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- Member of: ASU Electronic Theses and Dissertations
Description
In today's era a lot of the construction projects suffer from time delay, cost overrun and quality defect. Incentive provisions are found to be a contracting strategy to address this potential problem. During last decade incentive mechanisms have gained importance, and they are starting to become adopted in the construction projects. Most of the previous research done in this area was purely qualitative, with a few quantitative studies. This study aims to quantify the performance of incentives in construction by collecting the data from more than 30 projects in United States through a questionnaire survey. First, literature review addresses the previous research work related to incentive types, incentives in construction industry, incentives in other industry and benefits of incentives. Second, the collected data is analyzed with statistical methods to test the significance of observed changes between two data sets i.e. incentive projects and non-incentive projects. Finally, the analysis results provide evidence for the significant impact of having incentives; reduced the cost and schedule growth in construction projects in United States.
ContributorsPaladugu, Bala Sai Krishna (Author) / El Asmar, Mounir (Thesis advisor) / Ernzen, James (Committee member) / Sullivan, Kenneth (Committee member) / Arizona State University (Publisher)
Created2015
Description
Large-scale civil infrastructure systems are critical for the functioning and development of any society. However, these systems are often vulnerable to degradation and the effects of aging, necessitating consistent monitoring and maintenance. Current methods for infrastructure maintenance primarily rely on human intervention and need the implementation of advanced sensing and computing technologies in field operations and maintenance (O&M) tasks. This research aimed to address these gaps and provide novel contributions. Specifically, the objectives of this study were to leverage artificial intelligence models to enhance point cloud noise processing, to automate tree species detection using Mask R-CNN, and to integrate imagery data and LiDAR datasets for real-time terrain analysis. First, the study proposed leverages neural networks to eliminate unwanted noise from point cloud datasets, enhancing the accuracy and reliability of infrastructure data. Secondly, the research integrated Mask R-CNN into automated tree species detection. This component offers an efficient solution to identify and classify vegetation surrounding infrastructure, enabling infrastructure managers to devise proactive vegetation management strategies, thereby reducing risks associated with tree-related incidents. Lastly, the study fused image and LiDAR datasets to support real-time terrain analysis. This integrated approach provides a comprehensive understanding of terrain characteristics, allowing infrastructure managers to assess slope, elevation, and other relevant factors, facilitating proactive maintenance interventions and mitigating risks associated with erosion. These contributions collectively underscore the potential of artificial intelligence models in advancing the operations and maintenance practices of large civil infrastructure systems. By leveraging these models, infrastructure managers can optimize decision-making processes, streamline maintenance efforts, and enhance critical infrastructure networks' overall resilience and sustainability.
ContributorsPaladugu, Bala Sai Krishna (Author) / Grau, David (Thesis advisor) / Ernzen, James (Committee member) / Standage, Richard (Committee member) / Arizona State University (Publisher)
Created2023