ABSTRACT
Current standard approaches for monitoring site progress for lean construction favor weekly or bi-weekly meetings. All trade and construction site management representatives meet to synchronize the forthcoming schedule. Up-to-date information is often not available, causing poor coordination and resulting in delays, rework and waste of monetary resources. Furthermore, infrequent updates on work performance impact scheduling of critical activities. This paper investigates the possibility to automate some tasks in progress monitoring by applying an AI-system with abductive reasoning on real-time localization sensing data (RTLS) and domain expert knowledge. The work proposes a framework, consisting of three modules (data preparation, processing, and update) that utilize abductive reasoning. An experiment was conducted on previously collected data Teizer et al. (2013) to compare progress inferred from the framework with actual progress recorded. The preliminary results indicate the framework is able to reason about progress with high degree of similarity to the paper of Teizer et al. (2013), however, solely based on RTLS data and without any manual input. The future of the framework is promising since it supports the analysis of time series, allowing it to be applied nearly simultaneously to data collection, and thereby significantly increasing the update rate for information.
