A safety assessment method for prestressed concrete structures

Abstract

The present invention discloses a safety assessment method for prestressed concrete structures. A variety of intelligent sensors are used to conduct health monitoring on the key stressed members of the structure, including: monitoring the stress of prestressed tendons based on magneto-elastic stress sensors; monitoring the fracture and corrosion of steel bars based on guided wave technology; monitoring the surface cracks of concrete based on machine vision and image processing technology. The assessment method includes: based on the monitoring data, using deep learning algorithms to predict the degree and location of local damage to the structure; building and correcting a multi-scale finite element fine model of the structure to be measured, and reflecting the damage characteristics obtained by the deep learning algorithm on the finite element model; fusing multi-source monitoring data and multi-scale finite element models to evaluate the overall safety status of prestressed concrete structures. The proposed method fills the gap in the overall safety assessment of prestressed concrete structures, expands the application of deep learning algorithms in the field of civil engineering, and provides a guarantee for ensuring the operation safety of prestressed concrete structures.

Description

Technical Field

[0001] The present invention belongs to the technical field of construction engineering, and relates to multi-scale finite element modeling, deep learning algorithms, and structural health monitoring technologies. Background Art

[0002] Prestressed concrete structures are an important part of urban infrastructure. However, with the increase of service life, the influence of structural service conditions and environmental erosion, and improper design and construction, concrete material aging and structural damage will occur, resulting in deterioration of structural performance, decline of bearing capacity, and reduction of durability. The main types of damage faced by current prestressed concrete structures include: concrete carbonation, steel bar corrosion, prestressing tendon relaxation, concrete surface cracking, etc., and the generation of damage is usually caused by the appearance of cracks on the concrete surface. Steel bar corrosion is the primary factor affecting the durability of concrete structures. The concrete cover cracking along the steel bars caused by the expansion of steel bar corrosion, the decrease of the bond strength between steel bars and concrete, and the reduction of the effective area of steel bars will all lead to the loss of bearing capacity and safety of prestressed concrete structures. The stress relaxation of prestressing tendons directly affects the bearing capacity of prestressed concrete structures. As the most obvious type of disease, cracks directly reduce the strength and stiffness of concrete structures, affect the aesthetics of the structures, and at the same time make the steel bars lose their protective layer and are more likely to corrode.

[0003] Therefore, it is particularly important to establish an effective safety assessment method for prestressed concrete structures. Conduct health monitoring on key issues such as possible steel bar corrosion, stress reduction of prestressing tendons, and development of concrete surface cracks in prestressed concrete structures, and adopt effective methods to evaluate the influence of local damage on the overall bearing capacity of the structure, timely warn of potential hidden dangers existing in the structure, timely demolish and reinforce local components, and avoid the occurrence of catastrophic accidents.

[0004] With the progress of technology, intelligent sensors and sensing technologies have developed rapidly, and the corresponding signal processing technologies have become more and more complex. For the steel bar corrosion monitoring of prestressed concrete structures, the monitoring technologies adopted include fiber optic pH sensors, ultrasonic guided wave sensors, acoustic emission technologies, etc. The ultrasonic guided wave technology has been widely concerned due to its long detection range, high efficiency, and good accuracy. The magneto-elastic stress sensor has remarkable effects in the stress monitoring of prestressing tendons. The absolute stress of the prestressing tendons measured is accurate and not affected by environmental temperature changes, and it has strong engineering applicability. For the crack monitoring of the concrete surface, a complete set of technologies based on machine vision and image recognition have been widely used in recent years. Its characteristics include a wide detection range, fast detection speed, and high visualization degree of detection results. And for the detection of all high-rise prestressed concrete structures, drones or wall-climbing robots can be used instead.

[0005] However, the existing structural monitoring methods only extract the local damage characteristics of the structure and cannot effectively evaluate the overall bearing capacity of the structure through the local damage characteristics of the structure. At present, no effective method and research literature for combining multiple local damage characteristics of the structure to evaluate the overall safety status of the structure have been found. The Chinese patent document with the application number 201710054344.4 only describes the image acquisition and crack feature extraction of local cracks in the bridge, but does not explain how to judge the safety status of the structure through the extracted crack features. Summary of the Invention

[0006] The object of the present invention is to address the above technical problems and propose a safety assessment method for prestressed concrete structures. By combining the monitoring results of prestressed concrete structures by multiple intelligent sensors with a multi-scale finite element refined model, the overall bearing capacity and safety status of the structure are analyzed to ensure the safety of prestressed concrete structures during operation and prevent disasters from occurring.

[0007] To solve the above technical problems, the present invention adopts the following technical solutions:

[0008] A safety assessment method for prestressed concrete structures includes the following steps:

[0009] S1: Establish a multi-scale finite element refined model of the prestressed concrete structure, extract the natural frequency and modal vibration mode of the structure, compare with the actual test results of the structure, and update the finite element model;

[0010] S2: Simulate the structural response of the prestressed concrete structure under long-term operating loads and extreme loads, extract the stress distribution of the key load-bearing components of the structure, and provide a basis for the optimal layout of sensors;

[0011] S3: Use multiple intelligent sensors to monitor the safety status of the key load-bearing components of the structure and construct a monitoring data sample set;

[0012] S4: Use multiple deep learning algorithms to train the sample set, extract the effective components and damage characteristic values in the monitoring data, and accurately predict the damage degree and damage location of the local structure;

[0013] S5: Reflect the local damage condition of the structure onto the established multi-scale finite element model, combine multi-source monitoring data, analyze the impact of local damage on the key parts of the structure and the overall bearing capacity of the structure, and evaluate the overall safety status of the prestressed concrete structure.

[0014] Furthermore, the multi-scale finite element refined model of the prestressed concrete structure is characterized in that a segment refined model of the key load-bearing components is established, considering the time and space scale changes of the key load-bearing components, including the degradation of material properties over time, the changes of environmental variables and load actions over time, etc.

[0015] Further, the multiple intelligent sensor monitoring technologies include:

[0016] Monitoring of the prestress of prestressed tendons based on magnetoelastic stress sensors. The magnetoelastic stress sensors are installed at the ends of prestressed tendons. For existing prestressed concrete structures, they can be installed at the anchorage ends of prestressed tendons.

[0017] Monitoring of steel bar fracture and corrosion based on guided wave technology. The guided wave sensors are installed on the cross-sections of steel bars. The forms of guided wave sensors that can be used include piezoelectric sensors and magnetostrictive sensors, which are reasonably selected according to the actual situation.

[0018] Monitoring of concrete surface cracks based on machine vision and image processing technology. The acquisition of concrete surface images can be carried out using fixed cameras, drones, wall-climbing robots, etc., which are reasonably selected according to actual needs.

[0019] Further, the multiple deep learning algorithms include, but are not limited to, methods such as convolutional neural networks, recurrent neural networks, adversarial neural networks, and transfer learning.

[0020] Further, the image processing technology includes, but is not limited to, image grayscale conversion, image histogram equalization, image median filtering, image normalization, image binarization, image binary filtering, image pixel detection, etc.

[0021] Further, the damage characteristic values include, but are not limited to, the stress level of prestressed tendons, the damage degree of steel bars in concrete, the damage location of steel bars in concrete, the length of concrete surface cracks, the width of concrete surface cracks, the location and orientation of concrete surface cracks, etc.

[0022] Further, for the bearing capacity of the key parts of the structure, it is necessary to compare the bearing capacity of the components required in the specifications to judge whether the stress or strain conditions of the key parts of the structure exceed the bearing capacity requirements and whether damage occurs.

[0023] Further, for the assessment of the overall bearing capacity of the structure, it is necessary to judge whether there is a structural failure behavior under the action of loads and damage.

[0024] The intelligent sensor system proposed by the present invention has the advantages of low cost, small volume, light weight, convenient installation and use, and is convenient for large-scale popularization and use on prestressed concrete structures. The safety assessment method proposed by the present invention has the advantages of high detection efficiency and strong real-time performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Through the following detailed description in conjunction with the accompanying drawings, the above advantages of the present invention will become clearer and easier to understand. These drawings are only schematic and do not limit the present invention, where:

[0026] Figure 1 is the safety assessment system of the prestressed concrete structure described in the present invention; Detailed implementation manners

[0027] The following combines the accompanying drawings to elaborate in detail on a safety assessment method for a prestressed concrete structure of the present invention.

[0028] The embodiments described herein are specific specific implementation manners of the present invention and are used to illustrate the concept of the present invention. They are all explanatory and exemplary and should not be construed as limiting the implementation manners of the present invention and the scope of the present invention. Except for the embodiments described herein, those skilled in the art can also adopt other obvious technical solutions based on the content disclosed in the claims and the specification of this application. These technical solutions include technical solutions that make any obvious substitutions and modifications to the embodiments described herein.

[0029] As Figure 1 shown, the present invention provides a safety assessment method for a prestressed concrete structure, which specifically includes the following steps:

[0030] S1: Select the commonly used precast prestressed concrete box girder in the project as the monitoring object. Establish a multi-scale finite element refined model of the measured prestressed concrete beam, and make local refinement specifically for the prestressed tendons, structural steel bars, and concrete structures at common vulnerable parts, and consider the performance degradation of materials caused by time change, and extract the natural frequencies and modal vibration modes of the finite element model structure;

[0031] S2: Obtain the acceleration time history signal of the prestressed concrete beam under environmental loads by arranging multi-point acceleration sensors, and use the modal identification algorithm based on structural response (stochastic subspace method, frequency domain decomposition method, eigensystem realization algorithm, etc.) to identify the natural frequencies and modal vibration modes of the structure for updating the finite element model;

[0032] S3: Simulate the structural response of the prestressed concrete structure under long-term operating loads and extreme loads, and extract the stress distribution of the key stressed components of the structure to reasonably arrange intelligent monitoring sensors;

[0033] S4: Before grouting the prestressed duct, install magnetoelectric stress sensors and magnetostrictive guided wave sensors at the end of the prestressed tendon anchor, and then grout and seal the anchor. Long-term monitor the stress change of the prestressed tendon and collect stress monitoring and ultrasonic guided wave echo signals to establish a monitoring data sample set.

[0034] S5: Use a drone or wall-climbing robot equipped with a high-definition camera to collect images of the concrete surface features. Adopt image recognition technology (including image grayscale conversion, image histogram equalization, image median filtering, image normalization, image binarization, image binary filtering, image pixel detection, etc.) to extract the crack features on the concrete surface in the image, and construct an image recognition sample set;

[0035] S6: Classify the monitoring data and image samples and create labels. The label content includes the damage characteristic values of key stressed components (including the stress level of prestressed tendons, the damage degree of steel bars in concrete, the damage location of steel bars in concrete, the length of concrete surface cracks, the width of concrete surface cracks, the location and orientation of concrete surface cracks, etc.). Use the convolutional neural network algorithm for training and testing to enable it to accurately estimate the local damage characteristics of the structure;

[0036] S7: Reflect the local damage characteristics of the structure onto the established multi-scale finite element model, analyze the impact of local damage on the key parts of the structure and the overall bearing capacity of the structure, and set the local component failure criterion, so as to evaluate the overall safety status of the prestressed concrete structure.

[0037] The present invention is not limited to the above embodiments. Anyone can obtain other various forms of products under the inspiration of the present invention. However, no matter what changes are made in its shape or structure, as long as it has the same or similar technical solutions as the present application, it falls within the protection scope of the present invention.

Claims

1. A safety assessment method for a prestressed concrete structure, characterized in that It includes the following steps: S1: Establish a refined multi-scale finite element model of the prestressed concrete structure, extract the natural frequencies and modal vibration modes of the structure, compare with the actual test results of the structure, and update the finite element model; S2: Simulate the structural responses of the prestressed concrete structure under long-term operating loads and extreme loads, extract the stress distributions of the key load-bearing components of the structure, and provide a basis for the optimal layout of sensors; S3: Adopt a variety of intelligent sensors to monitor the safety conditions of the key load-bearing components of the structure, specifically including: (1) Monitoring the absolute stress of the prestressed tendons based on magneto-elastic stress sensors to monitor the reduction of the stress level of the prestressed tendons caused by prestress relaxation; (2) Monitoring the fracture and corrosion of steel bars based on guided wave technology to prevent the overall structure from being damaged due to the damage of the internal steel bars of the prestressed concrete structure; (3) Monitoring the surface cracks of concrete based on machine vision and image processing technology, evaluating the impact of local cracks on the overall bearing capacity, and constructing a monitoring data sample set; S4: Use a variety of deep learning algorithms to train the sample set, extract the effective components and damage characteristic values from the monitoring data, and accurately predict the damage degree and damage location of the local structure; S5: Reflect the local damage condition of the structure onto the established multi-scale finite element model, combine multi-source monitoring data, analyze the impact of local damage on the key parts of the structure and the overall bearing capacity of the structure, and evaluate the overall safety condition of the prestressed concrete structure.

2. The safety assessment method for a prestressed concrete structure according to claim 1, characterized in that For the refined multi-scale finite element model of the prestressed concrete structure, establish a sectional refined model of the key load-bearing components, considering the time and space scale changes of the key load-bearing components, including the degradation of material properties over time, the changes of environmental variables and load effects over time.

3. The safety assessment method for a prestressed concrete structure according to claim 1, characterized in that The various deep learning algorithms include convolutional neural network, recurrent neural network, adversarial neural network, and transfer learning.

4. The safety assessment method for a prestressed concrete structure according to claim 1, characterized in that For the image processing technology, it includes image grayscale, image histogram equalization, image median filtering, image normalization, image binarization, image binary filtering, and image pixel detection.

5. The safety assessment method for a prestressed concrete structure according to claim 1, characterized in that For the damage characteristic values, it includes the stress level of the prestressed tendons, the damage degree of the steel bars in the concrete, the damage location of the steel bars in the concrete, the length of the surface cracks of the concrete, the width of the surface cracks of the concrete, the location and orientation of the surface cracks of the concrete.

6. The safety assessment method for a prestressed concrete structure according to claim 1, characterized in that For the bearing capacity of the key parts of the structure, it is necessary to compare the bearing capacity of the components required in the code to judge whether the stress or strain conditions of the key parts of the structure exceed the bearing capacity requirements.

7. The safety assessment method for a prestressed concrete structure according to claim 1, characterized in that For the evaluation of the overall bearing capacity of the structure, it is necessary to judge whether there is a structural failure behavior under the action of loads and damage.

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