Orcesi A.D.,IFSTTAR |
Frangopol D.M.,Lehigh University
Engineering Structures | Year: 2011
Management of bridges under uncertainty is an important issue for stakeholders. The use of probabilistic approaches enables one to consider uncertainties in the structural deterioration, assessment, and maintenance processes. Combined with optimization techniques, it is possible to determine management strategies that simultaneously minimize failure, assessment, maintenance, and rehabilitation costs. Nevertheless, there is a strong need in developing practical and efficient frameworks that enable stakeholders to optimize future allocation of budgets for facilities under uncertain structural parameters. In particular, providing an approach that is in agreement with stakeholders constraints still remains a challenge. Moreover, the use of structural health monitoring (SHM) in future management frameworks, to update structural performance, still needs further development. The objective of this paper is threefold: (a) provide management strategies in agreement with fixed budgets, (b) provide management strategies that consider the time delay between the assessment and the intervention schedule, and (c) include information provided by SHM in the decision process and analyze the impact of monitoring strategies on the structural analysis accuracy. An event tree based approach is proposed to consider various uncertainties in the decision process. Optimal solutions are associated with multiple criteria such as minimum expected failure cost, minimum expected inspection/SHM/maintenance costs, maximum agreement of expected inspection/SHM/maintenance costs to available budgets, and maximum accuracy of monitoring results. The approach is illustrated on an existing highway bridge. © 2011 Elsevier Ltd.
Omikrine Metalssi O.,IFSTTAR |
Ait-Mokhtar A.,University of La Rochelle |
Turcry P.,University of La Rochelle |
Construction and Building Materials | Year: 2012
This paper presents the consequences of the carbonation phenomenon in the case of a mortar with cellulose ether as admixture on its mechanical properties, microstructure and length variations. Carbonation was found to improve mechanical strengths and decrease the global porosity with modifying the pore size distribution. The latter is beneficial regarding durability. However, carbonation also led to an increase of shrinkage, and thus to a probable increase of cracking. Carbonation and shrinkage kinetics could be slowed down by sheltering the material from carbonation during hardening. © 2012 Elsevier Ltd. All rights reserved.
Orcesi A.D.,IFSTTAR |
Frangopol D.M.,Lehigh University
Journal of Structural Engineering | Year: 2011
A model using lifetime functions is used to evaluate the probability of survival of bridge components. The possible outcomes associated with nondestructive inspections (NDIs) are incorporated in an event-tree model. Each time a bridge component is inspected, different decisions can be made. The use of a lifetime function for each component of the structural system enables one to express the probability that the component survives. In theory (i.e., perfect inspection), each NDI should be associated with two possible outcomes: survival or failure. In the first case, no damage is detected and the probability density function of time to failure is updated knowing that the component has survived until the inspection. In the second case, damage is detected and maintenance action is planned. In practice, NDIs are subjected to uncertainties (i.e., imperfect inspections) and detecting or not detecting damage depends on the inspection quality (i.e., probability of detection). For poor-quality inspections, there is a significant risk to overestimate the probability of safe performance. The aim of this paper is to provide a practical methodology for determining optimal NDI strategies for different components of steel bridges. The different types of inspections considered in this paper are visual, magnetic particle, and ultrasonic. An economic analysis is performed and NDI strategies are optimized by simultaneously minimizing both the expected inspection/maintenance cost (i.e., the sum of inspection and maintenance costs) and the expected failure cost. The proposed approach is applied to an existing steel bridge. © 2011 American Society of Civil Engineers.
Belaroussi R.,IFSTTAR |
Milgram M.,University Pierre and Marie Curie
Expert Systems with Applications | Year: 2012
Face localization is the first stage in many vision based applications and in human-computer interaction. The problem is to define the face location of a person in a color image. The four boosted classifiers embbeded in OpenCV, based on Haar-like features, are compared in terms of speed and efficiency. Skin color distribution is estimated using a non parametric approach. To avoid drifting in color estimate, this model is not updated during the sequence but renewed whenever the face is detected again, that gives the ability to our system to cope with different lighting conditions in a more robust way. Skin color model is then used to localize the face represented by an ellipse: connected component segmentation and a statistical approach, namely the coupled Camshift of Bradsky, are compared in terms of efficiency and speed. The pursuit algorithms are tested on various video sequences, corresponding to various scenarios in terms of illumination, face pose, face size and background complexity (distractor effects). © 2011 Elsevier Ltd. All rights reserved.
Cord A.,IFSTTAR |
Computer-Aided Civil and Infrastructure Engineering | Year: 2012
The state of roads is continuously degrading due to meteorological conditions, ground movements, and traffic, leading to the formation of defects, such as grabbing, holes, and cracks. In this article, a method to automatically distinguish images of road surfaces with defects from road surfaces without defects is presented. This method, based on supervised learning, is generic and may be applied to all type of defects present in those images. They typically present strong textural information with patterns that show fluctuations at small scales and some uniformity at larger scales. The textural information is described by applying a large set of linear and nonlinear filters. To select the most pertinent ones for the current application, a supervised learning based on AdaBoost is performed. The whole process is tested both on a textural recognition task based on the VisTex image database and on road images collected by a dedicated road imaging system. A comparison with a recent cracks detection algorithm from Oliveira and Correia demonstrates the proposed method's efficiency. © 2011 Computer-Aided Civil and Infrastructure Engineering.