Classifying the Level of Bid Price Volatility Based on Machine Learning with Parameters from Bid Documents as Risk Factors

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.

Automated summary

The study aims to classify bid price volatility levels using machine learning and bid document parameters as risk factors, with a focus on project-oriented risk factors and pre-bid clarification documents as important determinants of bid price uncertainty. Data samples were collected from Caltrans bid summaries and pre-bid clarification documents from 2011-2018, with analysis showing that models including bid document uncertainty as a parameter were more accurate in predicting bid price risks. The accuracy of the models was verified with 40 verification test datasets.