• Journal of the Korean Society for Precision Engineering
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• v.14 no.7
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• pp.91-98
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• 1997

Because of a complicated behavior of fatigue in mechanical structures, the analysis of fatigue is in need of much researches on life prediction. A method is developed for the dynamic tensile strength analysis by simple tensile test, which is for the failure life prediction by lethargy coefficient of various materials. Then it is programed to analyze the failure life prediction of mechanical system by virtue of fracture. Thus the dynamic tensile strength analysis is performed to evaluate life parameters as a numerical example, using the developed method.

• Journal of the Korean GEO-environmental Society
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• v.19 no.5
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• pp.13-22
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• 2018

The number of researches on the mechanical properties of cemented sand and gravel (CSG) materials and the application of the CSG Dam has been increased. In order to explain the technical scheme of strength prediction model about the artificial neural network, we obtained the sample data by orthogonal test using the PVA (Polyvinyl alcohol) fiber, different amount of cementing materials and age, and established the efficient evaluation and prediction system. Combined with the analysis about the importance of influence factors, the prediction accuracy was above 95%. This provides the scientific theory for the further application of CSG, and will also be the foundation to apply the artificial neural network theory further in water conservancy project for the future.

• Computers and Concrete
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• v.32 no.6
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• pp.577-594
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• 2023

Engineered cementitious composites with calcined clay limestone cement (LC³-ECC) as a kind of green, low-carbon and high toughness concrete, has recently received significant investigation. However, the complicated relationship between potential influential factors and LC³-ECC compressive strength makes the prediction of LC³-ECC compressive strength difficult. Regarding this, the machine learning-based prediction models for the compressive strength of LC³-ECC concrete is firstly proposed and developed. Models combine three novel meta-heuristic algorithms (golden jackal optimization algorithm, butterfly optimization algorithm and whale optimization algorithm) with support vector regression (SVR) to improve the accuracy of prediction. A new dataset about LC³-ECC compressive strength was integrated based on 156 data from previous studies and used to develop the SVR-based models. Thirteen potential factors affecting the compressive strength of LC³-ECC were comprehensively considered in the model. The results show all hybrid SVR prediction models can reach the Coefficient of determination (R²) above 0.95 for the testing set and 0.97 for the training set. Radar and Taylor plots also show better overall prediction performance of the hybrid SVR models than several traditional machine learning techniques, which confirms the superiority of the three proposed methods. The successful development of this predictive model can provide scientific guidance for LC³-ECC materials and further apply to such low-carbon, sustainable cement-based materials.

• Proceedings of the Korea Concrete Institute Conference
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• 1998.10a
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• pp.281-286
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• 1998

The aim of this study is to compare the development of compressive strength of high-Flowing concrete with maturity and to investigate the applicability of strength prediction models of concrete. An experiment was attempted on the high-flowing concrete mixes using Ordinary portland cement, High belite cement, Blast furance slage cement and replaced Fly-ash of 30% by weight of Ordinary portland cement, the water-binder ratios of mixes being 0.35 and the curing temperatures being 30, 20, 10, 5$^{\circ}C$. Test results of mixes are statistically analyzed to infer the correlation coefficient between the maturity and the compressive strength of high-flowing concrete.

• International Journal of Concrete Structures and Materials
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• v.10 no.4
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• pp.451-460
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• 2016

An anchorage system is necessary in most reinforced concrete structures for connecting attachments. It is very important to predict the strength of the anchor to safely maintain the attachments to the structures. However, according to experimental results, the existing design codes are not appropriate for large anchors because they offer prediction equations only for small size anchors with diameters under 50 mm. In this paper, a new prediction model for breakout shear strength is suggested from experimental results considering the characteristics of large anchors, such as the prying effect and size effect. The proposed equations by regression analysis of the derived model equations based on the prying effect and size effect can reasonably be used to predict the breakout shear strength of not only ordinary small size anchors but also large size anchors.

• Computers and Concrete
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• v.20 no.1
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• pp.49-56
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• 2017

A clear correlation exists between the compressive strength and elastic modulus of concrete. Unfortunately, determining the static elastic modulus requires destructive methods and determining the dynamic elastic modulus is greatly complicated by the shape and size of the specimens. This paper reports on a novel approach to the prediction of compressive strength in concrete cylinders using numerical calculations in conjunction with the impact-echo method. This non-destructive technique involves obtaining the speeds of P-waves and S-waves using correction factors through numerical calculation based on frequencies measured using the impact-echo method. This approach makes it possible to calculate the dynamic elastic modulus with relative ease, thereby enabling the prediction of compressive strength. Experiment results demonstrate the speed, convenience, and efficacy of the proposed method.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2013.05a
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• pp.89-90
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• 2013

This study is to predict the compressive strength for the concrete of ground granulated blast-furnace slag, and use Plowman's, Gompertz's model. The results are as follows; The prediction compressive strength were simiar using Rastrup's equivalent age model. but The prediction compressive strength using Freiesleben's equivalent age model weren't simiar in bfs replacement Ratio of 50%, because it is analyzed as the activation energy.

• Journal of Korean Association for Spatial Structures
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• v.20 no.4
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• pp.111-121
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• 2020

There has been increasing interest in UHPC (Ultra-High Performance Concrete) materials in recent years. Owing to the superior mechanical properties and durability, the UHPC has been widely used for the design of various types of structures. In this paper, machine learning based compressive strength prediction methods of the UHPC are proposed. Various regression-based machine learning models were built to train dataset. For train and validation, 110 data samples collected from the literatures were used. Because the proportion between the compressive strength and its composition is a highly nonlinear, more advanced regression models are demanded to obtain better results. The complex relationship between mixture proportion and concrete compressive strength can be predicted by using the selected regression method.

• Coupled systems mechanics
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• v.12 no.6
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• pp.539-555
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• 2023

Although some prediction models have successfully developed for ultra-high performance concrete (UHPC), they do not provide insights and explicit relations between all constituents and its consistency, and compressive strength. In the present study, based on the experimental results, several mathematical models have been evaluated to predict the consistency and the 28-day compressive strength of UHPC. The models used were Linear, Logarithmic, Inverse, Power, Compound, Quadratic, Cubic, Mixed, Sinusoidal and Cosine equations. The applicability and accuracy of these models were investigated using experimental data, which were collected from literature. The comparisons between the models and the experimental results confirm that the majority of models give acceptable prediction with a high accuracy and trivial error rates, except Linear, Mixed, Sinusoidal and Cosine equations. The assessment of the models using numerical methods revealed that the Quadratic and Inverse equations based models provide the highest predictability of the compressive strength at 28 days and consistency, respectively. Hence, they can be used as a reliable tool in mixture design of the UHPC.

• Computers and Concrete
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• v.19 no.4
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• pp.419-427
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• 2017

With the rising global environmental awareness on energy saving and carbon reduction, as well as the environmental transition and natural disasters resulted from the greenhouse effect, waste resources should be efficiently used to save environmental space and achieve environmental protection principle of "sustainable development and recycling". This study used recycled cement mortar and adopted the volumetric method for experimental design, which replaced cement (0%, 10%, 20%, 30%) with recycled materials (fly ash, slag, glass powder) to test compressive strength and ultrasonic pulse velocity (UPV). The hyperbolic function for nonlinear multivariate regression analysis was used to build prediction models, in order to study the effect of different recycled material addition levels (the function of $R_m$(F, S, G) was used and be a representative of the content of recycled materials, such as fly ash, slag and glass) on the compressive strength and UPV of cement mortar. The calculated results are in accordance with laboratory-measured data, which are the mortar compressive strength and UPV of various mix proportions. From the comparison between the prediction analysis values and test results, the coefficient of determination $R^2$ and MAPE (mean absolute percentage error) value of compressive strength are 0.970-0.988 and 5.57-8.84%, respectively. Furthermore, the $R^2$ and MAPE values for UPV are 0.960-0.987 and 1.52-1.74%, respectively. All of the $R^2$ and MAPE values are closely to 1.0 and less than 10%, respectively. Thus, the prediction models established in this study have excellent predictive ability of compressive strength and UPV for recycled materials applied in cement mortar.