John G. Everett

Ergonomics, Health, and Safety in Construction: Opportunities for Automation and Robotics

Automation and robotics have often been mentioned as possible solutions to health and safety problems in construction. Several studies have prioritized automation and robotics opportunities based in part on health and safety considerations. These and other studies conclude that automation and robotics will be most cost effective in tasks that require speed, repetitive motions, large forces, and operation in hostile environments. These are precisely the tasks that place craft workers at the highest risk for overexertion injuries and disorders. Overexertion injuries are the single largest classification of injury in construction in the United States, accounting for about 24% of all injuries. Overexertion injuries generally occur as a result of performing a given task as planned. While overexertion injuries are not intentional, the underlying causes of the injuries are built into the prescribed tools and work methods. This paper describes a current research project that will identify specific construction tasks that place craft workers at high risk for overexertion injuries and disorders. High risk tasks can then be targeted so that automation, robotics, and ergonomic principles can be applied to modify the task or work environment to accommodate human capabilities and limitations.

Ironworkers: Physiological demands during construction work

Construction craft work is a physically strenuous and demanding occupation. Physically demanding work leads to physical fatigue which is associated with decreased productivity. Early attempts to investigate the physical demands of construction work date back to the 1950s and were based on principles from work physiology. The work and workforce have changed since the 1950s warranting an investigation of todays construction work performed by todays construction workforce. This paper investigates the physical demands of construction ironworkers. Physiological measures of energy expenditure, including oxygen consumption and heart rate data, were collected for 7 ironworkers performing a total of 10 actual construction activities. The results indicate that these construction workers routinely exceed one or more published guidelines for acceptable levels of energy expenditure, oxygen consumption, and heart rate. The methods described in this paper have wide applications in identifying excessively demanding construction tasks so the work can be modified to accommodate the abilities of all workers, to reduce physical demands and resulting physical fatigue.

The validity of data dependent system modelling to predict relative physiological workload during construction work

The measurement of oxygen uptake during physical work activities is considered a good measure of the physiological workload experienced by construction workers. Many work physiologists advocate reporting the measured oxygen uptake as a percentage of maximum oxygen uptake, a ratio commonly known as relative workload. The main advantage of this ratio is the subject-specific workload it provides. This enables the tailoring of work demands to the ability of individual workers. Determining relative workload is arithmetically simple once maximum oxygen uptake is known. However, finding exact maximum oxygen uptake requires intricate and complicated laboratory procedures that may be dangerous for unfit individuals. Therefore, despite sacrificing accuracy, maximum oxygen uptake prediction techniques offer an attractive alternative. This paper presents a large scale validation of a maximum oxygen uptake prediction method that uses in-situ collected sub-maximal oxygen uptake data. The methodology, developed using twenty subjects and verified on eight construction workers, was based on the use of time series analysis techniques and the hypothesis that oxygen uptake data are serially dependent. The validation results using 100 subjects arrived at a ±0.65 liter·min -1

Predicting Workload during Physically Demanding Work Using Oxygen Uptake Data

Oxygen uptake measurements during the performance of actual work activities are considered a good measure of the absolute physiologic workload experienced by a worker. Many work physiologists recommend expressing absolute workload as a percentage of maximum oxygen uptake, commonly known as relative workload, since it provides a subject-specific workload. Determining relative workload is arithmetically simple but requires an additional and separate step to determine maximum oxygen uptake through exact or prediction techniques. This paper presents a method for predicating relative workload from in-situ collected sub-maximal oxygen uptake data without the need to determine maximum oxygen uptake. The methodology, developed using twenty subjects and verified on five, was based on modeling the human cellular utilization system as a stochastic system. The standard error in predicting relative workload for the validation subjects was ±3.2%. These initial results are quite promising and establish a starting point for further investigations.