The conventional approach that is used to monitor construction projects is to collect progress data from the construction site through visual investigation. This results in deficient and sometimes erroneous data, and leads to inefficiencies in project control, delays and cost overruns. To address these problems in building construction projects, an approach was developed to automatically monitor activity progress by tracking major construction equipment and bulk materials using sensor-based technologies that are cost-effective and easy to deploy. In this approach data obtained from sensors (e.g., load sensor) and/or other sensor-based technologies (i.e., Radio Frequency Identification (RFID)), which were deployed on major construction resources, were fused using rule-based algorithms to determine the activity progress. This progress data was compared with human-generated site related data (e.g., schedules, site reports) to determine the activity performance. This paper presents the developed data fusion approach and rule-based data fusion algorithms that incorporate the domain-specific heuristic information for determining the activity's overall progress. To validate the proposed approach, a proof-of-concept prototype was deployed and tested at a construction site for monitoring the progress of masonry work. The results show that the developed approach achieved 95% average accuracy in identifying the progress of the masonry work that was monitored during the field tests. The main contributions of this study are the rule-based data fusion approach and the rules that were developed for processing data from equipment and bulk materials. These rules can be used to determine the progress of other activities that use similar resources.