|dc.description.abstract||The physically strenuous and demanding nature of construction tasks exposes workers to injury risks, can reduce productivity, and contributes to undesirable early retirement. In spite of these risks, human performance in the workplace is often managed by over-simplified standards. Complex construction sites require continuous manual labor intervention. Site complexity also preclude objective and reliable quantification of labor exposure to ergonomic risk factors. It also impedes the introduction of automation and robotics in construction industry despite recent advancements in other construction technologies.
The overarching goal of this dissertation is to identify opportunities for human-centric advanced work assessment systems that can
1) objectively and simultaneously evaluate ergonomic risk levels and productivity in construction tasks involving heavy material handling,
2) effectively identify safe and productive working postures and techniques that workers develop as they gain experience, and
3) evaluate the impact of introducing new, semi-automated work systems on health and productivity in a construction context.
To achieve these goals, this research adopts wearable inertial measurement unit (IMU) based motion capture systems as means of data collection in construction worksites. It analyzes the resultant whole-body kinematic data using analytical tools including combined biomechanical-productivity analysis, rule-based postural ergonomic risk assessment, statistical analysis, and data clustering algorithms. This research specifically focuses its efforts on the masonry field, one of the most labor-intensive trades in construction. Over the span of four years, 45 masons at various levels of experience participated in field experiments within the framework of this study.
The acquired data was used to develop automated ergonomic assessment systems to evaluate risk levels via various rule-based assessment tools as well as biomechanical analysis. This approach enabled us to objectively evaluate ergonomic risk level in construction tasks, then analyze the relationships among body loads, experience, and work methods to quantitatively investigate differences in joint loads between experts and apprentices. Furthermore, motion data-driven identification of expert work technique was proposed as a guide to proper working methods and apprentice training. These approaches allowed us to identify proper work techniques adopted by experts and suggested the utilization of expert' techniques in apprentice training to reduce the prevalence of occupational injuries and to improve productivity.
Leveraging these insights, this study proposed a systematic and objective methodology to assess the value of a semi-automated work system in a construction context. The proposed methodology fills an important technology gap by representing a proactive approach for the evaluation of semi-automated work systems in terms of reduction in exposure to health risks and improvements of productivity. Ultimately, the present research seeks to maximize occupational performance by minimizing the level of human efforts in construction.||en