Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 이준성 | * |
dc.contributor.author | 손정욱 | * |
dc.date.accessioned | 2021-06-07T16:31:31Z | - |
dc.date.available | 2021-06-07T16:31:31Z | - |
dc.date.issued | 2021 | * |
dc.identifier.issn | 2071-1050 | * |
dc.identifier.other | OAK-29353 | * |
dc.identifier.uri | https://dspace.ewha.ac.kr/handle/2015.oak/257637 | - |
dc.description.abstract | The purpose of this study is to classify the bid price volatility level with machine learning and parameters from bid documents as risk factors. To this end, we studied project-oriented risk factors affecting the bid price and pre-bid clarification document as the uncertainty of bid documents through preliminary research. The authors collected Caltrans’s bid summary and pre-bid clarification document from 2011-2018 as data samples. To train the classification model, the data were preprocessed to create a final dataset of 269 projects consisting of input and output parameters. The projects in which the bid inquiries were not resolved in the pre-bid clarification had higher bid averages and bid ranges than the risk-resolved projects. Besides this, regarding the two classification models with neural network (NN) algorithms, Model 2, which included the uncertainty in the bid documents as a parameter, predicted the bid average risk and bid range risk more accurately (52.5% and 72.5%, respectively) than Model 1 (26.4% and 23.3%, respectively). The accuracy of Model 2 was verified with 40 verification test datasets. © 2021 by the authors. Licensee MDPI, Basel, Switzerland. | * |
dc.description.sponsorship | Yi, J.-S.; Department of Architectural & Urban Systems Engineering, South Korea; email: jsyi@ewha.ac.kr | * |
dc.language | English | * |
dc.publisher | MDPI AG | * |
dc.subject | Bid price volatility | * |
dc.subject | Classification model | * |
dc.subject | Machine learning (ML) | * |
dc.subject | Prebid clarification document | * |
dc.subject | Public project | * |
dc.subject | Risk analysis | * |
dc.subject | Risk management | * |
dc.subject | Sustainable project management | * |
dc.subject | Uncertainty in bid documents | * |
dc.title | Classifying the level of bid price volatility based on machine learning with parameters from bid documents as risk factors | * |
dc.type | Article | * |
dc.relation.issue | 7 | * |
dc.relation.volume | 13 | * |
dc.relation.index | SCIE | * |
dc.relation.index | SSCI | * |
dc.relation.index | SCOPUS | * |
dc.relation.journaltitle | Sustainability (Switzerland) | * |
dc.identifier.doi | 10.3390/su13073886 | * |
dc.identifier.wosid | WOS:000638893700001 | * |
dc.identifier.scopusid | 2-s2.0-85104025322 | * |
dc.author.google | Jang Y. | * |
dc.author.google | Son J. | * |
dc.author.google | Yi J.-S. | * |
dc.contributor.scopusid | 이준성(24172023400) | * |
dc.contributor.scopusid | 손정욱(34868873100) | * |
dc.date.modifydate | 20240322111640 | * |