CN-121999910-A - Method and system for predicting long-term bending-resistant bearing capacity of reinforced steel bar-FRP composite reinforced concrete

CN121999910ACN 121999910 ACN121999910 ACN 121999910ACN-121999910-A

Abstract

The invention provides a method and a system for predicting long-term bending-resistant bearing capacity of reinforced steel bar-FRP composite reinforced concrete, wherein the method comprises the steps of obtaining concrete parameters and mechanical property parameters of the reinforced steel bar-FRP composite reinforced concrete beam, obtaining a failure mode of the concrete beam, obtaining a short-term bending-resistant bearing capacity prediction model corresponding to the failure mode, obtaining the short-term bending-resistant bearing capacity of the concrete beam according to the concrete parameters and the mechanical property parameters by adopting the short-term bending-resistant bearing capacity prediction model, obtaining actual service time, seawater soaking time and load-carrying level of the concrete beam, calculating degradation influence coefficients of the concrete beam according to the actual service time, the seawater soaking time and the load-carrying level, and weighting the short-term bending-resistant bearing capacity by adopting the degradation influence coefficients of the concrete beam so as to obtain the long-term bending-resistant bearing capacity of the concrete beam. The method can improve the accuracy of predicting the long-term bending resistance bearing capacity of the concrete beam.

Inventors

CHANG YUFEI
XIAO SHUPENG
DU ZIHENG
HU XIAOFEI
WANG YIFEI

Assignees

安阳师范学院

Dates

Publication Date: 20260508
Application Date: 20260128

Claims (8)

1. The method for predicting the long-term bending-resistant bearing capacity of the reinforced bar-FRP composite reinforced concrete is characterized by comprising the following steps of: acquiring concrete parameters and mechanical property parameters of a reinforced steel bar-FRP composite reinforced concrete beam, and acquiring a damage mode of the concrete beam; acquiring a short-term bending-resistant bearing capacity prediction model corresponding to the failure mode, and acquiring the short-term bending-resistant bearing capacity of the concrete beam according to the concrete parameters and the mechanical performance parameters by adopting the short-term bending-resistant bearing capacity prediction model; acquiring the actual service time, the seawater soaking time and the load-carrying level of the concrete beam, and calculating the degradation influence coefficient of the concrete beam according to the actual service time, the seawater soaking time and the load-carrying level; and weighting the short-term bending load bearing capacity by adopting the degradation influence coefficient of the concrete beam so as to obtain the long-term bending load bearing capacity of the concrete beam.
2. The method for predicting long-term bending load bearing capacity according to claim 1, wherein, The step of obtaining a short-term bending-resistant bearing capacity prediction model corresponding to the failure mode comprises the following steps: If the failure mode is a concrete crushing failure mode, the short-term bending bearing capacity prediction model is a prediction model based on a preset force balance condition and a preset strain coordination condition; and if the failure mode is an FRP reinforcement fracture failure mode, the short-term bending-resistant bearing capacity prediction model is a prediction model based on the tensile strength and the ultimate tensile strain of the FRP reinforcement.
3. The method for predicting long-term bending load bearing capacity according to claim 1, wherein, The step of calculating the degradation influence coefficient of the concrete beam according to the actual service time, the seawater soaking time and the load carrying level comprises the following steps: Acquiring the actual environment temperature of the concrete beam, and calculating the equivalent service time of the concrete beam at the standard environment temperature according to the actual environment temperature and the actual service time; And acquiring a preset degradation influence prediction model, and calculating the degradation influence coefficient according to the equivalent service time length, the seawater soaking time length and the loading level by adopting the preset degradation influence prediction model.
4. The method for predicting long-term bending load bearing capacity according to claim 3, wherein, The step of calculating the equivalent service time of the concrete beam at the standard environment temperature according to the actual environment temperature and the actual service time comprises the following steps: Acquiring the actual degradation degree of the concrete beam after the actual service time length is passed at the actual environment temperature; And calculating the time length required by the concrete beam to reach the actual degradation degree at the standard environmental temperature, and taking the time length as the equivalent service time length.
5. The method for predicting long-term bending load bearing capacity according to claim 3, wherein, The step of obtaining the preset degradation influence prediction model comprises the following steps: acquiring degradation experimental data of the concrete beam, and acquiring a preset initial degradation influence model; and performing parameter fitting on the initial degradation influence model by adopting the degradation experimental data to obtain the preset degradation influence prediction model.
6. The method for predicting long-term bending load bearing capacity according to claim 3, wherein, After the step of performing parameter fitting on the initial degradation influence model by using the degradation experimental data to obtain the preset degradation influence prediction model, the method further comprises the following steps: Acquiring a preset test data set, and testing whether the preset degradation influence model meets a preset accuracy standard by adopting the test data set; and if not, returning to the step of performing parameter fitting on the initial degradation influence model by adopting the degradation experimental data.
7. The method for predicting long-term bending load bearing capacity according to claim 6, it is characterized in that the method comprises the steps of, The step of testing whether the preset degradation influence model meets a preset accuracy standard by adopting the test data set comprises the following steps: Calculating root mean square, average absolute error and decision coefficient of the preset degradation influence model according to the preset test data set; And if the root mean square is smaller than a preset root mean square threshold, the average absolute error is smaller than a preset error threshold and the decision coefficient is larger than a preset coefficient threshold, judging that the accuracy of the preset degradation influence model meets the preset accuracy standard.
8. A long-term bending-resistant bearing capacity prediction system of reinforced bar-FRP composite reinforced concrete, which comprises a memory, a processor and a computer program stored on the memory, and is characterized in that the processor executes the computer program to realize the long-term bending-resistant bearing capacity prediction steps of any one of claims 1 to 7.

Description

Method and system for predicting long-term bending-resistant bearing capacity of reinforced steel bar-FRP composite reinforced concrete Technical Field The invention relates to the technical field of bending-resistant bearing capacity prediction of concrete beams, in particular to a method and a system for predicting long-term bending-resistant bearing capacity of reinforced steel bar-FRP composite reinforced concrete. Background The reinforced bar-FRP (Fiber Reinforced Polymer ) composite reinforced concrete is reinforced by adopting reinforced bar-FRP mixed reinforced bars, wherein the reinforced bar-FRP mixed reinforced bars are concrete formed by continuous high-performance fibers and an organic matrix, have the advantages of remarkable light weight, corrosion resistance, excellent mechanical performance and the like compared with the traditional reinforced bars such as carbon steel or stainless steel screw-thread steel, and can improve the durability of a concrete structure in ocean engineering and high-chloride environment engineering. The bending bearing capacity of the concrete beam is the maximum capacity of resisting bending damage, is a core parameter of the structural design of a product, and can provide reliable basis for evaluating the stability and safety of the concrete beam by accurately predicting the bending bearing capacity. In the prior art, a method for predicting the bending bearing capacity of a concrete beam is to establish a short-term bending bearing capacity calculation model through the interface stress balance condition of the concrete beam, and predict the bending bearing capacity of the concrete according to the structural parameters of the steel bars in the concrete beam by adopting the model. The prediction mode can only predict the bending bearing capacity of the concrete beam in a short period, and the bending bearing capacity of the concrete beam can change along with the service time due to the fact that the concrete beam is soaked in seawater for a long time and under the action of continuous load, so that the long-term bending bearing capacity of the concrete beam cannot be accurately predicted by adopting the prediction method. Disclosure of Invention The invention provides a method and a system for predicting long-term bending-resistant bearing capacity of reinforced steel bar-FRP composite reinforced concrete, which are used for improving the accuracy of predicting the long-term bending-resistant bearing capacity of a concrete beam. Specifically, the invention provides a method for predicting the long-term bending-resistant bearing capacity of reinforced concrete of a steel bar-FRP composite reinforcement, which comprises the following steps: acquiring concrete parameters and mechanical property parameters of a reinforced steel bar-FRP composite reinforced concrete beam, and acquiring a damage mode of the concrete beam; acquiring a short-term bending-resistant bearing capacity prediction model corresponding to the failure mode, and acquiring the short-term bending-resistant bearing capacity of the concrete beam according to the concrete parameters and the mechanical performance parameters by adopting the short-term bending-resistant bearing capacity prediction model; acquiring the actual service time, the seawater soaking time and the load-carrying level of the concrete beam, and calculating the degradation influence coefficient of the concrete beam according to the actual service time, the seawater soaking time and the load-carrying level; and weighting the short-term bending load bearing capacity by adopting the degradation influence coefficient of the concrete beam so as to obtain the long-term bending load bearing capacity of the concrete beam. Further, the step of taking a short-term bending-resistant bearing capacity prediction model corresponding to the failure mode includes: If the failure mode is a concrete crushing failure mode, the short-term bending bearing capacity prediction model is a prediction model based on a preset force balance condition and a preset strain coordination condition; and if the failure mode is an FRP reinforcement fracture failure mode, the short-term bending-resistant bearing capacity prediction model is a prediction model based on the tensile strength and the ultimate tensile strain of the FRP reinforcement. Further, the step of calculating the degradation influence coefficient of the concrete beam according to the actual service time, the seawater soaking time and the loading level comprises the following steps: Acquiring the actual environment temperature of the concrete beam, and calculating the equivalent service time of the concrete beam at the standard environment temperature according to the actual environment temperature and the actual service time; And acquiring a preset degradation influence prediction model, and calculating the degradation influence coefficient according to the equivalent service time length, the seawa