• Journal of Distribution Science
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• v.19 no.9
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• pp.41-52
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• 2021

Purpose: Virtual reality is a unique technology to distribute data and demonstrates user's understanding towards complex products. The objective of this research is to investigate the impact of virtual reality on real world purchase intention of automotive cutting tools in Thailand's exhibitions. Hence, the research framework was constructed by telepresence, perception narrative, authenticity, trustworthiness, functional value, aesthetics, and purchase intention. Research design, data and methodology: Samples were collected from 500 visitors who participated in the selected top two metalworking exhibitions. Mix sampling approach is applied by using non-probability sampling methods of purposive or judgmental sampling, quota sampling, and convenience sampling method, respectively to reach target samples. Confirmatory Factor Analysis (CFA) and Structural Equation Model (SEM) were used to analyze and confirm goodness-of-fit of the model and hypothesis testing. Results: The results indicate that authenticity, functional value, and trustworthiness induced higher experiential value towards purchase intention. Those variables are stimulated by telepresence and perception narrative towards VR experience. Conclusions: Consumer's purchase intention towards VR experience on engineering cutting tools rely on consumer's sense of authenticity, trustworthiness, and functional value. Hence, marketing practitioners in automotive companies are encouraged to develop VR which focusing on significant factors to enhance consumers purchase intention.

• Journal of the Korea institute for structural maintenance and inspection
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• v.8 no.3
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• pp.169-176
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• 2004

The Mechanistic model, considering expansion stress, coefficient of diffusion etc. to time, is applied to predict the deterioration of concrete structures of the nuclear power plant(NPP) due to sulfate attack. Mix design for the test was three kinds of specified compressive strength 385, 280 and $210kgf/cm^2$ which are used to construct NPPs and cement was type I and V. The immersion test was performed with 10% $Na_2SO_4$ solution to cement type and strength for a year. The coefficient of diffusion on each concrete mix is calculated based on the results of immersion test, and it is used for predicting the sulfate attack of the concrete structures of NPP. The coefficient of diffusion of the target concrete ranged $0.5763{\sim}3.9002{\times}10^{-12}m^2/sec.$, and the sulfate attack rate of concrete structures of the NPP was predicted as 0.1~7.1 mm/year.

• Environmental Engineering Research
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• v.19 no.3
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• pp.269-274
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• 2014

On-site sanitation systems are typically installed to treat grey and toilet wastewaters in areas without sewer and centralized treatment systems. It is well known that, due to inappropriate design and operation, treatment performance of these systems in developing countries is not satisfactory in the removal of pathogens and organic matters. This research aimed to investigate the hydraulic conditions occurring in some on-site sanitation systems and the effects of hydraulic retention times (HRTs) on the system performance. The experiments were conducted with a laboratory-scale septic tank (40L in size) and an actual septic tank (600L in size), to test the hydraulic conditions by using tracer study with HRTs varying at 12, 24 and 48 hr. The experimental results showed the dispersion numbers to be in the range of 0.017-0.320 and the short-circuit ratios in the range of 0.014-0.031, indicating the reactors having a high level of sort-circuiting and approaching complete-mix conditions. The removal efficiency of $BOD_5$ was found to be 67% and the $k_{30}$ values for $BOD_5$ was $2.04day^{-1}$. A modified complete-mix model based on the relationship between $BOD_5$ removal efficiencies and HRTs was developed and validated with actual-scale septic tank data having a correlation coefficient ($R^2$) of 0.90. Therefore, to better protect our environment and minimizing health risks, new generation toilets should be developed that could minimize short-circuiting and improving treatment performance.

• Advances in concrete construction
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• v.11 no.5
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• pp.357-365
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• 2021

High deformable concrete (HDC) elements have compressive strength rates equal to conventional concrete and have got a high compressive strain at about 20% to 50%. These types of concrete elements as prefabricated parts have an abundance of applications in the construction industry which is the most used in the construction of tunnels in squeezing grounds, tunnel passwords from fault zones or swelling soils as soft supports. HDC elements after reaching to compressive yield stress, in nonlinear behavior have hardening combined with increasing strain and compressive strength. The main aim of this laboratory and numerical research is to construct concrete elements with the above properties so the compressive stress-strain behavior of different concrete elements with four categories of mix designs have been discussed and finally one of them has been defined as HDC element mix design. Furthermore, two columns with and without implementing of HDC elements have been made and stress-strain curves of them have been investigated experimentally. An analysis model is presented for columns using finite element method adopted by ABAQUS. The results obtained from the ABAQUS finite element method are compared with experimental data. The main comparison is made for stress-strain curve. The stress-strain curves from the finite element method agree well with experimental results. The results show that the dimension of the HDC samples is significant in the stress-strain behavior. The use of the element greatly increases energy absorption and ductility.

• Advances in concrete construction
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• v.13 no.3
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• pp.215-221
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• 2022

Considering their similar mass densities, an attempt was made to optimize the mix design of micro-concrete that used barite sand as an aggregate by substituting marble powder (5%, 10%, 20%, 30%, 40%, 50%, 70%), clay brick powder (30%, 50%, 70%), and fly ash (30%, 50%, 70%) for the concrete (by mass) to form specimens for shaking table tests. The test results showed that for these three groups of materials, the substitutions had little effect on the density. The barite sand played a decisive role in the density, and the overall density of the specimens reached approximately 2.9 g/cm³. The compressive strength and elastic modulus decreased with an increase in the substitution rates for the three types of materials. Among them, the 28 day compressive strength values of the 40% and 50% marble powder groups were 11.73 MPa and 8.33 MPa, respectively, which were 58.7% and 70.7% lower than the control group, respectively. Their elastic modulus values were 1.33×10⁴ MPa and 1.42×10⁴ MPa, respectively, which were 39.1% and 35% lower than those of the control group, respectively. The 28 day compressive strength values of the 50% and 70% clay brick powder groups were 13.13 MPa and 5.8 MPa, respectively, which were 53.8% and 79.6% lower than the control group, respectively. Their elastic modulus values were 1.54×10⁴ MPa and 1.19×10⁴ MPa, respectively, which were 29.7% and 45.4% lower than those of the control group, respectively. The 28 day compressive strength values of the 50% and 70% fly ash groups were 13.5 MPa and 7.1 MPa, respectively, which were 52.5% and 75% lower than those of the control group, respectively. Their elastic modulus values were 1.36×10⁴ MPa and 0.95×10⁴ MPa, respectively, which were 37.9% and 56.6% lower than those of the control group, respectively. There was a linear relationship between the 28 day compressive strength and elastic modulus, with the correlation coefficient reaching a value higher than 0.88. The test results showed that the model materials met the high density, low compressive strength, and low elastic modulus requirements for shaking table tests, and the test data of the three groups of different alternative materials were compared and analyzed to provide references and assistance for relevant model testers.

• Tunnel and Underground Space
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• v.20 no.1
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• pp.58-64
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• 2010

GLI model was verified to consider the interaction between a ground and a tunnel lining and to rationally reduce the ground load acting on the secondary lining(concrete lining) of a tunnel. In this study, the economy and the construction condition of tunnel concrete linings designed by a conventional frame model at Lot O of OO line were highly enhanced through a field design change using GLI model. For a few safe considerations, not only about 50% saving of reinforcing steel could reduce the material cost but also the wide space between bars could make it easy to pour concrete mix without voids. There was large saving effect of reinforcing steel for poor ground conditions because Terzaghi's load used in the conventional frame model produces too much high loads for those conditions.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.12
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• pp.7-13
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• 2018

The strength of concrete is affected significantly not only by the internal influence factors of cement, water, sand, aggregate, and admixture, but also by the external influence factors of concrete placement delay and curing temperature. The objective of this research was to predict the concrete strength considering both the internal and external influence factors when concrete is placed at the construction site. In this study, a concrete strength test was conducted on the 24 combinations of internal and external influence factors, and a neural network model was constructed using the test data. This neural network model can predict the concrete strength considering the external influence factors of the concrete placement delay and curing temperature when concrete is placed at the construction site. Contractors can use the concrete strength prediction neural network model to make concrete more robust to external influence factors during concrete placement at a construction site.

• Computers and Concrete
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• v.22 no.3
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• pp.337-353
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• 2018

The present study focuses on examining the structural behaviour of steel-fibre-reinforced concrete (SFRC) beams under high rates of loading largely associated with impact problems. Fibres are added to the concrete mix to enhance ductility and energy absorption, which is important for impact-resistant design. A simple, yet practical non-linear finite-element analysis (NLFEA) model was used in the present study. Experimental static and impact tests were also carried out on beams spanning 1.3 meter with weights dropped from heights of 1.5 m and 2.5 m, respectively. The numerical model realistically describes the fully-brittle tensile behaviour of plain concrete as well as the contribution of steel fibres to the post-cracking response (the latter was allowed for by conveniently adjusting the constitutive relations for plain concrete, mainly in uniaxial tension). Suitable material relations (describing compression, tension and shear) were selected for SFRC and incorporated into ABAQUS software Brittle Cracking concrete model. A more complex model (i.e., the Damaged Plasticity concrete model in ABAQUS) was also considered and it was found that the seemingly simple (but fundamental) Brittle Cracking model yielded reliable results. Published data obtained from drop-weight experimental tests on RC and SFRC beams indicates that there is an increase in the maximum load recorded (compared to the corresponding static one) and a reduction in the portion of the beam span reacting to the impact load. However, there is considerable scatter and the specimens were often tested to complete destruction and thus yielding post-failure characteristics of little design value and making it difficult to pinpoint the actual load-carrying capacity and identify the associated true ultimate limit state (ULS). To address this, dynamic NLFEA was employed and the impact load applied was reduced gradually and applied in pulses to pinpoint the actual failure point. Different case studies were considered covering impact loading responses at both the material and structural levels as well as comparisons between RC and SFRC specimens. Steel fibres were found to increase the load-carrying capacity and deformability by offering better control over the cracking process concrete undergoes and allowing the impact energy to be absorbed more effectively compared to conventional RC members. This is useful for impact-resistant design of SFRC beams.

• Computers and Concrete
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• v.19 no.5
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• pp.457-465
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• 2017

The Taiwanese liquid crystal display (LCD) industry has traditionally produced a huge amount of waste glass that is placed in landfills. Waste glass recycling can reduce the material costs of concrete and promote sustainable environmental protection activities. Concrete is always utilized as structural material; thus, the concrete compressive strength with a variety of mixtures must be studied using predictive models to achieve more precise results. To create an efficient waste LCD glass concrete (WLGC) design proportion, the related studies utilized a multivariable regression analysis to develop a compressive strength waste LCD glass concrete equation. The mix design proportion for waste LCD glass and the compressive strength relationship is complex and nonlinear. This results in a prediction weakness for the multivariable regression model during the initial growing phase of the compressive strength of waste LCD glass concrete. Thus, the R ratio for the predictive multivariable regression model is 0.96. Neural networks (NN) have a superior ability to handle nonlinear relationships between multiple variables by incorporating supervised learning. This study developed a multivariable prediction model for the determination of waste LCD glass concrete compressive strength by analyzing a series of laboratory test results and utilizing a neural network algorithm that was obtained in a related prior study. The current study also trained the prediction model for the compressive strength of waste LCD glass by calculating the effects of several types of factor combinations, such as the different number of input variables and the relevant filter for input variables. These types of factor combinations have been adjusted to enhance the predictive ability based on the training mechanism of the NN and the characteristics of waste LCD glass concrete. The selection priority of the input variable strategy is that evaluating relevance is better than adding dimensions for the NN prediction of the compressive strength of WLGC. The prediction ability of the model is examined using test results from the same data pool. The R ratio was determined to be approximately 0.996. Using the appropriate input variables from neural networks, the model validation results indicated that the model prediction attains greater accuracy than the multivariable regression model during the initial growing phase of compressive strength. Therefore, the neural-based predictive model for compressive strength promotes the application of waste LCD glass concrete.

• Journal of the Korea institute for structural maintenance and inspection
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• v.22 no.1
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• pp.32-39
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• 2018

Repairing timing and the extended service life with repairing are very important for cost estimation during operation. Conventionally used model for repair cost shows a step-shaped cost elevation without consideration of variability of extended service life due to repairing. In the work, RC(Reinforced Concrete) Column is considered for probabilistic evaluation of repairing number and cost. Two mix proportions are prepared and chloride behavior is evaluated with quantitative exterior conditions. The repairing frequency and cost are investigated with varying service life and the extended service life with repairing which were derived from the chloride behavior analysis. The effect of COV(Coefficient of Variation) on repairing frequency is small but the 1st repairing timing is shown to be major parameter. The probabilistic model for repairing cost is capable of reducing the number of repairing with changing the intended service life unlike deterministic model of repairing cost since it can provide continuous repair cost with time.