Abstract
We have completed the design of an early warning and evaluation analysis module based on machine learning algorithms. Aiming at the prestressed CFRP-strengthened reinforced concrete bridges under natural exposure, we developed a theoretical model to analyze the long-term prestress loss of reinforced parts and the adhesion behavior of the CFRP-concrete interface under natural exposure conditions. The analysis deeply reveals the technical and engineering geomechanics characteristics of the D bridge. At the same time, through a series of experimental studies on the D bridge condition monitoring system, the data acquisition and transmission, processing and control of the D bridge condition monitoring system, and the bridge condition monitoring and evaluation software are provided. Regarding how to repair the engineering geomechanical characteristics of D bridge, we mentioned the prestressed CFRP reinforcement technology. The prestressed carbon fiber reinforced composite (CFRP) structure made of reinforced concrete (RC) makes better use of the high-strength characteristics of CFRP and changes. It strengthens the stress distribution of the components and improves the overall strength of the components. It is more supported by engineers in the civil engineering and transportation departments. However, most prestressed CFRP-reinforced RC structures are located in natural exposure environments, and the effect of natural exposure environments on the long-term mechanical properties of prestressed C FRP-reinforced RC components is still unclear. This article mainly uses the research on the engineering geomechanics characteristics and reinforcement technology of the bridge body, so that people have a deep understanding of its concept, and provides reasonable use methods and measures for the maintenance and protection of the bridge body in the future. This paper studies the characteristics of engineering geomechanics based on machine learning algorithms and applies them to the research of CFRP reinforcement technology, aiming to promote its better development.
Highlights
At present, engineering practice and a number of research results show that prestressed CFRP reinforcement technology, as an active reinforcement technology, significantly improves the utilization efficiency of CFRP, provides a more convenient and efficient use plan for the application of CFPR, and makes better use of it [10]. e strength advantage lays a solid foundation for a wider range of applications
It can participate in the force before the reinforced component is subjected to the second force, and the stress distribution of the reinforcing element can be changed to close the original crack or prevent its expansion [11]. erefore, the prestressed CFRP reinforcement technology is more and more favored by traffic and civil engineers [12]. e use of prestressed CFRP reinforcement technology has become widespread in practice
After the fatigue test of the reinforced concrete beam by the Structural Laboratory of Chang’an University, the fatigue test was analyzed by finite element simulation ABAQUS. ree typical long-span bridges are selected as representatives: Bridge A, Bridge B, and Bridge C. ese were originally built in certain urban areas and are still in operation and are calculated by Midas Civil 2012 software. en, based on the “Specifications for Design of Concrete Structures”, the fatigue stress calculations of the three small and medium-sized bridges were confirmed, and the stress levels of the vertical steel bars under the design load of the three bridges were investigated to be 0.5 and 0.55, respectively [14]. e minimum stress level under load is 0.25
Summary
Since the 1990s, machine learning algorithms have gradually been applied to the study of geomechanical properties of bridge construction and related research on the properties of damaged parts of bridges [1]. e engineering geomechanical characteristic data of the bridge damage is sent to the machine, and the machine uses the learning algorithm calculation and pattern recognition to complete the work on the damage site [2]. e identification of the damage program can be traced back to the problem of functional adaptation [3]. e machine learning algorithm learns from historical data to set the geoengineering features of different damage degree projects and adapts the appropriate feature plane to the special space where the feature data is sent to the machine learning algorithm [4]. e learning algorithm calculates the data on the surface of the corresponding feature and performs interpolation to evaluate the degree of damage, and it plays a leading role in the repair of the bridge [5]. Fatigue cracks are generated at the local positions of the concrete members and propagate, and Scientific Programming fatigue fracture will occur It brings greater safety and social risks, and its maintenance brings a huge economic burden, which brings great troubles to people’s travel convenience [7]. Erefore, this article will focus on the research of carbon fiber reinforced composite material (CFRP) paste reinforcement technology to explore whether the engineering geomechanical properties of the damaged bridge can be restored [9]. E strength advantage lays a solid foundation for a wider range of applications It can participate in the force (effective force) before the reinforced component is subjected to the second force, and the stress distribution of the reinforcing element can be changed to close the original crack or prevent its expansion [11]. Selection of Material Parameters. (1) According to concrete reference materials, all concrete beams in the fatigue simulation test of this study are made of C40 concrete. (2) In the rod fatigue simulation test, the strength grade of the rod is HRB400
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