Browsing by Author "Behzadan, Amir"

Now showing 1 - 2 of 2

Collaborative Research: Transforming Teaching of Structural Analysis through Mobile Augmented Reality
Construction Science; TAMU; https://hdl.handle.net/20.500.14641/539; National Science Foundation
Structural Analysis is an introductory core course that is taught in almost all civil engineering, architectural engineering, and construction undergraduate programs across the U.S. Previous research unveils students' deficits in understanding the behavior of structural elements in a three-dimensional (3D) context due to the shortcomings of traditional lecturing approaches. In particular, such approaches often put too much emphasis on the analysis of individual structural members, thereby falling short in providing a solid, easy-to-follow, and holistic approach to comprehending and analyzing complex structures with a large number of interconnected elements. This research is transforming existing teaching pedagogy in structural analysis and filling a major gap in how structural engineering is currently taught to the nation's future workforce in U.S. institutions. In this project, mobile augmented reality (AR) is used to superimpose views of the real world with computer generated 3D models of structures under load. In doing so, the potential of AR for improving learning and increasing student engagement in the learning process is being systematically assessed. Building upon theoretical perspectives on how people learn, this project is also examining if pedagogy involving AR creates an effective learning environment for students, as well as if it enhances adaptive flexibility, which is deemed to be essential to be a successful engineer in the 21st Century. The broader impacts of this project extend beyond its confines through outreach activities such as integrating AR technology into lower-level engineering courses that aim to increase the number of ethnically-diverse students pursuing STEM degrees. The learning modules integrating AR into teaching of structural analysis will be made available online. A website and online repository are to include specific details about the learning objectives, implementation procedures, and instructor reflections on things to consider while implementing the teaching techniques. The developed software components and 3D models will be open source.
Real-time feedback-enabled simulation modeling of dynamic construction processes (ongoing)
Construction Science; TAMU; https://hdl.handle.net/20.500.14641/539; National Science Foundation
According to the U.S. Census Bureau, in 2015, the U.S. construction industry will surpass $1 Trillion Dollars in spending. Construction and infrastructure projects consist of interconnected networks of people, equipment, and materials. Most often, finding optimal work strategies, and making timely operational decisions that lead to maximum productivity while minimizing project completion cost and time is not trivial. Unlike manufacturing and industrial systems, construction projects involve dynamic (constantly evolving) layouts, complex resource interactions, uncertainties in workflows and processes, and unforeseen conditions that can result in deviations from plans and unwanted delays. Figures show that only 30 percent of construction projects finish on time and within budget. Therefore, the accuracy and timeliness of operational-level decision-making in construction projects is of utmost importance. This award supports fundamental research to enhance construction decision-making accuracy by reducing uncertainties through the seamless integration of process-level data into decision-making. This will be achieved by building the theoretical foundation and significantly advancing the current state of construction simulation modeling through enabling real-time interaction with a simulation model as the real project evolves, and communicating the simulation output through a feedback loop to steer the events in the real project. Therefore, results from this research will benefit the U.S. economy and the society since it leads to better decision-making which results in reducing waste, rework, cost, time, and ensures safety. The multi-disciplinary nature of this project will help broaden participation of underrepresented and diverse student groups in integrated research and pedagogical activities, and positively impact engineering education. The knowledge-based simulation modeling framework in this project enables process-level models to autonomously learn from and adapt to ever-changing and evolving construction systems. Process-level knowledge that serves as the input of such simulation models is obtained from ubiquitous sensory data that describe relationships, interactions, and uncertainty attributes of field processes, and enable the generation and maintenance of more accurate simulation models. In doing so, some scientific barriers are yet to be overcome to realize the full accreditation and application of this framework. The research team will design and test methods that draw from data mining, machine learning, forecasting, and control to fill the existing knowledge gaps in capturing and mining complex data and meta-data from equipment and human crew interactions. The resulting process-level knowledge will be rich enough to describe, model, analyze, and project the uncertainties of construction systems at any point in time and consequently help adjust resource allocations and operational scenarios on the job site.