Abdelaty, A F A (2017) Data-driven algorithms for enhanced transportation infrastructure asset management. PhD thesis, Iowa State University, USA.
Abstract
State highway agencies collect a considerable amount of digital data to document as well as support a variety of decision-making processes. This data is used to develop insights and extract information to enhance serval decision-making systems. However, digital data collected by highway agencies has been consistently underutilized especially in supporting data-driven or evidence-based decision-making systems. This underutilization is a result of a poor established connection between the data collected and its final possible usage. This study analyzes the digital data collected by highway agencies to enhance the reliability of decision-making systems by utilizing Geographic Information Systems (GIS) and data analytics. This study will a) develop an enhanced Life-Cycle Cost Analysis (LCCA) for pavement rehabilitation investment decisions by establishing a novel cost classification system, b) identifying the barriers and challenges faced by agencies to adopt a data-driven pavement performance evaluation process, and c) develop a dynamic pavement delineation algorithm that aggregates the pavement condition data at the distress level. In order to achieve these objectives, the study uses different digital dataset including a) pavement rehabilitation historical bid-data, b) pavement rehabilitation as-built drawings, c) pavement condition data, and d) pavement maintenance and rehabilitation geospatial data. The study developed an enhanced life-cycle cost analysis practice that would significantly improve the economic evaluation accuracy of investment decisions. Additionally, the study identified seven major barriers and challenges that hinder the adoption of a data-driven pavement performance evaluation. Finally, the study developed and automated a pavement delineation algorithm using Python programming language. This study is expected help highway agencies utilize their historical digital datasets to support a variety of decision-making systems. Furthermore, the study paves the way to adopting and implementing data-driven and evidence based decision-making processes.
| Item Type: | Thesis (Doctoral) |
|---|---|
| Thesis advisor: | Jeong, H D |
| Uncontrolled Keywords: | accuracy; economic evaluation; geographic information system; liability; reliability; highway; pavement; cost analysis; information system; investment; performance evaluation; programming; rehabilitation |
| Index terms: | dataset, information system, economic evaluation, as-built drawing, cost analysis, accuracy, geographic information system, investment decision, transportation infrastructure, distress, performance evaluation, evidence, decision-making, programming, agency, liability, aggregate, decision-making process, pavement maintenance and rehabilitation, asset management |
| Subjects: | structural engineering, maintenance engineering, decision analysis, evaluation and assessment methods, economic analysis, technical documentation, data management, liability law, financial analysis, materials science, sociology, programming, infrastructure and transport systems, financial and cost management, professional development, information systems, performance measurement, geographical techniques and analysis, asset management |
| Topics: | Research Practice, Legal Issues, Business Strategy, Digital Applications, Risk Management, Design Practice, Information Management, Engineering Principles, Cost Management, Quality Management, Geographical Context |
| Descriptive scope: | 4 PCTA |
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