AI-Based Environmental Code Checking Tool for Sustainability Best Management Practices of Infrastructure Construction Projects
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 lifecycle 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 Streamlit 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
Joseph J. Kim
Dr. Joseph J. Kim, PE (PI), is a Professor and Chair of the Department of Civil Engineering and Construction Engineering Management at California State University, Long Beach (CSULB). He supervised the graduate student assistant, coordinated all project activities, ensured successful project completion, and prepared the final MTI report. Dr. Kim has developed and taught three sustainability-focused courses for undergraduate and graduate students: CEM 481 Sustainability in the Built Environment, CEM 482 Sustainability in Infrastructure Systems, and CE 581 Sustainability and Green Construction. His research spans sustainability best management practices (BMPs) and the application of artificial intelligence to optimization problems in civil infrastructure systems. He has authored 115 peer-reviewed journal articles and conference papers and holds a minor in statistics, which supports his analytical research. His work has been funded by NSF TUES, Caltrans, MTI, CITT, the Metropolitan Water District, COAST, CSULB, and FDOT. Dr. Kim also played a key role in a Federal Highway Administration–funded project evaluating ITS-based pedestrian safety treatments. His responsibilities included project management, student supervision, data collection oversight, data organization, and statistical analysis using non-parametric methods. Findings from this work have been published in Transportation Research Record and presented at annual Transportation Research Board meetings. He has successfully completed multiple Caltrans and MTI projects and worked as a GIS specialist for the Gainesville Police Department in Florida.
Pooja D. Chavan
Pooja is a recent computer science graduate from the Department of Computer Engineering and Computer Science at CSULB who contributed to accomplishing the goals of this research project while she was a graduate student. The scope of her contributions includes assistance of the development of AI model and analysis of data. She has over three years of experience in software development, automation testing, and DevOps engineering. Her technical expertise spans building distributed systems, micro services architecture, artificial intelligence, and software quality engineering. She currently works as a Software Engineer at Daimler Truck North America, where she focuses on DevOps, process automation, and the development of scalable and efficient software solutions. She is passionate about leveraging artificial intelligence and automation technologies to solve real-world engineering and software challenges.
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