Evaluation of dynamic tensile strength of concrete using lattice-based simulations of spalling tests

Hwang, Young Kwang; Bolander, John E.; Lim, Yun Mook

doi:10.1007/s10704-020-00422-w

Cited by 23 publications

(18 citation statements)

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“…Such findings suggest the viability of machine learning modeling of the fracture mechanism of concrete using extensive experimental data in future work. Furthermore, fracture mechanics models have been mostly applied to simulate tensile or flexural strength of concrete, along with its ductility and impact behavior [82,83]. Data driven methods can further complement the findings in such studies considering the wide-ranging experimental data in the literature.…”

Section: Limitations Of the Modelmentioning

confidence: 99%

Predicting Ultra-High-Performance Concrete Compressive Strength Using Tabular Generative Adversarial Networks

2020

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There have been abundant experimental studies exploring ultra-high-performance concrete (UHPC) in recent years. However, the relationships between the engineering properties of UHPC and its mixture composition are highly nonlinear and difficult to delineate using traditional statistical methods. There is a need for robust and advanced methods that can streamline the diverse pertinent experimental data available to create predictive tools with superior accuracy and provide insight into its nonlinear materials science aspects. Machine learning is a powerful tool that can unravel underlying patterns in complex data. Accordingly, this study endeavors to employ state-of-the-art machine learning techniques to predict the compressive strength of UHPC using a comprehensive experimental database retrieved from the open literature consisting of 810 test observations and 15 input features. A novel approach based on tabular generative adversarial networks was used to generate 6513 plausible synthetic data for training robust machine learning models, including random forest, extra trees, and gradient boosting regression. While the models were trained using the synthetic data, their ability to generalize their predictions was tested on the 810 experimental data thus far unknown and never presented to the models. The results indicate that the developed models achieved outstanding predictive performance. Parametric studies using the models were able to provide insight into the strength development mechanisms of UHPC and the significance of the various influential parameters.

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Section: Limitations Of the Modelmentioning

confidence: 99%

Predicting Ultra-High-Performance Concrete Compressive Strength Using Tabular Generative Adversarial Networks

2020

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“…12 detail in refs. 13 The abrupt changes in the DIF values appeared near the strain rate 1 /s. Under an intermediate strain rate (< 1/s), the rate dependency attributed to the viscosity of free water present in the pores and pre-existing cracks of concrete is dominant; this is called the Stefan effect.…”

Section: Introductionmentioning

confidence: 96%

Mesh‐size independent rigid‐body spring network for simulation of the dynamic fracture behavior of concrete

Choo

Park

Nam

et al. 2023

Num Anal Meth Geomechanics

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In this study, a numerical approach is developed for simulating the rate‐dependent behaviors of concrete without mesh sensitivity. The rheological units (e.g., dashpot) are integrated with the six directional springs in the rigid‐body spring network (RBSN) elements to reflect the rate‐dependent behavior of concrete. Previously, the viscoplastic damage model was associated with the elemental degrees of freedom. However, in the present approach, a viscoelastic constitutive law is newly defined for the normal direction as a function of the strain rate, from which the internal forces can be updated from the regularized elemental stresses. Such improvements are validated through the numerical simulations of the direct tensile test and spalling test using the modified split‐Hopkinson pressure bar (SHPB). The simulated results show consistent structural responses without mesh dependence on the strain rate. In addition, the influences of the softening curve shapes on the crack localizations are examined. It is shown that the rheological parameters can be optimized and determined for consistent applications through virtual experiments. The proposed numerical approach enables the mesh construction procedure without concerning the mesh‐size sensitivity due to the strain rate, and various applications are expected for the simulations of detailed numerical models of concrete materials and structures under high loading rates.

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“…Firstly, to obtain the spalling strength a linear acoustic approximation is required [10,21]. Secondly, a linear elastic material behaviour of the concrete specimen is assumed before reaching the peak tensile strength [22,23]. Lastly, the rapture of specimen is assumed to be instant and is associated with a sudden disruption to the wave propagation [2].…”

Section: Introductionmentioning

confidence: 99%

Mesoscopic investigation of the dynamic tensile behaviour of concrete from spalling test and implication on interpretation of test data

Zhou

Chen

et al. 2022

International Journal of Impact Engineering

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Evaluation of dynamic tensile strength of concrete using lattice-based simulations of spalling tests

Cited by 23 publications

References 50 publications

Predicting Ultra-High-Performance Concrete Compressive Strength Using Tabular Generative Adversarial Networks

Predicting Ultra-High-Performance Concrete Compressive Strength Using Tabular Generative Adversarial Networks

Mesh‐size independent rigid‐body spring network for simulation of the dynamic fracture behavior of concrete

Mesoscopic investigation of the dynamic tensile behaviour of concrete from spalling test and implication on interpretation of test data

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