Mining Daily Work Report Data for Detecting Patterns of Construction Sequences

Abstract

Sequencing construction activities in highway projects is a complex planning process which requires not only considerable knowledge and practical experience of the planner/scheduler about various relevant aspects, such as the activities themselves, construction and procurement processes, and construction methods, but also input from other key members of the project regarding specific constraints and requirements. Moreover, sequencing is an iterative process; the sequence developed in the planning phase is likely to change in the construction phase. Therefore, learning from as-built schedules of past completed projects is needed to improve the planning and scheduling processes for future projects. In current practices, most state Departments of Transportation (DOTs) still mainly rely on schedulers' experience for schedule development. A data-driven systematic approach is still lacking, although the highway agencies have been spending a significant amount of money, time, and effort to collect various digital data during the construction process. This study aims to leverage historical digital daily work report data available in the DOTs' database to detect patterns of construction sequences in highway projects. Daily work report data collected from a state DOT were used to conduct a case study that developed a Sequential Pattern Mining algorithm to extract frequent sequential relationships among the activities for one major type of highway projects. Peer-review under responsibility of the scientific committee of the Creative Construction Conference 2019.

A Deep Learning Algorithms to Generate Activity Sequences Using Historical As-built Schedule Data

“…The method of SPM and the application on construction schedules is fully explained in Shrestha et al [8]. Given a sequence of activities, the SPM discovers all sequential patterns with various lengths.…”

“…Typical activity dependency rules have been identified based on physical and material constraints by analysing construction drawing and using experts' knowledge [7,4]. A recent study developed a data mining model to analyse historical schedules to obtain common sequential patterns among activities [8]. The mining model explored the dataset of historical as-built schedules and identified the most common activity sequential pattern among schedules.…”

“…The mining model explored the dataset of historical as-built schedules and identified the most common activity sequential pattern among schedules. Shrestha et al [8] identified that historical as-built schedules are a valuable resource for identifying the realistic sequence of activities. Although identifying sequential patterns out of historical schedules can provide useful insights for schedulers, there is still a need for a more comprehensive model that can discover sequence insights of the schedules.…”

“…Some researchers used data-driven methods and analyzed real projects' schedules to obtain common sequential patterns. Shrestha et al [8] used as-built schedules extracted from Daily Work Report (DWR) data of a State DOT and applied the Sequential Pattern Mining (SPM) technique to identify common activity sequential patterns. For example, they identified that in 78% of the projects in the dataset, the activity of "cold milling asphalt pavement" was implemented after the "maintenance of traffic".…”