A Cloud-based Mobile System to Manage Lessons-learned in Construction Projects.
Saved in:
| Title: | A Cloud-based Mobile System to Manage Lessons-learned in Construction Projects. |
|---|---|
| Authors: | Ferrada, Ximena1 xferrada@uc.cl, Núñez, Daniela2, Neyem, Andrés2, Serpell, Alfredo3, Sepúlveda, Marcos2 |
| Source: | Procedia Engineering. 2016, Vol. 164, p135-142. 8p. |
| Subjects: | Construction project management, Cloud computing, Mobile computing, Construction industry, Knowledge transfer, Shared workspaces |
| Abstract: | Knowledge and experience are vital assets within the construction industry. Nevertheless, small and medium construction companies still have problems to transfer the knowledge acquired in their projects to the rest of the organization. Lessons-learned are elements of knowledge management that could help companies to improve this process, and therefore, their global performance. This research presents a cloud-based mobile shared workspace to support knowledge management in construction. The article presents the original system and the modifications made to it based on an initial evaluation by construction professionals. The main upgrades were to include a notification system, letting users know when an action is required from them, and to improve the synchronization process for a better offline experience on site. The evaluators considered these were essentials features to be able to use the system on site. The 2.0 version of the system was validated with construction experts. The article concludes that one of the most relevant features of the system is its capacity to save information on site without an internet connection for later synchronization. Also, the proposed cloud-based shared workspace is a feasible option to improve knowledge management in small and medium Chilean construction companies, mainly because of mobility, usability and investment-related factors. [ABSTRACT FROM AUTHOR] |
| Copyright of Procedia Engineering is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
Be the first to leave a comment!
