Real world implementation of belief function theory to detect dislocation of materials in construction

S.N. Razavi 1 Carl Haas 1 Philippe Vanheeghe 2, 3 Emmanuel Duflos 2, 3, *
* Corresponding author
2 SEQUEL - Sequential Learning
LIFL - Laboratoire d'Informatique Fondamentale de Lille, LAGIS - Laboratoire d'Automatique, Génie Informatique et Signal, Inria Lille - Nord Europe
3 LAGIS-SI
LAGIS - Laboratoire d'Automatique, Génie Informatique et Signal
Abstract : Dislocations of construction materials on large sites represent critical state changes. The ability to detect dislocations automatically for tens of thousands of items can ultimately improve project performance significantly. A belief function based data fusion algorithm was developed to estimate materials locations and detect dislocations. Dislocation is defined as the change between discrete sequential locations of critical materials such as special valves or fabricated items, on a large construction project. Detecting these dislocations in a noisy information environment where low cost radio frequency identification tags are attached to each piece of material, and the material is moved sometimes only a few meters, is the main focus of this study. This work is a continuation of previous research, in which we tackled the location estimation problem by fusing the data from a simulation model. The results indicate the potential of the belief function based algorithm to detect object dislocation.
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https://hal.archives-ouvertes.fr/hal-00713047
Contributor : Emmanuel Duflos <>
Submitted on : Thursday, June 28, 2012 - 10:39:03 PM
Last modification on : Thursday, February 21, 2019 - 10:52:49 AM

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S.N. Razavi, Carl Haas, Philippe Vanheeghe, Emmanuel Duflos. Real world implementation of belief function theory to detect dislocation of materials in construction. FUSION 2009, Jul 2009, Seattle, WA, United States. pp.748-755. ⟨hal-00713047⟩

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