• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.10
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• pp.176-182
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• 2018

In order to understand and track the trends of construction safety accident, this study shows the topic trends in the construction safety accident with LDA(Latent Dirichlet Allocation)-based topic modeling method for data analytics. Especially, it performs to figure out the main issue of construction safety accident with unstructured data analysis based on the topic modeling rather than a variety of structured data analysis for preventing to safety accident in construction industry. To apply this methodology, I randomly collected to 540 news article data about construction accident from January 2017 to February 2018. Based on the unstructured data with the LDA-based topic modeling, I found the 10 topics and identified key issues through 10 keyword in each 10 topics. I forecasted the topic issue related to construction safety accident based on analysis of time-series trends about the news data from January 2017 to February 2018. With this method, this research gives a hint about ways of using unstructured news article data to anticipate safety policy and research field and to respond to construction accident safety issues in the future.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.3
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• pp.87-97
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• 2018

The traffic accident reports that are generated by the Traffic Broadcasting Networks(TBN) are unstructured data. It, however, has the value as some sort of real-time traffic information generated by the viewpoint of the drives and/or pedestrians that were on the roads, the time and spots, not the offender or the victim who caused the traffic accidents. However, the traffic accident reports, which are big data, were not applied to traffic accident analysis and traffic related research commonly. This study adopting text-mining technique was able to provide a clue for utilizing it for the impacts of traffic accidents. Seven years of traffic reports were grasped by this analysis. By analyzing the reports, it was possible to identify the road names, accident spot names, time, and to identify factors that have the greatest influence on other drivers due to traffic accidents. Authors plan to combine unstructured accident data with traffic reports for further study.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.2
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• pp.167-182
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• 2022

The accident data attributes of the traffic accident report are used not only in traditional traffic safety-related research to identify the cause of traffic accidents, but also as basis data for the development of the automated vehicle driving performance verification scenarios. However, since the data attributes of the traffic accident report are limited for the purpose of reconstructing the traffic situation and developing scenarios, this study aims to provide the directions for improvement of traffic accident report, ultimately for its expanded usability for the automated vehicle test scenarios. The directions for improvement of the traffic accident report are provided by categorizing the traffic situation before the accident (pre-crash), the situation immediately before or during the accident (on-crash), and the situation after the accident (post-crash), respectively. Additional data items or data processing methods are presented. Furthermore, data elements that can be extracted from the traffic accident process data in the unstructured narrative form are explored and provided.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.42 no.1
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• pp.127-134
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• 2022

Recently, policies and research to prevent increasing construction accidents have been actively conducted in the domestic construction industry. In previous studies, the prediction model developed to prevent construction accidents mainly used only structured data, so various characteristics of construction sites are not sufficiently considered. Therefore, in this study, we developed a machine learning-based construction accident prediction model that enables the characteristics of construction sites to be considered sufficiently by using both structured and text-type unstructured data. In this study, 6,826 cases of construction accident data were collected from the Construction Safety Management Integrated Information (CSI) for machine learning. The Decision forest algorithm and the BERT language model were used to train structured and unstructured data respectively. As a result of analysis using both types of data, it was confirmed that the prediction accuracy was 95.41 %, which is improved by about 20 % compared to the case of using only structured data. Conclusively, the performance of the predictive model was effectively improved by using the unstructured data together, and construction accidents can be expected to be reduced through more accurate prediction.

• Journal of the Korean Society of Safety
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• v.37 no.3
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• pp.9-15
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• 2022

The analysis of the causes of accidents in workplaces where machines and tools are used is essential to improve the effectiveness and efficiency of safety prevention policies in places of employment in Korea. The causes of workplace accidents are not fully understood mainly due to difficulties in analyzing available descriptive information. This study focuses on the automated accident cause analysis in workplaces based on the accident abstracts found in industrial accident reports written in an unstructured descriptive format. The method proposed in this paper is based on text data mining and uses the keyword search function of Excel software to automate the analysis. The analysis results indicate that the primary reason for the frequency of accidents is related to technical aspects at a stage in which dangerous situations occur in the workplace. Accidents due to managerial causes are typically observed when danger exists in the workplace; however, managerial actions play a more important role in reducing accident severity. A small company tends to use unsafe machines and devices, leading to further accidents due to technical causes, whereas managerial causes are more conspicuous as the company grows. To preclude the occurrence of accidents due to inadequate knowledge, the implementation of safety management and the provision of safety education to elderly workers at the early stage of their employment are particularly important for small companies with less than 100 workers.

• The Journal of the Convergence on Culture Technology
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• v.10 no.1
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• pp.609-615
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• 2024

The construction industry's fatality count stands at 402, comprising approximately 46% of total industrial accidents. Notably, construction costs less than 5 billion won account for about 69%, so strengthening safety management at small and medium-sized construction sites is required. In this study, 19,511 accident investigation data were collected using web scraping. Through statistical analysis of the collected structured data and text mining analysis of the unstructured data, accident types and causes of accidents were analyzed by construction costs at sites less than 5 billion won. As a result, it was confirmed that there were differences in accident types and causes depending on the construction costs. It is hoped that the results of this study will be used for customized safety management at small and medium-sized construction sites.

• The Journal of the Convergence on Culture Technology
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• v.8 no.5
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• pp.729-734
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• 2022

Various studies using accident cases are being conducted to identify the causes of accidents in the construction industry, but studies on the differences between public and private construction are insignificant. In this study, web scraping and text mining technologies were applied to analyze the causes of accidents by order type. Through statistical analysis and word cloud analysis of more than 10,000 structured and unstructured data collected, it was confirmed that there was a difference in the types and causes of accidents in public and private construction. In addition, it can contribute to the establishment of safety management measures in the future by identifying the correlation between major accident causes.

• Journal of Korea Water Resources Association
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• v.55 no.spc1
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• pp.1261-1270
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• 2022

The purpose of this study is to collect and analyze information related to water leaks that cannot be easily accessed, and utilized by using the news search results that people can easily access. We applied a web crawling technique for extracting big data news on water leakage accidents in the water supply system and presented an algorithm in a procedural way to obtain accurate leak accident news. In addition, a data analysis technique suitable for water leakage accident information analysis was developed so that additional information such as the date and time of occurrence, cause of occurrence, location of occurrence, damaged facilities, damage effect. The primary goal of value extraction through big data-based leak analysis proposed in this study is to extract a meaningful value through comparison with the existing waterworks statistical results. In addition, the proposed method can be used to effectively respond to consumers or determine the service level of water supply networks. In other words, the presentation of such analysis results suggests the need to inform the public of information such as accidents a little more, and can be used in conjunction to prepare a radio wave and response system that can quickly respond in case of an accident.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.6
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• pp.141-155
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• 2017

As the use of SNS becomes more active, many people are posting their thoughts about specific events in their SNS in the form of text. As a result, SNS is used in various fields such as finance and distribution to conduct service satisfaction surveys and consumer monitoring. However, in the transportation area, there are not enough cases to utilize unstructured data analysis such as emotional analysis. In this study, we developed an emotional analysis methodology that can be used in transportation by using highway VOC data, which is atypical data collected by Korea Expressway Corporation. The developed methodology consists of morpheme analysis, emotional dictionary construction, and emotional discrimination of the collected unstructured data. The developed methodology was verified using highway related tweet data. As a result of the analysis, it can be guessed that many information and information about the construction and the accident were related to the highway during the analysis period. Also, it seems that users complain about the delay caused by construction and accident.

• Nuclear Engineering and Technology
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• v.41 no.8
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• pp.1045-1052
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• 2009

A calculation model is developed to predict the transient free surface flow on the containment floor following a loss-of-coolant accident (LOCA) of pressurized water reactors (PWR) for the use of debris transport evaluation. The model solves the two-dimensional Shallow Water Equation (SWE) using a finite volume method (FVM) with unstructured triangular meshes. The numerical scheme is based on a fully explicit predictor-corrector method to achieve a fast-running capability and numerical accuracy. The Harten-Lax-van Leer (HLL) scheme is used to reserve a shock-capturing capability in determining the convective flux term at the cell interface where the dry-to-wet changing proceeds. An experiment simulating a sudden break of a water reservoir with L-shape open channel is calculated for validation of the present model. It is shown that the present model agrees well with the experiment data, thus it can be justified for the free surface flow with accuracy. From the calculation of flow field over the simplified containment floor of APR1400, the important phenomena of free surface flow including propagations and interactions of waves generated by local water level distribution and reflection with a solid wall are found and the transient flow rates entering the Holdup Volume Tank (HVT) are obtained within a practical computational resource.