1,720,997 research outputs found
On the Use of Singular Vectors for the Flexibility-Based Damage Detection under the Assumption of Unknown Structural Masses
Full text linkThe main purpose of this work is to investigate the usability of easily obtainable parameters instead of the modal traditional ones, in the context of a flexibility-based damage detection procedure, under the assumption of unknown structural masses. To this aim, a comparison is made between two different approaches: the first involves the calculation of the flexibility matrix by using traditional modal parameters, such as natural frequencies and modal vectors, normalized to unitary values, while the second involves the use of singular vectors, obtained through a simple matrix factorization. The modal parameters and the singular vectors necessary for the implementation of the damage detection procedure are evaluated through two different techniques: the Eigensystem Realization Algorithm and a wavelet-based procedure, for which a variant is proposed by introducing the energy reassignment concept into the original algorithm. Through the latter approach, in particular, it is possible to obtain a high number of singular vectors even in the case of reduced availability of sensors. The study is performed under the assumption of nonstationary excitation, in order to achieve general results, and the effectiveness of the procedures is evaluated through simulated tests regarding different structural schemes
Instantaneous identification of densely instrumented structures using line topology sensor networks
Full text linkIn this paper, a new strategy for vibration-based structural health monitoring is proposed, specifically designed for smart sensors with edge computing capabilities organized in a line topology. This solution is aimed at maximizing resource optimization and enables the identification of modal parameters even for large or densely instrumented structures, where star-topology monitoring systems are typically unsuitable. In particular, an efficient data management procedure is proposed to reduce data transmission, thus improving efficiency and minimizing maintenance interventions for battery replacement in wireless applications. The maximum volume of transmitted data can be selected by the user, based on the specific requirements of the network. Although the considerable reduction of data size, the proposed approach enables accurate estimation of the structural parameters in challenging scenarios where other techniques generally fail. Modal parameters are identified in an online fashion, enabling near real-time detection and localization of early damage. Applications to a real case study instrumented with a dense sensor network show the effectiveness of the proposed approach and the possibility of localizing structural defects in slightly damaged civil structures
Vibration-based structural damage detection using a decentralized network with limited sensors
No full textOne of the main purposes of modal identification is to estimate the structural mechanical properties, in order to detect a possible damage over time. Several researches have been conducted recently to implement identification techniques based on the assumption of modest number of sensors. Such methods are needed both in the case of decentralized systems of wireless smart sensors, where operating units interact with each other in small groups, and in the case of repeated tests, carried out to identify large structures with a low availability of sensors. In this paper we propose a hybrid adaptive technique for natural vibration-based dynamic identification in the time-frequency domain. The proposed method involves the introduction of a novel procedure for decoupling the modal responses contained in the recorded signals, led by a clustering-based decomposition and reconstruction process; subsequently, decoupled signals, considered as single degree of freedom responses, are subjected to identification procedures in time domain, with the aim of studying the trend of instantaneous modal parameters. Since the procedure involves the real time estimation of dynamic parameters through a wavelet-based technique, it is particularly suitable for non-stationary signals. From the estimated parameters, a structural damage detection procedure is applied by using a flexibility-based technique. In order to test the effectiveness of the proposed method, the identification and damage detection procedures are simulated on a finite element model that represents a reinforced concrete plane frame
Fusing Modal Parameters and Curvature Influence Lines for Damage Localization Under Vehicle Excitation
No full textTransport infrastructure serves as a vital component for facilitating the movement of people and goods, contributing significantly to sustainable development and territorial cohesion. However, bridges, which are crucial elements of transportation networks, are increasingly susceptible to degradation caused by growing traffic volumes and severe weather events. Recent research efforts have focused on developing damage-sensitive features specifically designed for bridges, with curvature being one of the most used indicators. Traditionally, curvature estimation requires multiple instrumented locations. However, dense networks introduce challenges related to data management, synchronization, and battery replacement. This study presents an algorithm that enables the identification of dense curvature estimates for civil infrastructure using a limited set of accelerometers. The algorithm combines sparse curvature estimates derived from modal parameters with curvature influence lines obtained from the vibration response recorded during vehicle passage. A Kalman filter is employed to fuse these two features, mitigating the error resulting from traffic variations. The effectiveness of the proposed method is demonstrated using data collected from a real steel truss bridge with artificially induced damage
Shared micromobility-driven modal identification of urban bridges
Full text linkRecent research in Indirect Structural Health Monitoring (ISHM) uses the dynamic response of instrumented vehicles to carry out “drive-by” monitoring of bridges. These vehicles are generally cars or trucks instrumented with different types of sensors. However, some urban bridges are inaccessible to regular vehicles. Also, cars and trucks have non-negligible weight and suspension systems that may affect the collected vibration data. Stiff, light, and standardized shared micromobility vehicles, such as bicycles and electric kick scooters, have never been explored for ISHM purposes. This paper proposes an innovative and automatic ISHM strategy based on the data collected by smartphones temporarily installed on shared micromobility vehicles. An identification procedure suitable for cloud computing is proposed to extract the dynamic parameters of bridges without needing any sensor deployment, becoming particularly appealing for monitoring a densely built environment at a territorial scale. The methodology is applied to a real footbridge in Bologna (Italy)
The value of monitoring a structural health monitoring system
Full text linkStructural Health Monitoring (SHM) systems are adopted to acquire timely and continuous data on the state of civil structures, aerospace vehicles, and industrial machines, which deteriorate due to slow processes, such as corrosion and fatigue, and shock events, including natural and man-made disasters. The components of SHM systems themselves are exposed to deterioration after their installation; thereby, they might provide altered information to decision-makers. To account for this issue, Sensor Validation Tools (SVTs) have been developed to give insight into the actual condition of the SHM systems. In the last decade, researchers have exploited the Value of Information (VoI) from Bayesian decision theory to quantify the benefit of the information provided by an SHM system, implicitly assuming that it is working correctly when interrogated. The benefit of the information provided by SVTs on the state of an SHM system has never been investigated. This paper addresses this topic and extends the classical VoI framework to quantify the additional benefit brought by the information on the state of the SHM system to the decision problems it is meant to support. A numerical analysis, as well as a methodology demonstration on a real bridge, are presented to discuss the framework
Damage detection of non-linear reinforced concrete structure by means of single sensing node
No full textThis paper presents an application of an output-only structural identification algorithm specifically designed for low-cost monitoring systems on a structure with strongly non-linear behavior. The identification procedure is conducted simulating the presence of a single smart sensor node on the specimen, in order to identify the natural frequencies and amplitudes of collected accelerations in real time. The estimated parameters are used to develop a non-linear frequency-amplitude model representing the structural response, subsequently employed for damage detection
Pressure mapping using nanocomposite-enhanced foam and machine learning
Full text linkPressure mapping has garnered considerable interest in the healthcare and robotic industries. Low-cost and large-area compliant devices, as well as fast and effective computational algorithms, have been proposed in the last few years to facilitate distributed pressure sensing. One approach is to use electrical impedance tomography (EIT) to reconstruct the contact pressure distribution of piezoresistive materials. While tremendous success has been demonstrated, conventional algorithms may be unsuitable for real-time monitoring due to its computational demand and runtime. Moreover, the low resolution of reconstructed images is a well-known issue related to the regularization strategies typically employed for traditional EIT methods. Therefore, in this study, two different supervised machine learning (ML) approaches, namely, radial basis function networks and deep neural networks, were employed to efficiently solve the inverse EIT problem and improve the resolution of reconstructed pressure maps. The demonstration of high-resolution pressure mapping, specifically, for identifying pressure hotspots, was achieved using a carbon nanotube-based thin film integrated with foam
Exploiting traffic-induced vibration for high-resolution damage identification in bridges with sparse instrumentation
No full textGrowing traffic demand on aging civil infrastructure raises the need for reliable structural health monitoring tools for "minor" bridges. Data management can be power-demanding in state-of-the-art dense wireless sensor networks. This study presents an original damage identification algorithm for viaducts to identify a dense curvature distribution of the loaded structure based on sparse acceleration recordings. Moving vehicles are exploited to obtain dense spatial information of the structural features and localize anomalies with high resolution. Modal parameters and curvature influence lines are obtained by pro- cessing the raw acceleration signals with a particular filter bank, efficiently implementable through edge computing technologies
Dynamic identification of a reinforced concrete structure by means of modal assurance distribution
No full textFor time-varying systems in structural engineering, such as bridges with vehicular traffic and structures during
construction or seismic events, the application of traditional vibration-based identification methods may not be
admissible, since the hypotheses of stationarity of the signal may be far from the real situation. Moreover, in some cases,
real-time approaches to visualize the identified parameters are desirable to allow for a rapid decision-making process,
which assumes the utmost importance for the early warning in structures near to excavation or demolition sites and
strategic infrastructures after seismic events. Time-frequency representations have been largely used for the instantaneous
identification of linear time-varying systems. However, traditional algorithms generally suffer from issues related to the
identification of closely-spaced modes, which make the modal decomposition a non-trivial task. Moreover, by tracking
only natural frequencies, some variations of the dynamic behavior could not be perceived, or the variations induced by
other environmental and operational conditions could prevail over those due to a modification in the structural properties
(e.g., due to ongoing damage). In this paper, a Decomposition Algorithm based on Modal Assurance (DAMA) is applied
to separate modal responses, in order to allow a near real-time identification of modal parameters of structures with time-varying
features. In particular, a Modal Assurance Distribution (MAD) obtained from the analysis of multi-variate signals
is used to track the variations of modal parameters, considering both natural frequencies and modal shapes. The DAMA
is applied to a full-scale structure subjected to a series of dynamic tests performed in different structural conditions. The
analysis of multi-variate signals consisting of acceleration recordings collected at different locations of the structure
allows the online identification of varying dynamic parameters, which can be used to assess the structural state of health
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