ORCID Identifier(s)


Graduation Semester and Year




Document Type


Degree Name

Doctor of Philosophy in Civil Engineering


Civil Engineering

First Advisor

Mohsen Shahandashti


The United States has been one of the top five countries most frequently hit by natural disasters. The post-disaster survival of cities and communities depend on their capabilities to reconstruct and repair damages to buildings and other infrastructure systems. The significant increase in the repair costs following large-scale natural disasters, also called “demand surge,” slows down the repair process that impacts many lives touched by large-scale natural disasters. Previous studies showed that post-disaster construction labor cost escalation drives the total post-disaster construction cost escalation in the U.S. The ultimate goal of this research is to (1) measure post-disaster construction labor wage changes in different sub-sectors of the construction sector and compare the sub-sectors against each other to determine which construction sub-sectors are most vulnerable to disasters, (2) assess the role of pre-disaster construction market conditions in influencing post-disaster construction labor changes, and (3) create Spatial Panel Data Models (SPDM) to find the spatial interaction effects as well as time-specific effects in the existing cross-sectional demand surge models. The historical county-level data of five construction market indicators (establishment count, contribution level, average weekly wages, employment level, and building permits) prior to disasters along with disaster magnitudes (property damages) were collected for more than 35 of the largest weather-related disasters (floods, storms, and tornadoes) in the United States. These disasters affected more than 600 counties from 2007 to 2014. It is expected that the results of this study will help cost engineers to prepare more accurate bids in the volatile post-disaster construction markets and help capital planners and post-disaster risk-mitigation agencies to identify the more vulnerable construction markets. It is also expected that the results of this study will help demand surge modelers to create more accurate models.


Construction cost changes, Construction labor wage, Natural disasters, Demand surge, Cross-sectional modeling, Regression analysis, Spatial econometric, spatial panel data model


Civil and Environmental Engineering | Civil Engineering | Engineering


Degree granted by The University of Texas at Arlington