As-built schedules prepared during and after construction are valuable tools for State Highway Agencies (SHAs) to monitor construction progress, evaluate contractor's schedule performance, and defend against any potential disputes. However, previous studies indicate that current as-built schedule development methods are manual and rely on information scattered in various field diaries and meeting minutes. SHAs have started to collect field activity data in digital databases that can be used to automatically generate as-built schedules if proper computational algorithms are developed. This study develops computational algorithms and a prototype system to automatically generate and visualize project level and activity level as-built schedules during and after construction. The algorithm is validated using a real highway project data. The study is expected to significantly aid SHAs in making better use of field data, facilitate as-built schedule development, monitor construction progress with higher granularity, and utilize as-built schedule for productivity analysis.

One vital task of a project's owner is to determine a reliable and reasonable construction time for the project. A U.S. highway agency typically uses the bar chart or critical path method for estimating project duration, which requires the determination of construction logic. The current practice of activity sequencing is challenging, time-consuming, and heavily dependent upon the agency schedulers' knowledge and experience. Several agencies have developed templates of repetitive projects based on expert inputs to save time and support schedulers in sequencing a new project. However, these templates are deterministic, dependent on expert judgments, and get outdated quickly. This study aims to enhance the current practice by proposing a data-driven approach that leverages the readily available daily work report data of past projects to develop a knowledge base of construction sequence patterns. With a novel application of sequential pattern mining, the proposed framework allows for the determination of common sequential patterns among work items and proposed domain measures such as the confidence level of applying a pattern for future projects under different project conditions. The framework also allows for the extraction of only relevant sequential patterns for future construction time estimation.