Current Institution: Virginia Tech
Bio: I am Omobolanle (Bola) Ogunseiju, a doctoral candidate of Building construction, at the Myers Lawson school of construction, Virginia Tech, and the outstanding doctoral student in the College of Architecture and Urban Studies at Virginia Tech. My research interests focus on advancing workforce development (safety, health, and well-being), and developing smart communities through the application of Artificial Intelligence (enabled by digital twin, cyber-physical systems, data sensing, data analytics, and reality capture technologies) and wearable robots. I am particularly interested in understanding and shaping the human–technological dynamics involved in workforce development, safety, and health, especially within the construction sector. Findings from my research have been published in premier conferences and journal outlets including AUTOcon, ITcon, ECAM, SSBE, ASC, ICCCE, and CONVR.
Abstract: Understanding Human-Technology Fluency for Workforce Development using Artificial Intelligence
The construction industry continues to be one of the most hazardous industries, with construction activities being physically demanding and repetitive in nature. This often results in cost and time overruns, health and safety problems, and workforce shortage. A future construction industry where cost and safety hazards are reduced, and productivity is increased can be achieved through human-technology fluency. This results from the co-adaptation between human and technology, driven by the skill deficiencies of humans and by the updates in automatic control policies based on humans. My research investigates human-technology fluency for workforce development and smart construction practices using sensing technologies and artificial intelligence techniques. Artificial intelligence, enabled by real-time sensing, virtual environments, machine learning, cyber-physical systems, digital twins, and wearable robots are leveraged in my work for 1) improving construction workforce health and safety, and 2) investigating competencies development for implementing sensing technologies on construction projects.