p>This paper presents the results of SensIT , an ongoing research initiative at Chalmers University of Technology aimed at developing a digital twin concept to improve the asset management strategies of reinforced concrete infrastructure. The developed concept relies on data collected from distributed optical fiber sensors (DOFS), which are then analysed to extract relevant features, such as deflections and crack widths, that can be used as indicators of the structural performance. Thereafter, intuitive contour plots are generated to deliver critical information about the element’s structural condition in a clear and straightforward manner. Last, both raw and analysed data are integrated into a collaborative web application where information can be readily accessed, and results can be visualized directly onto a 3D model of the element. The concept has been tested on a large-scale reinforced concrete beam subjected to flexural loading in laboratory conditions.</p
This paper explores the performance of distributed optical fiber sensors based on Rayleigh backscattering for the monitoring of strains in reinforced concrete elements subjected to different types of long-term external loading. In particular, the reliability and accuracy of robust fiber optic cables with an inner steel tube and an external protective polymeric cladding were investigated through a series of laboratory experiments involving large-scale reinforced concrete beams subjected to either sustained deflection or cyclic loading for 96 days. The unmatched spatial resolution of the strain measurements provided by the sensors allows for a level of detail that leads to new insights in the understanding of the structural behavior of reinforced concrete specimens. Moreover , the accuracy and stability of the sensors enabled the monitoring of subtle strain variations, both in the short-term due to changes of the external load and in the long-term due to time-dependent effects such as creep. Moreover, a comparison with Digital Image Correlation measurements revealed that the strain measurements and the calculation of deflection and crack widths derived thereof remain accurate over time. Therefore, the study concluded that this type of fiber optic has great potential to be used in real long-term monitoring applications in reinforced concrete structures .
This paper investigates the use of distributed optical fiber sensors (DOFS) based on Optical Frequency Domain Reflectometry of Rayleigh backscattering for Structural Health Monitoring purposes in civil engineering structures. More specifically, the results of a series of laboratory experiments aimed at assessing the suitability and accuracy of DOFS for crack monitoring in reinforced concrete members subjected to external loading are reported. The experiments consisted on three-point bending tests of concrete beams, where a polyamide-coated optical fiber sensor was bonded directly onto the surface of an unaltered reinforcement bar and protected by a layer of silicone. The strain measurements obtained by the DOFS system exhibited an accuracy equivalent to that provided by traditional electrical foil gauges. Moreover, the analysis of the high spatial resolution strain profiles provided by the DOFS enabled the effective detection of crack formation. Furthermore, the comparison of the reinforcement strain profiles with measurements from a digital image correlation system revealed that determining the location of cracks and tracking the evolution of the crack width over time were both feasible, with most errors being below ±3 cm and ±20 mm, for the crack location and crack width, respectively.
This paper reports the early findings of an ongoing project aimed at developing new methods to upgrade the current maintenance strategies of the civil and transport infrastructure. As part of these new methods, the use of Machine Learning (ML) algorithms is being investigated to constitute the core of a new generation of more accurate and robust structural health monitoring (SHM) systems for concrete structures. Unlike most of the existing SHM systems, relying on the analysis of the natural frequencies of the structure based on data obtained from accelerometers, the present study uses a distributed optic fiber system to monitor the strain distribution along steel reinforcing bars. The preliminary results of the study indicate that a semi-supervised Deep Autoencoder algorithm (DAE) can successfully quantify the damage attributable to transverse cracks in a reinforced concrete beam subjected to three-point loading. Future applications will feature the determination of crack locations, early detection of reinforcement corrosion as well as other types of damage such as splitting cracks or surface spalling.
Today, the accelerated degradation of many concrete structures poses a major challenge for the proper maintenance of the transport infrastructure. Therefore, inspection and maintenance operations constitute an important part of the recurrent costs of infrastructure. Furthermore, the increasing migration of population to urban areas has made sustainable development an imperative need. This need has become a driving force for innovation and new challenges such as the concept of smart cities and infrastructure. The successful utilization of newly available technologies will enable a whole new range of possibilities such as sensor driven cloud-based strategies for infrastructure management, which will promote an upgrade of the current infrastructure network to a new generation of safer, more efficient and more sustainable smart infrastructure: the infrastructure 2.0. The aim of this paper is to review the state-of-the-art of the different key technologies comprising a smart monitoring system, focusing on the aspects that are required to ensure a successful implementation of such system. The main result of the study is a scientific roadmap that can serve as a guide for traffic administrations and academic institutions in their task to develop and create a new infrastructure management strategy based on emerging technologies and innovative processes.