Shrestha, R; Ko, T and Lee, J (2025) Quantifying project uncertainties: Leveraging historical bid and change order data for automated detection of cost and schedule impacts in new projects. Journal of Construction Engineering and Management, 151(4): 04025017, ISSN 0733-9364
Abstract
Unexpected uncertainties often arise during construction project execution, impacting performance measures such as time, budget, scope, and quality. This study addresses these cost and schedule challenges by creating an automated risk management model that utilizes natural language processing (NLP) techniques. NLP techniques are powerful tools that can process and analyze natural language data, allowing us to uncover valuable insights from textual data. This method enables the extraction of meaningful information from bid, contract, and change order documentation. The bidirectional encoder representations from transformers (BERT) model, a widely recognized transformer-based model, transforms words and phrases into numerical representations. After that, cosine similarity is used to assess the similarity between new and old projects. All these techniques allow us to predict potential costs and schedule changes for upcoming projects based on data from past similar projects. The research question of this study is: How can historical bidding and change order documents be utilized to forecast uncertainties in project cost and schedule for new projects? To address this question, the authors proposed an approach using NLP, BERT, and cosine similarity to extract the relevant data from past similar projects to forecast the cost and schedule changes for upcoming new projects, thus providing proactive insights for project management. Using a case study of 113 projects, out of which 20% were set aside for testing, the model achieved an accuracy of 78.30% in forecasting cost changes and 75.0% in forecasting schedule changes, with an overall accuracy of more than 75% in predicting changes. This finding demonstrates the model's efficacy in anticipating project uncertainties, thus significantly contributing to improved project management. This data-driven approach to managing uncertainties ultimately enhances overall project success and performance by allowing construction professionals to anticipate and address potential risks and variations proactively.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | change order; construction bid documents; cost; lage language model; natural language processing; project uncertainties; schedule |
| Index terms: | documentation, forecasting, project success, construction project, project cost, testing, documents, construction professional, project uncertainty, variation, risk management, performance measure, bidding, case study, change order, accuracy, project management |
| Subjects: | production management, prediction and forecasting, contractual condition, bidding, risk assessment, professional practice, professional development, performance measurement, economics, data collection methods, project management theory and practice |
| Topics: | Project Management, Procurement, Cost Management, Quality Management, Risk Management, Information Management, Engineering Principles, Contract Administration, Research Practice |
| Descriptive scope: | 4 PCEA |
N.B. Descriptive scope is a count of how many of the five facets of empirical research are indicated by the words used in title, abstract and keywords. It is not intended as a judgement on the research; merely a count of the kind of word we would expect to indicate Phenomenon, Concepts, Theoretical framing, Empirical techniques, Analytical techniques. If all five are present, then a code of “5 PCTEA” will indicate this. If you feel the coding for this record is questionable, we welcome discussion around the terms we matched or the way we categorized them. The facet you would expect may not be coded, or a facet may be coded inappropriately. This can also bear on a larger question, of which facets should be treated as defining in construction management research. Please get in touch, and we will look at it. More details here