Special Issue "Sensor Networks in Structural Health Monitoring: From Theory to Practice"

A special issue of Journal of Sensor and Actuator Networks (ISSN 2224-2708).

Deadline for manuscript submissions: closed (31 January 2020).

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editors

Prof. Eleni Chatzi
E-Mail Website
Guest Editor
ETH Zurich, Institute of Structural Engineering, Department of Civil, Environmental and Geomatic Engineering, Stefano-Franscini-Platz 5, 8093 Zürich, Switzerland
Interests: structural health monitoring; data driven condition assessment and self sensing systems; identification and control of nonlinear structural systems; life-cycle assessment and decision support for predictive maintenance; smart sensor technology; smart materials and structures
Dr. Vasilis K. Dertimanis
E-Mail Website
Guest Editor
ETH Zurich, Department of Civil, Environmental and Geomatic Engineering, Stefano-Franscini-Platz 3, 8093 Zürich, Switzerland
Interests: structural Istructural identification and health monitoring; state estimation; active and passive structural control; hybrid testingdentification & Health Monitoring; State Estimation; Active & Passive Structural Control; Hybrid Testing

Special Issue Information

Dear Colleagues,

The growing attention that structural health monitoring (SHM) has enjoyed in recent years can be attributed, among others reasons, to the advent of low-cost and easily-deployable sensors. The enabling technology has brought forth a new era of structural diagnostic means and is continuously redefining the tools for information processing, data reduction/compression, feature extraction and smart assessment.

It is true that, within the SHM community, novel data-driven or hybrid methods are being developed, implementations for field deployments around the globe are being established, already showing significant indications of effectiveness. Nonetheless, a number of open issues remain to be addressed, associated with the type, number and placement of sensors that provide continuous behavioral signatures of the monitored structures. Despite the fact that the latest trends in SHM tend to promote analytical, instead of hardware, redundancy, a sensor network and its configuration remain the key aspects of any SHM scheme.

Within this context, the aim of this Special Issue is to discuss the latest advances in the field of sensor networks for SHM. The focus lies in both active research on the theoretical foundations of sensor networks, as well as technological developments that might define the next generation of SHM. Applications in structural dynamics, earthquake engineering, mechanical and aerospace engineering, as well as other relevant areas, will be accepted.

Topics relevant to the session include, but are not limited to:

  • Wired and wireless sensor networks
  • Structural state estimation and sensor fusion
  • Virtual Sensing and fault-tolerant sensor networks
  • Optimal strategies for sensor placement and fusion
  • Inverse methods for big data analysis and classification
  • Linear and nonlinear system identification
  • Model updating and verification,
  • Uncertainty quantification in model selection and parameter estimation
  • Feature extraction
  • Extraction of performance indicators
  • Damage detection/localization/assessment
  • Special topics in structural deterioration, including fatigue, wear, etc.

Papers dealing with experimental/field investigations and results of long-term monitoring deployments are especially welcomed.

Prof. Dr. Eleni Chatzi
Dr. Vasilis K. Dertimanis
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Sensor and Actuator Networks is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • structural health monitoring
  • condition assessment
  • sensor networks
  • damage detection
  • model updating
  • uncertainty quantification
  • feature extraction
  • optimal sensor placement
  • life-cycle assessment

Published Papers (7 papers)

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Editorial

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Editorial
Sensor Networks in Structural Health Monitoring: From Theory to Practice
J. Sens. Actuator Netw. 2020, 9(4), 47; https://doi.org/10.3390/jsan9040047 - 13 Oct 2020
Cited by 1 | Viewed by 630
Abstract
The growing attention that structural health monitoring (SHM) has enjoyed in recent years can be attributed, amongst other factors, to the advent of low-cost and easily deployable sensors [...] Full article

Research

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Article
GP-ARX-Based Structural Damage Detection and Localization under Varying Environmental Conditions
J. Sens. Actuator Netw. 2020, 9(3), 41; https://doi.org/10.3390/jsan9030041 - 08 Sep 2020
Cited by 1 | Viewed by 1030
Abstract
The representation of structural dynamics in the absence of physics-based models, is often accomplished through the identification of parametric models, such as the autoregressive with exogenous inputs, e.g. ARX models. When the structure is amenable to environmental variations, parameter-varying extensions of the original [...] Read more.
The representation of structural dynamics in the absence of physics-based models, is often accomplished through the identification of parametric models, such as the autoregressive with exogenous inputs, e.g. ARX models. When the structure is amenable to environmental variations, parameter-varying extensions of the original ARX model can be implemented, allowing for tracking of the operational variability. Yet, the latter occurs in sufficiently longer time-scales (days, weeks, months), as compared to system dynamics. For inferring a “global”, long time-scale varying ARX model, data from a full operational cycle has to typically become available. In addition, when the sensor network comprises multiple nodes, the identification of long time-scale varying, vector ARX models grow in complexity. We address these issues by proposing a distributed framework for structural identification, damage detection and localization. Its main features are: (i) the individual estimation of local, single-input-single-output ARX models at every operational point; (ii) the long time-scale representation of each individual ARX coefficient via a Gaussian process regression, which captures dependency on varying Environmental and Operational Conditions (EOCs); (iii) the establishment of a distributed residual generation algorithm for damage detection, which produces time-series of well-defined stationary statistics, with detected discrepancies used for damage diagnosis; and, (iv) exploitation of ARX-inferred mode shape curvatures, obtained via ARX-inferred global state-space models, of the healthy and damaged states, for damage localization. The method is assessed via application on two numerical case studies of different complexity, with the results confirming its efficacy for diagnostics under varying EOCs. Full article
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Article
The Design and Calibration of Instrumented Particles for Assessing Water Infrastructure Hazards
J. Sens. Actuator Netw. 2020, 9(3), 36; https://doi.org/10.3390/jsan9030036 - 30 Jul 2020
Cited by 4 | Viewed by 1817
Abstract
The highly dynamical entrainment and transport processes of solids due to geophysical flows is a major challenge studied by water infrastructure engineers and geoscientists alike. A miniaturised instrumented particle that can provide a direct, non-intrusive, low-cost and accessible method compared to traditional approaches [...] Read more.
The highly dynamical entrainment and transport processes of solids due to geophysical flows is a major challenge studied by water infrastructure engineers and geoscientists alike. A miniaturised instrumented particle that can provide a direct, non-intrusive, low-cost and accessible method compared to traditional approaches for the assessment of coarse sediment particle entrainment is developed, calibrated and tested. The instrumented particle presented here is fitted with inertial microelectromechanical sensors (MEMSs), such as a triaxial accelerometer, a magnetometer and angular displacement sensors, which enable the recording of the particle’s three-dimensional displacement. The sensor logs nine-axis data at a configurable rate of 200–1000 Hz and has a standard mode of deployment time of at least one hour. The data can be obtained and safely stored in an internal memory unit and are downloadable to a PC in an accessible manner and in a usable human-readable state. A plethora of improved design specifications have been implemented herein, including increased frequency, range and resolution of acceleration and gyroscopic sensing. Improvements in terms of power consumption, in comparison to previous designs, ensure longer periods of data logging. The embedded sensors are calibrated using simple physical motions to validate their operation. The uncertainties in the experiments and the sensors’ readings are quantified and an appropriate filter is used for inertial sensor fusion and noise reduction. The instrumented particle is tested under well-controlled lab conditions, where the beginning of the destabilisation of a bed surface in an open channel flow, is showcased. This is demonstrative of the potential that specifically designed and appropriately calibrated instrumented particles have in assessing the initiation and occurrence of water infrastructure hazards. Full article
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Article
Bayesian Model-Updating Using Features of Modal Data: Application to the Metsovo Bridge
J. Sens. Actuator Netw. 2020, 9(2), 27; https://doi.org/10.3390/jsan9020027 - 03 Jun 2020
Cited by 2 | Viewed by 1921
Abstract
A Bayesian framework is presented for finite element model-updating using experimental modal data. A novel likelihood formulation is proposed regarding the inclusion of the mode shapes, based on a probabilistic treatment of the MAC value between the model predicted and experimental mode shapes. [...] Read more.
A Bayesian framework is presented for finite element model-updating using experimental modal data. A novel likelihood formulation is proposed regarding the inclusion of the mode shapes, based on a probabilistic treatment of the MAC value between the model predicted and experimental mode shapes. The framework is demonstrated by performing model-updating for the Metsovo bridge using a reduced high-fidelity finite element model. Experimental modal identification methods are used in order to extract the modal characteristics of the bridge from ambient acceleration time histories obtained from field measurements exploiting a network of reference and roving sensors. The Transitional Markov Chain Monte Carlo algorithm is used to perform the model updating by drawing samples from the posterior distribution of the model parameters. The proposed framework yields reasonable uncertainty bounds for the model parameters, insensitive to the redundant information contained in the measured data due to closely spaced sensors. In contrast, conventional Bayesian formulations which use probabilistic models to characterize the components of the discrepancy vector between the measured and model-predicted mode shapes result in unrealistically thin uncertainty bounds for the model parameters for a large number of sensors. Full article
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Article
Swarm-based Parallel Control of Adjacent Irregular Buildings Considering Soil–structure Interaction
J. Sens. Actuator Netw. 2020, 9(2), 18; https://doi.org/10.3390/jsan9020018 - 30 Mar 2020
Cited by 3 | Viewed by 2535
Abstract
Seismic behavior of tall buildings depends upon the dynamic characteristics of the structure, as well as the base soil properties. To consider these factors, the equations of motion for a multi-story 3D building are developed to include irregularity and soil–structure interaction (SSI). Inspired [...] Read more.
Seismic behavior of tall buildings depends upon the dynamic characteristics of the structure, as well as the base soil properties. To consider these factors, the equations of motion for a multi-story 3D building are developed to include irregularity and soil–structure interaction (SSI). Inspired by swarm intelligence in nature, a new control method, known as swarm-based parallel control (SPC), is proposed in this study to improve the seismic performance and minimize the pounding hazards, by sharing response data among the adjacent buildings at each floor level, using a wireless-sensors network (WSN). The response of individual buildings is investigated under historic earthquake loads, and the efficiencies of each different control method are compared. To verify the effectiveness of the proposed method, the numerical example of a 15-story, 3D building is modeled, and the responses are mitigated, using semi-actively controlled magnetorheological (MR) dampers employing the proposed control algorithm and fuzzy logic control (FLC), as well as the passive-on/off methods. The main discussion of this paper is the efficiency of the proposed SPC over the independent FLC during an event where one building is damaged or uncontrolled, and an active control based upon the linear quadratic regulator (LQR) is considered for the purpose of having a benchmark ideal result. Results indicate that in case of failure in the control system, as well as the damage in the structural elements, the proposed method can sense the damage in the building, and update the control forces in the other adjacent buildings, using the modified FLC, so as to avoid pounding by minimizing the responses. Full article
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Article
Data-Interpretation Methodologies for Practical Asset-Management
J. Sens. Actuator Netw. 2019, 8(2), 36; https://doi.org/10.3390/jsan8020036 - 22 Jun 2019
Cited by 6 | Viewed by 3896
Abstract
Monitoring and interpreting structural response using structural-identification methodologies improves understanding of civil-infrastructure behavior. New sensing devices and inexpensive computation has made model-based data interpretation feasible in engineering practice. Many data-interpretation methodologies, such as Bayesian model updating and residual minimization, involve strong assumptions regarding [...] Read more.
Monitoring and interpreting structural response using structural-identification methodologies improves understanding of civil-infrastructure behavior. New sensing devices and inexpensive computation has made model-based data interpretation feasible in engineering practice. Many data-interpretation methodologies, such as Bayesian model updating and residual minimization, involve strong assumptions regarding uncertainty conditions. While much research has been conducted on the scientific development of these methodologies and some research has evaluated the applicability of underlying assumptions, little research is available on the suitability of these methodologies to satisfy practical engineering challenges. For use in practice, data-interpretation methodologies need to be able, for example, to respond to changes in a transparent manner and provide accurate model updating at minimal additional cost. This facilitates incremental and iterative increases in understanding of structural behavior as more information becomes available. In this paper, three data-interpretation methodologies, Bayesian model updating, residual minimization and error-domain model falsification, are compared based on their ability to provide robust, accurate, engineer-friendly and computationally inexpensive model updating. Comparisons are made using two full-scale case studies for which multiple scenarios are considered, including incremental acquisition of information through measurements. Evaluation of these scenarios suggests that, compared with other data-interpretation methodologies, error-domain model falsification is able to incorporate, iteratively and transparently, incremental information gain to provide accurate model updating at low additional computational cost. Full article
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Review

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Review
Emerging Trends in Optimal Structural Health Monitoring System Design: From Sensor Placement to System Evaluation
J. Sens. Actuator Netw. 2020, 9(3), 31; https://doi.org/10.3390/jsan9030031 - 01 Jul 2020
Cited by 5 | Viewed by 1714
Abstract
This paper presents a review of advances in the field of Sensor Placement Optimisation (SPO) strategies for Structural Health Monitoring (SHM). This task has received a great deal of attention in the research literature, from initial foundations in the control engineering literature to [...] Read more.
This paper presents a review of advances in the field of Sensor Placement Optimisation (SPO) strategies for Structural Health Monitoring (SHM). This task has received a great deal of attention in the research literature, from initial foundations in the control engineering literature to adoption in a modal or system identification context in the structural dynamics community. Recent years have seen an increasing focus on methods that are specific to damage identification, with the maximisation of correct classification outcomes being prioritised. The objectives of this article are to present the SPO for SHM problem, to provide an overview of the current state of the art in this area, and to identify promising emergent trends within the literature. The key conclusions drawn are that there remains a great deal of scope for research in a number of key areas, including the development of methods that promote robustness to modelling uncertainty, benign effects within measured data, and failures within the sensor network. There also remains a paucity of studies that demonstrate practical, experimental evaluation of developed SHM system designs. Finally, it is argued that the pursuit of novel or highly efficient optimisation methods may be considered to be of secondary importance in an SPO context, given that the optimisation effort is expended at the design stage. Full article
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