Exploring three pillars of construction robotics via dual-track quantitative analysis

Abstract

Construction robotics has emerged as a leading technology in the construction industry. This paper conducts an innovative dual-track quantitative comprehensive method to analyze the current literature and assess future trends. First, a bibliometric review of 955 journal articles published between 1974 and 2023 was performed, exploring keywords, journals, countries, and clusters. Furthermore, a neural topic model based on BERTopic addresses topic modeling repetition issues. The study identifies building information modeling (BIM), human–robot collaboration (HRC), and deep reinforcement learning (DRL) as “three pillars” in the field. Additionally, we systematically reviewed the relevant literature and nested symbiotic relationships. The outcome of this study is twofold: first, the findings provide quantitative and qualitative scientific guidance for future research on trends; second, the innovative dual-track quantitative analysis research methodology simultaneously stimulates critical thinking about the modeling of other similarly trending topics characterized to avoid high degree of homogeneity and corpus overlap.