Over the past two decades, hundreds of studies have been published that demonstrate the benefits of 4D building information modeling (BIM) for optimizing construction planning and scheduling. Nonetheless to date, 4D BIM has only been adopted by 35-40% of top engineering news-record (ENR) companies and only used on a small fraction of their projects. The longevity of using 4D BIM also rarely outlasts the pre-construction phase. While the value associated with using 4D BIMs during the construction phase is well documented, the level of effort required to create them has significantly impacted perceptions about their return of investment (ROI) and limited their adoption. To address these inefficiencies, this paper presents a new method - comprised of text mining and machine learning (ML) techniques. Our method parses the description of construction schedule activities, assembles a breakdown structure of work locations/areas, and maps each activity to its corresponding 3D BIM element. Our method also labels each activity to a project phase such that these activities can be animated with different visual cues. Experiment results on 10 real-world construction projects show that the method achieved 89% accuracy in the parsing task. The benefits of the proposed method are discussed in detail.