TY - GEN
T1 - A Study on Classifying Construction Disaster Cases in Report with CNN for Effective Management
AU - Kim, Ha Young
AU - Jang, Ye Eun
AU - Kang, Hyun Bin
AU - Yi, June Seong
N1 - Funding Information:
This work is supported by the Korea Agency for Infrastructure Technology Advancement (KAIA) grant funded by the Ministry of Land, Infrastructure and Transport (Grant 22ORPS-B158109-03).
Publisher Copyright:
© 2022 ASCE.
PY - 2022
Y1 - 2022
N2 - This study proposes an efficient management direction for Korean construction disaster cases from KOSHA (Korea Occupational Safety & Health Agency) through a text data classification model based on CNN Algorithm. Five classes were defined to classify construction accidents: fall, electric shock, flying object, collapse, and narrowness. After the initial test, the classification accuracy of fall disasters was relatively high, while other types were mainly classified as fall disasters. These results showed that (1) specific accident-causing behavior, (2) similar sentence structure, and (3) complex accidents corresponding to complex types affect the model accuracy. Two improvement experiments were then conducted: (1) reclassification, and (2) elimination of complex accidents. With complex accidents eliminated, the classification performance improved by 185.7%. This result indicated that the multicollinearity of complex accidents was solved during elimination. In conclusion, this study suggests the necessity to manage complex accidents independently while preparing a system to describe the situation of future accidents in detail.
AB - This study proposes an efficient management direction for Korean construction disaster cases from KOSHA (Korea Occupational Safety & Health Agency) through a text data classification model based on CNN Algorithm. Five classes were defined to classify construction accidents: fall, electric shock, flying object, collapse, and narrowness. After the initial test, the classification accuracy of fall disasters was relatively high, while other types were mainly classified as fall disasters. These results showed that (1) specific accident-causing behavior, (2) similar sentence structure, and (3) complex accidents corresponding to complex types affect the model accuracy. Two improvement experiments were then conducted: (1) reclassification, and (2) elimination of complex accidents. With complex accidents eliminated, the classification performance improved by 185.7%. This result indicated that the multicollinearity of complex accidents was solved during elimination. In conclusion, this study suggests the necessity to manage complex accidents independently while preparing a system to describe the situation of future accidents in detail.
UR - http://www.scopus.com/inward/record.url?scp=85128932549&partnerID=8YFLogxK
U2 - 10.1061/9780784483961.051
DO - 10.1061/9780784483961.051
M3 - Conference contribution
AN - SCOPUS:85128932549
T3 - Construction Research Congress 2022: Computer Applications, Automation, and Data Analytics - Selected Papers from Construction Research Congress 2022
SP - 483
EP - 491
BT - Construction Research Congress 2022
A2 - Jazizadeh, Farrokh
A2 - Shealy, Tripp
A2 - Garvin, Michael J.
PB - American Society of Civil Engineers (ASCE)
T2 - Construction Research Congress 2022: Computer Applications, Automation, and Data Analytics, CRC 2022
Y2 - 9 March 2022 through 12 March 2022
ER -