A platform for research: civil engineering, architecture and urbanism
Multisensor data fusion for on-site materials tracking in construction
AbstractAutomated materials tracking and locating on construction sites can significantly impact construction productivity. The ability to automatically detect the locations and multi-handling of thousands of items can improve the performance of material distribution, and ultimately improve project performance. Deploying a cost-effective, scalable, and easy to implement materials location sensing system in real world construction sites has very recently become technically and economically feasible. However, much opportunity still exists to improve accuracy, precision and robustness. In this study a data fusion model is used on an integrated solution for automated identification, location estimation, and dislocation detection of construction materials. The developed model is a modified functional data fusion model. Particular attention is focused on dislocation detection because it is closely coupled with location estimation, and because it can be used to detect multi-handling of materials. Multi-handling is a key indicator of inefficiency. This study has successfully addressed the challenges of fusing data from different simple sources of information within a very noisy and dynamic environment. The results indicate a potential for the proposed model to improve location estimation and movement detection as well as to automate multi-handling counts.
Multisensor data fusion for on-site materials tracking in construction
AbstractAutomated materials tracking and locating on construction sites can significantly impact construction productivity. The ability to automatically detect the locations and multi-handling of thousands of items can improve the performance of material distribution, and ultimately improve project performance. Deploying a cost-effective, scalable, and easy to implement materials location sensing system in real world construction sites has very recently become technically and economically feasible. However, much opportunity still exists to improve accuracy, precision and robustness. In this study a data fusion model is used on an integrated solution for automated identification, location estimation, and dislocation detection of construction materials. The developed model is a modified functional data fusion model. Particular attention is focused on dislocation detection because it is closely coupled with location estimation, and because it can be used to detect multi-handling of materials. Multi-handling is a key indicator of inefficiency. This study has successfully addressed the challenges of fusing data from different simple sources of information within a very noisy and dynamic environment. The results indicate a potential for the proposed model to improve location estimation and movement detection as well as to automate multi-handling counts.
Multisensor data fusion for on-site materials tracking in construction
Razavi, Saiedeh N. (author) / Haas, Carl T. (author)
Automation in Construction ; 19 ; 1037-1046
2010-07-26
10 pages
Article (Journal)
Electronic Resource
English
Multisensor data fusion for on-site materials tracking in construction
British Library Online Contents | 2010
|Data Association Algorithm for Multisensor Multitarget Tracking
British Library Online Contents | 1996
|Multisensor Data Fusion for Bridge Condition Assessment
British Library Online Contents | 2017
|Multisensor Data Fusion for Bridge Condition Assessment
Online Contents | 2017
|Multisensor Data Fusion for Bridge Condition Assessment
ASCE | 2017
|