Browsing by Subject "infrastructure management"
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Item GIS-Based Probabilistic Approach for Assessing and Enhancing Infrastructure Data Quality(2012-11-26) Saliminejad, Siamak 1983-The task of preserving and improving infrastructure systems is becoming extremely challenging because these systems are decaying due to aging and over utilization, have limited funding, and are complex in nature (geographically spread, and affect and are affected by technological, environmental, social, security, political, and economic factors). The infrastructure management paradigm has emerged to assist in the challenging task of managing infrastructure systems in a systematic and cost-effective manner. Infrastructure management is a data-driven process. It relies on large databases that contain information on the system?s inventory, condition, maintenance and rehabilitation (M&R) history, utilization, and cost. This data feeds into analytical models that assess infrastructure current conditions, predict future conditions, and develop optimal M&R strategies. Thus, complete and accurate data is essential to a reliable infrastructure management system. This study contributes to advancing the infrastructure management paradigm (with focus on pavement management) in two primary ways: (a) it provides in-depth understanding of the impact of errors in condition data on the outputs of infrastructure management systems, and (b) it provides efficient computational methods for improving infrastructure data quality. First, this research provides a quantitative assessment of the effects of error magnitude and type (both systematic and random) in pavement condition data on the accuracy of PMS outputs (i.e., forecasted needed budget and M&R activities in a multi-year planning period). Second, a new technique for detecting gross outliers and pseudo outliers in pavement condition data was developed and tested. Gross outliers are data values that are likely to be erroneous, whereas pseudo outliers are pavement sections performing exceptionally well or poor due to isolated local conditions. Third, a new technique for estimating construction and M&R history data from pavement condition data was developed and tested. This technique is especially beneficial when M&R data and condition data are stored in disparate heterogeneous databases that are difficult to integrate (i.e., legacy databases). The main merit of the developed techniques is their ability to integrate methods and principles from Bayesian and spatial statistics, GIS, and operations research in an efficient manner. The application of these techniques to a real-world cases study (pavement network in Bryan district) demonstrated the potential benefits of these techniques to infrastructure managers and engineers.Item Incorporating Risk and Uncertainty into Pavement Network Maintenance and Rehabilitation Budget Allocation Decisions(2014-07-30) Menendez Acurio, Jose RafaelAccording to the American Society of Civil Engineers, 33% of the United States? major roads are in poor or mediocre condition with a projected funding shortfall of $549.5 billion for 2010?2015. Environmental factors, increased traffic, and lack of adequate maintenance are causing many of these roads to deteriorate faster. The imbalance between maintenance needs and available funds tends to become more critical over time, demanding more reliable and advanced tools for allocating funds and prioritizing projects. In 2012, the U.S. Congress passed the Moving Ahead for Progress in the 21st Century Act (MAP-21) to fund surface transportation programs for 2013?2014 and beyond. MAP-21 establishes a framework for federal transportation investments with the goals of preserving the highway system while improving its condition and performance. This law requires states to develop risk-based asset management plans that include risk management analysis. In order to fulfill MAP-21 requirements, pavement management systems must be upgraded to incorporate risk management, permitting pavement management systems to serve as a more realistic decision support tool for planning and budget allocation in pavement maintenance and rehabilitation. This dissertation aims to incorporate risk assessment into maintenance and rehabilitation budget decisions at the planning stage. For risk assessment, uncertainty was incorporated into the analysis process, and factors influencing decisions are modeled as probability distributions. The factors included are pavement conditions, available funds, maintenance and rehabilitation costs, and performance prediction. The risk for each scenario is defined as the probability of failing to achieve pre-defined performance goals. The results of this research show that the benefit-cost budget allocation method has the lowest risk to fail to achieve the performance goals. The maintenance-first method has slightly higher risk but averages scores are better compared with benefit-cost. The method with highest risk is the rehabilitation-first, which have a significant difference with all the other allocation methods. This research demonstrates that incorporating uncertainty and risk assessment into pavement management can lead to better-informed decision and ultimately improved M&R budget allocation policies. This work provides DOTs with analytical tools and methods for meeting the requirements of MAP-21.