Review on pressure sensors for structural health monitoring

Sikarwar, Samiksha; Satyendra,; Singh, Shakti Pratap; Yadav, B. C.

doi:10.1007/s13320-017-0419-z

Cited by 42 publications

(23 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The force induced deformation of the sensing surface, generates an electric signal, which is sampled by an analogue to digital converter to a quantitative measure. There have been many developments of pressure sensors in the past, which vary in terms of performance, technology, design and cost [61]. Its main application areas can be found in industrial monitoring, such as flow measurement or leakage detection [61].…”

Section: Mechanicalmentioning

confidence: 99%

“…There have been many developments of pressure sensors in the past, which vary in terms of performance, technology, design and cost [61]. Its main application areas can be found in industrial monitoring, such as flow measurement or leakage detection [61]. In this subsection we will discuss two main categories of this sensing technology: resistive pressure sensing, and roomscaled pressure sensing with piezoelectric or fiber optical sensors.…”

Section: Mechanicalmentioning

confidence: 99%

See 1 more Smart Citation

Sensing Technology for Human Activity Recognition: A Comprehensive Survey

et al. 2020

View full text Add to dashboard Cite

Sensors are devices that quantify the physical aspects of the world around us. This ability is important to gain knowledge about human activities. Human Activity recognition plays an import role in people's everyday life. In order to solve many human-centered problems, such as health care, and individual assistance, the need to infer various simple to complex human activities is prominent. Therefore, having a well defined categorization of sensing technology is essential for the systematic design of human activity recognition systems. By extending the sensor categorization proposed by White, we survey the most prominent research works that utilize different sensing technologies for human activity recognition tasks. To the best of our knowledge, there is no thorough sensor-driven survey that considers all sensor categories in the domain of human activity recognition with respect to the sampled physical properties, including a detailed comparison across sensor categories. Thus, our contribution is to close this gap by providing an insight into the state-of-the-art developments. We identify the limitations with respect to the hardware and software characteristics of each sensor category and draw comparisons based on benchmark features retrieved from the research works introduced in this survey. Finally, we conclude with general remarks and provide future research directions for human activity recognition within the presented sensor categorization. INDEX TERMS Sensor categorization, human activity recognition, public databases for human activity recognition, physical sensors, sensor benchmark.

show abstract

Section: Mechanicalmentioning

confidence: 99%

Section: Mechanicalmentioning

confidence: 99%

Sensing Technology for Human Activity Recognition: A Comprehensive Survey

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Some of the works available in the literature have focused on problems related to a single level of SHM [4], a specific application [5], a specific technique [6], or a certain type of sensor for inspection [7]. For example, at the level of damage detection, aspects such as sensor locations and the use of wireless sensor networks [8] as well as the use of specific kinds of sensors or sensor networks, such as microelectromechanical systems (MEMS) [9], accelerometers, optical fibers [10], vibration sensors [11], and pressure-based sensors [12] have been addressed. Similarly, this level has been tackled using different techniques, as shown throughout this review.…”

Section: Introductionmentioning

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

Sensors

View full text Add to dashboard Cite

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.

show abstract

“…The cracks occurred easily in concrete due to its low tensile strength and poor crack resistance, which will do great harm to the quality and durability of concrete structures (Červenka and Papanikolaou, 2008; Cicekli et al, 2007; Contrafatto and Cuomo, 2006; Ripani et al, 2014; Voyiadjis and Abu-Lebdeh, 1994). In order to sense the cracks in time, the intelligent concrete which possess the characteristics of the crack self-sensing characteristic is becoming one of the hottest issues in our world (Downey et al, 2018; Feng et al, 2017; Le and Kim, 2017; Ou, 2005; Sikarwar et al, 2017; You et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

An analytical model for crack monitoring of the shape memory alloy intelligent concrete

Liu

Wang

Yin

et al. 2019

Journal of Intelligent Material Systems and Structures

View full text Add to dashboard Cite

A theoretical model for the crack monitoring of the shape memory alloy intelligent concrete is presented in this work. The mechanical properties of shape memory alloy materials are first given by the experimental test. The one-dimensional constitutive model of the shape memory alloys is reviewed by degenerating from a three-dimensional model, and the behaviors of the shape memory alloys under different working conditions are then discussed. By combining the electrical resistivity model and the one-dimensional shape memory alloy constitutive model, the crack monitoring model of the shape memory alloy intelligent concrete is given, and the relationships between the crack width of the concrete and the electrical resistance variation of the shape memory alloy materials for different crack monitoring processes of shape memory alloy intelligent concrete are finally presented. The numerical results of the present model are compared with the published experimental data to verify the correctness of the model.

show abstract

Review on pressure sensors for structural health monitoring

Cited by 42 publications

References 43 publications

Sensing Technology for Human Activity Recognition: A Comprehensive Survey

Sensing Technology for Human Activity Recognition: A Comprehensive Survey

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

An analytical model for crack monitoring of the shape memory alloy intelligent concrete

Contact Info

Product

Resources

About