Fuzzy Logic Pavement Maintenance and Rehabilitation Triggering Approach for Probabilistic Life-Cycle Cost Analysis

2007 ◽  
Vol 1990 (1) ◽  
pp. 80-91 ◽  
Author(s):  
Chen Chen ◽  
Gerardo W. Flintsch
Keyword(s):  
Fuzzy Logic ◽  
Life Cycle ◽  
Cost Analysis ◽  
Life Cycle Cost ◽  
Life Cycle Cost Analysis ◽  
Pavement Maintenance ◽  
Maintenance And Rehabilitation

Mechanistic-Empirical and Life-Cycle Cost Analysis for Optimizing Flexible Pavement Maintenance and Rehabilitation

2012 ◽  
Vol 138 (5) ◽  
pp. 625-633 ◽  
Author(s):  
Venkata Mandapaka ◽  
Imad Basheer ◽  
Khushminder Sahasi ◽  
Per Ullidtz ◽  
John T. Harvey ◽  
...  
Keyword(s):  
Life Cycle ◽  
Cost Analysis ◽  
Life Cycle Cost ◽  
Flexible Pavement ◽  
Life Cycle Cost Analysis ◽  
Pavement Maintenance ◽  
Maintenance And Rehabilitation

A Performance-Specified and Reliability-Based Approach for Life-Cycle Cost Analysis of Long Term Pavement Maintenance Contracts

2013 ◽  
Vol 723 ◽  
pp. 721-728
Author(s):  
Jih Chiang Lee ◽  
Jyh Dong Lin ◽  
Chin Rung Chiou ◽  
Han Yi Wang
Keyword(s):  
Life Cycle ◽  
Cost Analysis ◽  
Life Cycle Cost ◽  
Risk Estimation ◽  
Life Cycle Cost Analysis ◽  
International Roughness Index ◽  
Pavement Maintenance ◽  
Performance Specification ◽  
Pavement Deterioration

The objectives of this paper are to present the feasibility of utilizing reliability-based method to quantify life-cycle cost associated with performance specification. And a framework develops for quantifying the life-cycle cost. The framework consists of three components: (1) the pavement deterioration performance prediction; (2) the reliability-based risk estimation; and (3) the life-cycle cost analysis. An example is illustrated using the International Roughness Index (IRI) data to demonstrate how the approach works. The approach has potential for use in valuation of long term pavement maintenance contracts.

scholarly journals Development of a Statistical Model to Predict Materials’ Unit Prices for Future Maintenance and Rehabilitation in Highway Life Cycle Cost Analysis

2020 ◽  
Author(s):  
Changmo Kim ◽  
Ghazan Khan ◽  
Brent Nguyen ◽  
Emily L. Hoang
Keyword(s):  
Life Cycle ◽  
Cost Analysis ◽  
Statistical Models ◽  
Life Cycle Cost ◽  
Unit Price ◽  
Life Cycle Cost Analysis ◽  
Pavement Materials ◽  
Project Costs ◽  
Maintenance And Rehabilitation ◽  
The Future

The main objectives of this study are to investigate the trends in primary pavement materials’ unit price over time and to develop statistical models and guidelines for using predictive unit prices of pavement materials instead of uniform unit prices in life cycle cost analysis (LCCA) for future maintenance and rehabilitation (M&R) projects. Various socio-economic data were collected for the past 20 years (1997–2018) in California, including oil price, population, government expenditure in transportation, vehicle registration, and other key variables, in order to identify factors affecting pavement materials’ unit price. Additionally, the unit price records of the popular pavement materials were categorized by project size (small, medium, large, and extra-large). The critical variables were chosen after identifying their correlations, and the future values of each variable were predicted through time-series analysis. Multiple regression models using selected socio-economic variables were developed to predict the future values of pavement materials’ unit price. A case study was used to compare the results between the uniform unit prices in the current LCCA procedures and the unit prices predicted in this study. In LCCA, long-term prediction involves uncertainties due to unexpected economic trends and industrial demand and supply conditions. Economic recessions and a global pandemic are examples of unexpected events which can have a significant influence on variations in material unit prices and project costs. Nevertheless, the data-driven scientific approach as described in this research reduces risk caused by such uncertainties and enables reasonable predictions for the future. The statistical models developed to predict the future unit prices of the pavement materials through this research can be implemented to enhance the current LCCA procedure and predict more realistic unit prices and project costs for the future M&R activities, thus promoting the most cost-effective alternative in LCCA.

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