Testing automation adoption influencers in construction using light deep learning

Technology adoption is pivotal for the productivity growth in construction industry. This research paper attempts to fill this gap by addressing the following research objectives. First, the predictor factors stimulating project managers' adoption of construction automation innovations are rigorously analyzed using a mixed approach combining a systematic literature review, a knowledgeable panel, and a survey questionnaire. Secondly, the study implements a light deep learning model to track the progress of reinforcing bar placements by verifying completed rebar ties. By linking the progress of the bar to a single binary condition, the number of classes needed to train the neural network drops to only two resulting in a light CNN with a recall rate of 89.2% and precision rate of 95.7%. This model can be implemented on a low power GPU, making it more cost efficient and simpler to adopt on site. A similar approach can be used on other critical activities in construction. This approach can aid inspections, quality control, in combination with drones or robotic systems. The proposed system integrates the most important factors of a successful adoption by providing a proof of concept with potential use cases in construction sites. •Automation solutions cannot be adopted by managers due to many influencing factors.•Top predictor factors of construction automation innovations are analyzed.•Using the high influencers of adoption– a rebar machine learning system– is developed.•Experimental analysis is performed and results were compared with other methods.•A proof of concept with potential use cases in construction sites is provided.