Influence of global stiffness and equivalent model on prediction of impact response of RC beams

Pham, Thong M.; Hao, Hong

doi:10.1016/j.ijimpeng.2017.11.014

Cited by 90 publications

(51 citation statements)

References 35 publications

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“…In addition, there are some limitations in predicting the transient impact response of RC beams, especially the stress wave propagation. Based on their studies, Pham and Hao [19] and Yi et al [20] suggested that the impact-induced wave propagation should be taken into account in the local response phase.…”

Section: Introductionmentioning

confidence: 99%

Peak Response Prediction for RC Beams under Impact Loading

Zhao

Jia

2019

Shock and Vibration

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In this paper, a novel and simple method for predicting the peak response of RC beams subjected to impact loading is proposed. e theoretical basis for calculating the peak impact force originates from the contact law, the principle of conservation of energy, the impulse-momentum theorem, and the wave theory. Additionally, the conventional beam theory, in conjunction with the wellknown layered-section approach, is utilized to obtain the force-deflection relationship of the RC beam. Subsequently, by taking into account the strain rate effect, the maximum midspan deflection of RC beams under impact loading is determined based on the conservation of energy approach. A comparison with 143 impact tests has shown that the proposed method is able to estimate the maximum midspan deflection of RC beams under impact loading with high accuracy. e prediction of the peak impact force is shown to be slightly overestimated, which however can be used in the anti-impact design to preclude the shear failure near the impact point.

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Section: Introductionmentioning

confidence: 99%

Peak Response Prediction for RC Beams under Impact Loading

Zhao

Jia

2019

Shock and Vibration

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“…t 1 represents the instant of maximum impact force as mentioned above. t 1 1 , t 3 1 , t 5 1 represent the instant of 0.1, 0.3, and 0.5 ms after the maximum impact force, respectively. The length of the vector represents the magnitude of acceleration.…”

Section: Response Of Force and Midspan Deflectionmentioning

confidence: 99%

“…It is proved that the behavior of beams has improvement with the increase of reinforcement ratio. Pham et al [5] changed the beam span to investigate the influence of global stiffness on the response of RC beams. It is found that the behavior of RC beams during the first impact impulse was almost constant, but the displacement, failure mode, and reaction at later stages are changed.…”

Section: Introductionmentioning

confidence: 99%

Impact resistance of RC beams under different combinations of mass and velocity: mesoscale numerical analysis

Jin

Lan

Zhang

et al. 2020

Archiv.Civ.Mech.Eng

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The concrete structures under impact loading duress may be destroyed within an extremely short period of time. The importance and complexity of exploration on the impact resistance of concrete members make this area still open for discussion. In the present study, a 3-D mesoscale numerical model was established to investigate the effect of the combination of impact mass and velocity on the mechanical behavior of reinforced concrete (RC) beams subjected to impact loadings. Heterogeneity of concrete and strain rate effects of concrete and steel bars were taken into account. Furthermore, nonlinear interaction between the concrete and steel bars was considered herein. Results from macroscale and mesoscale simulation were compared with the available physical tests, indicating that the mesoscale numerical model can better represent the influence of heterogeneity of concrete on the mechanical behavior of RC beams. Five different impact energy levels were involved to study the effect of the combination of impact mass and velocity on the impact resistance of RC beams. At last, the residual bearing capacity and natural frequency of impacted RC beams were numerically calculated and their relationship was discussed. It is indicated that the deformation of RC beams is influenced strongly by the impulse, which increases with the increasing impact mass at identical impact energy. Besides, the failure mode of RC beams turns from shear-dominant failure mode to bending shear failure mode with the increase of impact mass, accompanied by the increase of energy dissipation of steel bars and the whole member. Despite this, in the present work, the combination of the impact mass and velocity had little influence on the damage extent (based on the performance) of the RC beams. Moreover, an empirical relationship between the residual bearing capacity and the natural frequency of the impacted RC beams was established as a rough reference for damage evaluation in engineering practice.

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“…As the crucial part of the rear axle, the condition of the main reducer has a direct impact on the level of vibration, safety and comfort, and any fault of the main reducer may lead to production downtime, economic loss and human injury [1,2]. Vibration signals generated during the running process can effectively reflect the variance of condition.…”

Section: Introductionmentioning

confidence: 99%

A Multichannel Data Fusion Method Based on Multiple Deep Belief Networks for Intelligent Fault Diagnosis of Main Reducer

Liu

2020

Symmetry

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Aiming at the problems of poor efficiency of the intelligent fault diagnosis method of the main reducer and the poor effectiveness of multichannel data fusion, this paper proposes a multichannel data fusion method based on deep belief networks and random forest fusion for fault diagnosis. Multiple deep belief networks (MDBNs) are constructed to obtain deep representative features from multiple modalities of multichannel data. Random forest can fuse deep representative features achieved from MDBNs to construct the model of multiple deep belief networks fusion (MDBNF). The proposed method is applied to fault diagnosis of the main reducer and evaluation of the performance. Multiple deep belief network model fusions (MD BN F) are constructed to improve the multichannel data fusion effect. Single sensory data, multichannel data, and two intelligent models based on support vector machine and deep belief networks are used as comparison in the experiments. The results indicate that the classification accuracy of the test set collected by sensor 1 and sensor 2 is 88.35% and 88.73%, respectively. The comparison results show that the method has good convergence. The data fusion of the proposed diagnostic model can effectively improve the correlation between the collected vibration signals and the failure mode, thereby improving the diagnostic performance by nearly 8%, representing improved diagnostic accuracy.

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Influence of global stiffness and equivalent model on prediction of impact response of RC beams

Cited by 90 publications

References 35 publications

Peak Response Prediction for RC Beams under Impact Loading

Peak Response Prediction for RC Beams under Impact Loading

Impact resistance of RC beams under different combinations of mass and velocity: mesoscale numerical analysis

A Multichannel Data Fusion Method Based on Multiple Deep Belief Networks for Intelligent Fault Diagnosis of Main Reducer

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