School of Civil and Environmental Engineering

Ph.D. Thesis Defense Announcement

Advancing Circular Life Cycle Sustainability Assessment for Civil Infrastructure: Bio-Based Materials, Built Environment, and Transport Electrification

By 

Arjun Thangaraj Ramshankar

Advisor:

Dr. Joe F Bozeman III

Committee Members: 

Dr. Akanksha Menon (ME)

Dr. Valerie Thomas (ISYE)

Dr. Adjo A. Amekudzi-Kennedy (CEE)

Dr. Sofía Pérez-Guzmán (CEE)

Date and Time:

June 4th, 2026.  11 AM EST

Location: Ford ES&T L1205

Meeting Link: PhD Defense - Arjun Thangaraj Ramshankar | Meeting-Join | Microsoft Teams

The decarbonization of the built environment and the transportation sector requires rigorous holistic assessment frameworks capable of capturing environmental, economic, and social dimensions of sustainability. This dissertation proposes the application of the Circular Life Cycle Sustainability Assessment (CLCSA) framework which integrates Life Cycle Assessment (LCA), Life Cycle Costing (LCC), Social Life Cycle Assessment (S-LCA), and Circularity Assessment (CA) across three interconnected civil infrastructure domains, namely, circular bio-based building materials, transport electrification, and the built environment. In the bio-based materials domain, the CLCSA framework is applied for the first time to hemp-based thermal insulation at scale, encompassing a Monte Carlo-based techno-economic model for supply chain scalability and an LCA supported by uncertainty and sensitivity analysis incorporating circularity-focused end-of-life scenarios for the southeastern United States. In the transport electrification domain, an integrated LCA-TEA framework is developed and applied to overhead catenary line (OCL) powered freight trucks and institutional vehicle fleet transitions, demonstrating that electrification outcomes are highly sensitive to regional grid characteristics, capital costs, and adoption rates. In the built environment domain, a statistical benchmarking framework characterizes carbon intensity baselines across a portfolio of multi-story buildings in the US, and a Gaussian Process Regression (GPR) model is developed for early-stage embodied carbon prediction from a dataset obtained through an industry-academia partnership, providing calibrated predictive uncertainty intervals to support carbon budgeting at the preconstruction stage. Social sustainability is treated as a common theme across all three domains, encompassing labor and equity implications associated with the electric transition, and an examination of fairness and bias in data-based approaches. Taken together, this dissertation examines the application of the CLCSA framework across the three domains to highlight the challenges specific to each domain and presents solutions and future directions for research.