Publication Detail

Life Cycle Cost Analysis Input Framework for Full Depth Recycling and Application on State Route 113 and State Route 84

UCD-ITS-RR-22-122

Technical Memorandum

UC Pavement Research Center

Suggested Citation:
Kedarisetty, Sampat, Changmo Kim, Ali A. Butt, John T. Harvey, Jon Lea, David Jones (2022) Life Cycle Cost Analysis Input Framework for Full Depth Recycling and Application on State Route 113 and State Route 84. Institute of Transportation Studies, University of California, Davis, Technical Memorandum UCD-ITS-RR-22-122

Full depth recycling (FDR) has emerged as a feasible rehabilitation alternative in California. This study focuses on addressing the economic feasibility of example FDR structures using life cycle cost analysis (LCCA) that included probabilistic and deterministic life cycle agency costs and deterministic life cycle road user costs. Two LCCA case studies were performed to provide an initial understanding of the agency cost variation. Estimating roadway construction costs plays a key role in pavement LCCA and long-term planning. Materials costs per functional unit are the major input values affecting pavement cost and total construction cost, and they are dependent on project scale, market, region, risk, climate, and economic circumstances. Publicly available contract cost data from past roadway construction activities on the California state highway network were used in this study. Economies of scale suggest that high quantities of materials would have lower unit costs. Unsupervised machine learning techniques were employed to divide the available data into four volume categories (low, medium, high, very high) based on material quantities in a project to accomplish the probabilistic LCCA. Work zone delay road user costs were estimated in RealCost-CA and incorporated into the life cycle cost of each alternative. Case studies were conducted for rehabilitation of two California highways, State Route 113 (SOL 113) and State Route 84 (YOL 84), for a 60-year design life. Two different pavement rehabilitation alternatives were considered for the project, an FDR structure and a hot mix asphalt HMA reconstruction, along with their respective maintenance and rehabilitation sequences. Two different pavement structural design methods were also included in the study to enable comparison: R-value and CalME.

Key words: life cycle cost analysis (LCCA), machine learning, Monte Carlo simulations, probabilistic LCCA
UCPRC-TM-2022-01