• Journal of the Korea Safety Management & Science
• /
• v.25 no.3
• /
• pp.63-71
• /
• 2023

The construction industry stands out for its higher incidence of accidents in comparison to other sectors. A causal analysis of the accidents is necessary for effective prevention. In this study, we propose a data-driven causal analysis to find significant factors of fatal construction accidents. We collected 14,318 cases of structured and text data of construction accidents from the Construction Safety Management Integrated Information (CSI). For the variables in the collected dataset, we first analyze their patterns and correlations with fatal construction accidents by statistical analysis. In addition, machine learning algorithms are employed to develop a classification model for fatal accidents. The integration of SHAP (SHapley Additive exPlanations) allows for the identification of root causes driving fatal incidents. As a result, the outcome reveals the significant factors and keywords wielding notable influence over fatal accidents within construction contexts.

• 대한공업교육학회지
• /
• v.37 no.2
• /
• pp.99-125
• /
• 2012

The objectives of this study was to apprehend the displaying components by comparing and analyzing the ten types of construction textbooks used in Technical high schools by elaboration theory by Ferigeluth, and to supply preliminary data for the following revising textbooks. To achieve them, first, comparable units were excerpted by analyzing between the national textbooks of 7th curriculum and the new revised curriculum used in Technical high schools. Second, each five types of 7th and new curriculum textbooks were compared and analyzed by seven requisites in the Elaboration theory by Reigeluth. As the results of the study, first, elaborating process like conceptual, procedural, and theoretical elaboration has been applied to the different subjects according to their properties. Second, cognitive strategy activator among the seven strategies was widely used. Whereas, analogy was not applied to all the ten types of the text books. In addition, Learning prerequisite sequence was presented with only simple picture and explanation, and it was inadequately used in practice. Third, summary was more used in 7th curriculum text than the new revised text. Fourth, six strategy components were widely used to all texts except construction and materials in the five types of texts. Lastly synthesis of theory and practice was more used than summary in theory subjects and learner control was more used in the subject of the synthesis of theory and practice.

• The Journal of the Convergence on Culture Technology
• /
• v.8 no.5
• /
• pp.489-495
• /
• 2022

It makes difference to LSTM D/L(Deep Learning) results for language model construction as the corpus preprocess changes. An LSTM model was trained with a famouse literaure poems(Ki Hyung-do's work) for training corpus in the study. You get the two wordvector sets for two corpus sets of the original text and eraised word ending text each once D/L training completed. It's been inspected of the similarity/analogy operation results, the positions of the wordvectors in 2D plane and the generated texts by the language models for the two different corpus sets. The suggested words by the silmilarity/analogy operations are changed for the corpus sets but they are related well considering the corpus characteristics as a literature work. The positions of the wordvectors are different for each corpus sets but the words sustained the basic meanings and the generated texts are different for each corpus sets also but they have the taste of the original style. It's supposed that the D/L language model can be a useful tool to enjoy the literature in object and in diverse with the analysis results shown in the study.

• Journal of English Language & Literature
• /
• v.57 no.5
• /
• pp.727-755
• /
• 2011

In this paper, I'll try to read Don Lee's Yellow intertextually with a more canonical text, Sherwood Anderson's Winesburg, Ohio, in order to see what kind of traditions and techniques Yellow references and/or rewrites as a way of tracking this production. Yellow's formal properties as a short story cycle are established through its use of particular conventions. For instance, Yellow follows the short story cycle model that includes the assemblage of recurring characters into one locale. Yellow's characters are all connected to and at some point located in the fictional small town of Rosarita Bay, California. The text form aligns it with established literary conventions and traditions and suggests the author's reliance upon or trust in those modes. Yellow's setting in a small town alludes to and has often been compared to Anderson's Winesburg, Ohio, which is perhaps one of the most well-known and extensively discussed short story cycles in American literature. Also following convention is Lee's construction of Rosarita Bay and the text's third person narrator as a member of that town. Both Rosarita Bay and the narrator become important figures through the related-tale nature of the text. The method of story-telling is similar to how the town Winesburg and its "seemingly sympathetic and non-overtly judgmental" narrator are operational in Anderson's text. In sum, Yellow is opportune for intertextual reading largely because it is a collection of stories that create a linked series.

• Journal of the Korea Institute of Building Construction
• /
• v.24 no.1
• /
• pp.121-131
• /
• 2024

This research explores the potential of leveraging unstructured data from construction supervision documents, which contain detailed inspection insights from independent third-party monitors of building construction processes. With the evolution of analytical methodologies, such unstructured data has been recognized as a valuable source of information, offering diverse insights. The study introduces a framework designed to assess cost performance by applying advanced analytical methods to the unstructured data found in final construction supervision reports. Specifically, key phrases were identified using text mining and social network analysis techniques, and these phrases were then analyzed through binomial logistic regression to assess cost performance. The study found that predictions of cost performance based on unstructured data from supervision documents achieved an accuracy rate of approximately 73%. The findings of this research are anticipated to serve as a foundational resource for analyzing various forms of unstructured data generated within the construction sector in future projects.

• Language and Information
• /
• v.17 no.1
• /
• pp.17-32
• /
• 2013

Birner (1998) analyzes the construction of inversion within the centering theory, claiming that the preposed constituent in the inversion structure represents the backward-looking center that connects the current utterance to the previous discourse. However, this paper refutes such a strong claim, pointing out various problems of her work. Instead, this paper argues that the preposed element in the inversion construction is merely the preferred center under the condition that the ranking of the forward-looking centers is determined by the surface word order, rather than by grammatical relations. Thus, this paper claims that the discourse function of the construction of inversion is not text development but merely prominence-giving, in the sense of Ili$\acute{c}$ (1998).

• Annual Conference of KIPS
• /
• 2011.04a
• /
• pp.1525-1527
• /
• 2011

현재 모든 분야에 IT산업이 융합되어 있지만 건설분야에서는 IT산업과의 융합이 많이 시도되고 있음에도 불구하고 타 산업에 비해 미비한 실정이다. 특히, 설계단계 공사비정보는 설계자의 의사결정을 지원하는 중요한 자료원임에도 불구하고 작성자에 따라 내역서에 쓰이는 작업항목 및 규격의 표현방식이 다르고 외래어 표음 및 오타, 그리고 부가정보 표기로 인해 단가축적의 시스템 및 DB화 가 이루어지지 않고 있다. 따라서 본 연구는 시공단계에서 발생된 실적단가를 설계단계에서 효과적으로 활용하기 위해 동일한 작업항목의 상이한 표현을 동일하게 인식할 수 있는 텍스트 인식시스템의 알고리즘을 제시한다. 텍스트 인식알고리즘에는 "유사어 및 단어테이블", "기준작업항목 테이블", "인식된 작업항목 테이블" 등으로 구성된 DB, 최소의미단위 단어를 비교 분절하기 위한 문자열 매칭 알고리즘, 그리고 동일하지 않은 텍스트를 인식하고 사용자의 의사결정을 지원하기 위한 유사도 계산으로 구성하였다.

• Journal of Broadcast Engineering
• /
• v.27 no.3
• /
• pp.391-401
• /
• 2022

As deep learning-based object detection and recognition research have been developed recently, the scope of application to industry and real life is expanding. But deep learning-based systems in the construction system are still much less studied. Calculating materials in the construction system is still manual, so it is a reality that transactions of wrong volumn calculation are generated due to a lot of time required and difficulty in accurate accumulation. A fast and accurate automatic drawing recognition system is required to solve this problem. Therefore, we propose an AI-based automatic drawing recognition accumulation system that detects and recognizes steel materials in construction drawings. To accurately detect steel materials in construction drawings, we propose data augmentation techniques and spatial attention modules for improving small object detection performance based on YOLOv4. The detected steel material area is recognized by text, and the number of steel materials is integrated based on the predicted characters. Experimental results show that the proposed method increases the accuracy and precision by 1.8% and 16%, respectively, compared with the conventional YOLOv4. As for the proposed method, Precision performance was 0.938. The recall was 1. Average Precision AP_0.5 was 99.4% and AP_0.5:0.95 was 67%. Accuracy for character recognition obtained 99.9.% by configuring and learning a suitable dataset that contains fonts used in construction drawings compared to the 75.6% using the existing dataset. The average time required per image was 0.013 seconds in the detection, 0.65 seconds in character recognition, and 0.16 seconds in the accumulation, resulting in 0.84 seconds.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.43 no.1
• /
• pp.109-117
• /
• 2023

Schedule delays present a major risk factor, as they can adversely affect construction projects, such as through increasing construction costs, claims from a client, and/or a decrease in construction quality due to trims to stages to catch up on lost time. Risk management has been conducted according to the importance and priority of schedule delay risk, but quantification of risk on the depth of schedule delay tends to be inadequate due to limitations in data collection. Therefore, this research used the BERT (Bidirectional Encoder Representations from Transformers) language model to convert the contents of aconstruction log, which comprised unstructured data, into WBS (Work Breakdown Structure)-based structured data, and to form a model of classification and quantification of risk. A process was applied to eight highway construction sites, and 75 cases of rain schedule delay risk were obtained from 8 out of 39 detailed work kinds. Through a K-S test, a significant probability distribution was derived for fourkinds of work, and the risk impact was compared. The process presented in this study can be used to derive various schedule delay risks in construction projects and to quantify their depth.

• Journal of the Korea Society of Computer and Information
• /
• v.19 no.10
• /
• pp.23-33
• /
• 2014

The development of the information retrieval field is evolving to the research field searching accurately for the information from thing finding rapidly a large amount of information. Personalization and the semantic web technology is a key technology. The automatic indexing technology about the web document and throughput go beyond the research stage and show up as the practical service. However, there is a lack of research on the document information retrieval field about the attached document type of except the web document. In this paper, we illustrate about the method in which it analyzed the text content of the unstructured documents prepared in the text, word, hwp form and it how to construction OWL ontology. To build TBox of the document ontology and the resources which can be obtained from the document is selected, and we implement with the system in order to utilize as the instant of the constructed document ontology. It is effectually usable in the information retrieval and document management system using the semantic technology of the correspondence document as the ontology automatic construction of this kind of the unstructured documents.