• International journal of steel structures
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• v.18 no.4
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• pp.1336-1349
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• 2018

Generally, a precast prestressed concrete (PSC) beam is used as girders for short-to-medium span (less than 30 m) bridges due to the advantages of simple design and construction, reduction of construction budget, maintenance convenience. In order to increase the span length beyond 50 m of precast PSC girder, PSC hollow box girder with unbonded prestressed H-type steel beam placed at the compressive region is proposed. The unbonded compressive prestressing in the H-type steel beams in the girder is made to recover plastic deflection of PSC girder when the pre-stressing is released. Also, the H-steel beams allow minimization of depth-to-length ratio of the girder by reducing the compressive region of the cross-section, thereby reducing the weight of the girder. A quasi-static 3-point bending test with 4 different loading steps is performed to verify safety and plastic deflection recovery of the girder. The experimental results showed that the maximum applied load exceeded the maximum design load and most of the plastic deflection was recovered when the compressive prestressing of H-type steel beams is released. Also using prestressed H-type steel as compression reinforcements in the upper part of cross section, repair and restoration difficulty and cost of PSC girders should be significantly reduced. The study result and analysis are discussed in detail in the paper.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.14 no.1
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• pp.139-149
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• 2019

The purpose of this study is to contribute to the minimization of the social cost due to the insolvency by improving the success rate of the startups by providing useful information to the founders and the start-up support institutions through analysis of non-financial information affecting the failure of the startups. This study is aimed at entrepreneurs. The entrepreneurs that are defined by the credit guarantee institutions generally refer to entrepreneurs within 5 years of establishment. The data used in the study are sampled from the companies that were supported by the start-up guarantee from January 2014 to December 2013 as the end of December 2017. The total number of sampled firms is 2,826, 2,267 companies (80.2%), and 559 non-performing companies (19.8%). The non-financial information of the entrepreneur was divided into the entrepreneur characteristics information, the entrepreneur characteristics information, the entrepreneur asset information and the entrepreneur 's credit information, and cross-tabulations and logistic regression analysis were conducted. As a result of cross-tabulations, univariate analysis showed that personal credit rating, presence in the industry, presence of residential housing, presence of employees, and presence of financial statements were selected as significant variables. As a result of the logistic regression analysis, three variables such as personal credit rating, occupation in the industry, and presence of residential house were found to be important factors affecting the failure of founding companies. This result shows the importance of entrepreneur 's personal credibility and experience and entrepreneur' s assets in business management. The start-up support institutions should reflect these results in the entrepreneur 's credit evaluation system, and the entrepreneurs need training on the importance of the personal credit and the management plan in the entrepreneurial education. The results of this analysis will contribute to the minimization of the incapacity of startups by providing useful non-financial information to founders and start-up support organizations.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.6
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• pp.26-32
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• 2016

Usually, there are many uncertainties regarding the error of an assumed load, material properties, member size, and structure analysis in a structure, and it may have a direct influence on the qualities of optimal design of structures. Probabilistic analysis has developed rapidly into a desirable process and structural reliability analysis is an increasingly important tool that assists engineers to consider uncertainties during the design, construction and life of a structure to calculate its probability of failure. This study deals with the applications of two optimization techniques to solve the reliability-based optimization problem of structures. The reliability-based optimization problem was formulated as a minimization of the structural volume subject to the constraints on the values of componential reliability index determined by the AFOSM approach. This presented method may be a useful tool for the reliability-based design optimization of structures.

• Journal of the Korean Society of Hazard Mitigation
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• v.8 no.6
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• pp.149-153
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• 2008

The pollutant capacity occurred before and after the development of a watershed should be quantitatively estimated and controlled for the minimization of water contamination. The Ministry of Environment suggested a guideline for the legal management of nonpoint source from 2006. However, the rational method for the determination of treatment capacity from nonpoint source proposed in the guideline has the problem in the field application because it does not reflect the project based cases and overestimates the pollutant load to be reduced. So, we perform the standard rainfall analysis by analytical probabilistic method for the estimation of an additional pollutant load occurred by a project and suggest a methodology for the estimation of contaminant capacity instead of a simple rational method. The suggested methodology in this study could determine the reasonable capacity and efficiency of a treatment facility through the estimation of pollutant load from nonpoint source and from this we can manage the watershed appropriately. We applied a suggested methodology to the projects of housing land development and a dam construction in the watersheds. When we determine the treatment capacity by a rational method without consideration of the types of projects we should treat the 90% of pollutant capacity occurred by the development and to do so, about 30% of the total cost for the development should be invested for the treatment facility. This requires too big cost and is not realistic. If we use the suggested method the target pollutant capacity to be reduced will be 10 to 30% of the capacity occurred by the development and about 5 to 10% of the total cost can be used. The control of nonpoint source must be performed for the water resources management. However it is not possible to treat the 90% of pollutant load occurred by the development. The proper pollutant capacity from nonpoint source should be estimated and controlled based on various project types and in reality, this is very important for the watershed management. Therefore the results of this study might be more reasonable than the rational method proposed in the Ministry of Environment.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.4
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• pp.1295-1302
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• 2013

The structural deformed shape is important information to structural analysis. If the sufficient measuring points are secured at the structural monitoring system, reasonable and accurate structural deformation shapes can be obtained and structural analysis is possible using this deformation. However, the accurate estimation of the global structural shapes might be difficult if sufficient measuring points are not secure under cost limitations. In this study, SFSM-LS algorithm, the economic and effective estimation method for the structural deformation shapes with limited displacement measuring points is developed and suggested. In the suggested method, the global structural deformation shape is determined by the superposition of the pre-investigated structural deformed shapes obtained by preliminary FE analyses, with their optimum weight factors which lead minimization of the estimate errors. 2-span continuous bridge model is used to verify developed algorithm and parametric studies are performed. By the parametric studies, the characteristics of the estimation results obtained by the suggested method were investigated considering essential parameters such as pre-investigated structural shapes, locations and numbers of displacement measuring points. By quantitative comparison of estimation results with the conventional methods such as polynomial, Lagrange and spline interpolation, the applicability and accuracy of the suggested method was validated.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.231-252
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• 2021

Artificial intelligence (AI) is a key technology that will change the future the most. It affects the industry as a whole and daily life in various ways. As data availability increases, artificial intelligence finds an optimal solution and infers/predicts through self-learning. Research and investment related to automation that discovers and solves problems on its own are ongoing continuously. Automation of artificial intelligence has benefits such as cost reduction, minimization of human intervention and the difference of human capability. However, there are side effects, such as limiting the artificial intelligence's autonomy and erroneous results due to algorithmic bias. In the labor market, it raises the fear of job replacement. Prior studies on the utilization of artificial intelligence have shown that individuals do not necessarily use the information (or advice) it provides. Algorithm error is more sensitive than human error; so, people avoid algorithms after seeing errors, which is called "algorithm aversion." Recently, artificial intelligence has begun to be understood from the perspective of the augmentation of human intelligence. We have started to be interested in Human-AI collaboration rather than AI alone without human. A study of 1500 companies in various industries found that human-AI collaboration outperformed AI alone. In the medicine area, pathologist-deep learning collaboration dropped the pathologist cancer diagnosis error rate by 85%. Leading AI companies, such as IBM and Microsoft, are starting to adopt the direction of AI as augmented intelligence. Human-AI collaboration is emphasized in the decision-making process, because artificial intelligence is superior in analysis ability based on information. Intuition is a unique human capability so that human-AI collaboration can make optimal decisions. In an environment where change is getting faster and uncertainty increases, the need for artificial intelligence in decision-making will increase. In addition, active discussions are expected on approaches that utilize artificial intelligence for rational decision-making. This study investigates the impact of artificial intelligence on decision-making focuses on human-AI collaboration and the interaction between the decision maker personal traits and advisor type. The advisors were classified into three types: human, artificial intelligence, and human-AI collaboration. We investigated perceived usefulness of advice and the utilization of advice in decision making and whether the decision-maker's personal traits are influencing factors. Three hundred and eleven adult male and female experimenters conducted a task that predicts the age of faces in photos and the results showed that the advisor type does not directly affect the utilization of advice. The decision-maker utilizes it only when they believed advice can improve prediction performance. In the case of human-AI collaboration, decision-makers higher evaluated the perceived usefulness of advice, regardless of the decision maker's personal traits and the advice was more actively utilized. If the type of advisor was artificial intelligence alone, decision-makers who scored high in conscientiousness, high in extroversion, or low in neuroticism, high evaluated the perceived usefulness of the advice so they utilized advice actively. This study has academic significance in that it focuses on human-AI collaboration that the recent growing interest in artificial intelligence roles. It has expanded the relevant research area by considering the role of artificial intelligence as an advisor of decision-making and judgment research, and in aspects of practical significance, suggested views that companies should consider in order to enhance AI capability. To improve the effectiveness of AI-based systems, companies not only must introduce high-performance systems, but also need employees who properly understand digital information presented by AI, and can add non-digital information to make decisions. Moreover, to increase utilization in AI-based systems, task-oriented competencies, such as analytical skills and information technology capabilities, are important. in addition, it is expected that greater performance will be achieved if employee's personal traits are considered.