Theses and dissertations

This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students. 

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Measuring the construction performance in Saudi Arabia and proposing new procurement model based on BV PIPS (a university case study)

Description
Saudi Arabia has been facing issues with completing construction projects on time and on budget. It has been documented that 70% of public construction projects are delayed. Studies have identified the low-bid delivery method as an important factor in causing such delays. The procurement system (low-bid) ignores contractors’ performance, and…

Saudi Arabia has been facing issues with completing construction projects on time and on budget. It has been documented that 70% of public construction projects are delayed. Studies have identified the low-bid delivery method as an important factor in causing such delays. The procurement system (low-bid) ignores contractors’ performance, and that is reflected in projects’ performance. A case study was performed, at a University campus in northern Saudi Arabia, identifying the major causes of project delays and cost overruns. The University was experiencing delays from 50% to 150%. Also, the actual project costs for four projects were examined and found that all four projects’ costs were higher than the original bid. The delay and cost overruns factors were gathered from the University engineers. A literature research identified one construction management method, best value performance information procurement system (BV PIPS), has documented multiple times its ability to improve project performance. In a comparison using the result of a case study and the results of (BV PIPS), Saudi Arabia’s delivery system was identified as a potential cause of project performance issues. The current procurement system was analyzed and modified to adapt with the (BV PIPS). The proposed procurement system using BV PIPS, which can be implemented in Saudi Arabia, was created with owner side. A large survey was conducted of 761 classified contractors and 43 universities’ representatives who rated causes of delay factors and cost overruns. The delay factors were then compared to delay factors experienced on Saudi construction projects, identified by performing a literature research. The comparison identified 14 important causes of delays. Moreover, the survey showed that classified contractors and universities’ representatives unsatisfied with low-bid, and they agreed with BV PIPS which selecting vendors based on performance with price. The proposed model required a submitted level of experience (LE), risk assessment (RA), and value added (VA). Besides, project managers of vendors should be interviewed during the clarification phase. In addition, venders should submit the project’s scope, technical schedule, milestone schedule, and risk management plan. In the execution phase, vendors should submit a weekly risk report (WRR) and director’s report (DR).
ContributorsAlzara, Majed (Author) / Kashiwagi, Dean (Thesis advisor) / Kashiwagi, Jacob (Committee member) / Al-Tassan, Abdulrahman (Committee member) / Arizona State University (Publisher)
Created2016
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Characterizing the Performance of Machine Learning Algorithms: A Study and Novel Techniques

Description

Classification in machine learning is quite crucial to solve many problems that the world is presented with today. Therefore, it is key to understand one’s problem and develop an efficient model to achieve a solution. One technique to achieve greater model selection and thus further ease in problem solving is…

Classification in machine learning is quite crucial to solve many problems that the world is presented with today. Therefore, it is key to understand one’s problem and develop an efficient model to achieve a solution. One technique to achieve greater model selection and thus further ease in problem solving is estimation of the Bayes Error Rate. This paper provides the development and analysis of two methods used to estimate the Bayes Error Rate on a given set of data to evaluate performance. The first method takes a “global” approach, looking at the data as a whole, and the second is more “local”—partitioning the data at the outset and then building up to a Bayes Error Estimation of the whole. It is found that one of the methods provides an accurate estimation of the true Bayes Error Rate when the dataset is at high dimension, while the other method provides accurate estimation at large sample size. This second conclusion, in particular, can have significant ramifications on “big data” problems, as one would be able to clarify the distribution with an accurate estimation of the Bayes Error Rate by using this method.
ContributorsLattus, Robert (Author) / Dasarathy, Gautam (Thesis director) / Berisha, Visar (Committee member) / Turaga, Pavan (Committee member) / Barrett, The Honors College (Contributor) / Electrical Engineering Program (Contributor)
Created2021-12