Building material structure performance grading progressive detection system and method

Abstract

The invention discloses a building material structure performance grading progressive detection method and system, and belongs to the technical field of building structure detection. The method comprises three progressive detection steps: firstly, detecting the appearance and the coating of the exposed steel structure of the building, and if the appearance and the coating are abnormal, marking the exposed steel structure as a low-risk component and carrying out next-stage detection; then geometric deformation, cracks and stress states of the low-risk components are detected, and if the low-risk components are abnormal, the low-risk components are marked as high-risk components, and next-level detection is carried out; and finally, carrying out destructive detection on the high-risk component, verifying the key performance of the high-risk component, and if the component is abnormal, marking the component as an unqualified component and immediately reinforcing or replacing the component. The method also comprises the functions of environment monitoring, life prediction and the like, and is matched with a corresponding detection system. Through a grading progressive detection strategy, early risk identification and accurate evaluation of the building steel structure are realized, the detection efficiency is improved, the detection cost is reduced, and an effective guarantee is provided for the safety of the building structure.

Description

Technical Field

[0001] The present invention belongs to the technical field of building material testing, and particularly relates to a progressive detection system and method for the structural performance grading of building materials. Background Art

[0002] In the field of construction engineering, the structural performance testing of building materials is of crucial importance, which is directly related to the safety, stability and service life of buildings. Especially in some special cases, such as before the resumption of work on unfinished buildings, a comprehensive and accurate detection and evaluation of their steel structures is an essential link. The steel in unfinished buildings has been exposed to the outdoor environment for a long time and will be affected by various adverse factors such as rain, dust, and corrosive gases. These factors may cause problems such as rusting, coating damage, geometric deformation, crack generation, and stress state change of the steel, thereby affecting the mechanical properties and service life of the steel.

[0003] When the traditional method is used to detect the structural performance of building materials, there are many limitations. First of all, traditional detection methods are often relatively single, usually only detecting a certain aspect of the performance of building materials, such as only detecting the rust situation of steel or only detecting its mechanical properties, lacking a comprehensive evaluation of the overall structural performance of building materials. This single detection method cannot accurately reflect the comprehensive situation of building materials in the actual use environment, and it is easy to ignore some potential risks.

[0004] Secondly, traditional detection methods have deficiencies in accuracy. Due to the limitations of detection means and equipment, traditional methods are difficult to accurately measure some subtle changes and performance parameters of building materials. For example, when detecting the rust layer thickness and coating quality of steel, traditional methods may not be able to accurately capture local subtle changes, thus affecting the judgment of the aging degree of steel. In addition, when detecting geometric deformation and cracks, the measurement accuracy of traditional methods is also limited, which may lead to inaccurate evaluation of structural performance.

[0005] Furthermore, the flexibility of traditional detection methods is poor. In the actual detection process, different building materials and detection scenarios may require different detection methods and equipment, but traditional methods often lack adaptability and are difficult to be flexibly adjusted according to specific situations. For example, for some complex building structures or large-scale detection projects, traditional methods may require a large amount of time and manpower, with low efficiency and difficult to meet the requirements of rapid detection and evaluation.

[0006] In addition, traditional detection methods also have unreasonable aspects in terms of resource allocation. Due to the lack of a hierarchical evaluation and dynamic adjustment mechanism for detection risks, traditional methods often adopt the same detection process and resource investment for all detection objects, resulting in resource waste in some low-risk or risk-free parts, while high-risk parts may not receive sufficient attention and detection. This not only increases the detection cost but also may affect the timely discovery and handling of high-risk problems.

[0007] Finally, traditional detection methods are not precise enough in terms of early warning and treatment plans. Due to the inability to accurately evaluate the risk levels of building materials, it is difficult for traditional methods to formulate corresponding countermeasures for different levels of risks. When abnormal situations occur, effective early warnings and treatment plans may not be provided in a timely manner, thus increasing the risk of safety accidents in buildings.

[0008] In summary, in order to overcome these limitations of traditional detection methods, improve the efficiency, accuracy, and flexibility of the structural performance detection of building materials, and achieve efficient resource allocation and precise risk control, it is necessary to develop a brand-new hierarchical progressive detection system and method for the structural performance of building materials. Summary of the Invention

[0009] In order to solve the defects existing in the above-mentioned prior art, the present invention proposes a detection system and method for the structural performance of building materials.

[0010] The technical solution adopted by the present invention is as follows: A hierarchical progressive detection method for the structural performance of building materials, comprising: Step 1: Detect the appearance and coating of the exposed steel structure of the building. If the detection result is abnormal, perform a risk mark, mark it as a low-risk component, and conduct the next-level risk detection; Step 2: Further detect and quantify the damage condition of the low-risk components. If the detection result is abnormal, perform a risk mark, mark it as a high-risk component, and conduct the next-level risk detection; Step 3: Conduct a destructive test on the high-risk components to detect and verify their key performance. If the detection result is abnormal, perform a risk mark and mark it as a non-conforming component.

[0011] Preferably, in Step 1, it specifically includes: detecting the rust area ratio, rust layer color, coating thickness, and coating adhesion of the appearance of the exposed steel structure of the building. If the detection results are rust area < 30%, rust layer color is black rust, coating thickness > 50% of the design value, and adhesion > 3 MPa, that is, the detection results are normal, mark it as a risk-free component and conduct a re-inspection every 12 months; otherwise, if the detection result is abnormal, perform a risk mark, mark it as a low-risk component, and conduct the next-level risk detection.

[0012] Furthermore, the temperature, humidity, and concentration in the air where the risk-free components are located are detected. If the relative humidity > 80%, and or , it is determined that the test result is abnormal, marked as the corrosion acceleration area, and the re-inspection period is shortened to 3 months.

[0013] Preferably, the damage conditions of the low-risk components in step 2 specifically include: geometric deformation, cracks, and stress state of the low-risk components.

[0014] Furthermore, step 2 specifically includes: Step 2.1: Measure the wall thickness of the component, record the thinning rate, scan the component morphology, calculate the deflection and node offset. If the wall thickness thinning rate > 15% or the deflection > L / 300 or the offset > H / 400, it is marked as a candidate component for structural failure, where L is the span of the component and H is the vertical height of the component; Step 2.2: Conduct UT inspection on the welds and stress concentration areas of the nodes, record the crack depth, conduct MT inspection on the surface crack sensitive areas, mark the crack length and distribution. If the crack depth > 1 / 6 of the wall thickness or the crack length > 10 mm, it is marked as a component at risk of fracture; Step 2.3: Install sensors on the high-stress components, record the stress amplitude and vibration spectrum for 72 hours. If the stress amplitude > 0.5f_y or the vibration frequency is close to the natural vibration frequency of the component, it is marked as a candidate component for dynamic instability, where f_y is the yield strength of the component material; Step 2.4: Conduct risk marking on the candidate components for structural failure, components at risk of fracture, and candidate components for dynamic instability, mark them as high-risk components, and conduct the next-level risk detection.

[0015] Furthermore, step 3 includes: Step 3.1: Cut specimens from the high-risk components and test the yield strength, tensile strength, and impact toughness; if the measured yield strength and tensile strength < 85% of the design value or the impact toughness , it is determined as a non-conforming component and needs to be immediately reinforced or replaced; Step 3.2: For the components with the measured yield strength ≥ 85% of the design value or the impact toughness , input the collected data into the finite element analysis software, train it in the corrosion rate model, calculate the remaining bearing capacity and corrosion life of the component. If the remaining life < 5 years, it is marked as an urgently processed component and included in the renovation plan.

[0016] A progressive detection system for the structural performance grading of building materials, which is used to execute the described detection method, specifically includes: a main control device and a rust layer detection device, a coating detection device, an environmental detection device, a geometric deformation detection device, a crack detection device, a stress state detection device, a mechanical property detection device, a life prediction device, and a strategy formulation device connected to the main control device; The rust layer detection device is used to detect the proportion of the rusted area on the appearance of the exposed steel structure of the building and the color of the rust layer; The coating detection device is used to detect the coating thickness and coating adhesion of the appearance of the exposed steel structure of the building; The environmental detection device is used to detect the temperature, humidity, and and concentration in the air; The geometric deformation detection device is used to measure the wall thickness of the component, record the thinning rate, scan the component morphology, and calculate the deflection and node offset; The crack detection device is used to perform UT detection on the welds and stress concentration areas of the nodes, record the crack depth, perform MT detection on the surface crack sensitive areas, and mark the crack length and distribution; The stress state detection device is used to detect the internal stress amplitude and vibration spectrum of the component within 72 hours; The mechanical property detection device is used to test the yield strength, tensile strength, elongation, and impact toughness; The life prediction device is used to predict and calculate the remaining bearing capacity and corrosion life of the component; The strategy formulation device is used to formulate corresponding risk elimination strategies according to the risk levels of the detected steel structure components.

[0017] Preferably, the main control device controls the rust layer detection device and the strategy formulation device to be always on, and controls the coating detection device, the environmental detection device, the geometric deformation detection device, the crack detection device, the stress state detection device, the mechanical property detection device, and the life prediction device to be always off; The rust layer detection device detects the proportion of the rusted area on the appearance of the exposed steel structure of the building and the color of the rust layer. If the detection result shows that the rusted area > 30% and the rust layer color is red rust, then there is a greater structural risk for this steel structure. The main control device controls the coating detection device to be turned on to detect the coating thickness and coating adhesion. If the detection result shows that the coating thickness < 50% of the design value or the adhesion < 3 MPa, that is, the detection result is abnormal, a risk mark is made and it is marked as a low-risk component. The main control device controls the next-level risk detection to be carried out; The main control device controls the geometric deformation detection device, the crack detection device, and the stress state detection device to start. The geometric deformation detection device measures the wall thickness of the component, records the thinning rate, scans the morphology of the component, calculates the deflection and the node offset. If the wall thickness thinning rate > 15% or the deflection > L / 300 or the offset > H / 400, it is marked as a candidate component for structural failure; the crack detection device performs UT detection on the welds and the stress concentration areas of the nodes, records the crack depth, performs MT detection on the surface crack sensitive areas, marks the crack length and distribution. If the crack depth > 1 / 6 of the wall thickness or the crack length > 10 mm, it is marked as a component at risk of fracture; the stress state detection device records the stress amplitude and the vibration spectrum of the steel structure within 72 hours. If the stress amplitude > 0.5f_y or the vibration frequency is close to the natural vibration frequency of the component, it is marked as a candidate component for dynamic instability; risk marking is performed on the candidate components for structural failure, the components at risk of fracture, and the candidate components for dynamic instability, marked as high-risk components, and the next-level risk detection is carried out; The main control device controls the mechanical property detection device to start. The mechanical property detection device tests the yield strength, the tensile strength, and the impact toughness; if the measured yield strength and tensile strength < 85% of the design value or the impact toughness is determined as a non-conforming component; If the measured yield strength ≥ 85% of the design value or the impact toughness the main control device controls the life prediction device to start, inputs the data collected by it into the finite element analysis software, trains in the corrosion rate model, calculates the remaining bearing capacity and the corrosion life of the component. If the remaining life < 5 years, it is marked as a component for emergency treatment; During the detection process of the steel structure materials, the main control device controls the strategy formulation device to start, and generates corresponding risk elimination strategies for the steel structure components with different detection results respectively.

[0018] Furthermore, if the detection result of the rust layer detection device is normal, it is marked as a component without risk. The temperature, humidity, and the concentration of and in the air of the environment where the component without risk is located are detected. If the relative humidity > 80%, and or then it is determined that the detection result is abnormal, marked as an area with accelerated corrosion, and the re-inspection period is shortened to 3 months.

[0019] A storage medium, on which a computer program is stored, and when the computer program is run, it executes the above-mentioned hierarchical progressive detection method for the structural performance of building materials.

[0020] In summary, due to the adoption of the above technical solutions, the beneficial effects of the present invention are: The hierarchical progressive detection system for the structural performance of building materials is a comprehensive, multi-sensor system that integrates software and hardware such as rust layer detection devices, strategy formulation devices, coating detection devices, environmental detection devices, geometric deformation detection devices, crack detection devices, stress state detection devices, mechanical property detection devices, and life prediction devices. Through these devices, a risk-oriented detection method for gradually grading the steel performance detection of unfinished buildings can be carried out, timely identifying four levels of low risk, medium risk, high risk, and emergency existing in steel structural members. The multi-sensor technology is adopted to further detect the internal and external parameter performance of the parts at risk of aging, including detecting abnormalities such as rust layer, coating, deformation, crack, environmental parameters, stress state, and mechanical properties, and generating corresponding risk elimination strategies according to the degree of abnormality.

[0021] Through the five core advantages of efficiency optimization, accuracy improvement, scenario adaptation, cost control, and technology expansion, the hierarchical progressive detection method and system can accurately evaluate the material risk level, quickly formulate countermeasures, improve material safety, extend service life, and provide accurate early warnings and treatment plans for abnormal situations, solving problems such as resource waste, insufficient accuracy, and poor flexibility in traditional detection methods, and ensuring efficient resource allocation and precise risk control. Its core value lies in achieving the high efficiency, precision, and intelligence of the detection process through hierarchical screening, dynamic adjustment, and resource focus, and is particularly suitable for complex systems, large-scale detections, or high-risk scenarios. Brief Description of the Drawings

[0022] The present invention will be described by way of examples with reference to the accompanying drawings, where: Figure 1 is the flowchart of the detection method in the present invention; Figure 2 is the logical flowchart of the detection method in the present invention; Figure 3 is the structural schematic diagram of the detection system in the present invention. Detailed Embodiments

[0023] Next, the technical solutions of the present invention will be clearly and completely described with the help of embodiments in conjunction with the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention.

[0024] Embodiment 1 When problems are found during the steel material inspection, it is necessary to follow the principles of "graded response, risk priority, and scientific handling", and formulate targeted treatment plans in combination with the problem type, severity, and importance of the components. The following is a redesigned graded risk-oriented inspection method for the steel performance inspection of unfinished buildings, which clarifies the inspection content, tools, and priorities for the four levels of low risk, medium risk, high risk, and emergency, ensuring efficient resource allocation and precise risk control.

[0025] A progressive inspection method for the structural performance grading of building materials, refer to Figure 1 , 2 , which consists of three stages respectively: Preliminary screening stage (non-destructive, quickly locate risks) Detailed assessment stage (semi-destructive / non-destructive testing, quantify damage) Verification and confirmation stage (destructive testing, verify key performance) Specifically, it includes the following steps: Step 1: Inspect the appearance and coating of the exposed steel structure of the building. If the inspection result is abnormal, make a risk mark, mark it as a low-risk component, and conduct the next-level risk inspection.

[0026] Specifically, inspect the rust area ratio, rust layer color, coating thickness, and coating adhesion of the exposed steel structure of the building. If the inspection results show that the rust area < 30%, the rust layer color is black rust, the coating thickness > 50% of the design value, and the adhesion > 3 MPa, that is, the inspection result is normal, mark it as a risk-free component, and conduct a re-inspection every 12 months; otherwise, if the inspection result is abnormal, make a risk mark, mark it as a low-risk component, and conduct the next-level risk inspection.

[0027] For the steel structure marked as a risk-free component, it is also necessary to detect the ambient temperature and humidity where it is located, and the and concentration in the air. If the relative humidity > 80%, and or

[0028] , then it is determined that the inspection result is abnormal, mark it as an accelerated corrosion area, and shorten the re-inspection cycle to 3 months.

[0029] Step 2: Further detect and quantify the damage of low-risk components. If the inspection result is abnormal, make a risk mark, mark it as a high-risk component, and conduct the next-level risk inspection.

[0030] The damage conditions of low-risk components specifically include the geometric deformation, cracks, and stress state of low-risk components. Step 2 specifically includes the following sub-steps: Step 2.1: Measure the wall thickness of the component (measure 1 point every 100 mm spacing), record the thinning rate (compared with the design value), scan the morphology of the component, calculate the deflection and the node offset. If the wall thickness thinning rate > 15% or the deflection > L / 300 or the offset > H / 400, mark it as a candidate component for structural failure.

[0031] In actual operation, use an ultrasonic thickness gauge to measure the wall thickness of steel structural components, set a measuring point every 100 mm to ensure that the measuring points cover the key areas of the component. By comparing with the designed wall thickness value, calculate the thinning rate. At the same time, use a 3D laser scanner to scan the overall morphology of the component, obtain the actual geometric shape data of the component, and calculate the deflection value and the node offset of the component.

[0032] For example, for a steel column with a designed wall thickness of 10 mm, if the measurement result shows that the actual wall thickness at a certain place is 8 mm, the thinning rate is 20%, exceeding the threshold of 15%, it should be marked as a candidate component for structural failure. Similarly, for a steel beam with a span of 6 m, if the measured deflection is 25 mm, exceeding the limit of L / 300 (i.e., 20 mm), it should also be marked as a candidate component for structural failure.

[0033] Step 2.2: Conduct UT inspection on stress concentration areas such as welds and nodes, record the crack depth, conduct MT inspection on surface crack sensitive areas, mark the crack length and distribution. If the crack depth > t / 6 (t is the wall thickness) or the crack length > 10 mm, mark it as a component at risk of fracture.

[0034] In this step, use ultrasonic testing (UT) technology to detect internal defects in stress concentration areas such as welds and nodes, focusing on the crack depth. At the same time, use magnetic particle testing (MT) technology to detect surface crack sensitive areas, and accurately measure the length and distribution of surface cracks.

[0035] For example, for a steel structural component with a wall thickness of 12 mm, if UT inspection finds a crack with a depth of 2.5 mm at the weld, exceeding the threshold of t / 6 (i.e., 2 mm), it should be marked as a component at risk of fracture. Similarly, if MT inspection finds a crack with a length of 15 mm on the surface, exceeding the limit of 10 mm, it should also be marked as a component at risk of fracture.

[0036] Step 2.3: Install sensors on high-stress components (such as the bottom of columns and the ends of beams), record the stress amplitude and vibration spectrum for 72 hours. If the stress amplitude > 0.5f_y (yield strength) or the vibration frequency is close to the natural vibration frequency of the component, mark it as a candidate component for dynamic instability.

[0037] In this step, high-stress areas in the steel structure, such as the bottom of columns and the ends of beams, are selected, and strain gauges and acceleration sensors are installed to continuously monitor the stress changes and vibration characteristics of the components within 72 hours. The stress amplitude and vibration spectrum are recorded through a data acquisition system and compared with the yield strength and natural vibration frequency of the component material.

[0038] For example, for a component made of Q235 steel (yield strength fy is 235 MPa), if the maximum stress amplitude monitored reaches 130 MPa, exceeding the threshold of 0.5fy (i.e., 117.5 MPa), it should be marked as a candidate component for dynamic instability. Similarly, if the natural vibration frequency of the component is 5 Hz and the main vibration frequency monitored is 4.8 Hz, close to the natural vibration frequency, it should also be marked as a candidate component for dynamic instability.

[0039] Step 2.4: Risk mark the candidate components for structural failure, components at risk of fracture, and candidate components for dynamic instability, mark them as high-risk components, and conduct the next-level risk detection.

[0040] After completing the detection of the above three sub-steps, all steel structure components marked as candidate components for structural failure, components at risk of fracture, or candidate components for dynamic instability are uniformly marked as high-risk components and annotated in the Building Information Modeling (BIM) system to prepare for the next-level risk detection.

[0041] Step 3: Conduct destructive testing on high-risk components to verify their key performance. If the test results are abnormal, conduct risk marking, mark them as unqualified components, and immediately reinforce or replace them.

[0042] Step 3.1: Cut specimens from high-risk components and test the yield strength, tensile strength, and impact toughness; if the measured yield strength and tensile strength < 85% of the design value or the impact toughness , it is determined to be an unqualified component and needs to be immediately reinforced or replaced.

[0043] The described hierarchical progressive detection method and system can effectively improve the detection efficiency, accuracy, and reliability by refining the detection process step by step and level by level, and gradually deepening according to different complexities or detection requirements. The beneficial effects are elaborated from multiple dimensions as follows: Hierarchical detection: First, quickly eliminate problem-free samples through primary detection, and only conduct in-depth detection on suspicious samples, avoiding the high-cost analysis of all samples.

[0044] Progressive analysis: Dynamically adjust the detection strategy according to the detection results to avoid repeated operations.

[0045] Resource allocation on demand: For samples that pass the primary detection, there is no need to invest in high-level detection resources (such as manpower, equipment, time), reducing the overall cost.

[0046] In this step, standard specimens are cut from non-critical parts of high-risk components, and yield strength, tensile strength, and impact toughness tests are carried out in accordance with the mechanical property test standards for metallic materials. When cutting specimens, the overall structural performance of the components should be avoided from being affected, and the specimen size and shape should meet the requirements of relevant test standards.

[0047] For example, for a Q345 steel component with a designed yield strength of 345 MPa and a tensile strength of 490 MPa, if the measured yield strength is 280 MPa (lower than 85% of the designed value, i.e., 293.25 MPa), or the measured tensile strength is 400 MPa (lower than 85% of the designed value, i.e., 416.5 MPa), or the impact toughness is (lower than the threshold value), it should be determined as a non-conforming component, and reinforcement or replacement measures should be taken immediately.

[0048] Step 3.2: For components with a measured yield strength ≥ 85% of the designed value or impact toughness , the data collected from them is input into finite element analysis software and trained in the corrosion rate model to calculate the remaining bearing capacity and corrosion life of the components. If the remaining life < 5 years, it is marked as an urgently processed component and given priority in the renovation plan.

[0049] For high-risk components whose mechanical property test results meet the requirements, it is necessary to further evaluate their long-term service performance. The data such as the geometric dimensions, material properties, and damage status of the components are input into finite element analysis software, and a corrosion rate model is established in combination with environmental parameters, and the remaining bearing capacity and expected corrosion life of the components in the current state are calculated through numerical simulation.

[0050] For example, after a steel column undergoes mechanical property tests, the yield strength is 320 MPa (higher than 85% of the designed value of 345 MPa), the tensile strength is 450 MPa (higher than 85% of the designed value of 490 MPa), and the impact toughness is (higher than the threshold value). The measured geometric dimensions, material properties, and corrosion status data of it are input into finite element analysis software and simulated in combination with environmental corrosion parameters. If the prediction result shows that the remaining service life of the steel column is 4 years, lower than the threshold value of 5 years, it should be marked as an urgently processed component and given priority in the renovation plan.

[0051] Example 2 A hierarchical progressive detection system for the structural performance of building materials, refer to Figure 3, for implementing the detection method described in Embodiment 1, specifically including: a main control device and a rust layer detection device, a coating detection device, an environmental detection device, a geometric deformation detection device, a crack detection device, a stress state detection device, a mechanical property detection device, a life prediction device, and a strategy formulation device connected to the main control device.

[0052] The rust layer detection device is used to detect the proportion of the rusted area on the appearance of the exposed steel structure of the building and the color of the rust layer. The rust layer detection device includes a high-resolution digital camera and image analysis software. By taking images of the steel structure surface, it automatically calculates the proportion of the rusted area using image processing technology and determines whether the rust layer color is black rust or red rust through a color recognition algorithm.

[0053] The coating detection device is used to detect the thickness and adhesion of the coating on the appearance of the exposed steel structure of the building. The coating detection device includes a coating thickness gauge and an adhesion tester. The coating thickness gauge uses the principle of electromagnetic induction and can non-destructively measure the thickness of non-magnetic coatings on a metal substrate; the adhesion tester uses the pull-off method to evaluate the adhesion of the coating by measuring the force required to pull the coating away from the substrate.

[0054] The environmental detection device is used to detect the temperature and humidity of the surrounding environment, and the and concentration in the air. The environmental detection device includes a temperature and humidity sensor, a chloride ion concentration detector, and a sulfur dioxide concentration detector. The temperature and humidity sensor uses a capacitive sensing element and has high precision and good long-term stability; the chloride ion concentration detector uses ion-selective electrode technology; the sulfur dioxide concentration detector uses electrochemical sensing technology and can real-time monitor the pollutant concentration in the air.

[0055] The geometric deformation detection device is used to measure the wall thickness of the component (measure 1 point every 100 mm spacing), record the thinning rate (compared with the design value), scan the component morphology, and calculate the deflection and node offset. The geometric deformation detection device includes an ultrasonic thickness gauge, a 3D laser scanner, and a data processing unit. The ultrasonic thickness gauge uses the pulse reflection principle and can accurately measure the wall thickness of metal components; the 3D laser scanner quickly obtains the three-dimensional geometric shape of the component by emitting laser beams and receiving reflected signals; the data processing unit compares the measured data with the design value and calculates the thinning rate, deflection, and node offset.

[0056] The crack detection device is used to perform UT detection on stress concentration areas such as welds and nodes, record the crack depth, perform MT detection on surface crack sensitive areas, and mark the crack length and distribution. The crack detection device includes an ultrasonic flaw detector (UT), a magnetic particle flaw detector (MT) and a defect data recording system. The ultrasonic flaw detector uses the pulse reflection principle and can detect cracks and defects inside the component; the magnetic particle flaw detector establishes a magnetic field on the surface of the component and uses the phenomenon of magnetic particles gathering at the crack to display surface cracks; the defect data recording system digitally records and visualizes the detected crack information.

[0057] The stress state detection device is used to detect the internal stress amplitude and vibration spectrum of the component within 72 hours. The stress state detection device includes a strain gauge, an acceleration sensor, a data acquisition system and a spectrum analysis software. The strain gauge indirectly measures the stress state by measuring the slight deformation on the surface of the component; the acceleration sensor is used to measure the vibration characteristics of the component; the data acquisition system continuously records the stress and vibration data within 72 hours; the spectrum analysis software performs Fourier transform on the collected data to obtain the vibration spectrum characteristics.

[0058] The mechanical properties testing device is used to test yield strength, tensile strength, elongation, and impact toughness. The mechanical properties testing device includes a universal material testing machine, an impact testing machine, and a sample preparation device. The universal material testing machine is used to perform tensile tests to measure yield strength, tensile strength, and elongation; the impact testing machine is used to measure the impact toughness of the material; and the sample preparation device is used to cut standard samples from the component and perform necessary processing.

[0059] The life prediction device is used to predict and calculate the remaining bearing capacity and corrosion life of the component. The life prediction device includes a high-performance computer, finite element analysis software and a corrosion rate model. The high-performance computer provides powerful computing power; the finite element analysis software is used to establish a numerical model of the component and perform structural analysis; the corrosion rate model is based on measured data and environmental parameters to predict the corrosion behavior and structural performance degradation of the component during future use.

[0060] The strategy formulation device is used to formulate a corresponding risk elimination strategy according to the risk level of the detected steel structure. The strategy formulation device includes an expert knowledge base, a decision support system and a report generation module. The expert knowledge base stores the treatment solutions and technical specifications of various steel structure problems; the decision support system recommends appropriate treatment measures according to the detection results and risk levels; and the report generation module automatically generates a detection report and maintenance suggestions.

[0061] The main control device controls the rust layer detection device and the strategy formulation device to be always on, and controls the coating detection device, the environmental detection device, the geometric deformation detection device, the crack detection device, the stress state detection device, the mechanical property detection device, and the life prediction device to be always off.

[0062] The rust layer detection device detects the proportion of the rusted area and the rust layer color on the appearance of the exposed steel structure of the building. If the detection result shows that the rusted area > 30% and the rust layer color is red rust, then there is a greater structural risk for this steel structure. The main control device controls the coating detection device to be turned on to detect the coating thickness and the coating adhesion. If the detection result shows that the coating thickness < 50% of the design value or the adhesion < 3 MPa, that is, the detection result is abnormal, a risk mark is made and it is marked as a low-risk component. The main control device controls the next-level risk detection to be carried out.

[0063] The main control device controls the geometric deformation detection device, the crack detection device, and the stress state detection device to be turned on. The geometric deformation detection device measures the wall thickness of the component (measuring 1 point every 100 mm spacing), records the thinning rate (compared with the design value), scans the component morphology, and calculates the deflection and the node offset. If the wall thickness thinning rate > 15% or the deflection > L / 300 (L is the span of the component) or the offset > H / 400 (H is the vertical height of the component), it is marked as a candidate component for structural failure; the crack detection device conducts UT detection on stress concentration areas such as welds and nodes, records the crack depth, conducts MT detection on the surface crack sensitive areas, and marks the crack length and distribution. If the crack depth > t / 6 (t is the wall thickness) or the crack length > 10 mm, it is marked as a component at risk of fracture; the stress state detection device records the stress amplitude and the vibration spectrum of the steel structure within 72 hours. If the stress amplitude > 0.5f_y (f_y is the yield strength of the material) or the vibration frequency is close to the natural vibration frequency of the component, it is marked as a candidate component for dynamic instability.

[0064] Risk marks are made for the candidate components for structural failure, the components at risk of fracture, and the candidate components for dynamic instability, and they are marked as high-risk components, and the next-level risk detection is carried out.

[0065] The main control device controls the mechanical property detection device to be turned on. The mechanical property detection device tests the yield strength, the tensile strength, and the impact toughness; if the measured yield strength and tensile strength < 85% of the design value or the impact toughness , it is determined to be a non-conforming component.

[0066] If the measured yield strength ≥ 85% of the design value or the impact toughness , the main control device controls the life prediction device to be turned on, inputs the data it collects into the finite element analysis software, trains in the corrosion rate model, calculates the remaining bearing capacity and the corrosion life of the component. If the remaining life < 5 years, it is marked as a component for emergency treatment.

[0067] During the inspection process of steel structure materials, the main control device controls the activation of the strategy formulation device, and corresponding risk elimination strategies are generated for steel structure components with different inspection results, specifically including: Risk-free components: Formulate a routine maintenance plan, including regular cleaning, anti-corrosion coating inspection, and environmental monitoring, with a comprehensive inspection every 12 months. For risk-free components in the corrosion acceleration area, shorten the inspection cycle to 3 months and increase the frequency of anti-corrosion coating maintenance.

[0068] Low-risk components: Formulate an enhanced maintenance plan, including local anti-corrosion treatment, coating repair, and regular monitoring, with a comprehensive inspection every 6 months. For components with a rust area approaching 30% or a coating thickness approaching the critical value, it is recommended to carry out preventive coating renewal.

[0069] High-risk components: Formulate a special monitoring plan, including installing permanent monitoring sensors, regularly conducting non-destructive testing and structural performance evaluation, with a comprehensive inspection every 3 months. For components with a remaining life of 5 - 10 years, it is recommended to include them in the medium-term renovation plan and increase the inspection frequency.

[0070] Unqualified components: Formulate an emergency treatment plan, including temporary support, load restriction, and emergency reinforcement or replacement. For load-bearing key components, it is recommended to immediately implement reinforcement or replacement; for non-key components, on the premise of ensuring safety, the treatment can be completed within 3 months.

[0071] The inspection result of the rust layer detection device is normal, marked as a risk-free component. Detect the ambient temperature, humidity, and air concentration where the risk-free component is located. If the relative humidity > 80% and or , then determine that the inspection result is abnormal, mark it as the corrosion acceleration area, and shorten the re-inspection cycle to 3 months.

[0072] Embodiment III A storage medium has a computer program stored thereon. When the computer program is run, it executes a method for hierarchical progressive inspection of the structural performance of building materials as described in Embodiment I.

[0073] The storage medium can be a read-only memory (ROM), a random access memory (RAM), a disk, an optical disc, or other forms of computer-readable storage media. The computer program stored on the storage medium includes program codes for implementing the method for hierarchical progressive inspection of the structural performance of building materials. When the program codes are executed by a processor, they can control the computer system to execute each step described in Embodiment I.

[0074] The main modules of the computer program include: Data acquisition module: responsible for receiving data from various detection devices, including rust layer detection data, coating detection data, environmental parameter data, geometric deformation data, crack detection data, stress state data and mechanical properties test data.

[0075] Data processing module: Process and analyze the collected data, including calculating parameters such as corrosion area ratio, thinning rate, deflection, offset, etc., and comparing them with preset thresholds.

[0076] Risk assessment module: Based on the data processing results, the risk levels of steel structures are divided into risk-free components, low-risk components, high-risk components and unqualified components.

[0077] Life prediction module: Calculates the remaining bearing capacity and expected service life of components based on finite element analysis and corrosion rate model.

[0078] Strategy generation module: Generates corresponding risk elimination strategies and maintenance recommendations based on risk assessment results and life prediction results.

[0079] Report generation module: automatically generates inspection reports, including component status, risk level, treatment suggestions and next inspection time.

[0080] When the computer program is run, the data acquisition module first obtains the various test data of the steel structure, and then the data processing module analyzes and processes these data. Next, the risk assessment module classifies the risk level of the components according to the processing results. For high-risk components, the life prediction module will further calculate their remaining service life. Finally, the strategy generation module and the report generation module generate risk elimination strategies and test reports respectively.

[0081] It should be noted that the first embodiment, the second embodiment and the third embodiment are all a type of graded progressive detection method for the structural performance of building materials.

[0082] In the technical solution system elaborated in detail in this application, each key device used to build the entire system, without exception, belongs to a software or hardware module that is clearly existing and widely recognized in the prior art field. These devices have clear definitions, functional descriptions, and application scenarios in the prior art system. Those skilled in the art, with their own professional knowledge, technical experience, and full understanding of the prior art, can implement and operate this solution completely without deviation, accurately, and efficiently after carefully reading the technical content recorded in this application, thereby ensuring that the technical solution can be smoothly implemented according to the expected goals and play its due technical effects.

[0083] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A progressive detection method for the structural performance grading of building materials, characterized in that, Including: Step 1: Detect the appearance and coating of the exposed steel structure of the building. If the detection result is abnormal, conduct a risk marking, mark it as a low-risk component, and conduct the next-level risk detection; Step 2: Further detect and quantify the damage condition of the low-risk components. If the detection result is abnormal, conduct a risk marking, mark it as a high-risk component, and conduct the next-level risk detection; Step 3: Conduct a destructive test on the high-risk components to detect and verify their key performances. If the detection result is abnormal, conduct a risk marking, mark it as a non-conforming component.

2. The progressive detection method for the structural performance grading of a building material according to claim 1, characterized in that, Specifically included in Step 1 are: detecting the rust area ratio, rust layer color, coating thickness, and coating adhesion of the appearance of the exposed steel structure of the building. If the detection results are rust area < 30%, rust layer color is black rust, coating thickness > 50% of the design value, and adhesion > 3 MPa, that is, the detection result is normal, mark it as a risk-free component and conduct a re-inspection every 12 months; otherwise, if the detection result is abnormal, conduct a risk marking, mark it as a low-risk component, and conduct the next-level risk detection.

3. A progressive detection method for the structural performance grading of a building material according to claim 2, characterized in that, Detect the temperature, humidity of the environment where the non-risk components are located, and the and concentrations in the air. If the relative humidity > 80%, and or , then it is determined that the test result is abnormal, marked as the corrosion acceleration area, and the re-inspection period is shortened to 3 months.

4. A progressive detection method for the structural performance grading of a building material according to claim 1, characterized in that, The damage condition of the low-risk components in Step 2 specifically includes: geometric deformation, cracks, and stress state of the low-risk components.

5. A progressive detection method for the structural performance grading of a building material according to claim 4, characterized in that, Step 2 specifically includes: Step 2.1: Measure the wall thickness of the component, record the thinning rate, scan the component morphology, calculate the deflection and node offset. If the wall thickness thinning rate > 15% or deflection > L / 300 or offset > H / 400, mark it as a candidate component for structural failure, where L is the span of the component and H is the vertical height of the component; Step 2.2: Conduct UT detection on the welds and stress concentration areas of the nodes, record the crack depth, conduct MT detection on the surface crack sensitive areas, mark the crack length and distribution. If the crack depth > 1 / 6 of the wall thickness or the crack length > 10 mm, mark it as a component at risk of fracture; Step 2.3: Install sensors on the high-stress components, record the stress amplitude and vibration spectrum for 72 hours. If the stress amplitude > 0.5f_y or the vibration frequency is close to the natural vibration frequency of the component, mark it as a candidate component for dynamic instability, where f_y is the yield strength of the component material; Step 2.4: Conduct a risk marking on the candidate components for structural failure, components at risk of fracture, and candidate components for dynamic instability, mark them as high-risk components, and conduct the next-level risk detection.

6. A progressive detection method for the structural performance grading of a building material according to claim 1, characterized in that, Step 3 includes: Step 3.1: Cut specimens from high-risk components and test the yield strength, tensile strength, and impact toughness; if the measured yield strength and tensile strength < 85% of the design value or the impact toughness , it is determined as a non-conforming component and immediate reinforcement or replacement is required; Step 3.2: For members with measured yield strength ≥ 85% of the design value or impact toughness of the members, input the collected data into finite element analysis software, train in the corrosion rate model, calculate the remaining bearing capacity and corrosion life of the members. If the remaining life < 5 years, mark them as members for emergency treatment and include them in the renovation plan.

7. A progressive detection system for the structural performance grading of building materials, characterized in that, For implementing the detection method described in any one of claims 1-6, specifically including: a main control device and a rust layer detection device, a coating detection device, an environment detection device, a geometric deformation detection device, a crack detection device, a stress state detection device, a mechanical property detection device, a life prediction device, and a strategy formulation device connected to the main control device; The rust layer detection device is used to detect the rust area ratio and rust layer color of the appearance of the exposed steel structure of the building; The coating detection device is used to detect the coating thickness and coating adhesion of the appearance of the exposed steel structure of the building; The environmental detection device is used to detect the temperature, humidity, and and concentration in the air; The geometric deformation detection device is used to measure the wall thickness of the component, record the thinning rate, scan the component morphology, and calculate the deflection and node offset; The crack detection device is used for UT detection of welds and stress concentration areas of joints, recording the crack depth, and MT detection of surface crack sensitive areas, marking the crack length and distribution; The stress state detection device is used for detecting the internal stress amplitude and vibration spectrum of components within 72 hours; The mechanical property detection device is used for testing yield strength, tensile strength, elongation, and impact toughness; The life prediction device is used for predicting and calculating the remaining bearing capacity and corrosion life of components; The strategy formulation device is used for formulating corresponding risk elimination strategies according to the risk levels of the steel structure components detected; 8. The progressive detection system for the structural performance grading of a building material according to claim 7, wherein, The main control device controls the rust layer detection device and the strategy formulation device to be always on, and controls the coating detection device, environment detection device, geometric deformation detection device, crack detection device, stress state detection device, mechanical property detection device, and life prediction device to be always off; The rust layer detection device detects the proportion of the rusted area and the rust layer color on the appearance of the exposed steel structure of the building. If the detection result shows that the rusted area > 30% and the rust layer color is red rust, then there is a greater structural risk for this steel structure. The main control device controls the coating detection device to be turned on to detect the coating thickness and coating adhesion. If the detection result shows that the coating thickness < 50% of the design value or the adhesion < 3 MPa, that is, the detection result is abnormal, a risk mark is made and it is marked as a low-risk component. The main control device controls the next-level risk detection to be carried out; The main control device controls the geometric deformation detection device, crack detection device, and stress state detection device to be turned on. The geometric deformation detection device measures the wall thickness of the component, records the thinning rate, scans the component morphology, calculates the deflection and node offset. If the wall thickness thinning rate > 15% or the deflection > L / 300 or the offset > H / 400, it is marked as a candidate component for structural failure; The crack detection device conducts UT detection on welds and stress concentration areas of joints, records the crack depth, conducts MT detection on surface crack sensitive areas, and marks the crack length and distribution. If the crack depth > 1 / 6 of the wall thickness or the crack length > 10 mm, it is marked as a component at risk of fracture; The stress state detection device records the stress amplitude and vibration spectrum of the steel structure within 72 hours. If the stress amplitude > 0.5f_y or the vibration frequency is close to the natural vibration frequency of the component, it is marked as a candidate component for dynamic instability; Risk marks are made for the candidate components for structural failure, components at risk of fracture, and candidate components for dynamic instability, and they are marked as high-risk components, and the next-level risk detection is carried out; The main control device controls the mechanical property detection device to be turned on, and the mechanical property detection device tests yield strength, tensile strength, and impact toughness; If the measured yield strength and tensile strength < 85% of the design value or the impact toughness , it is determined as a non-conforming component; If the measured yield strength ≥ 85% of the design value or the impact toughness , the main control device controls the life prediction device to start, inputs the collected data into the finite element analysis software, trains in the corrosion rate model, calculates the remaining bearing capacity and corrosion life of the component. If the remaining life < 5 years, it is marked as a component for emergency treatment; During the detection process of the steel structure material, the main control device controls the strategy formulation device to be turned on, and generates corresponding risk elimination strategies for steel structure components with different detection results respectively.

9. The progressive detection system for the structural performance grading of a building material according to claim 8, wherein The detection result of the rust layer detection device is normal, marked as a component without risk. Detect the ambient temperature and humidity of the component without risk, and the and concentration in the air. If the relative humidity > 80%, and or , then it is determined that the detection result is abnormal, marked as the corrosion acceleration area, and the re-inspection period is shortened to 3 months.

10. A storage medium, characterized in that, A computer program is stored on the storage medium, and when the computer program is run, it executes a hierarchical progressive detection method for the structural performance of building materials as described in any one of claims 1-6.

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