Experimental Study of Friction Resistance between Steel and Concrete in Prefabricated Composite Beam with High‐Strength Frictional Bolt

Guo, Qianqian; Chen, Qingwei; Xing, Ying; Xu, Ya-ning; Zhu, Yi Jie

doi:10.1155/2020/1292513

Cited by 14 publications

(4 citation statements)

References 10 publications

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“…Material properties of UHPC and steel rebar used in defining the CDP model are illustrated in Table 1. The contact between supporting and loading steel plates and the concrete was surface to surface of two types of behavior; tangential contact with penalty friction formulation having 0.5 friction coefficient [14,28], besides normal behavior with hard contact to prevent the penetration between the adjacent surfaces. The steel plates were constrained as a rigid body.…”

Section: Calibration Of Cdp Model Parameters For the Practical Beammentioning

confidence: 99%

Impact of Failure-surface Parameters of Concrete Damage Plasticity Model on the Behavior of Reinforced Ultra-high Performance Concrete Beams

Mahdi

2023

Period. Polytech. Civil Eng.

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The failure surface of elements can numerically represent by a concrete damage plasticity (CDP) model in Abaqus software which depends on five parameters. The parameters are eccentricity, shape, biaxial to uniaxial compressive strength ratio, dilation angle, and viscosity. This paper studies the effect of changing the values of the failure surface parameters on the bearing capacity, deflection, and overall structural behavior of ultra-high performance concrete (UHPC) beams. The parameters' changes include reasonable values higher or lower than the values adopted by Abaqus. An experimentally performed reinforced UHPC beam is simulated by Abaqus, and the five parameters are calibrated to coincide with the practical results. Then, one of the parameters is changed while others remain constant for each alteration to study its effect on the UHPC beam behavior. The numerical analysis results show that all five parameters do not affect the loading capacity and the corresponding deflection at the first cracking state. At peak state, the eccentricity does not affect the load and deflection. The influence of shape and the biaxial to uniaxial compressive strength ratio are confined to the deflection at peak load only without affecting the bearing capacity. The dilation angle effect appears when it is greater than 30 degrees to 56 degrees, while when it is less than 30, it does not affect the load and deflection. Rising the dilation angle and viscosity value promotes the peak load and the deflection.

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Section: Calibration Of Cdp Model Parameters For the Practical Beammentioning

confidence: 99%

Impact of Failure-surface Parameters of Concrete Damage Plasticity Model on the Behavior of Reinforced Ultra-high Performance Concrete Beams

Mahdi

2023

Period. Polytech. Civil Eng.

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“…The interaction between the steel bolsters and concrete column is selected to be a contact with the normal behavior of hard contact type to prevent penetration between them and allow separation after contact, and tangential behavior with a friction coefficient of 0.45 between concrete and steel. The friction coefficient between concrete and steel ranges between 0.29 to 0.6, as stated by Guo et al [40], Rinker et al [41], and Zhao and Zhu [42]. Friction coefficient sensitivity was checked by the software and chosen to be 0.45 throughout the numerical analysis due to the compressive stress considering the effect of a compressed rubber pad between concrete and steel.…”

Section: Numerical Simulation Of Columnsmentioning

confidence: 99%

Experimental and Numerical Behavior of Basalt Fiber Reinforced Short Concrete Columns Under Axial Loading

Mohammed

Jabbar

Hasan

2023

Period. Polytech. Civil Eng.

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This paper presents experimental and numerical investigations to reveal effecting of incorporating basalt fibers into a concrete matrix on the structural behavior and loading capacity of axially loaded short columns. Six volume fractions of chopped basalt fibers are added to the same concrete mixture to prepare six identically reinforced columns. The results illustrate that the bonding forces between microfilaments and matrix increase to provide good internal confinement for concrete ingredients, which enhances compressive strength and column loading capacity. The 0.3 % basalt fiber awarded the best compressive strength, while 0.15 % and 0.3 % awarded the best load capacity to the column. The Addition of basalt fibers delays cracking to increase the cracking load by about 50 % more than no fiber column, which indicates that it needs more energy to overcome the bonding strength between filaments and matrix. At the ultimate state, the loading capacity increases by 15 % and 17 % for 0.15 % and 0.3 % of basalt fibers and by 10 % and 12 % for 0.45% and 0.6% of basalt fiber. The 0.75 % decreased compressive strength by about 6 % but raised the column's ultimate load by 18 %. Therefore, basalt fiber benefits the cracking load more than the maximum load. The finite element showed approaching the peak load in numerical and experimental results. The longitudinal rebars and ties do not yield at the ultimate state. Increasing the reinforcement ratio raises loading capacity while lowering the yield stress of bars minimizes the maximum load.

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“…To characterize the frictional behavior, a friction coefficient of 0.52 was assigned to the interfaces between steel and concrete. This value was determined based on the average obtained from the interface friction values reported by Rabbat et al [23] and Guo et al [24]. In addition, a friction coefficient of 0.25 was applied to all steel interfaces.…”

Section: Interaction Conditionsmentioning

confidence: 99%

Data-Driven Prediction Model for High-Strength Bolts in Composite Beams

Li,

Yin,

Sha

et al. 2023

Buildings

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In recent years, the application of artificial intelligence-based methods to engineering problems has received consistent praise for their high predictive accuracy. This paper utilizes a BP neural network to predict the strength of steel–concrete composite beam shear connectors with high-strength friction-grip bolts (HSFGBs). These connectors are widely used in bridge and building construction due to their superior strength and stiffness compared to traditional beams. A validated finite element model was used to predict the strength of HSFGB shear connectors. A reliable database was created by analyzing 208 models with different characteristics for machine learning modeling. Previous studies have identified issues with result variation and overestimation or underestimation of shear connection strength. Among the machine learning methods evaluated, the backpropagation neural network model performed the best. It achieved a goodness of fit of over 93% in both the training and testing sets, with a low coefficient of variation of 6.50%. Concrete strength, bolt diameter, and bolt tensile strength were found to be important variables influencing the strength of shear connectors. Other variables showed a proportional or inverse relationship with compressive strength, except for concrete strength and bolt pretension. This study presents an accurate machine learning approach for predicting the strength of HSFGB shear connectors in steel–concrete composite beams. The study offers valuable insights into the effects of various variables on the performance of shear connection strength, providing support for structural design and analysis.

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Experimental Study of Friction Resistance between Steel and Concrete in Prefabricated Composite Beam with High‐Strength Frictional Bolt

Cited by 14 publications

References 10 publications

Impact of Failure-surface Parameters of Concrete Damage Plasticity Model on the Behavior of Reinforced Ultra-high Performance Concrete Beams

Impact of Failure-surface Parameters of Concrete Damage Plasticity Model on the Behavior of Reinforced Ultra-high Performance Concrete Beams

Experimental and Numerical Behavior of Basalt Fiber Reinforced Short Concrete Columns Under Axial Loading

Data-Driven Prediction Model for High-Strength Bolts in Composite Beams

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