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2024 Vol.40, Issue 4 Preview Page
Automated Data Extraction from Unstructured Geotechnical Report based on AI and Text-mining Techniques

AI 및 텍스트 마이닝 기법을 활용한 지반조사보고서 데이터 추출 자동화

Jimin Park1
,
Wanhyuk Seo2
,
Dong-Hee Seo3
,
Tae-Sup Yun4*
박 지민1
,
서 완혁2
,
서 동희3
,
윤 태섭4*
1Member, Graduate Student, Dept. of Civil and Environmental Eng., Yonsei Univ.
2Member, Graduate Student, Dept. of Civil and Environmental Eng., Yonsei Univ.
3Leader, Geotechnical Eng. Team, RIF tech., GS E&C
4Member, Prof., Dept. of Civil and Environmental Eng., Yonsei Univ.
1정회원, 연세대학교 건설환경공학과 박사과정
2정회원, 연세대학교 건설환경공학과 박사수료
3비회원, GS건설 RIF Tech. 인프라기술연구센터 지반지질팀 책임연구원
4정회원, 연세대학교 건설환경공학과 교수
*Corresponding Author
31 August 2024. pp. 69-79
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Abstract

Field geotechnical data are obtained from various field and laboratory tests and are documented in geotechnical investigation reports. For efficient design and construction, digitizing these geotechnical parameters is essential. However, current practices involve manual data entry, which is time-consuming, labor-intensive, and prone to errors. Thus, this study proposes an automatic data extraction method from geotechnical investigation reports using image-based deep learning models and text-mining techniques. A deep-learning-based page classification model and a text-searching algorithm were employed to classify geotechnical investigation report pages with 100% accuracy. Computer vision algorithms were utilized to identify valid data regions within report pages, and text analysis was used to match and extract the corresponding geotechnical data. The proposed model was validated using a dataset of 205 geotechnical investigation reports, achieving an average data extraction accuracy of 93.0%. Finally, a user-interface-based program was developed to enhance the practical application of the extraction model. It allowed users to upload PDF files of geotechnical investigation reports, automatically analyze these reports, and extract and edit data. This approach is expected to improve the efficiency and accuracy of digitizing geotechnical investigation reports and building geotechnical databases.

Keywords
Convolutional neural network
Data extraction
Geotechnical investigation report
Text mining
User interface program

현장 지반정수 데이터는 다양한 현장 및 실내시험을 통해 획득된 후 지반조사보고서의 형태로 작성되어 유통된다. 효율적인 설계 및 시공을 위해선 지반정수의 디지털 데이터베이스화가 필수적이나, 현재 지반조사보고서 데이터는 수동 입력 방식으로 많은 시간과 인력이 소요되며, 오류가 발생하기도 한다. 본 연구는 이미지 기반 딥러닝 모델 및 텍스트 마이닝 기법을 사용하여 지반조사보고서에서 데이터를 자동으로 추출하는 방법을 제안하였다. 딥러닝 기반의 페이지 분류 모델과 텍스트 서칭 알고리즘을 사용하여 지반조사보고서 부록 내 세부 지반시험 결과 보고서를 100%의 정확도로 분류할 수 있었다. 컴퓨터 비전 알고리즘을 통해 보고서 페이지 내 유효한 데이터 영역을 결정하고, 텍스트 분석을 통해 추출 데이터 항목과 상응하는 지반 데이터를 짝지어 데이터를 추출했다. 제안한 모델은 205개의 지반조사보고서로 구성된 데이터셋을 통해 검증되었으며, 평균 93.0%의 데이터 추출 정확도를 기록하였다. 마지막으로, 추출 모델의 실무 적용성을 위해 사용자 인터페이스 기반 프로그램을 개발하였다. 프로그램 내 사용자 상호작용을 통해 지반조사보고서 PDF 파일을 업로드하고 자동으로 보고서를 분석 및 데이터를 추출, 편집할 수 있도록 했다. 이를 통해 지반조사보고서의 디지털화 및 지반 데이터베이스 구축이 더욱 효율적이고 정확하게 이루어질 수 있을 것으로 판단된다.

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Information
  • Publisher :The Korean Geotechnical Society
  • Publisher(Ko) :한국지반공학회
  • Journal Title :Journal of the Korean Geotechnical Society
  • Journal Title(Ko) :한국지반공학회 논문집
  • Volume : 40
  • No :4
  • Pages :69-79
  • Received Date : 2024-06-28
  • Revised Date : 2024-07-10
  • Accepted Date : 2024-07-12
  • DOI :https://doi.org/10.7843/kgs.2024.40.4.69
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