Pipe installation work in offshore plant construction is a major construction process that accounts for more than 40% of the total construction by work type (Kim and Shin, 2014). Previous studies have continuously minimized the loss and cost of work by efficiently managing the production and installation processes of numerous pipes made of various materials according to the characteristics of work on offshore plants (Ham et al., 2016). To examine the contents of major studies, Ham et al. (2016) attempted to minimize the inefficiency of delayed pipe delivery by designing a regression analysis predictive model for pipe production and installation. This study demonstrated the possibility of more accurate pipe lead time prediction if high-quality data are acquired and appropriate variables are selected as a result of the nature of regression analysis. Wei and Nienhyuis (2012) applied an assembly sequence algorithm for fittings to the pipe installation plan and examined the possibility of interference and processing. They implemented a system that derives an automatic assembly sequence by reflecting constraints in the actual assembly; however, there were partial limitations in improving the entire piping process. DSME (2015) suggested an automatic method for cross-verification of 2D drawings and 3D models in 2D and 3D pipe systems using the ISO 15926 xml file format. Furthermore, SHI (2015) proposed a piping work management system composed of a unit for pipe information extraction from pipe design information, a work difficulty setting unit, an individual work ratio setting unit, a performance management unit, and a performance database. It manages only the engineering information, excluding the 3D computer-aided design (CAD) model. Park and Woo (2018) proposed a data structure for an integrated piping process management system by defining items that need to be improved for each process of offshore plant piping materials. Oh et al. (2018) presented a method for predicting the required amount of pipe materials for offshore structures based on big data analysis for more accurate material