Assessment and development of radio frequency identification applications for effective management of construction activities
The construction industry suffers from quality problems, cost over-runs, and project delays, which can be traced back to the lack of accurate and real-time information among the players. Recent research has indicated that even though construction materials and components may constitute more than 50% of total project costs, existing methods for managing them still depend on human skills. These traditional data collection are time and labour-intensive, error-prone, and unreliable due to reluctance of workforces to monitor and record the presence of large numbers of materials. Advances in Automated Data Collection (ADC) technologies make them technically and economically feasible and viable. However, the construction industry is a late adopter of ADC technologies and the deployment of a cost-effective, scalable, and easy-to-implement materials management system has not yet been addressed. In order to investigate the late adoption of ADC technologies and to present a new solution to automate the task of materials management, the study provides a useful insight into the ADC technologies adoption barriers within the construction industry by identifying 19 key factors. An online questionnaire survey was conducted among the professionals in the industry from 16 countries. In view of these, a new approach for integrating the latest innovations in ADC technologies for real-time data collection in construction was proposed. A combination of Radio Frequency Identification (RFID), Global Positioning System (GPS), and Global System for Mobile Communications (GSM) technologies was proposed in this study. This proposed integrated system can facilitate extremely low-cost and infrastructure-free solutions to uniquely identify materials, components, and equipment and to locate and track them in three phases namely; production sites (off-site), en-route (shipping), and construction job sites (on-site) almost instantaneously. The proposed system is fully automatic, thus reducing the labour costs and eliminating human error associated with data collection process during construction materials management.