Nanoscale resolution interrogation scheme for simultaneous static and dynamic fiber Bragg grating strain sensing

A combined interrogation and signal processing technique which facilitates high-speed simultaneous static and dynamic strain demodulation of multiplexed fiber Bragg grating sensors is described. The scheme integrates passive, interferometric wavelength-demodulation and fast optical switching between wavelength division multiplexer channels with signal extraction via a software lock-in amplifier and fast Fourier transform. Static and dynamic strain measurements with noise floors of 1 nanostrain and 10 nanostrain/sqrt(Hz), between 5 mHz and 2 kHz were obtained. An inverse analysis applied to a cantilever beam set up was used to characterise and verify strain measurements using finite element modeling. By providing distributed measurements of both ultahigh-resolution static and dynamic strain, the proposed scheme will facilitate advanced structural health monitoring

Modeling of Traffic Excitation for System Identification of Bridge Structures

In long-term health monitoring of bridge structures, system identification is often performed based only on the system output (bridge vibration responses) because the system input (traffic excitation) is difficult to measure. To facilitate the identification of the bridge properties, traffic excitation is commonly modeled as spatially uncorrelated white noise. A physical model of a stationary stream of vehicles (moving loads) arriving in accordance with a Poisson process, traversing an elastic beam, shows that the traffic excitation is spatially correlated. Employing the dynamic nodal loading approach, this spatial correlation results in a frequency-dependent excitation spectrum density matrix, and shifts the response spectra obtained from those excited by spatially uncorrelated white noise. It is shown that the application of system identification techniques based on the conventional excitation model may result in misleading structural properties. Hence, this study further proposes an output-only gray-box identification technique for bridge structures, in which knowledge about the nature of the traffic excitation, such as its spatial correlation, is implanted into an autoregressive-moving-average (ARMA) model. The identifiability of the ARMA model so constructed is assured and the feasibility of the proposed identification technique is demonstrated by a numerical example

Displacement filed calculation of large-scale structures using computer vision with physical constrains

Because of the advantages of easy deployment, low cost and non-contact, computer vision-based structural displacement acquisition technique has received wide attention and research in recent years. However, the displacement field acquisition of large-scale structures is a challenging topic due to the contradiction of camera field of view and resolution. This paper presents a large-scale structural displacement field calculation framework with integrated computer vision and physical constraints using only one camera. Firstly, the full-field image of the large-scale structure is obtained by processing the multi-view image using image stitching technique; secondly, the full-field image is meshed and the node displacements are calculated using an improved template matching method; and finally, the non-node displacements are described using shape functions considering physical constraints. The developed framework was validated using a scaled bridge model and evaluated by the proposed evaluation index for displacement field calculation accuracy. This paper can provide an effective way to obtain displacement fields of large-scale structures efficiently and cost-effectively

Improving Distributed Fiber-Optic Sensor Measures by Digital Image Correlation: Two-Stage Structural Health Monitoring

This paper deals with the integrated use of distributed fiber-optic sensors and digital image correlation techniques to develop a two-stage monitoring method for damage detection, localization, and quantification. The proposed methodology was applied in the laboratory on reinforced concrete beam specimens and is suitable for further field developments in concrete structures of large dimensions. The first stage is based on distributed strain monitoring through Brillouin scattering-based fiber-optic sensors to detect and locate potential damage zones within the entire structure, while the second focuses on verification of the critical regions identified by the optical-fiber sensor using the digital image correlation technique

Strategies of anomalies detection in bridges and tunnels as a tool for structural health management. New approaches and AI support.

L'abstract è presente nell'allegato / the abstract is in the attachmen

Innovative Methods and Materials in Structural Health Monitoring of Civil Infrastructures

In the past, when elements in sructures were composed of perishable materials, such as wood, the maintenance of houses, bridges, etc., was considered of vital importance for their safe use and to preserve their efficiency. With the advent of materials such as reinforced concrete and steel, given their relatively long useful life, periodic and constant maintenance has often been considered a secondary concern. When it was realized that even for structures fabricated with these materials that the useful life has an end and that it was being approached, planning maintenance became an important and non-negligible aspect. Thus, the concept of structural health monitoring (SHM) was introduced, designed, and implemented as a multidisciplinary method. Computational mechanics, static and dynamic analysis of structures, electronics, sensors, and, recently, the Internet of Things (IoT) and artificial intelligence (AI) are required, but it is also important to consider new materials, especially those with intrinsic self-diagnosis characteristics, and to use measurement and survey methods typical of modern geomatics, such as satellite surveys and highly sophisticated laser tools

Health monitoring of civil infrastructures by subspace system identification method: an overview

Structural health monitoring (SHM) is the main contributor of the future's smart city to deal with the need for safety, lower maintenance costs, and reliable condition assessment of structures. Among the algorithms used for SHM to identify the system parameters of structures, subspace system identification (SSI) is a reliable method in the time-domain that takes advantages of using extended observability matrices. Considerable numbers of studies have specifically concentrated on practical applications of SSI in recent years. To the best of author's knowledge, no study has been undertaken to review and investigate the application of SSI in the monitoring of civil engineering structures. This paper aims to review studies that have used the SSI algorithm for the damage identification and modal analysis of structures. The fundamental focus is on data-driven and covariance-driven SSI algorithms. In this review, we consider the subspace algorithm to resolve the problem of a real-world application for SHM. With regard to performance, a comparison between SSI and other methods is provided in order to investigate its advantages and disadvantages. The applied methods of SHM in civil engineering structures are categorized into three classes, from simple one-dimensional (1D) to very complex structures, and the detectability of the SSI for different damage scenarios are reported. Finally, the available software incorporating SSI as their system identification technique are investigated

Modeling of Traffic Excitation for System Identification of Bridge Structures

In long-term health monitoring of bridge structures, system identification is often performed based only on the system output (bridge vibration responses) because the system input (traffic excitation) is difficult to measure. To facilitate the identification of the bridge properties, traffic excitation is commonly modeled as spatially uncorrelated white noise. A physical model of a stationary stream of vehicles (moving loads) arriving in accordance with a Poisson process, traversing an elastic beam, shows that the traffic excitation is spatially correlated. Employing the dynamic nodal loading approach, this spatial correlation results in a frequency-dependent excitation spectrum density matrix, and shifts the response spectra obtained from those excited by spatially uncorrelated white noise. It is shown that the application of system identification techniques based on the conventional excitation model may result in misleading structural properties. Hence, this study further proposes an output-only gray-box identification technique for bridge structures, in which knowledge about the nature of the traffic excitation, such as its spatial correlation, is implanted into an autoregressive-moving-average (ARMA) model. The identifiability of the ARMA model so constructed is assured and the feasibility of the proposed identification technique is demonstrated by a numerical example

Study on Modern Bridge Structure Health Monitoring System Based on Damage Identification

With the rapid growth of traffic, the loads\u27 design of many existing bridges can no longer meet the current vehicle load requirements, and the structural safety is seriously threatened. To ensure the structural safety of the bridge, it is necessary to monitor the bridge health and establish an early warning mechanism to prevent major accidents. The modern concrete bridge structure health monitoring based on damage identification proposed in this paper carried out principal component analysis of modern concrete bridges, and then this paper used principal component analysis (PCA) to locate the nonlinear damage source of the experimental model, which obtained the following conclusions. The maximum shear stress of the steel beam web is about 80 MPa，and the bulk stress of steel is reached at 7.5 MPa. Furthermore, to reduce the original data\u27s dimensionality, PCA effectively retains the characteristic information of the original data; empirical examples from external factor are presented. The major advantage of applying this framework is that the structural damage identification is simple and reliable with its advantages of dimensionality reduction, noise reduction, and exclusion of out-of-bounds interference factors