2023 journal article
Developing a Construction Domain-Specific Artificial Intelligence Language Model for NCDOT's CLEAR Program to Promote Organizational Innovation and Institutional Knowledge
JOURNAL OF COMPUTING IN CIVIL ENGINEERING, 37(3).
author keywords: Natural language processing; Knowledge management; Lessons learned; Neural language model; Construction industry
UN Sustainable Development Goal Categories
9. Industry, Innovation and Infrastructure
(Web of Science)
17. Partnerships for the Goals
(Web of Science)
Source: Web Of Science
Added: April 11, 2023
Transportation agency personnel gain valuable knowledge through their work, but such knowledge is lost if it is not documented properly after the worker leaves the organization. The risk of losing institutional knowledge is a current problem at state departments of transportation, including the North Carolina Department of Transportation (NCDOT), due to high personnel turnover. State transportation agencies have implemented knowledge repositories in the form of lessons learned/best practices databases to address this problem. However, motivating end-users to use such databases is challenging. This paper addresses this challenge through novel artificial intelligence technology whereby a neural network–based language model is implemented as part of the NCDOT's new knowledge management program: Communicate Lessons, Exchange Advice, Record (CLEAR). The CLEAR program encompasses a database of lessons learned/best practices and a website to access and search the database. The developed methodology involves training a language model on transportation construction texts and using that trained model in a novel algorithm enabling users to search the CLEAR database easily. The developed language-processing model provides an easily accessible interface to suggest the most relevant CLEAR data based on the end-user's searched keywords. The model learns an inference model of construction domain–specific vocabulary extracted from various sources, such as contract documents, textbooks, and specifications, to make meaningful connections between lessons learned/best practices in the CLEAR database and project-specific knowledge. The developed model has been validated by project managers for projects at various life cycle stages. The automation of information retrieval is intended to encourage NCDOT personnel to use and embrace the CLEAR program as part of their routine work to improve project workflow. In the long run, the NCDOT will benefit from consistent usage of the CLEAR program and its high quality content, thereby leading to enhanced institutional knowledge and organizational innovation.