ETDs

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.

Displaying 1 - 4 of 4
Filtering by

Clear all filters

168625-Thumbnail Image.png

A Performance Study of Different Deep Learning Architectures For Detecting Construction Equipment in Sites

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…

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
156647-Thumbnail Image.png

Teaching Non-Technological Skills for Successful Building Information Modeling (BIM) Projects

Description
Implementing Building Information Modeling (BIM) in construction projects has many potential benefits, but issues of projects can hinder its realization in practice. Although BIM involves using the technology, more than four-fifths of the recurring issues in current BIM-based construction projects are related to the people and processes (i.e., the non-technological…

Implementing Building Information Modeling (BIM) in construction projects has many potential benefits, but issues of projects can hinder its realization in practice. Although BIM involves using the technology, more than four-fifths of the recurring issues in current BIM-based construction projects are related to the people and processes (i.e., the non-technological elements of BIM). Therefore, in addition to the technological skills required for using BIM, educators should also prepare university graduates with the non-technological skills required for managing the people and processes of BIM. This research’s objective is to develop a learning module that teaches the non-technological skills for addressing common, people- and process-related, issues in BIM-based construction projects. To achieve this objective, this research outlines the steps taken to create the learning module and identify its impact on a BIM course. The contribution of this research is in the understanding of the pedagogical value of the developed problem-based learning module and documenting the learning module’s development process.
ContributorsAbdul Rahman, Abdul Rahimi Bin (Author) / Ayer, Steven K (Thesis advisor) / Tang, Pingbo (Committee member) / Wiezel, Avi (Committee member) / Arizona State University (Publisher)
Created2018
158495-Thumbnail Image.png

Developing an Augmented Reality Solution for Mapping Underground Infrastructure

Description

Underground infrastructure is a critical part of the essential utility services provided to society and the backbone of modern civilization. However, now more than ever before, the disastrous events of a striking underground utilities cost billions of dollars each year in societal damages. Advanced technology and sophisticated visualization techniques such…

Underground infrastructure is a critical part of the essential utility services provided to society and the backbone of modern civilization. However, now more than ever before, the disastrous events of a striking underground utilities cost billions of dollars each year in societal damages. Advanced technology and sophisticated visualization techniques such as augmented reality (AR) now play a significant role in mitigating such devastating consequences. Therefore, it is vitally important to coordinate resources, share information, and ensure efficient communication between construction personnel and utility owners. Besides, geographic information systems (GIS) provide a solution for interoperability in the construction industry. Applying such technologies in the field of underground construction requires accurate and up-to-date information. However, there is currently limited research that has integrated AR and GIS and evaluated the effectiveness and usability of the combination in this domain. The main objective of this research was to develop an integrated AR-GIS for mapping and capturing underground utilities using a mobile device. To achieve these objectives, a design research approach utilized to develop and evaluate a mobile extended-reality (XR-GIS) application. This research has produced an efficient solution for data collection and sharing among stakeholders in the underground construction industry. The main challenge in creating a reliable and adaptive outdoor AR system is the accurate registration of virtual objects in the real world. Due to the limited accuracy of smartphones, this study used an external Global Positioning System (GPS) devices to reduce positional error. The primary motivation behind this research is to make the construction industry more aware of the benefits of leveraging AR to prevent utility strikes and enhance public safety.

This dissertation fills the gap in the knowledge regarding applying Augmented Reality (AR) in the underground infrastructure mapping. This study’s three research objectives are:

(1) Identify the challenges and barriers facing the underground construction industry when applying AR.

(2) Develop an integrated AR-GIS for mapping and capturing underground utilities using a mobile device.

(3) Evaluate the horizontal accuracy of the captured data used by the AR phone application XR-GIS that has been developed by the author.
ContributorsFenais, Amr (Author) / Ariaratnam, Samuel T (Thesis advisor) / Ayer, Steven K (Committee member) / Kaloush, Kamil (Committee member) / Arizona State University (Publisher)
Created2020
158609-Thumbnail Image.png

Predictive Control of Interpersonal Communication Processes in Civil Infrastructure Systems Operations

Description
Interpersonal communications during civil infrastructure systems operation and maintenance (CIS O&M) are processes for CIS O&M participants to exchange critical information. Poor communications that provide misleading information can jeopardize CIS O&M safety and efficiency. Previous studies suggest that communication contexts and features could be indicators of communication errors and relevant…

Interpersonal communications during civil infrastructure systems operation and maintenance (CIS O&M) are processes for CIS O&M participants to exchange critical information. Poor communications that provide misleading information can jeopardize CIS O&M safety and efficiency. Previous studies suggest that communication contexts and features could be indicators of communication errors and relevant CIS O&M risks. However, challenges remain for reliable prediction of communication errors to ensure CIS O&M safety and efficiency. For example, existing studies lack a systematic summarization of risky contexts and features of communication processes for predicting communication errors. Limited studies examined quantitative methods for incorporating expert opinions as constraints for reliable communication error prediction. How to examine mitigation strategies (e.g., adjustments of communication protocols) for reducing communication-related CIS O&M risks is also challenging. The main reason is the lack of causal analysis about how various factors influence the occurrences and impacts of communication errors so that engineers lack the basis for intervention.

This dissertation presents a method that integrates Bayesian Network (BN) modeling and simulation for communication-related risk prediction and mitigation. The proposed method aims at tackling the three challenges mentioned above for ensuring CIS O&M safety and efficiency. The proposed method contains three parts: 1) Communication Data Collection and Error Detection – designing lab experiments for collecting communication data in CIS O&M workflows and using the collected data for identifying risky communication contexts and features; 2) Communication Error Classification and Prediction – encoding expert knowledge as constraints through BN model updating to improve the accuracy of communication error prediction based on given communication contexts and features, and 3) Communication Risk Mitigation – carrying out simulations to adjust communication protocols for reducing communication-related CIS O&M risks.

This dissertation uses two CIS O&M case studies (air traffic control and NPP outages) to validate the proposed method. The results indicate that the proposed method can 1) identify risky communication contexts and features, 2) predict communication errors and CIS O&M risks, and 3) reduce CIS O&M risks triggered by communication errors. The author envisions that the proposed method will shed light on achieving predictive control of interpersonal communications in dynamic and complex CIS O&M.
ContributorsSun, Zhe (Author) / Tang, Pingbo (Thesis advisor) / Ayer, Steven K (Committee member) / Cooke, Nancy J. (Committee member) / Liu, Yongming (Committee member) / Arizona State University (Publisher)
Created2020