaton model of the target area to determine the corrosion trend data of the target area.

[0032] Based on the corrosion trend data, it is determined whether to send a warning command to the warning terminal, so that the warning terminal responds to the warning command and outputs warning information.

[0033] In some possible implementations, after determining the stress distribution data of the target region and before driving the cellular automata model of the target region using the stress distribution data, the method further includes:

[0034] Determine the timestamp corresponding to the stress distribution data; and

[0035] After determining the corrosion trend data of the target area, the method further includes:

[0036] Based on the corrosion trend data and the timestamp, determine whether to send an early warning command to the early warning terminal.

[0037] In some possible implementations, constructing the finite element model of the target region includes:

[0038] Determine the crack geometry, material properties, and environmental loads in the target region;

[0039] Based on the crack geometry, the material properties, and the environmental load, a three-dimensional model of the target region is constructed. The three-dimensional model includes a first mesh element and a second mesh element. The crack tip radius corresponding to the first mesh element is smaller than the crack tip radius corresponding to the second mesh element, and the size of the first mesh element is smaller than the size of the second mesh element.

[0040] A finite element model of the target region is constructed by determining displacement boundary conditions that match the support type of the metal roof and mapping the environmental loads to nodal forces or surface pressures of the three-dimensional model.

[0041] In some possible implementations, the step of driving a cellular automaton model of the target region using the stress distribution data to determine corrosion trend data of the target region includes:

[0042] Establish the spatial correspondence between the nodes of the finite element model and the cells of the cellular automata model to be constructed;

[0043] Based on the spatial correspondence, the stress distribution data, and the property parameters of the metal roof, the initial state parameters of each cell in the cellular automata model are determined, wherein the initial state parameters include: stress state parameters, corrosion depth, damage variables, and crack propagation probability.

[0044] According to the preset update rules, the initial state parameters are iteratively calculated to obtain multiple updated state parameters;

[0045] Based on the initial state parameters and the updated multiple state parameters, the corrosion trend data of the target area is determined.

[0046] In some possible implementations, after iteratively calculating the initial state parameters according to a preset update rule to obtain updated state parameters, the method further includes:

[0047] The corrosion depth, damage variables, and crack propagation probability from the updated multiple state parameters are used as corrosion damage results, and the finite element model is updated based on the corrosion damage results.

[0048] Based on the updated finite element model, new stress distribution data are calculated so as to update the cellular automata model of the target region based on the new stress distribution data.

[0049] In some possible implementations, determining whether to send a warning command to the warning terminal based on the corrosion trend data, so that the warning terminal responds to the warning command and outputs warning information, includes:

[0050] Based on the corrosion trend data, the target stress state, target corrosion depth, target damage variables, and target crack propagation probability of the metal roof at the target time are determined.

[0051] Based on the target stress state and the target damage variable, the first remaining life of the metal roof at the target time is determined;

[0052] Based on the target corrosion depth, determine the second remaining life of the metal roof at the target time;

[0053] Based on the target crack propagation probability, the third remaining lifetime of the metal roof at the target time is determined;

[0054] Based on the first remaining lifetime, the second remaining lifetime, and the third remaining lifetime, determine whether to send an early warning command to the early warning terminal.

[0055] This disclosure provides a metal roof stress corrosion early warning system using acoustic emission technology, comprising an acoustic emission sensor array, a server, and an early warning terminal. Each acoustic emission sensor in the acoustic emission sensor array is distributed across the metal roof. The server is communicatively connected to each acoustic emission sensor in the acoustic emission sensor array and the early warning terminal. The acoustic emission sensor array is configured to: collect acoustic emission signals; and transmit the acoustic emission signals to the server. The server is configured to: locate potential crack areas and associated corrosion areas in the metal roof based on the acoustic emission signals; construct a finite element model of the target area using the potential crack areas and the associated corrosion areas as target areas to determine stress distribution data of the target areas; drive a cellular automaton model of the target areas using the stress distribution data to determine corrosion trend data of the target areas; and determine whether to send an early warning command to the early warning terminal based on the corrosion trend data. The early warning terminal is configured to: output early warning information in response to the early warning command. Therefore, stress distribution data of potential crack regions and associated corrosion regions can be determined through finite element models. Then, the stress distribution data can be used to drive cellular automata models of potential crack regions and associated corrosion regions to determine the corrosion trends of these regions. In this way, by combining finite element models and cellular automata models to determine the corrosion trends of potential crack regions and associated corrosion regions, the stress corrosion situation of metal roofs can be determined more accurately, thereby improving the accuracy of stress corrosion early warning for metal roofs.

[0056] This disclosure provides a method for early warning of stress corrosion on metal roofs using acoustic emission technology. The method is applied to a server, which is communicatively connected to each acoustic emission sensor in an acoustic emission sensor array and an early warning terminal. The acoustic emission sensors in the array are distributed across the metal roof. The method acquires acoustic emission signals collected by the acoustic emission sensor array. Then, based on the acoustic emission signals, it locates potential crack areas and associated corrosion areas in the metal roof. Next, it constructs a finite element model of the target area, using the potential crack areas and associated corrosion areas as target areas, to determine the stress distribution data of the target area. Subsequently, it drives a cellular automaton model of the target area using the stress distribution data to determine the corrosion trend data of the target area. Finally, based on the corrosion trend data, it determines whether to send an early warning command to the early warning terminal, so that the early warning terminal responds to the early warning command and outputs early warning information. Therefore, stress distribution data of potential crack regions and associated corrosion regions can be determined through finite element models. Then, the stress distribution data can be used to drive cellular automata models of potential crack regions and associated corrosion regions to determine the corrosion trends of these regions. In this way, by combining finite element models and cellular automata models to determine the corrosion trends of potential crack regions and associated corrosion regions, the stress corrosion situation of metal roofs can be determined more accurately, thereby improving the accuracy of stress corrosion early warning for metal roofs. Attached Figure Description

[0057] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.

[0058] To more clearly illustrate the technical solutions in the embodiments of this disclosure or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0059] One or more embodiments are illustrated by way of example with reference numerals in the accompanying drawings. These illustrations do not constitute a limitation on the embodiments. Elements with the same reference numerals in the drawings are denoted as similar elements. Unless otherwise stated, the figures in the drawings are not to be limited by scale.

[0060] Figure 1 An interactive schematic diagram of a metal roof stress corrosion early warning system using acoustic emission technology provided in an embodiment of this disclosure;

[0061] Figure 2An interactive schematic diagram of another metal roof stress corrosion early warning system using acoustic emission technology provided in this embodiment of the present disclosure;

[0062] Figure 3 A schematic flowchart illustrating a method for early warning of stress corrosion of metal roofs using acoustic emission technology, provided in an embodiment of this disclosure;

[0063] Figure 4 A schematic diagram showing the distribution of an acoustic emission sensor array in a metal roof stress corrosion early warning system employing acoustic emission technology, provided as an embodiment of this disclosure;

[0064] Figure 5 This is a schematic diagram of the structure of a server provided in an embodiment of the present disclosure. Detailed Implementation

[0065] Various exemplary embodiments of the present disclosure will now be described in detail with reference to the accompanying drawings. It is obvious that the described embodiments are only a part of, and not all, of the embodiments described herein. It should be noted that, unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps set forth in these embodiments do not limit the scope of the present disclosure.

[0066] Those skilled in the art will understand that the terms "first" and "second" in the embodiments of this disclosure are only used to distinguish different steps, devices or modules, and do not represent any specific technical meaning, nor do they indicate the logical order between them.

[0067] It should also be understood that in this embodiment, "multiple" can refer to two or more, and "at least one" can refer to one, two or more.

[0068] It should also be understood that any component, data or structure mentioned in the embodiments of this disclosure can generally be understood as one or more unless expressly defined or given to the contrary in the context.

[0069] Furthermore, the term "and / or" in this disclosure is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this disclosure generally indicates that the preceding and following related objects have an "or" relationship.

[0070] It should also be understood that the description of the various embodiments in this disclosure emphasizes the differences between the various embodiments, and the similarities or similarities can be referred to each other. For the sake of brevity, they will not be described in detail.

[0071] The following description of at least one exemplary embodiment is merely illustrative and is not intended to limit the scope of this disclosure or its application or use.

[0072] Techniques, methods, and equipment known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and equipment should be considered part of the specification.

[0073] It should be noted that similar labels and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be discussed further in subsequent figures.

[0074] It should be noted that, unless otherwise specified, the embodiments and features described in this disclosure can be combined with each other. To facilitate understanding of the embodiments of this disclosure, the disclosure will be described in detail below with reference to the accompanying drawings and embodiments. Obviously, the described embodiments are only some, not all, of the embodiments of this disclosure. All other embodiments obtained by those skilled in the art based on the embodiments of this disclosure without creative effort are within the scope of protection of this disclosure.

[0075] To address the low accuracy of existing acoustic emission technology for early warning of stress corrosion on metal roofs, this disclosure provides a stress corrosion early warning system for metal roofs using acoustic emission technology. This system can determine the stress distribution data of potential crack regions and associated corrosion regions through a finite element model. Then, the stress distribution data drives cellular automata models of the potential crack regions and associated corrosion regions to determine their corrosion trends. Thus, by combining the finite element model and the cellular automata model to determine the corrosion trends of potential crack regions and associated corrosion regions, the stress corrosion situation of the metal roof can be determined more accurately, thereby improving the accuracy of stress corrosion early warning for metal roofs.

[0076] Figure 1 This is a schematic diagram of a stress corrosion early warning system for metal roofs using acoustic technology, provided as an embodiment of this disclosure.

[0077] like Figure 1 As shown, the system includes an acoustic emission sensor array, a server, and an early warning terminal. The acoustic emission sensor array includes multiple acoustic emission sensors. These sensors are distributed across the metal roof. For example, the acoustic emission sensors can be evenly distributed across the metal roof. Alternatively, the number and location of the acoustic emission sensors can be determined according to the complexity of the metal roof structure. For example, a more complex structure can use a larger number of acoustic emission sensors; a simpler structure can use a smaller number.

[0078] For example, such as Figure 4 As shown, Figure 4This diagram illustrates the distribution of the acoustic emission sensor array in a stress corrosion early warning method for metal roofs employing acoustic emission technology, as provided in an embodiment of this disclosure. Figure 4 The metal roof 401 shown is equipped with multiple acoustic emission sensors 402, that is, an acoustic emission sensor array.

[0079] The server is communicatively connected to each acoustic emission sensor in the acoustic emission sensor array and the early warning terminal.

[0080] Metal roofing refers to a roofing system that uses metal sheets as the roofing material, combining the structural layer and the waterproofing layer into one. Examples include metal roofs used in chemical workshops and warehouses, metal roofs in coastal or high-humidity areas, large-span or heavy-duty metal roofs (such as stadiums and airports), and metal roofs in environments with large temperature differences or frequent thermal expansion and contraction.

[0081] Acoustic emission sensors can be used to detect acoustic emission signals generated by materials or structures during stress due to the propagation of internal defects, the formation of cracks, etc.

[0082] The server can include one or more servers.

[0083] The early warning system can be used to issue risk warnings when it is determined that there is an existing or impending risk of stress corrosion on metal roofs.

[0084] In step 101, the acoustic emission sensor array acquires acoustic emission signals.

[0085] In practice, when materials are subjected to external forces or deformation, they release energy and generate elastic waves, which are known as acoustic emission signals. By analyzing acoustic emission signals, information such as internal structural changes and damage to the material can be obtained.

[0086] The acoustic emission sensors in the acoustic emission sensor array can be, for example, acoustic emission piezoelectric sensors, fiber optic acoustic emission sensors, etc.

[0087] In step 102, the acoustic emission sensor array transmits the acoustic emission signal to the server.

[0088] Here, after the acoustic emission sensor array collects the acoustic emission signal, the acoustic emission sensor array can transmit the acoustic emission signal to the server so that the server can analyze it.

[0089] In step 103, the server locates potential crack areas in the metal roof and associated corrosion areas of the potential crack areas based on the acoustic emission signal.

[0090] Potential crack zones refer to areas within the metal roofing material where microscopic defects (such as dislocation slip and grain boundary separation) have already appeared, but have not yet formed macroscopically visible cracks. These areas are usually at the critical state of crack initiation due to stress concentration or corrosive media, and are potential risk points for structural failure.

[0091] Associated corrosion zones refer to corrosion sites that are spatially or mechanistically related to potential crack areas. Corrosion can accelerate crack initiation (such as stress corrosion cracking), while crack propagation can provide pathways for corrosive media to penetrate, often creating a vicious cycle. Associated corrosion zones can be in direct contact with crack initiation sites or indirectly linked through the electrochemical properties of the material.

[0092] Here, based on the acoustic emission signal, the time difference data of at least three acoustic emission sensors can be determined. Therefore, a time difference positioning algorithm can be used to solve the three-dimensional coordinates by solving simultaneous equations in order to locate potential crack areas in the metal roof.

[0093] Furthermore, associated corrosion regions of potential crack areas can be determined based on pre-defined correspondence data. This correspondence data represents the relationship between potential crack areas and their associated corrosion regions. Alternatively, a pre-trained machine learning model can be used to determine the associated corrosion regions of potential crack areas. This machine learning model can be a convolutional neural network trained using training samples containing data on potential crack areas and their associated corrosion regions.

[0094] In some cases, the server can obtain acoustic emission signals collected in real time by an acoustic emission sensor array, removing noise such as electromagnetic interference and mechanical vibration. Then, a time-difference positioning algorithm is used to calculate the three-dimensional coordinates of the signal source, generating a spatial distribution heat map of potential crack areas. Subsequently, by combining multi-source data such as electrochemical and thermal imaging, the correlation between signal characteristics and corrosion parameters is analyzed, and associated corrosion areas are delineated. Potential crack areas and associated corrosion areas are marked on the three-dimensional model of the metal roof, providing an intuitive basis for maintenance decisions. Through this method, the entire chain of monitoring for stress corrosion defects in metal roofs—from initiation to propagation and correlation—can be achieved, improving the accuracy of structural safety assessments.

[0095] In step 104, the server uses the potential crack region and the associated corrosion region as target regions to construct a finite element model of the target regions in order to determine the stress distribution data of the target regions.

[0096] The finite element model of the target area is a numerical model constructed using the finite element analysis method for potential crack areas and associated corrosion areas located by acoustic emission signals in a metal roof. This model discretizes the continuous target area into a finite number of elements and, combining theories of materials mechanics, fracture mechanics, and corrosion mechanics, simulates its mechanical behavior under actual loads and environmental conditions to analyze stress distribution and predict defect propagation trends.

[0097] Here, the finite element model of the target region can be constructed in the following way:

[0098] First, the geometric model is extracted and simplified: Based on the acoustic emission positioning results, the three-dimensional coordinate range of the target area is obtained (e.g., through point cloud data annotation). Combined with the metal roof drawings, the geometric features of the target area (e.g., plate thickness, joint locations, bolt holes, etc.) are extracted. The model can be simplified as follows: details unrelated to stress distribution (e.g., small protrusions) are ignored, while retaining the geometric morphology of key defects such as cracks and corrosion pits. For corroded areas, an equivalent geometric model (e.g., local thickness reduction or porosity distribution) is constructed based on detection data (e.g., corrosion depth, area).

[0099] Next, mesh generation can be performed: for potential crack tips, a denser mesh (e.g., element size ≤ 0.1 mm) can be used to capture stress singularities; for corrosion boundary regions, a medium-density mesh can be used to reflect material property gradient changes; for regions far from defects, a sparse mesh can be used to balance computational efficiency and accuracy. Regarding mesh type selection, singular elements (such as 6-node triangular elements) are used at crack tips to satisfy the stress field attenuation characteristics in fracture mechanics; tetrahedral or hexahedral elements are used in corrosion regions, combined with swept meshing techniques to improve computational accuracy.

[0100] Then, differentiated definitions of material properties are made: for basic metallic materials, parameters such as elastic modulus (e.g., 210 GPa for steel), Poisson's ratio (0.3), and yield strength can be used; for corrosion-affected zones, material properties can be reduced according to the degree of corrosion. For example, in the case of uniform corrosion, the elastic modulus is reduced proportionally to the corrosion depth (e.g., when the corrosion depth reaches 10%, the modulus drops to 90%). In the case of pitting corrosion, porosity is defined locally, and equivalent elastic parameters are calculated using the mixing rule. For crack regions, a cohesive zone model can be used to define the relationship between crack opening displacement and traction force, simulating crack propagation resistance.

[0101] Then, boundary conditions and loads can be applied: for mechanical boundaries, the actual load borne by the metal roof can be applied; for static loads, self-weight, snow load (e.g., 1.5 kN / m²), and wind load (calculated based on the wind pressure coefficient) can be used; for dynamic loads, thermal stress caused by temperature cycling can be used. For constraint conditions, displacement boundaries can be defined according to the roof support method (e.g., bolted fixing, welding). For corrosion-stress coupling boundaries, corrosion medium concentration boundaries can be applied in the associated corrosion region, and corrosion potential can be defined in conjunction with electrochemical theory.

[0102] Therefore, the stress distribution data of the target area can be determined in the following way:

[0103] First, configure the finite element solver: you can choose static analysis or transient analysis. Static analysis can be used to calculate steady-state stress fields, such as stress distribution under self-weight and static loads. Transient analysis can consider the time-varying effects of dynamic loads (such as temperature changes and wind vibration) on stress.

[0104] Submodeling is employed near the crack tip to perform detailed local calculations in the target region after the global model is solved. The Newton-Raphson method is used iteratively to solve nonlinear problems (such as material plasticity and crack contact).

[0105] Next, stress field post-processing and feature extraction are performed. Mises equivalent stress contour maps are extracted to identify stress concentration regions. Principal stress direction vector maps are plotted to analyze the influence of stress state on crack propagation (e.g., tensile stress promotes open-type crack propagation). Stress distribution data may include, for example, stress intensity factor, corrosion stress ratio, and stress gradient.

[0106] Then, multiphysics coupling verification was performed: the stress concentration region calculated by the finite element method was compared with the acoustic emission event location results, and the finite element model parameters (such as sound velocity and material damping) were corrected. Referring to the corrosion current density measured by the electrochemical workstation, the degree of material softening in the corrosion region was adjusted to ensure that the error between the calculated stress and the experimental results was less than a preset threshold.

[0107] In step 105, the server drives the cellular automata model of the target area using the stress distribution data to determine the corrosion trend data of the target area.

[0108] Cellular automata are a discretized dynamic system simulation method that simulates the evolution of complex systems by dividing a continuous target region into regularly arranged "cells". Each cell iteratively updates its state according to preset local rules (based on the states of neighboring cells and external inputs).

[0109] Corrosion trend data refers to quantitative indicators that reflect the corrosion evolution law of the target area, obtained through cellular automata simulation.

[0110] Here, the stress distribution data can be used to drive a cellular automata model of the target region to determine the corrosion trend data of the target region:

[0111] The first step is data preprocessing and mapping of stress data dimensionality reduction: The three-dimensional stress field (such as Mises stress and stress intensity factor) obtained from finite element analysis is mapped to a cellular mesh using an interpolation algorithm, with each cell corresponding to a unique stress value. A correlation function between stress and corrosion kinetics is established; for example, stress-induced surface film rupture: a stress threshold is used to determine whether a cell has entered an "active corrosion" state.

[0112] The second step is to define and initialize the basic state of the cell, including the initial corrosion depth, material electrode potential, environmental ion concentration, and stress coupling state.

[0113] The third step involves constructing corrosion evolution rules. Electrochemical corrosion rules can be based on Faraday's law, combined with stress correction terms to calculate corrosion depth increments. At crack tips or high-stress cells, an additional "stress corrosion cracking (SCC)" rule is introduced. This rule applies when the actual stress value at a point within the target area exceeds the stress corrosion cracking (SCC) threshold stress (i.e., the subsequent...). When corrosion occurs, the corrosion depth increases exponentially. Furthermore, the accumulation of corrosion products or the diffusion of ion concentrations should be considered; for example, corrosion products in one cell may reduce the oxygen concentration in adjacent cells (representing the oxygen content in the environment near the metal surface), promoting the formation of localized galvanic cells.

[0114] The fourth step is to set the time step according to the corrosion rate accuracy requirements, such as one time step per day or week; the preset number of steps per iteration (e.g., 10-100) is used to output the corrosion depth distribution and rate cloud map once, and the following corrosion trend data are obtained through statistical analysis: the location and depth growth curve of the most severely corroded area; the comparison of the average corrosion rate in different stress ranges; and the spatial overlap index between corrosion pits and high stress areas.

[0115] In step 106, the server determines whether to send a warning command to the warning terminal based on the corrosion trend data.

[0116] Here, the server can determine the current severity of corrosion and the time remaining until the preset corrosion severity is reached based on the corrosion trend data, and then determine whether to send an early warning command to the early warning terminal.

[0117] In some optional implementations of this embodiment, the server may also perform the following steps:

[0118] The first step is to determine the target stress state, target corrosion depth, target damage variables, and target crack propagation probability of the metal roof at the target time based on the corrosion trend data.

[0119] The target time can be the present or a future time.

[0120] Corrosion trend data can include the corrosion rate and depth of the metal roof at different points in time, environmental factors (such as humidity, temperature, chemical concentration, etc.), and historical stress data (i.e. stress values).

[0121] The target stress state can be the stress distribution of the entire area or part of the metal roof at the target time.

[0122] Target stress state The damage rate is affected in the following ways. :when At that time, the corrosion damage rate increases: , denoted as the corrosion damage rate under no stress, and k is the stress sensitivity coefficient. The stress corrosion cracking threshold (SCCT) refers to the critical stress value at which a material will undergo stress corrosion cracking in a specific corrosive environment.

[0123] Stress concentration promotes fatigue damage: The fatigue damage rate in high-stress areas is proportional to the m-th power of the stress amplitude, where m is the fatigue index.

[0124] The target corrosion depth can be the corrosion depth of the entire area or a portion of the metal roof at the target time.

[0125] The target damage variable D(t) can be the degree of material property degradation of the entire or part of the metal roof at the target time, ranging from 0 (no damage) to 1 (complete failure), and can be determined by the following formula: .

[0126] in, This indicates damage caused by corrosion, and is related to the depth of corrosion. α is a material constant, usually 1≤α≤2. Given the current corrosion depth, This refers to the critical corrosion depth, such as the depth at which 30% of the cross-sectional thickness remains. This indicates damage caused by stress, based on fatigue accumulation or creep. , The current stress cycle number is given, and n represents the total number of stress cycles. The fatigue life under the corresponding stress amplitude is determined by the SN (stress-life) curve, which describes the relationship between the stress amplitude (S) and the number of failure cycles (N) under alternating stress.

[0127] The target crack propagation probability can be the crack propagation probability of the entire area or a part of the metal roof at the target time.

[0128] The second step is to determine the first remaining life of the metal roof at the target time based on the target stress state and the target damage variable.

[0129] The first remaining life can be the remaining life of the metal roof at the target time, determined based on the target stress state and target damage variables.

[0130] Here, a critical damage value can be defined. (e.g., 0.9), then the first remaining lifetime The current damage evolution rate It can be obtained through fitting historical monitoring data or finite element simulation. This indicates the current cumulative damage.

[0131] The third step is to determine the second remaining life of the metal roof at the target time based on the target corrosion depth.

[0132] The second remaining lifetime can be the remaining lifetime of the metal roof at the target time, determined based on the target corrosion depth.

[0133] Here, we can first calculate the corrosion depth increment. ,in, Let be the stress correction function, when hour, The k-factor, an empirical coefficient, is related to the material's inherent properties and the corrosive environment it is exposed to. It is determined through experiments or engineering experience and is used to quantify stress exceeding a critical value. The extent of the impact on the correction function. Indicates the target stress state. Indicates the stress corrosion cracking threshold. It represents the yield strength of a material, which is the critical stress value at which a material transitions from elastic deformation to plastic deformation. It reflects the material's ability to resist plastic deformation, and its unit is the same as that of stress. The corrosion depth of structures such as metal roofs is the increment over a certain time interval Δt, used to calculate the development of corrosion over time. The unit is usually meters (m) or millimeters (mm). K represents a constant related to electrochemical corrosion, which is related to electrode reactions, the corrosion system, etc., and may involve factors such as the electrochemical equivalent related to Faraday's law. Different corrosion systems have different values. i(t) represents the corrosion current density as a function of time t, reflecting the strength of the current during corrosion and indirectly indicating the rate of the corrosion reaction. The unit is usually amperes per square meter (A / m²). Generally, the higher the current density, the faster the corrosion rate. This represents the time interval for calculating the corrosion depth increment, such as a day or a month, set according to monitoring or calculation needs, and the unit is a second (s) or other time unit (if using the International System of Units, unit conversion will be required later). 'n' represents the number of electrons transferred in the electrode reaction, determined by the specific electrochemical corrosion reaction, such as the corrosion reaction of iron. In this context, n=2 is a fundamental parameter for electrochemical corrosion. The density of a metallic material is expressed in kilograms per cubic meter (kg / m³). It is used to convert the mass change in electrochemical corrosion into a depth change. Different metallic materials have different densities. F represents the Faraday constant, a fundamental constant in electrochemistry used to relate the amount of charge to the amount of substance. It plays a crucial role in calculating corrosion mass and depth using electrochemical corrosion current.

[0134] Second Remaining Life ,in, This indicates the critical corrosion depth, such as when a metal sheet fails when its thickness is reduced to 50% of its initial value. The current corrosion rate can be predicted by fitting recent corrosion data (e.g., linear regression) or by a mechano-electrochemical model. d(t) represents the current corrosion amount.

[0135] The fourth step is to determine the third remaining life of the metal roof at the target time based on the target crack propagation probability.

[0136] The third remaining lifetime can be the remaining lifetime of the metal roof at the target time, determined based on the target crack propagation probability.

[0137] Here, we can first calculate the crack propagation rate. Where 'a' is the crack length and 'N' represents the number of cycles. Let C be the stress intensity factor amplitude, and Y be the geometric factor. C and m are empirical constants related to the material and environment. C reflects the "basic rate" characteristic of crack propagation under specific environments (such as corrosive media, temperature, etc.), reflecting the inherent sensitivity of the material to fatigue stress corrosion crack propagation. It can be obtained through fatigue tests or stress corrosion tests, and C varies greatly under different materials and environments (such as air, seawater, high temperature). m describes the crack propagation rate as a function of stress intensity factor amplitude and temperature. The "sensitivity" to change. This indicates the target stress state.

[0138] Next, the critical crack length is defined. Current crack length Structural failure was observed. Monte Carlo simulations were used to account for parameter randomness (such as fluctuations in stress and material constants), and the crack propagation rate was used to calculate the crack propagation extent. The time probability distribution yields the crack propagation probability. Curve showing how it changes over time.

[0139] Set failure probability threshold The current crack propagation probability is Then the third remaining lifetime , The probability growth rate can be estimated using the time derivative of the crack propagation model or the slope of historical data. P(t) represents the probability growth amount.

[0140] The fifth step is to determine whether to send a warning command to the warning terminal based on the first remaining lifetime L1, the second remaining lifetime L2, and the third remaining lifetime L3.

[0141] Here, if the minimum value or the weighted sum of the first remaining lifetime, the second remaining lifetime, and the third remaining lifetime is less than the preset remaining lifetime, an early warning command can be sent to the early warning terminal; otherwise, no early warning command needs to be sent to the early warning terminal.

[0142] It is understood that, in the above-mentioned optional implementation methods, the first remaining lifetime, the second remaining lifetime, and the third remaining lifetime can be determined by the target stress state, the target corrosion depth, the target damage variable, and the target crack propagation probability, thereby determining whether to send an early warning command to the early warning terminal. This improves the timeliness of sending early warning commands to the early warning terminal.

[0143] In step 107, the warning terminal responds to the warning command and outputs warning information.

[0144] Here, after receiving the warning command, the warning terminal can output warning information to issue a warning.

[0145] This disclosure provides a metal roof stress corrosion early warning system using acoustic emission technology, comprising an acoustic emission sensor array, a server, and an early warning terminal. Each acoustic emission sensor in the acoustic emission sensor array is distributed across the metal roof. The server is communicatively connected to each acoustic emission sensor in the acoustic emission sensor array and the early warning terminal. The acoustic emission sensor array is configured to: collect acoustic emission signals; and transmit the acoustic emission signals to the server. The server is configured to: locate potential crack areas and associated corrosion areas in the metal roof based on the acoustic emission signals; construct a finite element model of the target area using the potential crack areas and the associated corrosion areas as target areas to determine stress distribution data of the target areas; drive a cellular automaton model of the target areas using the stress distribution data to determine corrosion trend data of the target areas; and determine whether to send an early warning command to the early warning terminal based on the corrosion trend data. The early warning terminal is configured to: output early warning information in response to the early warning command. Therefore, stress distribution data of potential crack regions and associated corrosion regions can be determined through finite element models. Then, the stress distribution data can be used to drive cellular automata models of potential crack regions and associated corrosion regions to determine the corrosion trends of these regions. In this way, by combining finite element models and cellular automata models to determine the corrosion trends of potential crack regions and their associated corrosion regions, the stress corrosion situation of metal roofs can be determined more accurately, thereby improving the accuracy of stress corrosion early warning for metal roofs.

[0146] The following describes the embodiments of this disclosure by way of example. However, it should be noted that the following content is only used to understand the technical solutions of the embodiments of this disclosure and does not constitute a limitation on the protection scope of the embodiments of this disclosure.

[0147] Metal roofs are prone to corrosion and cracking due to environmental factors and loads during long-term use, which can lead to safety accidents in severe cases. Current traditional monitoring methods suffer from low efficiency and poor accuracy, thus necessitating a more efficient and accurate monitoring and early warning system.

[0148] In view of this, this solution can realize real-time monitoring, precise location and early warning of corrosion and cracks in metal roofs, providing reliable data support for the maintenance and management of metal roofs and reducing safety risks.

[0149] In terms of architecture, the system mainly consists of a front-end data acquisition module, an intermediate data transmission module, a back-end data processing and analysis module, and an early warning module.

[0150] Front-end data acquisition module: Employs a distributed acoustic emission sensor array, evenly distributed or arranged on the metal roof according to the probability of stress corrosion. The acoustic emission sensors feature high sensitivity and low power consumption, enabling real-time capture of acoustic emission signals generated by corrosion and crack propagation on the metal roof. Each acoustic emission sensor is equipped with independent identification and positioning information, facilitating accurate data acquisition and analysis.

[0151] Intermediate data transmission module: Uses wireless or wired networks (such as fiber optic or Ethernet) to transmit the acoustic emission signals collected by the front-end acoustic emission sensor to the back-end processing platform, i.e., the aforementioned server.

[0152] Backend data processing and analysis module:

[0153] First, the acquired acoustic emission signals are preprocessed by filtering and denoising to improve signal quality and provide reliable data for subsequent analysis.

[0154] Then, based on information such as the time difference of arrival of the acoustic emission signals, potential crack areas in the metal roof were located. Simultaneously, characteristic parameters related to corrosion and cracking, such as signal intensity and frequency distribution, were extracted. Furthermore, the associated corrosion areas of the potential crack areas were determined.

[0155] Then, based on the extracted feature parameters, a finite element model and a cellular automata model of the metal roof were constructed. The finite element model was used to simulate the mechanical behavior of the metal roof and analyze the stress distribution; the cellular automata model was used to track the corrosion and crack propagation process. Through the synergistic analysis of the two models, the damage development trend of the metal roof was predicted.

[0156] In some cases, finite element models and cellular automata models can be maintained by different devices. Through continuous iteration and data interaction between the finite element models and cellular automata models, the corrosion trend of metal roofs can be analyzed more accurately.

[0157] Early warning module: After setting the early warning threshold, when the monitored data indicates that the corrosion or crack expansion of the metal roof has reached the early warning threshold, the early warning module (i.e., the aforementioned early warning terminal) can promptly issue an early warning message. Early warning methods include audible and visual alarms, SMS notifications, and email pushes, ensuring that relevant personnel can obtain information and take measures immediately.

[0158] It should be noted that, in addition to the contents described above, this embodiment may also include the technical features described in the above embodiments, thereby achieving the technical effect of the metal roof stress corrosion early warning method using acoustic emission technology shown above. Please refer to the above description for details. For the sake of brevity, it will not be elaborated here.

[0159] The stress corrosion early warning method for metal roofs using acoustic emission technology provided in this disclosure combines the advantages of finite element model and cellular automata model. Furthermore, it not only models potential crack areas but also models associated corrosion areas, thus enabling more accurate prediction of corrosion and cracks in metal roofs.

[0160] Please continue reading Figure 2 , Figure 2 This is an interactive schematic diagram of another metal roof stress corrosion early warning system using acoustic emission technology, provided as an embodiment of this disclosure.

[0161] like Figure 2 As shown, the system includes an acoustic emission sensor array, a server, and an early warning terminal. The acoustic emission sensors in the array are distributed across the metal roof. The server is communicatively connected to each acoustic emission sensor in the array and the early warning terminal. The server includes a first server and a second server, which are communicatively connected.

[0162] In step 201, the acoustic emission sensor array acquires acoustic emission signals.

[0163] In step 202, the acoustic emission sensor array transmits the acoustic emission signal to the first server.

[0164] In step 203, the first server locates potential crack areas in the metal roof and associated corrosion areas of the potential crack areas based on the acoustic emission signal.

[0165] In step 204, the first server uses the potential crack region and the associated corrosion region as target regions, and constructs a finite element model of the target regions to determine the stress distribution data of the target regions.

[0166] In step 205, the first server determines the timestamp corresponding to the stress distribution data.

[0167] The timestamp corresponding to the stress distribution data can represent the generation time of the stress distribution data or the acquisition time of the acoustic emission signal.

[0168] In step 206, the first server sends the stress distribution data and the timestamp to the second server.

[0169] In step 207, the second server drives the cellular automata model of the target area using the stress distribution data to determine the corrosion trend data of the target area.

[0170] In step 208, the second server determines whether to send a warning instruction to the warning terminal based on the corrosion trend data and the timestamp.

[0171] Here, based on corrosion trend data, the stress corrosion status of the metal roof at the time indicated by the timestamp can be determined, thereby determining whether to send an early warning command to the early warning terminal now or in the future.

[0172] In step 209, the warning terminal responds to the warning command and outputs warning information.

[0173] In some optional implementations of this embodiment, the first server is specifically configured as follows:

[0174] The first step is to determine the crack geometry, material properties, and environmental loads of the target region.

[0175] Crack geometry can be used to define the crack's shape (e.g., surface cracks, penetrating cracks), size (length, depth, width), and location (specific distribution within the target area, such as near a joint in a metal roof). These parameters determine the initial state of the crack and form the basis for subsequent analysis of crack propagation and structural failure. Cracks with different geometries exhibit significantly different stress concentration effects and propagation patterns. For example, a semi-elliptical surface crack and a penetrating straight crack show completely different stress distributions and propagation trends under the same load.

[0176] Material properties are used to obtain the mechanical performance parameters of metallic materials in the target area, such as elastic modulus, Poisson's ratio, yield strength, fracture toughness, and environment-related parameters (such as corrosion rate and stress corrosion sensitivity coefficient under specific corrosive environments, if environmental interactions are involved). Material properties are the core input of constitutive relations in finite element calculations, directly affecting the stress-strain response of the structure. For example, high-strength steel and ordinary carbon steel have drastically different stress distributions and deformations under the same load due to differences in elastic modulus and yield strength.

[0177] Environmental loads are used to determine the external forces acting on a target area, including mechanical loads (such as wind loads, snow loads, and localized loads caused by pedestrian traffic on metal roofs), temperature loads (stress caused by thermal expansion and contraction due to changes in ambient temperature), and corrosive environments (such as the chemical effects of humid air and acid rain, which may be manifested in terms of corrosive medium concentration and temperature). Environmental loads are the external driving forces that cause stress, deformation, and damage (such as crack propagation) in a structure. Accurately defining the type and magnitude of the loads is essential to simulating the actual stress state of the structure and assessing the impact of cracks on structural safety.

[0178] The second step is to construct a three-dimensional model of the target region based on the crack geometry, the material properties, and the environmental load.

[0179] The three-dimensional model includes a first mesh unit and a second mesh unit. The crack tip radius corresponding to the first mesh unit is smaller than the crack tip radius corresponding to the second mesh unit, and the size of the first mesh unit is smaller than the size of the second mesh unit.

[0180] Here, using the crack geometry, material properties, and environmental loads obtained in the first step, a three-dimensional solid model that reflects the true structure and crack characteristics of the target area can be built with the help of three-dimensional modeling software.

[0181] The purpose of mesh generation is to discretize a continuous 3D model into a finite number of mesh elements, and then obtain the overall structure by solving the mechanical response of each element.

[0182] The first mesh element is located near the crack tip, with a small crack tip radius and size. This is because severe stress concentration and an extremely high stress gradient exist at the crack tip, requiring a fine mesh to accurately capture the stress field distribution. A small mesh size and small tip radius can more accurately simulate the singularity of the crack tip (the rapid changes in stress and strain near the tip according to a specific pattern), ensuring the accuracy of crack propagation analysis. This allows for the precise calculation of the stress intensity factor at the crack tip (a parameter used to determine whether a crack will propagate), providing a reliable basis for subsequent crack propagation analysis.

[0183] The second mesh element is located far from the crack region, with a large tip radius and size. Its stress distribution is relatively gentle, so an overly fine mesh is unnecessary. Larger mesh sizes and tip radii can reduce the number of elements and improve computational efficiency while maintaining a certain level of accuracy. This allows for a reduction in computational scale without compromising overall analysis accuracy, thus balancing computational precision and cost.

[0184] In this way, by using a strategy of "focusing on critical areas with a dense grid (first grid cell) and simplifying non-critical areas with a sparse grid (second grid cell)," both computational accuracy and efficiency can be achieved.

[0185] The third step involves constructing a finite element model of the target region by determining displacement boundary conditions that match the support type of the metal roof and mapping the environmental loads to nodal forces or surface pressures of the three-dimensional model.

[0186] Displacement boundary conditions are used to match support types. Specifically, based on the actual support type of the metal roof (such as fixed supports, hinged supports, sliding supports, etc.), corresponding displacement constraints are set in the finite element model. For example, fixed supports restrict displacement and rotation in all directions; hinged supports restrict linear displacement but allow rotation about the hinge axis. This simulates the actual constraint state of the structure, ensuring that the stress and deformation of the structure in the finite element calculation conform to actual engineering conditions. If the boundary conditions are set incorrectly, the calculated stress and deformation results will deviate greatly from reality, rendering the analysis meaningless.

[0187] Environmental load mapping (nodal forces or surface pressure): The environmental loads determined in the first step are converted into loading forms that the model can recognize, according to the requirements of finite element analysis. If it is a concentrated force (such as a local impact load), it can be mapped as a nodal force applied to the corresponding node; if it is a distributed force (such as wind load, liquid pressure, etc.), it is converted into surface pressure and applied uniformly or non-uniformly to the model surface.

[0188] It is understandable that among the above-mentioned optional implementation methods, the finite element model can be subjected to external loads consistent with reality, and the calculated structural response (stress, deformation, crack propagation trend, etc.) can reflect the performance under real working conditions, providing data support for subsequent evaluation of the safety and remaining life of metal roofs in the cracked state.

[0189] In some optional implementations of this embodiment, the second server is specifically configured as follows:

[0190] The first step is to establish the spatial correspondence between the nodes of the finite element model and the cells of the cellular automata model to be constructed.

[0191] Cellular automata are discretized mesh models (cells can be viewed as discrete elements), while finite element methods (FEM) are discretized solutions for a continuum (nodes can be viewed as discrete points). It is necessary to "map" the nodes of the finite element method to the cells of the cellular automata to achieve a one-to-one spatial correspondence.

[0192] If the node coordinates of the finite element model are Cellular automata divides cells into the same coordinate system, ensuring that the spatial position of each cell coincides with (or contains) a node. Example: In a finite element model of a metal roof, if the node spacing is 5 mm, then the cell size of the cellular automaton is also set to 5 mm, and the cell center is aligned with the node coordinates.

[0193] The second step is to determine the initial state parameters of each cell in the cellular automata model based on the spatial correspondence, the stress distribution data, and the property parameters of the metal roof.

[0194] The initial state parameters include: stress state parameters, corrosion depth, damage variables, and crack propagation probability.

[0195] The definitions and determination methods for stress state parameters, corrosion depth, damage variables, and crack propagation probability can be found in the description above, and will not be repeated here.

[0196] The third step is to iteratively calculate the initial state parameters according to the preset update rules to obtain the updated state parameters.

[0197] The local rules of cellular automata, namely that the next state of each cell is determined only by its current state and the states of its neighboring cells, embody the evolutionary logic of "local interaction".

[0198] Here, different update rules can be determined for the stress state parameters, corrosion depth, damage variables, and crack propagation probability to iteratively calculate the initial state parameters. As an example, the corrosion depth can be updated based on the following formula. .in, It is a correction function of stress value and current corrosion depth on corrosion rate (e.g., higher stress accelerates corrosion). Indicates the current corrosion depth. Indicates the next corrosion depth. express and The time interval. k represents the increment of the baseline corrosion depth per unit time and under unit correction conditions.

[0199] The fourth step is to determine the corrosion trend data of the target area based on the initial state parameters and the updated multiple state parameters.

[0200] Here, curve fitting can be used to determine the corrosion trend data of the target area based on the initial state parameters and the updated multiple state parameters.

[0201] It is understood that, among the above-mentioned optional implementation methods, the initial state parameters such as stress state parameters, corrosion depth, damage variables, and crack propagation probability of each cell in the cellular automaton model can be determined based on the spatial correspondence between the finite element model and the cellular automaton model, stress distribution data, and the property parameters of the metal roof. Corrosion trend data can be determined accordingly, thereby further improving the accuracy of determining the corrosion trend.

[0202] In some application scenarios of the above optional implementation methods, the second server is further configured to transmit the corrosion depth, damage variables and crack propagation probability from the updated multiple state parameters as corrosion damage results to the first server.

[0203] The first server is further configured to: update the finite element model based on the corrosion damage results; calculate new stress distribution data based on the updated finite element model; and transmit the new stress distribution data to the second server.

[0204] The method for updating the finite element model here can refer to the process of constructing the finite element model described above. The calculation of the new stress distribution data can be found in the description above. It will not be repeated here.

[0205] It is understandable that in the above application scenarios, the finite element model and the cellular automaton model can be continuously updated and iterated through data interaction. This allows the updated finite element model to more accurately determine the stress distribution, and the updated cellular automaton model to more accurately determine the corrosion trend, thereby providing a more accurate early warning of stress corrosion on metal roofs.

[0206] The stress corrosion early warning system for metal roofs using acoustic emission technology provided in this embodiment can more accurately determine the time point corresponding to corrosion trend data by using the timestamp corresponding to the stress distribution data. This improves the accuracy of determining corrosion trend data and thus enhances the timeliness of early warning.

[0207] Please continue reading Figure 3 , Figure 3 This is a flowchart illustrating a method for early warning of stress corrosion of metal roofs using acoustic emission technology, provided in an embodiment of this disclosure. This method can be applied to a server, which may include one or more servers, such as a first server and a second server. Furthermore, the execution entity of this method can be hardware or software. When the execution entity is hardware, it can be one or more of the aforementioned servers. For example, a single server can execute this method, or multiple servers can cooperate with each other to execute this method. When the execution entity is software, this method can be implemented as multiple software programs or software modules, or as a single software program or software module. No specific limitations are imposed here.

[0208] The server is communicatively connected to each acoustic emission sensor in the acoustic emission sensor array and the early warning terminal, and each acoustic emission sensor in the acoustic emission sensor array is distributed on the metal roof.

[0209] like Figure 3 As shown, the method specifically includes:

[0210] Step 301: Acquire the acoustic emission signal collected by the acoustic emission sensor array.

[0211] Step 302: Based on the acoustic emission signal, locate the potential crack area in the metal roof and the associated corrosion area of ​​the potential crack area.

[0212] Step 303: Using the potential crack region and the associated corrosion region as target regions, construct a finite element model of the target regions to determine the stress distribution data of the target regions.

[0213] Step 304: Drive the cellular automata model of the target area using the stress distribution data to determine the corrosion trend data of the target area.

[0214] Step 305: Based on the corrosion trend data, determine whether to send a warning command to the warning terminal, so that the warning terminal responds to the warning command and outputs warning information.

[0215] In some optional implementations of this embodiment, after determining the stress distribution data of the target region and before driving the cellular automata model of the target region using the stress distribution data, the method further includes:

[0216] Determine the timestamp corresponding to the stress distribution data; and

[0217] After determining the corrosion trend data of the target area, the method further includes:

[0218] Based on the corrosion trend data and the timestamp, determine whether to send an early warning command to the early warning terminal.

[0219] In some application scenarios of the above optional implementation methods, constructing the finite element model of the target region includes:

[0220] Determine the crack geometry, material properties, and environmental loads in the target region;

[0221] Based on the crack geometry, the material properties, and the environmental load, a three-dimensional model of the target region is constructed. The three-dimensional model includes a first mesh unit and a second mesh unit. The crack tip radius corresponding to the first mesh unit is smaller than the crack tip radius corresponding to the second mesh unit, and the size of the first mesh unit is smaller than the size of the second mesh unit.

[0222] A finite element model of the target region is constructed by determining displacement boundary conditions that match the support type of the metal roof and mapping the environmental loads to nodal forces or surface pressures of the three-dimensional model.

[0223] In some application scenarios of the above-mentioned optional implementations, the step of using the stress distribution data to drive the cellular automata model of the target region to determine the corrosion trend data of the target region includes:

[0224] Establish the spatial correspondence between the nodes of the finite element model and the cells of the cellular automata model to be constructed;

[0225] Based on the spatial correspondence, the stress distribution data, and the property parameters of the metal roof, the initial state parameters of each cell in the cellular automata model are determined, wherein the initial state parameters include: stress state parameters, corrosion depth, damage variables, and crack propagation probability.

[0226] According to the preset update rules, the initial state parameters are iteratively calculated to obtain multiple updated state parameters;

[0227] Based on the initial state parameters and the updated multiple state parameters, the corrosion trend data of the target area is determined.

[0228] In some of the above application scenarios, after iteratively calculating the initial state parameters according to a preset update rule to obtain multiple updated state parameters, the method further includes:

[0229] The corrosion depth, damage variables, and crack propagation probability from the updated multiple state parameters are used as corrosion damage results, and the finite element model is updated based on the corrosion damage results.

[0230] Based on the updated finite element model, new stress distribution data are calculated so as to update the cellular automata model of the target region based on the new stress distribution data.

[0231] In some optional implementations of this embodiment, determining whether to send a warning command to the warning terminal based on the corrosion trend data, so that the warning terminal responds to the warning command and outputs warning information, includes:

[0232] Based on the corrosion trend data, the target stress state, target corrosion depth, target damage variables, and target crack propagation probability of the metal roof at the target time are determined.

[0233] Based on the target stress state and the target damage variable, the first remaining life of the metal roof at the target time is determined;

[0234] Based on the target corrosion depth, determine the second remaining life of the metal roof at the target time;

[0235] Based on the target crack propagation probability, the third remaining lifetime of the metal roof at the target time is determined;

[0236] Based on the first remaining lifetime, the second remaining lifetime, and the third remaining lifetime, determine whether to send an early warning command to the early warning terminal.

[0237] It should be noted that, where there is no conflict, the technical features described in different alternative implementations can be included in the same embodiment. For the sake of brevity, they will not be elaborated here.

[0238] It should also be noted that, in addition to the contents described above, this embodiment may also include the corresponding technical features described in the above embodiments, thereby achieving the technical effect of the above-mentioned metal roof stress corrosion early warning system using acoustic emission technology. Please refer to the above description for details. For the sake of brevity, it will not be elaborated here.

[0239] This disclosure provides a method for early warning of stress corrosion on metal roofs using acoustic emission technology. The method is applied to a server, which is communicatively connected to each acoustic emission sensor in an acoustic emission sensor array and an early warning terminal. The acoustic emission sensors in the array are distributed across the metal roof. The method acquires acoustic emission signals collected by the acoustic emission sensor array. Then, based on the acoustic emission signals, it locates potential crack areas and associated corrosion areas in the metal roof. Next, it constructs a finite element model of the target area, using the potential crack areas and associated corrosion areas as target areas, to determine the stress distribution data of the target area. Subsequently, it drives a cellular automaton model of the target area using the stress distribution data to determine the corrosion trend data of the target area. Finally, based on the corrosion trend data, it determines whether to send an early warning command to the early warning terminal, so that the early warning terminal responds to the early warning command and outputs early warning information. Therefore, stress distribution data of potential crack regions and associated corrosion regions can be determined through finite element models. Then, the stress distribution data can be used to drive cellular automata models of potential crack regions and associated corrosion regions to determine the corrosion trends of these regions. In this way, by combining finite element models and cellular automata models to determine the corrosion trends of potential crack regions and their associated corrosion regions, the stress corrosion situation of metal roofs can be determined more accurately, thereby improving the accuracy of stress corrosion early warning for metal roofs.

[0240] Figure 5 This is a schematic diagram of the structure of a server provided in an embodiment of the present disclosure. Figure 5The server 500 shown includes at least one processor 501, memory 502, at least one network interface 504, and other user interfaces 503. The various components in the server 500 are coupled together via a bus system 505. It is understood that the bus system 505 is used to implement communication between these components. In addition to a data bus, the bus system 505 also includes a power bus, a control bus, and a status signal bus. However, for clarity, ... Figure 5 The general designated all buses as Bus System 505.

[0241] The user interface 503 may include a display, keyboard, or clicking device (e.g., mouse, trackball, touchpad, or touchscreen).

[0242] It is understood that the memory 502 in this embodiment of the present disclosure may be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory may be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory may be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced Synchronous DRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), and Direct Rambus RAM (DRRAM). The memory 502 described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0243] In some implementations, memory 502 stores elements, executable units or data structures, or subsets thereof, or extended sets thereof: operating system 5021 and application program 5022.

[0244] The operating system 5021 includes various system programs, such as the framework layer, core library layer, and driver layer, used to implement various basic business functions and handle hardware-based tasks. The application program 5022 includes various applications, such as a media player and a browser, used to implement various application functions. The program implementing the method of this embodiment can be included in the application program 5022.

[0245] In this embodiment, by calling the program or instructions stored in memory 502, specifically the program or instructions stored in application program 5022, processor 501 executes the method steps provided in each method embodiment, including, for example:

[0246] Acquire the acoustic emission signals collected by the acoustic emission sensor array;

[0247] Based on the acoustic emission signal, the potential crack area in the metal roof and the associated corrosion area of ​​the potential crack area are located.

[0248] Using the potential crack region and the associated corrosion region as target regions, a finite element model of the target regions is constructed to determine the stress distribution data of the target regions.

[0249] The stress distribution data is used to drive a cellular automaton model of the target area to determine the corrosion trend data of the target area.

[0250] Based on the corrosion trend data, it is determined whether to send a warning command to the warning terminal, so that the warning terminal responds to the warning command and outputs warning information.

[0251] The methods disclosed in the above embodiments of this disclosure can be applied to or implemented by processor 501. Processor 501 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the integrated logic circuit of the hardware or by instructions in the form of software in processor 501. The processor 501 may be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this disclosure. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this disclosure can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software units in the decoding processor. The software units may be located in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The storage medium is located in memory 502. Processor 501 reads the information in memory 502 and, in conjunction with its hardware, completes the steps of the above method.

[0252] It is understood that the embodiments described herein can be implemented in hardware, software, firmware, middleware, microcode, or a combination thereof. For hardware implementation, the server can be implemented in one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), general-purpose processors, controllers, microcontrollers, microprocessors, other electronic units for performing the functions described above in this disclosure, or combinations thereof.

[0253] For software implementation, the techniques described herein can be implemented by units that perform the functions described above. The software code can be stored in memory and executed by a processor. The memory can be implemented within the processor or external to the processor.

[0254] The server provided in this embodiment can be as follows: Figure 5 The server shown can execute all the steps of the above-described methods for early warning of stress corrosion of metal roofs using acoustic emission technology, thereby achieving the technical effects of the above-described methods for early warning of stress corrosion of metal roofs using acoustic emission technology. For details, please refer to the above descriptions. For the sake of brevity, further details are omitted here.

[0255] This disclosure also provides a storage medium (computer-readable storage medium). This storage medium stores one or more programs. The storage medium may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as read-only memory, flash memory, hard disk, or solid-state drive; the memory may also include combinations of the above types of memory.

[0256] When one or more programs in the storage medium can be executed by one or more processors to implement the above-mentioned early warning method for stress corrosion of metal roofs using acoustic emission technology executed on the server side.

[0257] The processor described above is used to execute a stress corrosion early warning program for metal roofs using acoustic emission technology stored in memory, in order to implement the following steps of a stress corrosion early warning method for metal roofs using acoustic emission technology executed on the server side:

[0258] Acquire the acoustic emission signals collected by the acoustic emission sensor array;

[0259] Based on the acoustic emission signal, the potential crack area in the metal roof and the associated corrosion area of ​​the potential crack area are located.

[0260] Using the potential crack region and the associated corrosion region as target regions, a finite element model of the target regions is constructed to determine the stress distribution data of the target regions.

[0261] The stress distribution data is used to drive a cellular automaton model of the target area to determine the corrosion trend data of the target area.

[0262] Based on the corrosion trend data, it is determined whether to send a warning command to the warning terminal, so that the warning terminal responds to the warning command and outputs warning information.

[0263] Furthermore, the computer program product provided in this disclosure embodiment may include computer-readable code that, when executed on a device, causes a processor in the device to implement the following steps of a stress corrosion early warning method for metal roofs using acoustic emission technology, executed on the server side:

[0264] Acquire the acoustic emission signals collected by the acoustic emission sensor array;

[0265] Based on the acoustic emission signal, the potential crack area in the metal roof and the associated corrosion area of ​​the potential crack area are located.

[0266] Using the potential crack region and the associated corrosion region as target regions, a finite element model of the target regions is constructed to determine the stress distribution data of the target regions.

[0267] The stress distribution data is used to drive a cellular automaton model of the target area to determine the corrosion trend data of the target area.

[0268] Based on the corrosion trend data, it is determined whether to send a warning command to the warning terminal, so that the warning terminal responds to the warning command and outputs warning information.

[0269] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this disclosure.

[0270] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented in hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0271] It should be understood that the terminology used herein is for the purpose of describing particular exemplary embodiments only and is not intended to be limiting. Unless the context clearly indicates otherwise, the singular forms “a,” “an,” and “described” as used herein may also include the plural forms. The terms “comprising,” “including,” “containing,” and “having” are inclusive and therefore indicate the presence of the stated features, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, elements, components, and / or combinations thereof. The method steps, processes, and operations described herein are not construed as requiring them to be performed in a particular order described or illustrated unless the order of performance is explicitly indicated. It should also be understood that additional or alternative steps may be used.

[0272] The above description is merely a specific embodiment of this disclosure, enabling those skilled in the art to understand or implement it. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this disclosure. Therefore, this disclosure is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A stress corrosion early warning system for a metal roof using acoustic emission technology, characterized in that, The system comprises an acoustic emission sensor array, a service end and an early warning end; each acoustic emission sensor in the acoustic emission sensor array is distributed and arranged on the metal roof; the service end is in communication connection with each acoustic emission sensor in the acoustic emission sensor array and the early warning end respectively; wherein: The acoustic emission sensor array is configured to: collect an acoustic emission signal; and transmit the acoustic emission signal to the service end; The service end is configured to: based on the acoustic emission signal, locate a potential crack area in the metal roof and an associated corrosion area of the potential crack area; take the potential crack area and the associated corrosion area as a target area, construct a finite element model of the target area to determine stress distribution data of the target area; drive a cellular automaton model of the target area by the stress distribution data to determine corrosion trend data of the target area; and based on the corrosion trend data, determine whether to send a warning instruction to the early warning end; The early warning end is configured to: in response to the warning instruction, output warning information.

2. The system of claim 1, wherein, The service end comprises a first server and a second server, and the first server and the second server are in communication connection; and The first server is configured to: construct a finite element model of the target area to determine stress distribution data of the target area; determine a timestamp corresponding to the stress distribution data; send the stress distribution data and the timestamp to the second server; The second server is configured to: drive a cellular automaton model of the target area by the stress distribution data to determine corrosion trend data of the target area; and based on the corrosion trend data and the timestamp, determine whether to send a warning instruction to the early warning end. The first server is specifically configured to:

3. The system of claim 2, wherein, determine crack geometric characteristics, material properties and environmental loads of the target area; based on the crack geometric characteristics, the material properties and the environmental loads, construct a three-dimensional model of the target area, wherein the three-dimensional model comprises first grid cells and second grid cells, a crack tip radius corresponding to the first grid cells is smaller than a crack tip radius corresponding to the second grid cells, and a size of the first grid cells is smaller than a size of the second grid cells; construct a finite element model of the target area by determining displacement boundary conditions matched with support types of the metal roof and mapping the environmental loads as node forces or surface pressures of the three-dimensional model. The second server is specifically configured to:

4. The system of claim 2, wherein, establish a spatial correspondence between nodes of the finite element model and cells of a cellular automaton model to be constructed; based on the spatial correspondence, the stress distribution data and attribute parameters of the metal roof, determine initial state parameters of each cell in the cellular automaton model, wherein the initial state parameters comprise stress state parameters, corrosion depths, damage variables and crack propagation probabilities; iteratively calculate the initial state parameters according to a preset updating rule to obtain updated state parameters; and ​ determine corrosion tendency data of the target region based on the initial state parameter and the updated plurality of state parameters.

5. The system of claim 4, wherein: the second server is further configured to transmit, to the first server, a corrosion damage result including a corrosion depth, a damage variable, and a crack propagation probability of the updated plurality of state parameters; the first server is further configured to update the finite element model based on the corrosion damage result, calculate new stress distribution data based on the updated finite element model, and transmit the new stress distribution data to the second server.

6. The system of claim 1, wherein, the server is configured to: determine a target stress state, a target corrosion depth, a target damage variable, and a target crack propagation probability of the metal roof at a target time based on the corrosion tendency data; determine a first remaining life of the metal roof at the target time based on the target stress state and the target damage variable; determine a second remaining life of the metal roof at the target time based on the target corrosion depth; determine a third remaining life of the metal roof at the target time based on the target crack propagation probability; determine whether to send a warning instruction to the warning end based on the first remaining life, the second remaining life, and the third remaining life.

7. A method for stress corrosion early warning of a metal roof using acoustic emission technology, characterized in that, The method is applied to a server, which is in communication connection with each acoustic emission sensor in an acoustic emission sensor array and a warning end. Each acoustic emission sensor in the acoustic emission sensor array is distributed on the metal roof. The method comprises: acquiring acoustic emission signals collected by the acoustic emission sensor array; locating a potential crack region in the metal roof and an associated corrosion region of the potential crack region based on the acoustic emission signals; constructing a finite element model of the target region by taking the potential crack region and the associated corrosion region as the target region, to determine stress distribution data of the target region; driving a cellular automaton model of the target region by the stress distribution data, to determine corrosion tendency data of the target region; determining whether to send a warning instruction to the warning end based on the corrosion tendency data, so that the warning end outputs warning information in response to the warning instruction.

8. The method of claim 7, wherein, After determining the stress distribution data of the target region, before driving the cellular automaton model of the target region by the stress distribution data, the method further comprises: determining a timestamp corresponding to the stress distribution data; and After determining the corrosion tendency data of the target region, the method further comprises: determining whether to send a warning instruction to the warning end based on the corrosion tendency data and the timestamp.

9. The method of claim 8, wherein, The construction of the finite element model of the target region comprises: determining crack geometric characteristics, material properties, and environmental loads of the target region; constructing a three-dimensional model of the target region based on the crack geometric characteristics, the material properties and the environmental load, wherein the three-dimensional model comprises first mesh units and second mesh units, the crack tip radius corresponding to the first mesh units is smaller than the crack tip radius corresponding to the second mesh units, and the size of the first mesh units is smaller than the size of the second mesh units; constructing a finite element model of the target region by determining displacement boundary conditions matched with the support type of the metal roof and mapping the environmental load as node forces or surface pressures of the three-dimensional model.

10. The method according to claim 8 or 9, characterized in that, The driving the cellular automaton model of the target region by the stress distribution data to determine the corrosion tendency data of the target region comprises: establishing a spatial correspondence between nodes of the finite element model and cells of the cellular automaton model to be constructed; determining initial state parameters of each cell in the cellular automaton model based on the spatial correspondence, the stress distribution data and the attribute parameters of the metal roof, wherein the initial state parameters comprise stress state parameters, corrosion depths, damage variables and crack propagation probabilities; iteratively calculating the initial state parameters according to a preset updating rule to obtain updated state parameters; determining the corrosion tendency data of the target region based on the initial state parameters and the updated state parameters.

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