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Joan R. Casas

Researcher at Polytechnic University of Catalonia

Publications -  163
Citations -  3158

Joan R. Casas is an academic researcher from Polytechnic University of Catalonia. The author has contributed to research in topics: Structural health monitoring & Bridge (interpersonal). The author has an hindex of 24, co-authored 147 publications receiving 2259 citations. Previous affiliations of Joan R. Casas include ETSI.

Papers
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A review of distributed optical fiber sensors for civil engineering applications

TL;DR: The latest developments related with the improvement of these products are presented by presenting a wide range of laboratory experiments as well as an extended review of their diverse applications in civil engineering structures.
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Fiber Optic Sensors for Bridge Monitoring

TL;DR: An overview of the intensity modulated and spectrometric fiber optic sensors and techniques to assess the condition of existing structures in order to enhance the durability of the new bridges, increasing lifetime and reliability and decreasing maintenance activities is provided.
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Application of optical fiber distributed sensing to health monitoring of concrete structures

TL;DR: The use of optical backscatter reflectometer (OBR) sensors is a promising measurement technology for Structural Health Monitoring (SHM) as it offers the possibility of continuous monitoring of strain and temperature along the fiber.
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A review of recent distributed optical fiber sensors applications for civil engineering structural health monitoring

TL;DR: In this paper, the authors present a comprehensive collection of recently published research articles on Structural Health Monitoring (SHM) campaigns performed by means of Distributed Optical Fiber Sensors (DOFS).
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Bridge damage detection based on vibration data: past and new developments

TL;DR: In this article, a review of the current state-of-the-art developments in vibration-based damage detection in small to medium span bridges with particular focus on the utilization of advanced computational methods that avoid traditional damage detection pitfalls.