ASU Electronic Theses and Dissertations
This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.
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Description
The fact that the lean construction approach, a project-based production management approach, is considered as a best practice in the construction industry and a key solution to alleviate the implications of various forms of waste on the construction projects performance in general, and the Lebanese ones in particular, motivates the author to conduct a study to evaluate it as a strategic option. For that to happen, a bibliographic analysis has been developed to serve the key project objective. The bibliographic analysis is expected to help construction professionals to deepen their knowledge in Lean philosophy and its applications in the construction industry. After developing a solid background of understanding of Lean Construction, a survey to collect information from construction companies within the Lebanese territory has been conducted, followed by analysis and interpretations of the findings to examine lean construction inside the Lebanese construction Industry; that has been achieved in terms of understanding and analyzing the suitability, acceptability, and applicability of lean construction principles, tools, and techniques by Lebanese construction firms. Performed Revision has been crowned with a detailed explanation of the lean construction approach accompanied with an applicable lean construction implementation guideline. Besides that, survey results showed a wide acceptance of most lean construction principles (namely, waste elimination and continuous improvement) by Lebanese construction professionals. It has been shown as well, that lean construction tools and techniques are applied by a major portion of the Lebanese construction firms due to the significant impact these tools and techniques have on the project quality, schedule, and cost. However, all analyzed results confirm one main conclusion, that a significant portion of the Lebanese construction industry lack that adequate knowledge and understanding of lean construction philosophy, which necessitates the development of “Lean Construction Education Programs” as a principal enabler for successful lean construction adoption. This paper has been developed mainly to guide Lebanese construction professionals, especially project and construction managers, towards understanding and adopting lean construction as a mean to deliver projects of value and to inform Lebanese construction industry leaders about the current state of lean construction inside the Lebanese construction Industry.
ContributorsMetlej, Kamal (Author) / Grau Torrent, David (Thesis advisor) / Ariaratnam, Samuel (Committee member) / Czerniawski, Thomas (Committee member) / Arizona State University (Publisher)
Created2021
Description
There are relatively few available construction equipment detectors models thatuse deep learning architectures; many of these use old object detection architectures
like CNN (Convolutional Neural Networks), RCNN (Region-Based Convolutional
Neural Network), and early versions of You Only Look Once (YOLO) V1. It can be
challenging to deploy these models in practice for tracking construction equipment
while working on site.
This thesis aims to provide a clear guide on how to train and evaluate the
performance of different deep learning architecture models to detect different kinds of
construction equipment on-site using two You Only Look Once (YOLO) architecturesYOLO v5s and YOLO R to detect three classes of different construction equipment onsite, including Excavators, Dump Trucks, and Loaders. The thesis also provides a
simple solution to deploy the trained models. Additionally, this thesis describes a
specialized, high-quality dataset with three thousand pictures created to train these
models on real data by considering a typical worksite scene, various motions, varying
perspectives, and angles of construction equipment on the site.
The results presented herein show that after 150 epochs of training, the YOLORP6 has the best mAP at 0.981, while the YOLO v5s mAP is 0.936. However, YOLO v5s
had the fastest and the shortest training time on Tesla P100 GPU as a processing
unit on the Google Colab notebook. The YOLOv5s needed 4 hours and 52 minutes, but
the YOLOR-P6 needed 14 hours and 35 minutes to finish the training.ii
The final findings of this study show that the YOLOv5s model is the most efficient
model to use when building an artificial intelligence model to detect construction
equipment because of the size of its weights file relative to other versions of YOLO
models- 14.4 MB for YOLOV5s vs. 288 MB for YOLOR-P6.
This hugely impacts the processing unit’s performance, which is used to predict
the construction equipment on site. In addition, the constructed database is published
on a public dataset on the Roboflow platform, which can be used later as a foundation
for future research and improvement for the newer deep learning architectures.
Contributorssabek, mohamed mamdooh (Author) / Parrish, Kristen (Thesis advisor) / Czerniawski, Thomas (Committee member) / Ayer, Steven K (Committee member) / Arizona State University (Publisher)
Created2022
Description
The management of underground utilities is a complex and challenging task due to the uncertainty regarding the location of existing infrastructure. The lack of accurate information often leads to excavation-related damages, which pose a threat to public safety. In recent years, advanced underground utilities management systems have been developed to improve the safety and efficiency of excavation work. This dissertation aims to explore the potential applications of blockchain technology in the management of underground utilities and reduction of excavation-related damage. The literature review provides an overview of the current systems for managing underground infrastructure, including Underground Infrastructure Management (UIM) and 811, and highlights the benefits of advanced underground utilities management systems in enhancing safety and efficiency on construction sites. The review also examines the limitations and challenges of the existing systems and identifies the opportunities for integrating blockchain technology to improve their performance. The proposed application involves the creation of a shared database of information about the location and condition of pipes, cables, and other underground infrastructure, which can be updated in real time by authorized users such as utility companies and government agencies. The use of blockchain technology can provide an additional layer of security and transparency to the system, ensuring the reliability and accuracy of the information. Contractors and excavation companies can access this information before commencing work, reducing the risk of accidental damage to underground utilities.
ContributorsAlnahari, Mohammed S (Author) / Ariaratnam, Samuel T (Thesis advisor) / El Asmar, Mounir (Committee member) / Czerniawski, Thomas (Committee member) / Arizona State University (Publisher)
Created2023