• Korean Journal of Construction Engineering and Management
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• v.4 no.1 s.13
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• pp.97-105
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• 2003

Although Construction Failure Information has been reported in literatures, reports of research, and etc., it Is difficult to utilize the information because the information classification does not exist. Therefore, this study investigated and analyzed literatures of domestic and abroad research Institutions and suggested the Construction Failure Information Classification(CFIC). The CFIC is composed of four classified items; facility general information, failure situation information, failure cause Information, and failure counterplan information. Each item is divided sub-items. Through CFIC, Construction Failure Information can be standardized and utilized for useful data to prevent recurrences of construction failure.

• Korean Journal of Construction Engineering and Management
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• v.9 no.6
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• pp.257-267
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• 2008

In order to encourage construction practitioners to acknowledge failures and disseminate the information, the failure information must be documented and accumulated with a well-structured format, which contains not only the fact and result but also the circumstance and cause of the failure. In the Korean construction industry, many failures are not explained clearly and often not even reported publicly, partly because due to the lack of understanding positive aspects of failures, which can improve construction practices as a result of learning from failures. The purpose of this study is to develop a web-based construction failure information system using the case-based reasoning techniques, which can systematically accumulate, manage, and share the valuable failure information using a structured failure cases database. It can be utilized for planning proactive solutions on future failures by searching the very similar past failure cases.

• Korean Journal of Construction Engineering and Management
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• v.13 no.3
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• pp.78-88
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• 2012

Previous construction failure researches were focused on the utilization plan based on failure information and development of failure classification. However, it has limitation to set up the plan for prevention of construction failure due to the lack of the number of on-site staffs. In order to prevent effectively construction failure, a prevention plan should be established through quantitative evaluation of failure causes. The purpose of this study is to suggest the assessment method for selection Critical Management Factor(CMF) and to analyze the CMF on the apartment structural framework using FMEA(Failure Mode and Effective Analysis) which is one of the methods of quantitative evaluation. The element of risk evaluation separated degree of failure risk and prevention respectively. The assessment method for selection of CMF can be utilized for planning proactive solutions on the failure, and it can be also selected critical factors about each project phases, type of facility and construction work.

• Journal of Intelligence and Information Systems
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• v.9 no.1
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• pp.227-249
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• 2003

Prediction of corporate failure using past financial data is a well-documented topic. Early studies of bankruptcy prediction used statistical techniques such as multiple discriminant analysis, logit and probit. Recently, however, numerous studies have demonstrated that artificial intelligence such as neural networks can be an alternative methodology for classification problems to which traditional statistical methods have long been applied. In building neural network model, the selection of independent and dependent variables should be approached with great care and should be treated as model construction process. Irrespective of the efficiency of a teaming procedure in terms of convergence, generalization and stability, the ultimate performance of the estimator will depend on the relevance of the selected input variables and the quality of the data used. Approaches developed in statistical methods such as correlation analysis and stepwise selection method are often very useful. These methods, however, may not be the optimal ones for the development of neural network model. In this paper, we propose a genetic algorithms approach to find an optimal or near optimal input variables fur neural network modeling. The proposed approach is demonstrated by applications to bankruptcy prediction modeling. Our experimental results show that this approach increases overall classification accuracy rate significantly.

• Journal of the Korean GEO-environmental Society
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• v.16 no.4
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• pp.5-12
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• 2015

Civil engineers have taken the numerous slope monitoring data for an engineering project subjected to hazard potential of slide. However, the topics on how to deal with and draw out proper information from the data related to the slope behavior have not been widely discussed. Recently, several researchers had installed the real-time monitoring system to cope with slope failure; however they are mainly focused on the hardware system installation. Therefore, this study tries to show how the measured data could be grouped and connected each other. The basic idea of analyzing method studied in this paper came from the clustering, which is the part of data mining analysis. Therefore, at the base of classification of time series data, the authors suggest three mathematical data analyzing methods; Average Index of different displacement ($AD_{i,j}$), Difference of average relative displacement ($\overline{RD}_{i,j}$) and Coordinate system of average and relative displacement ($\overline{RD}$, AD). These analyzing methods are based on the statistical method and failure mechanism of slope. Therefore they showed clustering relationships of the similar parts of the slope which makes the same sliding mechanism.