• 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.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.97-100
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• 2015

Analysis of construction tender documents in overseas projects is a very important issue from a risk management point of view. Unfortunately, majority of construction firms are biased by winning contracts without in-depth analysis of tender documents. As a result, many contractors have incurred loss in overseas projects. Although a lot of risk analysis techniques have been introduced, most of them focus project's external unexpected risks such as country conditions and owner's financial standing. However, because those external risks are difficult to control and take preemptive action, we need to concentrate on project inherent risks. Based on this premise, this paper proposes a project-oriented risk mining approach which could detect and extract project risk factors automatically before they are materialized and assess them. This study presents a methodology regarding how to extract potential risks which exist in owner's project requirements and project tender documents using state of the art data analysis method such as text mining, data mining, and information visualization. The project-oriented risk mining approach is expected to effectively reflect project characteristics to the project risk management and could provide construction firms with valuable business intelligence.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.39-40
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• 2015

As international construction market increases, the importance of risk management in international construction project is emphasized. Unfortunately, current risk management practice does not sufficiently deal with project risks. Although a lot of risk analysis techniques have been introduced, most of them focus on project's external unexpected risks such as country conditions and owner's financial standing. However, because those external risks are difficult to manage and take preemptive action, we need to concentrate on project inherent risks. Based on this premise, this paper proposes a project-oriented risk mining approach which could detect and extract project risk factors automatically before they are materialized. This study presents a methodology regarding how to extract potential risks which exist in owner's project requirements and project tender documents using state of the art data analysis method such as text mining. The project-oriented risk mining approach is expected to effectively reflect project characteristics to the project risk management and could provide construction firms with valuable business intelligence.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2022.04a
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• pp.39-40
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• 2022

The use of asbestos has been completely prohibited in Korea since 2015. Therefore, nationally, the asbestos demolitions in the building are actively underway. In the process of demolishing asbestos, scattering dust occurs, which poses a risk to human body. These dusts causes fatal disease, and especially there is an increasing concern of safety about construction workers and building users. Until this day, however, only few researches have been conducted on asbestos demolishing process. Accordingly, it is necessary to analyze key factors and to develop a safety prediction model for workers. This study is an early stage of building quantified DB, and aims to actualize the safety problems of asbestos demolishing process using text mining method.

• Journal of the Korean Society of Industry Convergence
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• v.27 no.3
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• pp.581-590
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• 2024

This study aims to extract key topics through text mining of incident records (incident history, post-incident measures, preventive measures) from construction safety accident case data available on the public data portal. It also seeks to provide fundamental insights contributing to the establishment of manuals for disaster prevention by identifying correlations between these topics. After pre-processing the input data, we used the LDA-based topic modeling technique to derive the main topics. Consequently, we obtained five topics related to incident history, and four topics each related to post-incident measures and preventive measures. Although no dominant patterns emerged from the topic pattern analysis, the study holds significance as it provides quantitative information on the follow-up actions related to the incident history, thereby suggesting practical implications for the establishment of a preventive decision-making system through the linkage between accident history and subsequent measures for reccurrence prevention.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.94-102
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• 2022

Construction workers are engaged in many activities that may expose them to serious hazards, such as falling, unguarded machinery, or being struck by heavy construction equipment. Despite extensive research in building information modeling (BIM) for safety management, current approaches, detecting safety issues after design completion, may limit the opportunities to prevent predictable and potential accidents when decisions of building materials and systems are made. In this respect, this research proposes a proactive approach to detecting safety issues from the early design phase. This research aims to explore accident precursors and integrate them into BIM for tracking safety hazards during the design development process. Accident precursors can be identified from construction incident reports published by OSHA using a text mining technique. Through BIM-integrated accident precursors, construction safety hazards can be identified during the design phase. The results will contribute to supporting a successful transition from the design stage to the construction stage that considers a safe construction workplace. This will advance the body of knowledge about construction safety management by elucidating a hypothesis that safety hazards can be detected during the design phase involving decisions about materials, building elements, and equipment. In addition, the proactive approach will help the Architecture, Engineering and Construction (AEC) industry eliminate occupational safety hazards before near-miss situations appear on construction sites.

• Korean Journal of Construction Engineering and Management
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• v.17 no.6
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• pp.13-23
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• 2016

Construction automation based on information and communication technology(ICT) has been studied for improving productivity in the construction industry. This study investigates domestic research trends in ICT-based construction automation using text mining techniques. The results show that 'Technology to collect and analyze project progress(26%)' and 'Technology to analyze and apply the automation element of construction machinery(28%)' are the major research area. The word of 'construction information' is showed as important keywords in the area of 'Technology to collect and analyze project progress', and researches focusing on resource management, site management, information management, and real-time information monitoring have been mainly conducted. The word of 'ubiquitous' is shown as important keywords in the area of 'Technology to analyze and apply the automation element of construction machinery', and researches focusing on ubiquitous information management, ubiquitous site management, and measurement system have been mainly conducted.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.847-854
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• 2022

The aim of this study is to develop a Named Entity Recognition (NER) model to automatically identify construction-related organizations from news articles. This study collected news articles using web crawling technique and construction-related organizations were labeled within a total of 1,000 news articles. The Bidirectional Encoder Representations from Transformers (BERT) model was used to recognize clients, constructors, consultants, engineers, and others. As a pilot experiment of this study, the best average F1 score of NER was 0.692. The result of this study is expected to contribute to the establishment of international business strategies by collecting timely information and analyzing it automatically.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.41 no.3
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• pp.305-316
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• 2021

Construction accidents occur due to a number of reasons-worker carelessness, non-adoption of safety equipment, and failure to comply with safety rules are some examples. Because much construction work is done outdoors, weather conditions can also be a factor in accidents. Past construction accident data are useful for accident prevention, but since construction accident data are often in a text format consisting of natural language, extracting construction hazards from construction accident data can take a lot of time and that entails extra cost. Therefore, in this study, we extracted construction hazards from 2,026 domestic construction accident reports using text mining and performed a seasonal analysis of construction hazards through frequency analysis and centrality analysis. Of the 254 construction hazards defined by Korea's Ministry of Land, Infrastructure, and Transport, we extracted 51 risk factors from the construction accident data. The results showed that a significant hazard was "Formwork" in spring and autumn, "Scaffold" in summer, and "Crane" in winter. The proposed method would enable construction safety managers to prepare better safety measures against outdoor construction accidents according to weather, season, and climate.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.6
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• pp.347-353
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• 2022

In this paper, we propose a method that performs a text-video retrieval model by replacing video properties using captions. In general, the exisiting embedding-based models consist of both joint embedding space construction and the CNN-based video encoding process, which requires a lot of computation in the training as well as the inference process. To overcome this problem, we introduce a video-captioning module to replace the visual property of video with captions generated by the video-captioning module. To be specific, we adopt the caption generator that converts candidate videos into captions in the inference process, thereby enabling direct comparison between the text given as a query and candidate videos without joint embedding space. Through the experiment, the proposed model successfully reduces the amount of computation and inference time by skipping the visual processing process and joint embedding space construction on two benchmark dataset, MSR-VTT and VATEX.