Date of Award

6-2012

Degree Name

Master of Science in Engineering

Department

Civil and Construction Engineering

First Advisor

Dr. Pingbo Tang

Second Advisor

Dr. Jun-Seok Oh

Third Advisor

Dr. Valerian Kwigizile

Keywords

Bridge management, project control, laser scanner, data analysis, statistical analysis

Access Setting

Masters Thesis-Open Access

Abstract

Civil engineers frequently face the challenges of acquiring sufficient data to satisfy the informational needs of various decision making scenarios having time, budget, and resource constraints. This thesis focuses on exploring how statistical analysis of historical data sets can be used to improve the efficiency and effectiveness of future data collection activities in transportation engineering and construction project control. Pearson's correlation, multiple regression, Akaike information criterion, and Bayesian information criterion are applied to two cases of data collection and analysis to understand the relative importance of various parameters within each case. Case I analyzes National Bridge Inventory (NBI) data to identify the cross-regional variations and the relative importance of various bridge parameters as they relate to bridge deterioration across regions in the U.S.. Substantial variations in bridge deterioration influential items are observed across different regions and rating items while some items are consistently identified as important. Case II analyzes and ranks several scanner-based, environmental, and object-based factors influencing point cloud data quality and creates a scan-error prediction model for more effective scan planning using 3D laser scanners in construction project control.

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