Prediction of Compressive Strength of Partially Saturated Concrete Using Machine Learning Methods
- Title
- Prediction of Compressive Strength of Partially Saturated Concrete Using Machine Learning Methods
- Author
- 이강석
- Keywords
- data fusion; ultrasonic pulse velocity; electrical resistivity; marine environment; compressive strength; Technology; Electrical engineering. Electronics. Nuclear engineering; TK1-9971; Engineering (General). Civil engineering (General); TA1-2040; Microscopy; QH201-278.5; Descriptive and experimental mechanics; QC120-168.85
- Issue Date
- 2022-02
- Publisher
- MDPI
- Citation
- MATERIALS, v. 2022, NO 15, Page. 1-25
- Abstract
- The aim of this research is to recommend a set of criteria for estimating the compressive
strength of concrete under marine environment with various saturation and salinity conditions.
Cylindrical specimens from three different design mixtures are used as concrete samples. The specimens
are subjected to different saturation levels (oven-dry, saturated-surface dry and three partially
dry conditions: 25%, 50% and 75%) on water and water–NaCl solutions. Three parameters (P- and
S-wave velocities and electrical resistivity) of concrete are measured using two NDT equipment in
the laboratory while two parameters (density and water-to-binder ratio) are obtained from the design
documents of the concrete cylinders. Three different machine learning methods, which include, artificial
neural network (ANN), support vector machine (SVM) and Gaussian process regression (GPR),
are used to obtain multivariate prediction models for compressive strength from multiple parameters.
Based on the R-squared value, ANN results in the highest accuracy of estimation while GPR gives
the lowest root-mean-squared error (RMSE). Considering both the data analysis and practicality of
the method, the prediction model based on two NDE parameters (P-wave velocity measurement and
electrical resistivity) and one design parameter (water-to-binder ratio) is recommended for assessing
compressive strength under marine environment.
- URI
- https://www.proquest.com/docview/2637751764/fulltextPDF/6C4B4F4FE6334E75PQ/1?accountid=11283https://repository.hanyang.ac.kr/handle/20.500.11754/171045
- ISSN
- 1996-1944
- DOI
- 10.3390/ma15051662
- Appears in Collections:
- COLLEGE OF ENGINEERING SCIENCES[E](공학대학) > ARCHITECTURE(건축학부) > Articles
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