GIS-Based Probabilistic Approach for Assessing and Enhancing Infrastructure Data Quality

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.