AI-Based Environmental Code Checking Tool for Sustainability Best Management Practices of Infrastructure Construction Projects

Details

• Creators:
  Kim, Joseph J. ; Chavan, Pooja D.
• Corporate Creators:
  San Jose State University. College of Business. Mineta Transportation Institute ; California State University, Long Beach
• Corporate Contributors:
  State of California SB1 2017/2018, Trustees of the California State University Sponsored Programs Administration ; United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology
• Subject/TRT Terms:
  • Artificial intelligence
  • Sustainable transportation
  • Life cycle analysis
  • Infrastructure
  • Greenhouse gases
  • Best practices
  • Construction projects
  • Emissions testing
• Publication/ Report Number:
  26-29 ; CA-MTI-2524
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Corporate Publisher:
  San Jose State University. College of Business. Mineta Transportation Institute
• Description:
  Infrastructure construction and operation are major contributors to global greenhouse gas emissions, which account for approximately one-third of global CO₂ emissions and add to long-term environmental and air quality challenges. Sustainability rating systems such as Envision for infrastructure construction projects provide structured guidance to help teams reduce these impacts, but verifying compliance requires reviewing large volumes of project documents by hand, making the process time consuming and difficult to scale. Therefore, this research develops an AI-based environmental code checking tool that automates the interpretation of project documents and evaluates compliance on life cycle GHG assessment under Credit CR1.2 of Envision-in other words, determining whether a project successfully reduces greenhouse gas emissions throughout its lifecycle. This research integrates natural language processing, life-cycle assessment, and Envision-based scoring into a unified analytical pipeline. Using a custom spaCy-based Named Entity Recognition (NER) model, the authors trained the system on a representative case study based on real-world empirical data of infrastructure to extract project metadata, material quantities, and activity data from PDF documents. Extracted values were standardized and processed through an LCA module to calculate total, annualized, and intensity-based emissions, which were then benchmarked against baseline scenarios to determine Envision performance levels. A Stream lit web application was created to enable rapid document review with automated extraction, emissions calculation, visualization, and credit scoring. Based on a bridge case study, the system achieved high extraction accuracy with an F1-score of 95.6%, completed assessments in under five seconds, and correctly classified the project as "Improved" with a 17.5% GHG reduction. These findings demonstrate that AI can significantly reduce the time and effort required to evaluate sustainability performance while improving consistency and scalability, supporting broader adoption of standardized digital documentation, early-stage LCA integration, and AI-assisted verification practices in infrastructure construction projects.
• Format:
  PDF
• Funding:
  SB1-SJAUX_2023-26
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/93577/dot_93577_DS1.pdf
• File Type:
  [PDF - 6.67 MB]
• Collection(s):
  University Transportation Centers
• Main Document Checksum:
  urn:sha-512:97e59d0ab5409b8767acd5c9a59327f26a1af8e2ec234dde53fbee490b3b5cb164dcd8d62afd6e9d6b2e4ecb07e3f8b24d99f7a0da312d18dab3b26db424cc3f