• Korean Journal of Construction Engineering and Management
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• v.24 no.2
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• pp.50-58
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• 2023

Bid-rigging is a common issue in public construction projects, and appropriate sanctions are required from the relevant authorities. This study analyzes the need for an optimal enforcement model to prevent bid-rigging by considering both civil and criminal aspects. Recently, there have been overlapping sanctions under the Fair Trade Act, such as fines imposed by the Fair Trade Commission and civil lawsuits filed by the client for damages. The purpose of this study is to evaluate the effectiveness of penalty surcharges and compensation systems for preventing bid-rigging, and to consider the possibility of overlapping sanctions in public construction projects. It was found that overlapping sanctions under the Fair Trade Act can be helpful in improving the system. However, in cases where the state is the plaintiff for damages in a lawsuit, it is necessary to consider the penalty surcharge and sentence, reduce the penalty surcharge for joint acts, refund the surcharge after a final judgment, and consider the damage compensation system when imposing a surcharge. This study contributes to the development of an efficient enforcement model to suppress bid-rigging in public construction projects by analyzing the improvement effects of sanctions and compensation.

• Journal of the Korean Society of Safety
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• v.36 no.2
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• pp.49-57
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• 2021

In the case of military supplies, any potential failure and causes of failures must be considered. This study is aimed at examining the failure modes of a rotorcraft landing system to identify the priority items. Failure mode and effects analysis (FMEA) is applied to the rotorcraft landing system. In general, the FMEA is used to evaluate the reliability in engineering fields. Three elements, specifically, the severity, occurrence, and detectability are used to evaluate the failure modes. The risk priority number (RPN) can be obtained by multiplying the scores or the risk levels pertaining to severity, occurrence, and detectability. In this study, different weights of the three elements are considered for the RPN assessment to implement the FMEA. Furthermore, the FMEA is implemented using a fuzzy rule base, similarity aggregation model (SAM), and grey theory model (GTM) to perform a comparative analysis. The same input data are used for all models to enable a fair comparison. The FMEA is applied to military supplies by considering methodological issues. In general, the fuzzy theory is based on a hypothesis regarding the likelihood of the conversion of the crisp value to the fuzzy input. Fuzzy FMEA is the basic method to obtain the fuzzy RPN. The three elements of the FMEA are used as five linguistic terms. The membership functions as triangular fuzzy sets are the simplest models defined by the three elements. In addition, a fuzzy set is described using a membership function mapping the elements to the intervals 0 and 1. The fuzzy rule base is designed to identify the failure modes according to the expert knowledge. The IF-THEN criterion of the fuzzy rule base is formulated to convert a fuzzy input into a fuzzy output. The total number of rules is 125 in the fuzzy rule base. The SAM expresses the judgment corresponding to the individual experiences of the experts performing FMEA as weights. Implementing the SAM is of significance when operating fuzzy sets regarding the expert opinion and can confirm the concurrence of expert opinion. The GTM can perform defuzzification to obtain a crisp value from a fuzzy membership function and determine the priorities by considering the degree of relation and the form of a matrix and weights for the severity, occurrence, and detectability. The proposed models prioritize the failure modes of the rotorcraft landing system. The conventional FMEA and fuzzy rule base can set the same priorities. SAM and GTM can set different priorities with objectivity through weight setting.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.6
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• pp.841-849
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• 2023

Construction projects have risks due to various factors such as construction delays and construction accidents. Based on these construction risks, the method of calculating the construction period of the construction project is mainly made by subjective judgment that relies on supervisor experience. In addition, unreasonable shortening construction to meet construction project schedules delayed by construction delays and construction disasters causes negative consequences such as poor construction, and economic losses are caused by the absence of infrastructure due to delayed schedules. Data-based scientific approaches and statistical analysis are needed to solve the risks of such construction projects. Data collected in actual construction projects is stored in unstructured text, so to apply data-based risks, data pre-processing involves a lot of manpower and cost, so basic data through a data classification model using text mining is required. Therefore, in this study, a document-based data generation classification model for risk management was developed through a data classification model based on SVM (Support Vector Machine) by collecting construction project documents and utilizing text mining. Through quantitative analysis through future research results, it is expected that risk management will be possible by being used as efficient and objective basic data for construction project process management.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.39 no.6
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• pp.743-750
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• 2019

Most of the water supply facilities that use rivers as sources do not have monitoring facilities such as precipitation and stream flow measurement, and there is no judgment standard for drought response such as water intake control in river flow during dry season. In addition, it was confirmed that local government officials, who deal with actual drought work, have limitations in applying the drought index (SPI, PDSI, etc.) and diffusion models that have been proposed so far in advance. Therefore, in this study, the drought prediction system was constructed to determine the number of water-intake available days using SWAT (Soil and Water Assessment Tool) and the water supply network from the intake source to the beneficiary area, suggesting the drought spreading time and space.

• Journal of the Korean Geosynthetics Society
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• v.17 no.2
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• pp.9-18
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• 2018

In this study, a field model experiment was performed to analyze the bahavior of slope during failure. It was analyzed through x-MR control chart method with inverse displacement and K-value. As a result, the portent was confirmed at 4 minutes before slope failure in Case 1. The change of the control limit line according to moving range was analyzed and it was effective to apply K = 3. Use of the inverse displacement and x-MR control chart method will be useful for the prediction of abnormal behavior through quick and objective judgment. Prediction of slope failure using control chart method can be used as basic data of slope measurement management standard, and it can contribute in reduction of life and property damage caused by slope disaster.

• Journal of the Korea institute for structural maintenance and inspection
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• v.9 no.3
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• pp.129-138
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• 2005

This paper presents a methodology for the determination of optimal managing criteria for cable tension force in cable-stayed bridges using acceleration data acquired by monitoring system. There are many long span bridges installed with monitoring system in Korea. The monitoring systems are installed to diagnose abnormal behavior or damages in bridges and to warn these to bridge management agency. In cable-stayed bridges, the cable tension force could be an important indicator of abnormal behavior because of the geometric configuration of the cable-stayed bridge. If the management value of cable tension force is set too high or too low, then the monitoring system could not warn properly for the abnormal behavior of a bridge. Generally, the management value is set by empirical or engineering judgment, but in this paper, a new methodology for the determination of managing criteria for cable tension force is proposed based on the probability distribution model for tension force and reliability analysis. The proposed methodology is applied to a real concrete cable-stayed bridge in order to investigate its applicability.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.11
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• pp.479-488
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• 2022

In recent years, with the rapid development of machine learning technology, research utilizing machine learning has been actively conducted in fields such as cognition, reasoning and judgment, and action among various technologies constituting intelligent systems. In order to utilize this machine learning, it is indispensable to collect data for learning. However, the types of data generated vary according to the environment in which the data is generated, and the types and forms of data required are different depending on the learning model to be used for machine learning. Due to this, there is a problem that the existing data collection method cannot be reused in a new environment, and a specialized data collection module must be developed each time. In this paper, we propose a specification-based methology for data collection in artificial intelligence system to solve the above problems, ensure the reusability of the data collection method according to the data collection environment, and automate the implementation of the data collection function.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.6
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• pp.243-250
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• 2023

Chest X-ray is radiological examination for xeamining the lungs and haert, and is particularly widely used for diagnosing lung disease. Since the quality of these chest X-rays can affect the doctor's diagnosis, the process of evaluating the quality must necessarily go through. This process can involve the subjectivity of radiologists and is manual, so it takes a lot of time and csot. Therefore, in this paper, based on the chest X-ray quality assessment guidelines used in clinical settings, we propose a tool that automates the five quality assessments of artificial shadow, coverage, patient posture, inspiratory level, and permeability. The proposed tool reduces the time and cost required for quality judgment, and can be further utilized in the pre-processing process of selecting high-quality learning data for the development of a learning model for diagnosing chest lesions.

• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.2
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• pp.24-33
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• 2018

DSM and orthoimage, which are representative results of UAV photogrammetry, are high-quality spatial information data and are widely used in various fields of spatial information industry in recent years. However, the UAV photogrammetry has a problem that the quality of the output of the UAV deteriorates due to the altitude of the UAV, the camera calibration, the weather conditions at the time of shooting, the performance of the GPS / IMU and the number of the ground reference points. The purpose of this study is to analyze the location accuracy of unmanned aerial photogrammetry according to the change of the number if ground control points. Experiments were made with fixed wing, and the shooting altitude was set at 130m and 260m. The number of ground reference points used was 9, 8, 5, and 4, respectively. Ten checkpoints were used. XY RMSE for orthoimage and Z RMSE for DSM were compared and analyzed. In addition, the resolution of the orthoimage was determined to affect the judgment of the operator in the verification of the planimetric position accuracy, and the visual resolution was analyzed using the Siemens star target. As a result of the analysis, the variation of the vertical position accuracy is larger than the variation of the planimetric position accuracy when the number of the ground reference points are different. Also The higher the flying height, the greater the effect of change of ground control points on position accuracy.

• Journal of the Korea Institute of Building Construction
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• v.21 no.6
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• pp.665-676
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• 2021

As the number of deteriorated buildings increases, the importance of safety diagnosis and maintenance of buildings has been rising. Existing visual investigations and building safety diagnosis objectivity and reliability are poor due to their reliance on the subjective judgment of the examiner. Therefore, this study presented the limitations of the previously conducted appearance investigation and proposed 3D Point Cloud data to increase the accuracy of existing detailed inspection data. In addition, this study conducted a calculation of an objective building safety grade using a Deep-Neural Network(DNN) structure. The DNN structure is generated using the existing detailed inspection data and precise safety diagnosis data, and the safety grade is calculated after applying the state evaluation data obtained using a 3D Point Cloud model. This proposed process was applied to 10 deteriorated buildings through the case study, and achieved a time reduction of about 50% compared to a conventional manual safety diagnosis based on the same building area. Subsequently, in this study, the accuracy of the safety grade calculation process was verified by comparing the safety grade result value with the existing value, and a DNN with a high accuracy of about 90% was constructed. This is expected to improve economic feasibility in the future by increasing the reliability of calculated safety ratings of old buildings, saving money and time compared to existing technologies.