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Sim, Sung-Han
Smart Infrastructure Systems Lab
Research Interests
  • Structural Dynamics, Smart Sensor and Sensor Network, Structural Health Monitoring, Digital Image Processing

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Prediction model for mechanical properties of lightweight aggregate concrete using artificial neural network

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Title
Prediction model for mechanical properties of lightweight aggregate concrete using artificial neural network
Author
Yoon, Jin YoungKim, HyunjunLee, Young-JooSim, Sung-Han
Issue Date
2019-08
Publisher
MDPI AG
Citation
MATERIALS, v.12, no.17, pp.2678
Abstract
The mechanical properties of lightweight aggregate concrete (LWAC) depend on the mixing ratio of its binders, normal weight aggregate (NWA), and lightweight aggregate (LWA). To characterize the relation between various concrete components and the mechanical characteristics of LWAC, extensive studies have been conducted, proposing empirical equations using regression models based on their experimental results. However, these results obtained from laboratory experiments do not provide consistent prediction accuracy due to the complicated relation between materials and mix proportions, and a general prediction model is needed, considering several mix proportions and concrete constituents. This study adopts the artificial neural network (ANN) for modeling the complex and nonlinear relation between constituents and the resulting compressive strength and elastic modulus of LWAC. To construct a database for the ANN model, a vast amount of detailed and extensive data was collected from the literature including various mix proportions, material properties, and mechanical characteristics of concrete. The optimal ANN architecture is determined to enhance prediction accuracy in terms of the numbers of hidden layers and neurons. Using this database and the optimal ANN model, the performance of the ANN-based prediction model is evaluated in terms of the compressive strength and elastic modulus of LWAC. Furthermore, these prediction accuracies are compared to the results of previous ANN-based analyses, as well as those obtained from the commonly used linear and nonlinear regression models.
URI
https://scholarworks.unist.ac.kr/handle/201301/27821
URL
https://www.mdpi.com/1996-1944/12/17/2678
DOI
10.3390/ma12172678
ISSN
1996-1944
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UEE_Journal Papers
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