CN114722858A - Safety assessment method for prestressed concrete structure - Google Patents

Safety assessment method for prestressed concrete structure Download PDF

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CN114722858A
CN114722858A CN202210223362.1A CN202210223362A CN114722858A CN 114722858 A CN114722858 A CN 114722858A CN 202210223362 A CN202210223362 A CN 202210223362A CN 114722858 A CN114722858 A CN 114722858A
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monitoring
damage
prestressed concrete
prestressed
concrete structure
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段元锋
隋晓东
章红梅
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Zhejiang University ZJU
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Abstract

The invention discloses a safety assessment method for a prestressed concrete structure. Adopt multiple intelligent sensor to carry out health monitoring to structural key atress component, include: monitoring the stress of the prestressed tendon based on a magnetic-elastic stress sensor; monitoring the breakage and corrosion of the steel bar based on the guided wave technology; and (3) monitoring the crack of the concrete surface based on machine vision and image processing technology. The evaluation method comprises the following steps: predicting the local damage degree and the damage position of the structure by adopting a deep learning algorithm based on the monitoring data; building and correcting a multi-scale finite element fine model of the measured structure, and reflecting damage characteristics obtained by a deep learning algorithm on the finite element model; and integrating multi-source monitoring data and the multi-scale finite element model to evaluate the overall safety condition of the prestressed concrete structure. The method fills the gap of the whole safety assessment of the prestressed concrete structure, expands the application of the deep learning algorithm in the field of civil engineering, and provides guarantee for ensuring the operation safety of the prestressed concrete structure.

Description

Safety assessment method for prestressed concrete structure
Technical Field
The invention belongs to the technical field of building engineering, and relates to multi-scale finite element modeling, a deep learning algorithm and a structure health monitoring technology.
Background
The prestressed concrete structure is an important component of urban infrastructure, however, with the increase of service life, the influence of factors such as structural service conditions and environmental erosion, and the like, and the improper design and construction, the concrete material aging and structural damage occur, which leads to the deterioration of structural performance, the reduction of bearing capacity and the reduction of durability. The main types of damage faced by current prestressed concrete structures include: concrete carbonization, steel bar corrosion, prestressed tendon relaxation, concrete surface cracking and the like, and the damage is usually caused by cracks on the concrete surface. The corrosion of the steel bars is the primary factor influencing the durability of the concrete structure, and the loss of the bearing capacity and the safety of the prestressed concrete structure can be caused by the cracking of the concrete along the steel bars, the reduction of the bonding force between the steel bars and the concrete, the reduction of the effective area of the steel bars and the like caused by the corrosion and the expansion of the steel bars. The stress relaxation of the prestressed tendons directly affects the bearing capacity of the prestressed concrete structure. The cracks are the most obvious disease types, the strength and the rigidity of the concrete structure are directly reduced, the attractiveness of the structure is influenced, and meanwhile, the reinforcing steel bar is corroded more easily due to the fact that a protective layer is lost.
Therefore, it is important to establish an effective prestressed concrete structure safety assessment method. The method is used for carrying out health monitoring on key problems of reinforcement corrosion, stress reduction of prestressed reinforcements, crack development of concrete surfaces and the like which possibly occur in the prestressed concrete structure, evaluating the influence of local damage on the whole bearing capacity of the structure in an effective mode, timely early warning potential hazards in the structure, timely dismantling and reinforcing local members and avoiding catastrophic accidents.
With the progress of science and technology, intelligent sensors and sensing technologies are rapidly developed, and corresponding signal processing technologies are more and more complex. Aiming at the reinforcement corrosion monitoring of the prestressed concrete structure, the adopted monitoring technology comprises an optical fiber pH sensor, an ultrasonic guided wave sensor, a sound emission technology and the like, and the ultrasonic guided wave technology is widely concerned due to long detection range, high efficiency and good precision. The magnetic elastic type stress sensor has obvious effect on stress monitoring of the prestressed tendon, the measured absolute stress of the prestressed tendon is accurate and is not influenced by the change of the environmental temperature, and the engineering applicability is strong. For crack monitoring on the concrete surface, a complete set of technology based on machine vision and image recognition is widely applied in recent years, and the method has the characteristics of wide detection range, high detection speed and high visualization degree of detection results. And the detection of all high prestressed concrete structures can be replaced by an unmanned aerial vehicle or a wall climbing robot.
However, the existing structure monitoring method only extracts the local damage features of the structure, and cannot effectively evaluate the overall bearing capacity of the structure through the local damage features of the structure, and an effective method and research literature for evaluating the overall safety condition of the structure by combining multiple local damage features of the structure are not found at present. The chinese patent document with application number 201710054344.4 only describes image acquisition and crack feature extraction of a local crack of a bridge, and does not describe how to judge the safety condition of a structure through the extracted crack features.
Disclosure of Invention
The invention aims to solve the technical problems and provides a safety assessment method for a prestressed concrete structure, which analyzes the integral bearing capacity and safety state of the structure by combining the monitoring results of various intelligent sensors on the prestressed concrete structure and a multi-scale finite element fine model, ensures the safety of the prestressed concrete structure during the operation and prevents disasters.
In order to solve the technical problem, the invention adopts the following technical scheme:
a safety assessment method for a prestressed concrete structure comprises the following steps:
s1: establishing a multi-scale finite element refined model of the prestressed concrete structure, extracting the natural frequency and the modal shape of the structure, comparing the natural frequency and the modal shape with an actual test result of the structure, and updating the finite element model;
s2: simulating the structural response of the prestressed concrete structure under long-term operation load and extreme load, extracting the stress distribution of a key stress member of the structure, and providing a basis for the optimal arrangement of the sensor;
s3: monitoring the safety condition of a key stress component of a structure by adopting various intelligent sensors, and constructing a monitoring data sample set;
s4: training the sample set by adopting various deep learning algorithms, extracting effective components and damage characteristic values in monitoring data, and realizing accurate prediction of the damage degree and the damage position of the local structure;
s5: and reflecting the local damage condition of the structure on the established multi-scale finite element model, analyzing the influence of the local damage on the key part of the structure and the whole bearing capacity of the structure by combining the multi-source monitoring data, and evaluating the whole safety condition of the prestressed concrete structure.
Furthermore, the multi-scale finite element fine model of the prestressed concrete structure is characterized in that a segment fine model of a key stress member is established, and time and space scale changes of the key stress member are considered, wherein the time and space scale changes comprise material characteristic degradation along with time, environmental variable changes and load action changes along with time and the like.
Further, the multiple intelligent sensor monitoring technologies include:
the method comprises the following steps of (1) monitoring the stress of a prestressed tendon based on a magnetic-elastic stress sensor, wherein the magnetic-elastic stress sensor is arranged at the end part of the prestressed tendon and can be arranged at the anchoring end of the prestressed tendon for the built prestressed concrete structure;
the method comprises the following steps of monitoring steel bar fracture and corrosion based on a guided wave technology, wherein a guided wave sensor is arranged on the section of a steel bar, and the guided wave sensor can be adopted in a form of a piezoelectric sensor and a magnetostrictive sensor and is reasonably selected according to actual conditions;
based on the concrete surface crack monitoring of machine vision and image processing technique, the acquisition of concrete surface image can adopt fixed camera, unmanned aerial vehicle or wall climbing robot etc. select rationally according to actual need.
Further, the various deep learning algorithms include, but are not limited to, convolutional neural networks, cyclic neural networks, antagonistic neural networks, transfer learning, and the like;
further, the image processing technology includes, but is not limited to, image graying, image histogram equalization, image median filtering, image normalization, image binarization filtering, image pixel detection, and the like.
Further, the damage characteristic value includes, but is not limited to, a stress level of the tendon, a damage degree of the steel bar in the concrete, a damage position of the steel bar in the concrete, a length of the concrete surface crack, a width of the concrete surface crack, a position and a direction of the concrete surface crack, and the like.
Furthermore, the bearing capacity of the key part of the structure needs to be compared with the bearing capacity of the member required in the specification to judge whether the stress or strain condition of the key part of the structure exceeds the requirement of the bearing capacity, whether damage occurs or not, and the like.
Further, the evaluation of the overall bearing capacity of the structure needs to judge whether the structure fails under the action of load and damage.
The intelligent sensor system provided by the invention has the advantages of low manufacturing cost, small volume, light weight, convenience in installation and use, and convenience in large-scale popularization and use on a prestressed concrete structure. The safety assessment method provided by the invention has the advantages of high detection efficiency, strong real-time performance and the like.
Drawings
The above advantages of the present invention will become more apparent and more readily appreciated from the detailed description set forth below when taken in conjunction with the drawings, which are intended to be illustrative, not limiting, of the invention and in which:
FIG. 1 is a safety evaluation system of a prestressed concrete structure according to the present invention;
Detailed Description
The safety evaluation method of a prestressed concrete structure according to the present invention will be described in detail with reference to the accompanying drawings.
The examples described herein are specific embodiments of the present invention, are intended to be illustrative and exemplary in nature, and are not to be construed as limiting the scope of the invention. In addition to the embodiments described herein, those skilled in the art will be able to employ other technical solutions which are obvious based on the disclosure of the claims and the specification of the present application, and these technical solutions include technical solutions which make any obvious replacement or modification for the embodiments described herein.
As shown in fig. 1, the present invention provides a safety assessment method for a prestressed concrete structure, which specifically comprises the following steps:
s1: and selecting the common precast prestressed concrete box girder in the engineering as a monitoring object. Establishing a multi-scale finite element fine model of the tested prestressed concrete beam, particularly performing local fine treatment on prestressed tendons, construction steel bars and a common concrete structure at a position easy to damage, considering performance degradation of materials caused by time change, and extracting natural frequency and modal vibration mode of the finite element model structure;
s2: acquiring an acceleration time-course signal of the prestressed concrete beam under the action of an environmental load by arranging a multipoint acceleration sensor, and identifying the natural frequency and the modal vibration mode of the structure by adopting a modal identification algorithm (a random subspace method, a frequency domain decomposition method, a characteristic system realization algorithm and the like) based on structural response for updating a finite element model;
s3: simulating the structural response of the prestressed concrete structure under long-term operation load and extreme load, and extracting the stress distribution of key stressed components of the structure so as to reasonably arrange intelligent monitoring sensors;
s4: before the prestressed pipeline is grouted, a magnetoelectric stress sensor and a magnetostrictive guided wave sensor are installed at the end of a prestressed tendon anchorage device, and then grouting and anchoring are carried out. And monitoring the stress change of the prestressed tendon for a long time, collecting stress monitoring and ultrasonic guided wave echo signals, and establishing a monitoring data sample set.
S5: collecting a concrete surface characteristic image by adopting an unmanned aerial vehicle or a wall-climbing robot carrying a high-definition camera, extracting crack characteristics of the concrete surface in the image by adopting an image identification technology (including image graying, image histogram equalization, image median filtering, image normalization, image binarization filtering, image pixel detection and the like), and constructing an image identification sample set;
s6: classifying the monitoring data and the image samples and creating labels, wherein the label contents comprise damage characteristic values of key stress members (including the stress level of prestressed tendons, the damage degree of steel bars in concrete, the damage position of the steel bars in the concrete, the length of concrete surface cracks, the width of the concrete surface cracks, the position and the trend of the concrete surface cracks and the like), and training and testing by using a convolutional neural network algorithm so that the damage characteristics of the local structure can be accurately estimated;
s7: reflecting the local damage characteristics of the structure on the established multi-scale finite element model, analyzing the influence of the local damage on the key parts of the structure and the whole bearing capacity of the structure, and setting a local member failure criterion, thereby evaluating the whole safety condition of the prestressed concrete structure.
The present invention is not limited to the above embodiments, and any other products in various forms can be obtained by the teaching of the present invention, but any changes in the shape or structure thereof, which are the same as or similar to the technical solutions of the present invention, fall within the protection scope of the present invention.

Claims (8)

1. A safety assessment method for a prestressed concrete structure is characterized by comprising the following steps:
s1: establishing a multi-scale finite element refined model of the prestressed concrete structure, extracting the natural frequency and the modal shape of the structure, comparing the natural frequency and the modal shape with an actual test result of the structure, and updating the finite element model;
s2: simulating the structural response of the prestressed concrete structure under long-term operation load and extreme load, extracting the stress distribution of a key stress member of the structure, and providing a basis for the optimal arrangement of the sensor;
s3: monitoring the safety condition of a key stress component of a structure by adopting various intelligent sensors, and constructing a monitoring data sample set;
s4: training the sample set by adopting various deep learning algorithms, extracting effective components and damage characteristic values in monitoring data, and realizing accurate prediction of the damage degree and the damage position of the local structure;
s5: and reflecting the local damage condition of the structure on the established multi-scale finite element model, analyzing the influence of the local damage on the key part of the structure and the whole bearing capacity of the structure by combining the multi-source monitoring data, and evaluating the whole safety condition of the prestressed concrete structure.
2. The method of claim 1, wherein for the multi-scale finite element fine model of the prestressed concrete structure, a segment fine model of the key stressed member is established, and the time and space scale changes of the key stressed member are considered, including the degradation of material characteristics with time, the changes of environmental variables and load effect with time, and the like.
3. The method for safety evaluation of prestressed concrete structure according to claim 1, wherein said plurality of intelligent sensor monitoring techniques for step S2 includes:
monitoring the absolute stress of the prestressed tendon based on a magnetic elastic type stress sensor, and monitoring the reduction of the stress level of the prestressed tendon caused by the relaxation of the prestress;
monitoring the fracture and corrosion of the steel bar based on the guided wave technology, and preventing the damage of the whole structure caused by the damage of the steel bar in the prestressed concrete structure;
and monitoring the concrete surface cracks based on machine vision and image processing technology, and evaluating the influence of local cracks on the whole bearing capacity.
4. The method of claim 1, wherein the plurality of deep learning algorithms comprises convolutional neural network, cyclic neural network, antagonistic neural network, and transfer learning.
5. The method of claim 1, wherein the image processing technology comprises image graying, image histogram equalization, image median filtering, image normalization, image binarization filtering, and image pixel detection.
6. The method of claim 1, wherein the damage characteristic values include stress level of the prestressed reinforcement, damage degree of the reinforcement in the concrete, damage position of the reinforcement in the concrete, length of the crack on the concrete surface, width of the crack on the concrete surface, position and direction of the crack on the concrete surface.
7. The method as claimed in claim 1, wherein the load-bearing capacity of the critical portion of the structure is compared with the load-bearing capacity of the member required by the specification to determine whether the stress or strain condition of the critical portion of the structure exceeds the load-bearing capacity requirement.
8. The method of claim 1, wherein the evaluation of the overall bearing capacity of the structure is carried out by determining whether the structure fails under the action of load and damage.
CN202210223362.1A 2022-03-09 2022-03-09 Safety assessment method for prestressed concrete structure Pending CN114722858A (en)

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115100176A (en) * 2022-07-14 2022-09-23 中国海洋大学 Neural network-based reinforced concrete column damage assessment method
CN117114436A (en) * 2023-07-27 2023-11-24 中冶建筑研究总院有限公司 Existing prestressed concrete member performance evaluation method based on measured data

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115100176A (en) * 2022-07-14 2022-09-23 中国海洋大学 Neural network-based reinforced concrete column damage assessment method
CN115100176B (en) * 2022-07-14 2024-05-14 中国海洋大学 Reinforced concrete column damage assessment method based on neural network
CN117114436A (en) * 2023-07-27 2023-11-24 中冶建筑研究总院有限公司 Existing prestressed concrete member performance evaluation method based on measured data

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