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Construction Ergonomic Risk And Productivity Assessment Using Mobile Technology And Machine Learning, Nipun Deb Nath
Construction Ergonomic Risk And Productivity Assessment Using Mobile Technology And Machine Learning, Nipun Deb Nath
MSU Graduate Theses
The construction industry has one of the lowest productivity rates of all industries. To remedy this problem, project managers tend to increase personnel's workload (growing output), or assign more (often insufficiently trained) workers to certain tasks (reducing time). This, however, can expose personnel to work-related musculoskeletal disorders which if sustained over time, lead to health problems and financial loss. This Thesis presents a scientific methodology for collecting time-motion data via smartphone sensors, and analyzing the data for rigorous health and productivity assessment, thus creating new opportunities in research and development within the architecture, engineering, and construction (AEC) domain. In particular, …
Coupling Mobile Technology, Position Data Mining, And Attitude Toward Risk To Improve Construction Site Safety, Khandakar Mamunur Rashid
Coupling Mobile Technology, Position Data Mining, And Attitude Toward Risk To Improve Construction Site Safety, Khandakar Mamunur Rashid
MSU Graduate Theses
Construction sites comprise constantly moving heterogeneous resources that interact in close proximity of each other. The sporadic nature of such interactions creates an accident prone physical space surrounding workers. Despite efforts to improve site safety using location-aware proximity sensing techniques, major scientific gaps still remain in reliably forecasting impending hazardous scenarios before they occur. In the research documented in this thesis, spatiotemporal data of workers and site hazards are fused with a quantifiable model of an individual's attitude toward risk to generate proximity-based safety alerts in real time. In particular, two trajectory prediction models, namely polynomial regression (PR) and hidden …