Some Effects of Shearing Velocity on the Shear Stress-Deformation Behaviour of Hard–Soft Artificial Material Interfaces

Atapour, Hashem; Moosavi, M.

doi:10.1007/s10706-013-9687-6

Cited by 23 publications

(2 citation statements)

References 25 publications

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“…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].…”

Section: Introductionmentioning

confidence: 99%

Research on Engineering Geomechanics Characteristics and CFRP Reinforcement Technology Based on Machine Learning Algorithms

Yan

Zhang

et al. 2022

Scientific Programming

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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.

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Section: Introductionmentioning

confidence: 99%

Research on Engineering Geomechanics Characteristics and CFRP Reinforcement Technology Based on Machine Learning Algorithms

Yan

Zhang

et al. 2022

Scientific Programming

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“…In recent studies, artificial rock joints are generally used to study the rate-dependent strength behaviour of rock joints. Plaster of Paris is used to model the rock joints of soft rocks (Atapour and Moosavi 2013;Atapour and Moosavi 2014;Mirzaghorbanali et al 2014;Tang and Wong 2016;Wang et al 2016) and concrete/mortar is used to model rock joints of hard rocks (Jafari et al 2004;Atapour and Moosavi 2013;Atapour and Moosavi 2014). From these studies on artificial rock joints, it has been concluded that, with increasing shear velocity, the shear resistance of the rock joints increases for harder rocks while it reduces for softer rocks.…”

Section: Introductionmentioning

confidence: 99%

Shear velocity-based uncertainty quantification for rock joint shear strength

Tiwari

Latha

2019

Bull Eng Geol Environ

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The shear strength of rock joints is an important property required in order to analyze the stability of rock slopes and tunnels. However, estimation of the shear strength of rock joints for in situ conditions is a complex task due to various influencing factors present in the field. Among these factors, the shear velocity or the shear displacement rate along the rock joints are important parameters which are relatively less studied since their effect is considered to be of second order compared to other factors. However, some recent studies in the literature suggest that shear velocity has a significant influence on the shear strength of rock joints, and hence the shear strength of joints estimated at low shear velocities in laboratories cannot be used under in situ conditions where the possibility of higher shear velocities exist due to the presence of different factors, such as blasting, excavation, and thermal and seismic loads. In this paper, we have addressed these issues in three steps. In the first step, an experimental study on jointed rock specimens is presented to investigate the influence of the displacement rate on the shear strength of rock joints. In the second step, a probabilistic method is developed based on the experimental results and the compiled data from the literature to estimate the in situ shear strength of joints under higher displacement rate conditions, i.e., blasting, excavation, and seismic loads from laboratory-estimated shear strength at the International Society for Rock Mechanics suggested low displacement rates. In the third step, a case study of a Himalayan rock tunnel was used to demonstrate the described approach. It was observed that the shear strength of discontinuities reduced with ncreasing shear velocity and that the rate dependency was higher for low-density rocks and under high confining stress. Further, a considerable effect was observed on the probability of failure of the rock tunnel when the effect of shear velocity was considered in the stability analysis.

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Detection of Seismic Precursors in Converted Ultrasonic Waves to Shear Failure of Natural Sandstone Rock Joints

Gheibi¹,

Hedayat

2021

Rock Mech Rock Eng

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Some Effects of Shearing Velocity on the Shear Stress-Deformation Behaviour of Hard–Soft Artificial Material Interfaces

Cited by 23 publications

References 25 publications

Research on Engineering Geomechanics Characteristics and CFRP Reinforcement Technology Based on Machine Learning Algorithms

Research on Engineering Geomechanics Characteristics and CFRP Reinforcement Technology Based on Machine Learning Algorithms

Shear velocity-based uncertainty quantification for rock joint shear strength

Detection of Seismic Precursors in Converted Ultrasonic Waves to Shear Failure of Natural Sandstone Rock Joints

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