Engineering design motivations : a qualitative and quantitative analysis of university students

What motivates students to perform and pursue engineering design tasks? This study examines this question by way of three Learning Through Service (LTS) programs: 1) an on-going longitudinal study examining the impacts of service on engineering students, 2) an on-going analysis of an international senior design capstone program, and 3) an on-going evaluation of an international graduate-level research program. The evaluation of these programs incorporates both qualitative and quantitative methods, utilizing surveys, questionnaires, and interviews, which help to provide insight on what motivates students to do engineering design work. The quantitative methods were utilized in analyzing various instruments including: a Readiness assessment inventory, Intercultural Development Inventory, Sustainable Engineering through Service Learning survey, the Impacts of Service on Engineering Students’ survey, Motivational narratives, as well as some analysis for interview text. The results of these instruments help to provide some much needed insight on how prepared students are to participate in engineering programs. Additional qualitative methods include: Word clouds, Motivational narratives, as well as interview analysis. This thesis focused on how these instruments help to determine what motivates engineering students to pursue engineering design tasks. These instruments aim to collect some more in-depth information than the quantitative instruments will allow. Preliminary results suggest that of the 120 interviews analyzed Interest/Enjoyment, Application of knowledge and skills, as well as gaining knowledge are key motivating factors regardless of gender or academic level. Together these findings begin to shed light on what motivates students to perform engineering design tasks, which can be applied for better recruitment and retention in university programs.

POSE ESTIMATION AND 3D RECONSTRUCTION USING SENSOR FUSION

A camera maps 3-dimensional (3D) world space to a 2-dimensional (2D) image space. In the process it loses the depth information, i.e., the distance from the camera focal point to the imaged objects. It is impossible to recover this information from a single image. However, by using two or more images from different viewing angles this information can be recovered, which in turn can be used to obtain the pose (position and orientation) of the camera. Using this pose, a 3D reconstruction of imaged objects in the world can be computed. Numerous algorithms have been proposed and implemented to solve the above problem; these algorithms are commonly called Structure from Motion (SfM). State-of-the-art SfM techniques have been shown to give promising results. However, unlike a Global Positioning System (GPS) or an Inertial Measurement Unit (IMU) which directly give the position and orientation respectively, the camera system estimates it after implementing SfM as mentioned above. This makes the pose obtained from a camera highly sensitive to the images captured and other effects, such as low lighting conditions, poor focus or improper viewing angles. In some applications, for example, an Unmanned Aerial Vehicle (UAV) inspecting a bridge or a robot mapping an environment using Simultaneous Localization and Mapping (SLAM), it is often difficult to capture images with ideal conditions. This report examines the use of SfM methods in such applications and the role of combining multiple sensors, viz., sensor fusion, to achieve more accurate and usable position and reconstruction information. This project investigates the role of sensor fusion in accurately estimating the pose of a camera for the application of 3D reconstruction of a scene. The first set of experiments is conducted in a motion capture room. These results are assumed as ground truth in order to evaluate the strengths and weaknesses of each sensor and to map their coordinate systems. Then a number of scenarios are targeted where SfM fails. The pose estimates obtained from SfM are replaced by those obtained from other sensors and the 3D reconstruction is completed. Quantitative and qualitative comparisons are made between the 3D reconstruction obtained by using only a camera versus that obtained by using the camera along with a LIDAR and/or an IMU. Additionally, the project also works towards the performance issue faced while handling large data sets of high-resolution images by implementing the system on the Superior high performance computing cluster at Michigan Technological University.

IMPLEMENTATION OF PHOTOGRAMMETRY TO IMPROVE PROACTIVE ASSESSMENT OF RETAINING WALLS ALONG TRANSPORTATION CORRIDORS

Retaining walls are important assets in the transportation infrastructure and assessing their condition is important to prolong their performance and ultimately their design life. Retaining walls are often overlooked and only a few transportation asset management programs consider them in their inventory. Because these programs are few, the techniques used to assess their condition focus on a qualitative assessment as opposed to a quantitative approach. The work presented in this thesis focuses on using photogrammetry to quantitatively assess the condition of retaining walls. Multitemporal photogrammetry is used to develop 3D models of the retaining walls, from which offset displacements are measured to assess their condition. This study presents a case study from a site along M-10 highway in Detroit, MI were several sections of retaining walls have experienced horizontal displacement towards the highway. The results are validated by comparing with field observations and measurements. The limitations of photogrammetry were also studied by using a small scale model in the laboratory. The analysis found that the accuracy of the offset displacement measurements is dependent on the distance between the retaining wall and the sensor, location of the reference points in 3D space, and the focal length of the lenses used by the camera. These parameters were not ideal for the case study at the M-10 highway site, but the results provided consistent trends in the movement of the retaining wall that couldn’t be validated from offset measurements. The findings of this study confirm that photogrammetry shows promise in generating 3D models to provide a quantitative condition assessment for retaining walls within its limitations.