Health and productivity impact of semi-automated work systems in construction

https://doi.org/10.1016/j.autcon.2020.103396Get rights and content

Highlights

  • Proposed methodology assesses values of semi-automated work system in construction.

  • Health and productivity impact of semi-automated system was objectively evaluated.

  • Self-leveling pallet reduced 40% joint-load and increased 10% productivity.

  • Significantly lower joint angles at pick-up with the pallet were observed.

  • Compatibility of methodology with existing processes increases its value and applicability.

Abstract

Variability of construction sites and tasks make their automation prohibitively complex. Workers continue to carry out physically demanding tasks which adversely affect their health, safety, and productivity. The flexibility of semi-automated work systems, where operators work in conjunction with machines and robots, is an attractive alternative. It is critical to estimate the anticipated effectiveness of these interventions before integrating them into the current work processes. This study proposes a systematic and objective methodology to assess the value of a semi-automated work system in a construction context, as it pertains to reduced exposure to musculoskeletal disorder risks and productivity improvements. Additional assessments are also suggested for a complete analysis of efficacy. The proposed methodology was validated through an experimental evaluation of a force-assist self-leveling pallet in a masonry task. It provides an objective evaluation of impact on the task showing 40% reduction in joint loads and 10% increase in productivity.

Introduction

Due to the physically strenuous and demanding tasks in construction fieldwork, work-related musculoskeletal disorders (MSDs) have been widely reported as a considerable challenge in the construction industry [[1], [2], [3], [4], [5], [6]]. The estimated risk of MSDs for construction workers was 50% higher than all other workers [6]. In addition, in 2017, the Bureau of Labor Statistics reported that the incidence rate of MSDs was 31.2 for every 10,000 full-time workers in the construction industry across the U.S. [7].

Despite being one of the oldest industries, the level of automation and robotics in the construction industry has only begun to advance in the past few decades, lagging behind other industries such as automotive and manufacturing [[8], [9], [10]]. Furthermore, continuous changes in construction sites demand manual intervention by laborers, which is a concern due to the high number of injuries and MSDs associated with the trades [11].

Howe [12] states that the automation of manual labor in building processes can be categorized as a robotic system that consists of: (a) independent work cells, which can be integrated into traditional building method techniques; (b) stationary on-site factories; and (c) dynamic on-site factories which move as the building is completed. Modern research developments promote fully automated or near-fully automated building processes—such as the Shimizu Manufacturing System by Advanced Robotics Technology (SMART) system [13], autonomous mobile robots [14], contour crafting [15], and other 3D printing techniques [16]. However, full automation is not yet feasible based on the available technologies and the current challenges of the industry [12,14,16]. These limitations have led to the emergence of semi-automated work systems in construction where human operators work in conjunction with automation and robotics [8,[17], [18], [19], [20]].

In particular, semi-automated force-assist systems have the potential to mitigate MSD risk exposure from physically demanding tasks by directly targeting task demands and biomechanical exposures for operators. For example, robotic arms that externally support weight and maneuver tools on construction sites offload physical demands from the operator onto the mounting system, thereby reducing the associated risk [21]. Furthermore, Warszawski and Navon [22] suggested that robotics could generate economic value by performing the most physically demanding tasks (e.g., heavy lifting, reaching to the ceiling and/or floors) to reduce costs caused by injuries and time loss, leaving the more complex finishing tasks to humans. As such, several streams of semi-automation research in construction have been targeted towards improving the safety of operators concerning MSDs [8,[22], [23], [24]].

The primary goal and driving factor for the development of automation in construction has been the production and economic value [8,22,23]. A market research questionnaire about the importance of various attributes when implementing automation into construction revealed that the number one priority was increased productivity, followed by improved quality control and reliability, with safety being listed as the third most important attribute [[25], [26], [27]]. Nevertheless, well-designed and implemented ergonomic interventions often have economic value as well, either through the reduction of injuries, worker compensation, lost days or other associated indirect and direct costs of MSDs, or through increased productivity [[28], [29], [30], [31]]. According to the National Safety Council (NSC) [32], estimated industrial work injury costs were $161.5 billion in 2017, including wage and productivity losses, medical expenses, and administrative expenses. Therefore, integrating automation into the work system could reduce exposures to injury risks, and consequently save billions of dollars annually by reducing industrial work injuries.

For effective MSD risk control in construction, most current practices rely on “briefly and generally” written safety guidelines or expert assessments [33]. However, these methods are limited for providing quantitative assessments and can result in subjective evaluation consequences. Therefore, quantitative and objective assessment of MSD risk exposure as well as work productivity of operators is critical to evaluate the impact of implementing automation work systems into traditional work processes.

In this paper, semi-automated work systems are defined as construction equipment which automates a component of a task and is designed to be used in conjunction with manual laborers to complete the task, with a focus on force-assist systems. This study targets the implementation and integration of semi-automated systems with traditional working processes. Previous methodologies have been proposed to assess the value of integrating new work systems into traditional systems in a construction context. Those methodologies focused on either project-level evaluation of full automation [23,34] or were limited to worker productivity [35,36]. This work proposes a systematic and objective methodology to assess the value of semi-automated work systems in a construction context, where value is assessed in terms of both reduction in risk exposure to MSDs and improvements in productivity. A broader and more complete assessment of value prior to implementing such a system would also include analysis of: (1) net present value, (2) safety impact, (3) morale impact, (4) quality effects, (5) competitiveness impacts, and (6) process changers required. Validation of this methodology is demonstrated through an experimental evaluation of a force-assist self-leveling pallet in a masonry task.

Section snippets

Methodology

The flowchart of the methodology to evaluate semi-automated work systems is outlined in Fig. 1. The general steps of the methodology involve the following components: the identification of at-risk tasks within the job, a quantitative assessment of biomechanical demands and productivity, a proposal of semi-automated work systems and their integration into current work processes, experimental evaluation of the proposed equipment, and a final implementation decision and plan. Expanding the system

Validation of the methodology

The authors evaluated a semi-automated work system in a masonry task to experimentally validate the methodology. The study protocols were approved by the Research Ethics Committee at the University of Waterloo. In this case, a self-leveling pallet was used both for force assistance and improved positioning while building a standard wall out of 20 cm concrete masonry units (CMUs).

Discussion and conclusions

This study presented a methodology to evaluate the impact of semi-automated work systems on health and productivity in the construction industry. The proposed methodology integrates wearable motion capture suits and analytical tools in the assessment of masonry tasks. The methodology was implemented to assess the use of a self-leveling pallet in masonry tasks. Thirteen participants completed a standard wall using 45 CMUs under two conditions: traditional and semi-automated workstations.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgement

The authors would like to thank and acknowledge the Canada Masonry Design Centre (CMDC) and Ontario Masonry Training Centre (OMTC) in Mississauga, Ontario, Canada, for their considerable help in the data collection effort. The work presented in this paper was supported financially by CMDC, the Canadian Concrete Masonry Producers Association (CCMPA) and the Natural Sciences and Engineering Research Council of Canada (NSERC) (CRDPJ 494786-16). The authors also thank to Omar Elefai for providing

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