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Predicting Project's Uncertainty Risk in the Bidding Process by Integrating Unstructured Text Data and Structured Numerical Data Using Text Mining

Title
Predicting Project's Uncertainty Risk in the Bidding Process by Integrating Unstructured Text Data and Structured Numerical Data Using Text Mining
Authors
Yi, June-SeongLee, JeeHee
Ewha Authors
이준성
SCOPUS Author ID
이준성scopus
Issue Date
2017
Journal Title
APPLIED SCIENCES-BASEL
ISSN
2076-3417JCR Link
Citation
vol. 7, no. 11
Keywords
construction bidding riskpre-bid clarificationtext miningrisk prediction modelingunstructured text data
Publisher
MDPI AG
Indexed
SCIE; SCOPUS WOS scopus
Abstract
The construction bidding process takes place in the early stages of a construction project. The bidding process involves many uncertainties since the construction risk factors are predicted and reflected in the bid price before construction commences. Therefore, bidders should thoroughly understand the uncertainty factors of the project before make bidding decisions. The uncertainty risk of construction projects is determined by the content that is contained in the bidding document. If the information provided in the bidding document is not accurate and is unclear, the uncertainty of the projects increases. Thus, this study is performed to predict risks in the bidding process of construction projects by analyzing the uncertainty of the bidding document and using it as factors to predict a project's bidding risk. To achieve this, bidding risk prediction modeling was conducted using the pre-bid clarification information of each project. In addition, text mining on pre-bid RFI documents, which are in an unstructured text data format, was performed and the results of text mining were used as major influencing factors for the risk prediction models. As a result, the accuracy of the risk prediction model including text data was improved (72.92%) when compared to the prediction model using only numeric data (52.08%). The results of this study are expected to strengthen the possibility of further similar studies in the future since it enhances the predictive accuracy by incorporating the uncertainty of the bidding document, which is rarely considered in previous studies.
DOI
10.3390/app7111141
Appears in Collections:
엘텍공과대학 > 건축공학전공 > Journal papers
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