Relevant Data in the Rising Tide of Big Data: A Text-Mining Analysis in Construction Safety Index

Li, Rita Yi Man

doi:10.1007/978-3-319-12430-8_4

Cited by 2 publications

(2 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Li et al used R language and text mining methods to carry out word segmentation processing, feature item selection, vector space model construction, and co-occurrence rule recognition for accident reports and visualized text mining results by using word cloud and network structure graphs. Six key risk factors and 23 general risk factors of subway construction safety accidents were found [31].…”

Section: Application Of Text Mining In Subway Constructionmentioning

confidence: 99%

Identification of Safety Risk Factors in Metro Shield Construction

Tang,

Shen,

Zhang

et al. 2024

Buildings

View full text Add to dashboard Cite

Among the construction methods for subway projects, shield method construction technology has become a more widely used construction method for urban subway construction due to the advantages of a high degree of construction mechanization, low impact of the construction process on the environment, and strong adaptability of the shield machine to the stratum, etc. However, because of the complexity of the surrounding buildings (structures) in the subway construction, coupled with the diversity of the subway shield method construction activities and the uncertainties in the construction environment, to a certain extent, it is determined that the subway construction process is very complicated. The purpose of this study is based on the text mining method, where text is mined and utilized to realize the identification, extraction, and display of safety risk factors. Thus, it guides the safety management on site and provides a basis for knowledge reuse in other metro shield construction projects. Firstly, we analyze the shortcomings of safety risk management in domestic and international metro shield construction via a literature review, especially the utilization of safety risk text data. Secondly, we collect the risk reports submitted by all parties via the “Metro Project Safety Risk Early Warning System”, and manually screen the hidden danger statements with risk characterization to establish a corpus. Thirdly, we use the Jieba word separation package to extract and display the safety risk factors, so as to guide the on-site safety management. Subsequently, with the help of the Jieba word segmentation package for Chinese word segmentation, we develop a professional thesaurus to improve the effect of word segmentation; then, we use the TF-IDF parameter assignment to achieve the structural transformation of the text to extract high-frequency vocabulary; finally, from the high-frequency vocabulary to screen words containing the semantics of the risk to establish the risk of an initial set of words, we use the existing standards and norms to form the collection of safety risk factors of subway shield construction and generate the cloud diagram for visual display.

show abstract

Section: Application Of Text Mining In Subway Constructionmentioning

confidence: 99%

Identification of Safety Risk Factors in Metro Shield Construction

Tang,

Shen,

Zhang

et al. 2024

Buildings

View full text Add to dashboard Cite

show abstract

“…Term frequency-inverse document frequency (TF-IDF) is a representative method of traditional text feature extraction that is generally used to extract the features of onboard equipment faults [18,19]. Additionally, Li [20] and Zhou [21] used a vector space model (VSM) to transform a safety log into vectors for subway safety risk system fault diagnosis. Then, Zhao [22] and Zhong [23] applied a probabilistic subject model to extract the features of fault text data for on-board equipment and turnout fault diagnosis.…”

Section: Fault Diagnosis Of Rail Transit With Text Datamentioning

confidence: 99%

Fault Diagnosis of a Switch Machine to Prevent High-Speed Railway Accidents Combining Bi-Directional Long Short-Term Memory with the Multiple Learning Classification Based on Associations Model

Lin,

Hu,

et al. 2023

Machines

View full text Add to dashboard Cite

The fault diagnosis of a switch machine is vital for high-speed railway operations because switch machines play an important role in the safe operation of high-speed railways, which often have faults because of their complicated working conditions. To improve the accuracy of turnout fault diagnosis for high-speed railways and prevent accidents from occurring, a combination of bi-directional long short-term memory (BiLSTM) with the multiple learning classification based on associations (MLCBA) model using the operation and maintenance text data of switch machines is proposed in this research. Due to the small probability of faults for a switch machine, it is difficult to form a diagnosis with the small amount of sample data, and more fault text features can be extracted with feedforward in a BiLSTM model. Then, the high-quality rules of the text data can be acquired by replacing the SoftMax classification with MLCBA in the output of the BiLSTM model. In this way, the identification of switch machine faults in a high-speed railway can be realized, and the experimental results show that the Accuracy and Recall of the fault diagnosis can reach 95.66% and 96.29%, respectively, as shown in the analysis of the ZYJ7 turnout fault text data of a Chinese railway bureau from five recent years. Therefore, the combined BiLSTM and MLCBA model can not only realize the accurate diagnosis of small-probability turnout faults but can also prevent high-speed railway accidents from occurring and ensure the safe operation of high-speed railways.

show abstract

Relevant Data in the Rising Tide of Big Data: A Text-Mining Analysis in Construction Safety Index

Cited by 2 publications

References 44 publications

Identification of Safety Risk Factors in Metro Shield Construction

Identification of Safety Risk Factors in Metro Shield Construction

Fault Diagnosis of a Switch Machine to Prevent High-Speed Railway Accidents Combining Bi-Directional Long Short-Term Memory with the Multiple Learning Classification Based on Associations Model

Contact Info

Product

Resources

About