Developing Automation Adoption Readiness Index for Quality Management Focused on Highway Construction

Abstract

The construction industry is stigmatized by low-quality performance at the end of product delivery. The poor performance quality sometimes observed in highway construction leads to reduced service life and requires urgent attention. Although innovations such as increased reliance on automation have helped improved quality in other industries, the construction industry relies primarily on traditional and conventional procedures. To improve the adoption of automation in highway construction, stakeholders should be provided with information that can enhance their decision making. This study identifies the critical readiness indicators of construction automation adoption readiness (AAR) and proposes a process for assessing quality management (QM) AAR using a fuzzy index model. A survey was conducted among experienced highway construction professionals with knowledge of QM and automated systems within the US. Results indicate that the external indicators have the most impact on determining AAR, whereas technological indicators have the least impact. Findings from the study facilitate prioritization of adoption indicators and a robust readiness assessment during technology integration. Using the results of the present study and presented process, organizations involved in integrating automation with construction QM operations can improve the odds of successful implementation of automation on highway construction projects.