Sensors Used in Structural Health Monitoring

The damage identification process provides relevant information about the current state of a structure under inspection, and it can be approached from two different points of view. The first approach uses data-driven algorithms, which are usually associated with the collection of data using sensors. Data are subsequently processed and analyzed. The second approach uses models to analyze information about the structure. In the latter case, the overall performance of the approach is associated with the accuracy of the model and the information that is used to define it. Although both approaches are widely used, data-driven algorithms are preferred in most cases because they afford the ability to analyze data acquired from sensors and to provide a real-time solution for decision making; however, these approaches involve high-performance processors due to the high computational cost. As a contribution to the researchers working with data-driven algorithms and applications, this work presents a brief review of data-driven algorithms for damage identification in structural health-monitoring applications. This review covers damage detection, localization, classification, extension, and prognosis, as well as the development of smart structures. The literature is systematically reviewed according to the natural steps of a structural health-monitoring system. This review also includes information on the types of sensors used as well as on the development of data-driven algorithms for damage identification.

Section: Location and Networkingmentioning

confidence: 99%

Damage Identification in Structural Health Monitoring: A Brief Review from its Implementation to the Use of Data-Driven Applications

Burgos

Vargas

Pedraza

et al. 2020

“…The effects of such factors are today examined by means of various types of sensors [87,88]. At present, monitoring is often carried out for short periods because of the costs and of the logistic difficulties to maintain long-term campaigns; this inevitably leads to poor technical and scientific results.…”

Section: Application Of Capacitive Mems Accelerometers To Seismologymentioning

confidence: 99%

A Review of the Capacitive MEMS for Seismology

D’Alessandro

Scudero

Vitale

2019

109

MEMS (Micro Electro-Mechanical Systems) sensors enable a vast range of applications: among others, the use of MEMS accelerometers for seismology related applications has been emerging considerably in the last decade. In this paper, we provide a comprehensive review of the capacitive MEMS accelerometers: from the physical functioning principles, to the details of the technical precautions, and to the manufacturing procedures. We introduce the applications within seismology and earth sciences related disciplines, namely: earthquake observation and seismological studies, seismic surveying and imaging, structural health monitoring of buildings. Moreover, we describe how the use of the miniaturized technologies is revolutionizing these fields and we present some cutting edge applications that, in the very last years, are taking advantage from the use of MEMS sensors, such as rotational seismology and gravity measurements. In a ten-year outlook, the capability of MEMS sensors will certainly improve through the optimization of existing technologies, the development of new materials, and the implementation of innovative production processes. In particular, the next generation of MEMS seismometers could be capable of reaching a noise floor under the lower seismic noise (few tenths of ng/ H z ) and expanding the bandwidth towards lower frequencies (∼0.01 Hz).

“…SHM consists of integrating the sensor devices with the structural components to extract continuous information related to the mechanical behaviour of a structure in various operational conditions [ 5 , 6 ]. Different types of sensors such as microelectromechanical systems (MEMS), accelerometers, optical fibres, vibration sensors, pressure-based sensors [ 7 ], and global positioning system (GPS) sensors [ 8 ] can be used during the SHM process for measuring a wide range of critical structural parameters, such as strain, temperature, displacement, pressure, and vibration [ 9 ]. Among all the structural parameters, strain can be considered as an important parameter, which can be utilised for SHM.…”

Section: Introductionmentioning

confidence: 99%

Distributed Fibre Optic Sensor-Based Continuous Strain Measurement along Semicircular Paths Using Strain Transformation Approach

Nagulapally¹,

Shamsuddoha²,

Rajan

et al. 2021

Distributed fibre optic sensors (DFOS) are popular for structural health monitoring applications in large engineering infrastructure because of their ability to provide spatial strain measurements continuously along their lengths. Curved paths, particularly semicircular paths, are quite common for optical fibre placement in large structures in addition to straight paths. Optical fibre sensors embedded in a curved path configuration typically measure a component of strain, which often cannot be validated using traditional approaches. Thus, for most applications, strain measured along curved paths is ignored as there is no proper validation tool to ensure the accuracy of the measured strains. To overcome this, an analytical strain transformation equation has been developed and is presented here. This equation transforms the horizontal and vertical strain components obtained along a curved semicircular path into a strain component, which acts tangentially as it travels along the curved fibre path. This approach is validated numerically and experimentally for a DFOS installed on a steel specimen with straight and curved paths. Under tensile and flexural loading scenarios, the horizontal and vertical strain components were obtained numerically using finite element analysis and experimentally using strain rosettes and then, substituted into the proposed strain transformation equation for deriving the transformed strain values. Subsequently, the derived strain values obtained from the proposed transformation equation were validated by comparing them with the experimentally measured DFOS strains in the curved region. Additionally, this study has also shown that a localised damage to the DFOS coating will not impact the functionality of the sensor at the remaining locations along its length. In summary, this paper presents a valid strain transformation equation, which can be used for transforming the numerical simulation results into the DFOS measurements along a semicircular path. This would allow for a larger scope of spatial strains measurements, which would otherwise be ignored in practice.