• Journal of the Korean Geotechnical Society
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• v.35 no.6
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• pp.17-26
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• 2019

In this study, the statistical characteristics of the resistance bias factors were analyzed using a high-quality field load test database, and the total resistance bias factors were estimated considering the soil uncertainty and construction errors for the application of the limit state design of aggregate pier foundation. The MLR model by Bong and Kim (2017), which has a higher prediction performance than the previous models was used for estimating the resistance bias factors, and its suitability was evaluated. The chi-square goodness of fit test was performed to estimate the probability distribution of the resistance bias factors, and the normal distribution was found to be most suitable. The total variability in the nominal resistance was estimated including the uncertainty of undrained shear strength and construction errors that can occur during the aggregate pier construction. Finally, the probability distribution of the total resistance bias factors is shown to follow a log-normal distribution. The parameters of the probability distribution according to the coefficient of variation of total resistance bias factors were estimated by Monte Carlo simulation, and their regression equations were proposed for simple application.

• Journal of the Korean Geotechnical Society
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• v.28 no.2
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• pp.43-55
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• 2012

In this study, the resistance bias factors are calculated to determine the resistance factor of Gravel Compaction Piles which is one of the soft ground improvement methods. In order to calculate resistance bias factors for gravel compaction piles, two ultimate bearing capacities were analyzed. One is the ultimate bearing capacity in 2.54 cm settlement measured using data of the field loading test on 41 piles and the other is the ultimate bearing capacity calculated using the seven equations concerning bulging failure. The results of analysis show that the probability density function of the calculated ultimate bearing capacities has a lognormal distribution. Resistance bias factor and the coefficient of variation for Greenwood equation are 0.91 and 0.38, respectively, and for those of Hughes & Withers are 1.19 and 0.39. The two equations are suitable for calculating resistance factors for LRFD of soil improvement using gravel compaction piles.

• Journal of the Korean Geotechnical Society
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• v.30 no.8
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• pp.25-37
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• 2014

In load and resistance factor design (LRFD) method, resistance factors are typically calibrated using resistance bias factors obtained from either only the data within ${\pm}2{\sigma}$ or the data except the tail values of an assumed probability distribution to increase the reliability of the database. However, the data selection approach has a shortcoming that any low-quality data inadvertently included in the database may not be removed. In this study, a data quality evaluation method, developed based on the quality of static load test results, the engineering characteristics of in-situ soil, and the dimension of aggregate piers, is proposed for use in constructing database. For the evaluation of the method, a total 65 static load test results collected from various literatures, including static load test reports, were analyzed. Depending on the quality of the database, the comparison between bias factors, coefficients of variation, and resistance factors showed that uncertainty in estimating bias factors can be reduced by using the proposed data quality evaluation method when constructing database.

• Journal of the Korean Geotechnical Society
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• v.31 no.10
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• pp.5-16
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• 2015

Considering the conversion of the Korea Construction Standards to Limit State Design (LSD), we analyzed the resistance bias factor for pullout resistance, as a part of the development of the Load and Resistance Factor Design (LRFD) for soil nailing; very few studies have been conducted on soil nailing. In order to reflect the local characteristics of soil nailing, such as the design and construction level, we collected statistics on pullout tests conducted on slopes and excavation construction sites around the country. In this study a database was built based on the geotechnical properties, soil nailing specifications, and pullout test results. The resistance bias factors are calculated to determine the resistance factor of the pullout resistance for gravity and pressurized grouting method, which are the most commonly used methods in Korea; moreover, we have relatively sufficient data on these methods. We found the resistance bias factors to be 1.144 and 1.325, which are relatively conservative values for predicting the actual ultimate pullout resistance. It showed that our designs are safer than those found in a research case in the United States (NCHRP Report); however, there was an uncertainty, $COV_R$, of 0.27-0.43 in the pullout resistance, which is relatively high. In addition, the pressurized grouting method has a greater margin of safety than the gravity grouting method, and the actual ultimate pullout resistance determined using the pressurized grouting method has low uncertainty.

• The Journal of Information Systems
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• v.27 no.1
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• pp.133-151
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• 2018

Purpose Global fintech industry is proliferating. Although domestic investment in fintech service is also increasing fast, user acceptance of fintech payment service seems slower than expected. Previous fintech literature mainly focuses on overall characteristics and technical aspects of fintech including security issues and explores factors affecting the service adoption. This study aims to examine crucial factors affecting user's resistance intention to fintech payment service adoption. The research model was formulated based on innovation diffusion theory and status quo bias theory and validated empirically. Design/methodology/approach The proposed research model was empirically validated with 149 responses from college students who have used fintech payment service. The component-based SEM was employed for data analysis. Findings The significant findings are as follow. First, compatibility has significant negative effect and complexity, and perceived risk has a positive effect on user resistance. Second, institutional trust does not show a significant effect on user resistance but has an indirect effect through the mediation of trust in service provider. Finally, trust in service provider shows the significant negative effect on user resistance.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.5C
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• pp.343-350
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• 2006

The resistance bias factors for driven steel pipe piles are evaluated as a part of study to develop the LRFD(Load and Resistance Factor Design) for foundation structures in Korea. The 43 data sets of static load tests and soil property tests performed in the whole domestic area were collected and analyzed to determine the representative bearing capacities of the piles using various methods. Based on the statistical analysis of the data, the Davisson's criterion is proved to be the most reasonable method for estimation of pile bearing capacity among the methods used. The static bearing capacity formulas and the Meyerhof method using N values are applied to calculate the design bearing capacity of the piles. The resistance bias factors of the driven steel pipe piles are evaluated respectively as 0.98 and 1.46 by comparison of the bearing capacities for both of the static bearing capacity formulas and the Meyerhof method. It is also shown that uncertainty of the static bearing capacity formulas is relatively less than that of the Meyerhof method.

• Journal of the Korean GEO-environmental Society
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• v.16 no.6
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• pp.5-11
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• 2015

Recently the necessity of developing the Load and Resistance Factor Design (LRFD) for shallow foundation has been raised to implement to the domestic design codes related to geotechnical engineering since the limit state design is requested as international technical standard for the foundation of structures. In this study, applicability of LRFD for shallow foundation on weathered soils was investigated and resistance factor for this case was proposed. The quantitative analyses on the uncertainty and resistance bias for shallow foundation on weathered soil ground were performed by collecting the statistical data about domestic case studies for design and construction of shallow foundation. Reliability analyses for shallow foundation were first performed using FDA (First-order Design value Approach) method. Resistance factors were calibrated using the load factors obtained from the specifications of shallow foundations on weathered soil ground. The influence of the load factors developed in this study on the resistance factors were discussed by comparing with the resistance factor obtained from using AASHTO load factors.

• Asia-Pacific Journal of Business
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• v.12 no.4
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• pp.313-329
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• 2021

Purpose - Digital trade, which started in the early 2000s, is showing a sharp increase due to the recent pandemic. However, despite this proliferation, users' acceptance of innovation is very slow. Cloud services are at the center of digital trade activation. This study aims to contribute to the spread of digital trade through empirical analysis of the resistance factors that hinder the use of cloud service-based digital trade using the innovation resistance theory and the status quo bias theory. Design/Methodology/Approach - In order to achieve the research purpose, this study was conducted with 171 entrepreneurs using cloud service-based digital trade. Structural equation model(SEM) was used through empirical analysis. Findings - As a result of the study, it was found that the complexity of technology, perceived risk, compatibility, and trust in service providers had a significant effect on innovation resistance, and policy trust did not affect innovation resistance. Also, security concerns and institutional trust were analyzed to have a significant effect on the trust of service providers. Research Implications - This study is meaningful to help the rapid diffusion of innovative technologies through empirical analysis of factors that lower the intention to accept cloud service-based digital trade.

• Proceedings of the Korean Geotechical Society Conference
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• 2008.10a
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• pp.779-783
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• 2008

LRFD Resistance factors for static bearing capacity of driven steel pipe piles were calibrated in the freamework of reliability theory. Reliability analysis was performed by the First Order Reliability Method (FORM) using resistance bias factor statistics.The target reliability indices are selected as 2.0 and 2.33 for group pile case and 2.5 for single pile case, based on the reliability level of the current design practice and considering redundancy of pile group, acceptable risk level, construction quality control, and significance of individual structure.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.6C
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• pp.367-377
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• 2008

As part of study to develop LRFD (Load and Resistance Factor Design) codes for foundation structures in Korea, resistance factors for static bearing capacity of driven steel pipe piles were calibrated in the framework of reliability theory. The 57 data sets of static load tests and soil property tests conducted in the whole domestic area were collected and these load test piles were sorted into two cases: SPT N at pile tip less than 50, SPT N at pile tip equal to or more than 50. The static bearing capacity formula and the Meyerhof method using N values were applied to calculate the expected design bearing capacities of the piles. The resistance bias factors were evaluated for the two static design methods by comparing the representative measured bearing capacities with the expected design values. Reliability analysis was performed by two types of advanced methods: the First Order Reliability Method (FORM), and the Monte Carlo Simulation (MCS) method using resistance bias factor statistics. The target reliability indices are selected as 2.0 and 2.33 for group pile case and 2.5 for single pile case, in consideration of the reliability level of the current design practice, redundancy of pile group, acceptable risk level, construction quality control, and significance of individual structure. Resistance factors of driven steel pipe piles were recommended based on the results derived from the First Order Reliability Method and the Monte Carlo Simulation method.