Structural Health Monitoring

The role of transverse shear is crucial in modeling ultrasonic wave propagation in composites. The approximate plate theories have been extremely useful in providing solutions to problems involving static and dynamic loadings of uniform or laminated plates of finite dimensions. Moreover, since their simple formulations, they can also be useful for solving wave propagation problems in laminates of finite thickness and large lateral dimensions. A four-node plate bending element based on first order shear deformation theory is used in this work to predict the propagation velocity of first antisymmetric Lamb wave mode in graphite/epoxy composite plates by numerical simulation. Calculations are made using material properties obtained from a unidirectional panel via mechanical tests. For investigation, propagation along multiple directions is evaluated. Experimental and numerical results are compared at different angles of propagation and agreement between experimental and numerical approaches is found. © 2014 Elsevier Ltd.

The high-performance composite materials based on carbon fiber are increasingly used in critical security areas (aeronautics, civil engineering). Thus, it is important to know their characteristics during curing process or their use. We present herein a technique of electrical impedance analysis to extract some specific material properties (resistance and capacitance) in order to know its behavior. As the material microstructure contains a conductor part (carbon fiber) and an insulator part (resin), a model of electrical conduction in the material was established by using a network of a resistance RP connected in parallel with a capacitance CP (impedance Z). Spectroscopic impedance analysis of the studied samples informs about the intrinsic properties of material and shows a sensitivity of these electrical parameters (RP, CP) according to the curing cycle. Then the sensitivity to some physical parameters (temperature, deformations, etc.) will be demonstrated in order to know the health of the material, thus ensure Structural Health Monitoring (SHM).

A vision-based approach has been employed in Structural Health Monitoring (SHM) of bridge infrastructure. The approach has many advantages: non-contact, non-destructive, longdistance, high precision, immunity from electromagnetic interference, and multiple-target monitoring. This review aims to summarise the vision- and Digital Image Correlation (DIC)-based SHM methods for bridge infrastructure because of their strategic significance and security concerns. Four different bridge types were studied: concrete, suspension, masonry, and steel bridge. DIC applications in SHM have recently garnered attention in aiding to assess the bridges’ structural response mechanisms under loading. Different non-destructive diagnostics methods for SHM in civil infrastructure have been used; however, vision-based techniques like DIC were only developed over the last two decades, intending to facilitate damage detection in bridge systems with prompt and accurate data for efficient and sustainable operation of the bridge structure throughout its service life. Research works reviewed in this article demonstrated the DIC capability to detect damage such as cracks, spalling, and structural parameters such as deformation, strains, vibration, deflection, and rotation. In addition, the reviewed works indicated that the DIC as an efficient and reliable technique could provide sustainable monitoring solutions for different bridge infrastructures.

The pavement is a complex structure that is influenced by various environmental and loading conditions. The regular assessment of pavement performance is essential for road network maintenance. International roughness index (IRI) and pavement condition index (PCI) are well-known indices used for smoothness and surface condition assessment, respectively. Machine learning techniques have recently made significant advancements in pavement engineering. This paper presents a novel roughness-distress study using random forest (RF). After determining the PCI and IRI values for the sample units, the PCI prediction process is advanced using RF and random forest trained with a genetic algorithm (RF-GA). The models are validated using correlation coefficient (CC), scatter index (SI), and Willmott's index of agreement (WI) criteria. For the RF method, the values of the three parameters mentioned were −0.177, 0.296, and 0.281, respectively, whereas in the RF-GA method, −0.031, 0.238, and 0.297 values were obtained for these parameters. This paper aims to fulfill the literature's identified gaps and help pavement engineers overcome the challenges with the conventional pavement maintenance systems.

Apps and fitness trackers have changed the application culture of bio-surveillance. Their feedback technologies not only facilitate healthy lifestyle practices, but also multiply social control. At the interface of digital media systems and bioinformatic knowledge media, in the trend of self-measurement, they help to consider the body as a media object of regulated design and numerical forms of expression. The article deals with the logic of digital fitness and health monitoring devices and examines its importance in the context of self-management practices in process-oriented negotiation processes.

Apps und Fitness-Tracker haben die Anwendungskultur der Bio-Surveillance verändert. Ihre Feedbacktechnologien erleichtern nicht nur Praktiken der gesunden Lebensführung, sondern multiplizieren auch soziale Kontrolle. An der Schnittstelle von digitalen Mediensystemen und bioinformatischen Wissensmedien tragen sie im Trend der Selbstvermessung dazu bei, den Körper als Medienobjekt geregelter Gestaltung und numerischer Ausdrucksformen zu betrachten. Der Beitrag beschäftigt sich mit der Programmlogik dieses digitalen Fitness- und Gesundheitsmonitoring und untersucht ihren Stellenwert im Kontext von Praktiken der Selbstführung in prozessorientierten Aushandlungsprozessen.

One of the main tasks in structural health monitoring process is to create reliable algorithms that are capable of translating the measured response into meaningful information reflecting the actual condition of the monitored structure. The authors have recently introduced a novel unsupervised vibration-based damage detection algorithm that utilizes self-organizing maps to quantify structural damage and assess the overall condition of structures. Previously, this algorithm had been tested using the experimental data of Phase II Experimental Benchmark Problem of Structural Health Monitoring, introduced by the IASC (International Association for Structural Control) and ASCE (American Society of Civil Engineers). In this paper, the ability of this algorithm to quantify structural damage is tested analytically using an experimentally validated finite element model of a laboratory structure constructed at Qatar University.

In this study, a novel dual implementation of the Kalman filter proposed by Eftekhar Azam et al. (2014, 2015) is
experimentally validated for simultaneous estimation of the states and input of structural systems. By means of numerical
simulations, it has been shown that the proposed method outperforms existing techniques in terms of robustness and
accuracy for the estimated displacement and velocity time histories. Herein, dynamic response measurements, in the
form of displacement and acceleration time histories from a small-scale laboratory building structure excited at the base
by a shake table, are considered for evaluating the performance of the proposed Dual Kalman filter and in order to
compare this with available alternatives, such as the augmented Kalman filter (Lourens et al., 2012b) and the Gillijn De
Moore filter (GDF) (2007b). The suggested Bayesian approach requires the availability of a physical model of the system
in addition to output-only measurements from limited degrees of freedom. Two categories of such physical models are
herein studied to evaluate the effect of model error on the filter performances; the first, is a model that comprises
identified modal parameters, i.e., natural frequencies, mode shapes, modal damping ratios and modal participation
factors; the second, is a model that is extracted from a recently developed subspace identification procedure, namely
the transformed stochastic subspace identification method. The results are encouraging for the further use of the dual
Kalman filter and its available alternatives for addressing the important problems of full response reconstruction and
fatigue estimation in the entire body of linear structures, using a limited number of output-only vibration measurements.

This paper presents the results of using machine vision to relate surface observations to quantitative load level estimates in structural
components. In particular, image-processing and machine-learning regression techniques were used to build estimation models capable
of quantifying internal load levels (i.e., shear and moment) in shear-critical RC beams and slabs based on surface crack pattern images. The
estimation models were generated and tested using image data sets obtained from 10 different earlier published studies, which provided 558
crack pattern images captured from 84 shear-critical RC beams and slabs across a range of load and damage levels. Working with these
existing image data sets, various textural and geometric attributes of surface crack patterns were defined and evaluated with respect to their
effectiveness in building useful estimation models. Multiple statistical error measures and cross-validation methods are used to quantify
predictive accuracy, and several training/test methodologies were considered relative to potential field application scenarios. The results show
that the estimation models based on surface crack image data can work well across a wide range of geometries, loadings, concrete strengths,
and reinforcement details. Size effects can be accounted for by including specimen physical dimensions in the feature sets used for model training, and fundamental design relations can be used to develop useful nondimensional prediction parameters

The paper presents an overview of the results of diagnostic and monitoring activities carried out in the last years through satellite radar interferometry (2011-2016) and in situ measurements (2017-2019) in the historical city of Gubbio, Italy. The study is aimed at contributing to understanding the potential of remote sensing technologies in measuring complex deformation phenomena in historic buildings, such as those caused by soil-structural interaction and earthquakes, considering the monumental Consoli Palace and the Town Walls as case studies. The research activities have been carried out within the European HERACLES project, funded in the framework of Horizon (2020) and aimed at proposing novel diagnostic monitoring solutions for enhancing heritage resilience against various types of hazards. Particular attention is focused on the cross-correlation of the outputs of satellite radar interferometry and in-situ structural monitoring data aimed at achieving engineering meaningful results supporting decision making and proactive interventions.

We would like to invite you to join this exciting new project as a chapter contributor. Since this is a textbook, a great deal of this chapter entails a survey on the topic under the paradigm of cyber-physical systems, what can be done onboard and remotely, the distributed nature of the system and some exercises on futurology (anticipating trends can shed some light on upcoming designs). IET will bring great visibility to your work. You are welcome to suggest another topic/chapter title if you feel it would be more suitable. Each chapter should be around 20-25 pages each and can be submitted as a Word or Latex File. The IET will send you additional information (formatting, permission form, etc.) with the contributor's agreement once you have agreed to contribute to the book. Visit http:// www.theiet.org/resources/author-hub/books/index.cfm to get all information you need as a contributor to an IET research-level book. Each book is expected to have a total number of 500 printed pages (based on approximately 550 words per page with a 20% allowance for figures and tables). We have included a tentative schedule and list of topics below. If this is something you would consider, please send me the title of your chapter, a short description/abstract of the chapter content, and your full contact details. We will expect original content and new results for this book. You can, of course, reuse published material but the percentage of material reuse for the chapter should be less than 40%. The IET will run a piracy software on the full manuscript to control that you are including original material and will reject chapters who contain a large amount of already-published material so please do take this into consideration.

We introduce the realization of the first European real-time urban seismic network based on Micro Electro-Mechanical Systems (MEMS) technology. The project is called Monitoring Earthquake through MEMS Sensors (MEMS) and encompass the installation of about 100 seismic station in the municipality of Acireale (Sicily, Italy). The stations, specifically designed and assembled, are equipped with a triaxial MEMS accelerometer, and gyroscope and GPS. In this paper we present the characteristics of the station and of the network. The main objective of the network is a rapid, post-earthquake disaster assessment through the automatic production of shake maps. The network also allow the implementation of an automatic recognition system for damage assessment from remotely piloted aircraft, and of a site-specific Earthquake Early Warning system.

Co-edited collection by Marcia H. Rioux, Paula C. Pinto, and Gillian Parekh.

Disability, Rights Monitoring, and Social Change is a timely collection that addresses the current theoretical, methodological, and practical issues surrounding disability rights monitoring and offers a detailed look at law and policy reforms, best practices, and holistic methods. This unique compilation crosses the divide between the global South and North and explores the complex issues of intersectionality that arise for women with disabilities, Indigenous peoples with disabilities, and people with diverse disabilities. Appropriate for courses on human rights, social justice, policy, and advocacy, this volume serves as a guide and learning tool for anyone interested in disability rights monitoring and, more generally, the effective practice of monitoring human rights.

— Development of SCC is a desirable achievement in the construction industry in order to overcome the problems associated with cast-in-place concrete. SCC is not affected by the skills of workers, shape, size and amount of reinforcing bars used or arrangement of different structural elements, but due to its high fluidity and resistance to segregation, it can be pumped to longer distances. The main reasons for the employment of self-compacting concrete is to shorten the construction period, and to assure compactions in the structure, especially in confined zones where vibration is difficult, and to eliminate noise due to vibration. A simple mix design for SCC proposed by Nan su et al. is used for achieving the trial mixes. The trial mix which satisfies the rheological properties as per EFNARC guidelines and the corresponding developed mix which gives maximum compressive and splitting tensile strengths are used in the present work. Cement is replaced with varying percentages of silica fume (SF) ranging from (0-15%) at an interval of 2.5%. SCC with 5% silica fume shows better results when compared to other replacement levels. Hence it is found that silica fume can be effectively used as an alternate for cement replacement.

Objective:  This paper intends to evaluate inflammatory proteins and
hematological factors  concurrently blood obtained during the
cupping and with the venous blood.
Methods:  About 30 samples of 10 men participated in this clinical
evaluative study aged between 23 and 58 years. The hematological
factors were evaluated by by KX-. Cytokines (IL-10) &  hs-CRP were
measured by a sensitive sandwich ELISA kit.
Results:  Analysis of the results showed  a significant difference  in
many of the inflammatory proteins, hematological factors between
the venous blood and the cupping blood.
Conclusion: Based on the results of the comparison between venous
blood and cupping blood, the cupping blood is different, both in
components and immunologic response.

The use of self-organizing maps and artificial neural networks for structural health monitoring is presented in this paper. The authors recently developed a nonparametric structural damage detection algorithm for extracting damage indices from the ambient vibration response of a structure. The algorithm is based on self-organizing maps with a multilayer feedforward pattern recognition neural network. After the training of the self-organizing maps, the algorithm was tested analytically under various damage scenarios based on stiffness reduction of beam members and boundary condition changes of a grid structure. The results indicated that proposed algorithm can successfully locate and quantify damage on the structure.

Il periodo di vibrazione fondamentale svolge un ruolo fondamentale nella valutazione della domanda sismica. Il suo valore dipende dalle distribuzioni di massa e rigidezza nella struttura in esame ed è in genere valutato mediante analisi numeriche o, per alcune tipologie strutturali ricorrenti, mediante formulazioni empiriche semplificate. Nel presente articolo vengono presentati i risultati di una campagna sperimentale di identificazione dinamica di torri in muratura ricadenti nel territorio molisano. Queste ...

The advancement of wireless sensor networks yields a variety of wireless sensor network for wildlife tracking. One typical application for wireless sensor networks is in animal tracking and monitoring in wildlife environments. A significant number of studies have been done in tracking animals with sensor networks. However from the recent literature it is observed that there is no much study has been done on an intelligent real time sensor network that is capable to alerting the rangers an incidence of animal poaching before it happened. In this paper an intelligent wireless sensor system for tracking and monitoring rhinos and elephants is proposed.

Output-only algorithms are needed for modal identification when only structural responses are available. The recent years have witnessed the fast development of blind source separation (BSS) as a promising signal processing technique, pursuing to recover the sources using only the measured mixtures. As the most popular tool solving the BSS problem, independent component analysis (ICA) is able to directly extract the time-domain modal responses, which are viewed as virtual sources, from the observed system responses; however, it has been shown that ICA loses accuracy in the presence of higher-level damping. In this study, the modal identification issue, which is incorporated into the BSS formulation, is transformed into a time-frequency framework. The sparse time-frequency representations of the monotone modal responses are proposed as the targeted independent sources hidden in those of the system responses which have been short-time Fourier-transformed (STFT); they can then be efficiently extracted by ICA, whereby the time-domain modal responses are recovered such that the modal parameters are readily obtained. The simulation results of a multidegree-of-freedom system illustrate that the proposed output-only STFT-ICA method is capable of accurately identifying modal information of lightly and highly damped structures, even in the presence of heavy noise and nonstationary excitation. The laboratory experiment on a highly damped three-story frame and the analysis of the real measured seismic responses of the University of Southern California hospital building demonstrate the capability of the method to perform blind modal identification in practical applications.

Tall  building  designs  are  changing  with  the  emerging  demand  of  various  architectural
features and efficient design based on  structural geometry. With the growing demand, tall
building  structures  are  paving  way  towards  challenging  aspects  of  design  with
unconventional  expressions.  More  slender  structures  are  proposed  with  economic  and
structurally efficient design. This  necessitates the study of wind loading effect on various
geometrical  shapes  and  also  the  interference  behaviour  due  to  surrounding  buildings  and
their spacing in the neighbourhood. Sufficient availability of pressure and force coefficients
for  symmetric  and  regular  buildings  are  provided  in  various  international  codes,  but  they
lack the study of interference effect and unsymmetrical building plans. The present research
work  is  a  numerical  study  of  wind  interference  effect  and  its  optimization  caused  by  the
change of inter-spacing of interfering buildings and also the varying wind angle of attack on
a pentagonal plan shaped building which serves as principle building

ABSTRACT: The present paper examines how statistics of extremes can be used to enhance the assessment and performance prediction of monitored highway bridges. This is achieved by proposing an approach to obtain a monitoring-based live-load for use in a reliability analysis. Time effects that correlate observed data to code required return periods are considered. Additionally, a method to identify, minimise and properly account for the epistemic uncertainty inherent to any monitoring record within a reliability analysis is presented. The approach can be utilised to plan data collection efforts or to maximise the utility of a limited amount of data. The extension of extreme value statistics to the monitoring of highway bridges is developed first using simulations and is then demonstrated on a case study using 90 days of in-service data collected from a bridge located in Pennsylvania, USA.

KEYWORDS: monitoring; statistics of extremes; reliability assessment; uncertainty; live loads; highway bridges

In this paper, we propose a novel, secure, and intelligent IoT approach based on agent, we have implemented in the health care domain, where we developed an intelligent patient monitoring system for monitoring the patients heart rate automatically. Our system is more intelligent that can anticipate the critical condition before it even happens, send a message to the patient family, doctors, nurses, as well as hospital in-charge personal, and launch an alarm to be assisted by the nearest people in place. Also, our architecture ensures the authentication, authorization, and data sensing confidentiality. Hospitals and medical clinics can utilize our system to monitor their outpatients who are in danger of unpredictable health conditions. The approach presented in the paper can also be applied to other IoT domains.

This paper explores the challenges, opportunities, benefits, and limitations of reliability assessment of major and critical components of infrastructure using structural health monitoring (SHM) data. The focus is on bridges but the discussion is intentionally kept at a general level so that it is applicable to a wide range of infrastructural systems. This paper argues that holistic and strategic approaches are required to pave the way to the realization of the latent befits of SHM and maximising the value of information derived from SHM data. Firstly, it discusses these benefits and situations were implementing SHM can be considered to be potentially most useful. It later discusses two frameworks for seeing the SHM technologies in a broader context of asset management decision making. The first framework is underpinned by prioritisation of structures for monitoring, moves to guidelines for instrumentation and SHM data analysis, and ends with the integration of SHM results into asset management and disaster emergency plans and decisions. The second framework understands SHM as a starting point in a value chain of enabling technologies delivering information to infrastructure stakeholders. A separate section is devoted to deliberations on the emerging and important way of thinking of SHM outputs as ‘big data’ and associated challenges. One of the opportunities brought about by abundant SHM big data is the prospect of creating better ‘digital twins’ – a high fidelity, multi-physics, multi-scale models of structural systems underpinned by and calibrated using SHM data to assess and forecast reliability. Finally, an example of a recently constructed 12-span, 690 m long post-tensioned concrete viaduct equipped with a continuously operating SHM system comprising around 90 channels of strain, temperature, displacement and environmental data, and also subjected to one-off ambient dynamic testing campaigns is presented. The proposed framework for structural reliability assessment of the bridge integrates information on the mechanical properties of construction materials obtained via laboratory and in situ tests, the heterogeneous SHM and dynamic testing data from the full scale structure, and predictions from numerical models of the bridge. Several aspects of this information fusion will be discussed. Dynamic tests and static responses, including time dependent creep and shrinkage behaviour, enable system identification of the structure and creation, via model updating, of numerical (finite element) structural models, or ‘digital twins’, of the physical structure. These models can in turn be used for assessing quantitatively the bridge time dependent condition, health, safety and performance via more realistic numerical reliability evaluations. The collected temperature and strain and displacement data form the basis for generating realistic spatial and temporal load fields for reliability assessment utilizing calibrated numerical models of the structure. The monitoring of responses to traffic loads enables formulation of the dynamic live load demands actually acting on the bridge and response to these.

IoT in human services is the central member in giving better clinical offices to the patients and encourages specialists and hospitals. The proposed framework here comprises different clinical gadgets, for example, sensors and online or versatile-based applications that impart system-associated gadgets and assist with checking and recording patients' well being information and clinical data. The proposed result of the paper is to manufacture a framework to give a world-class clinical guide to the patients even in the remotest territories without any hospitals in their zones by interfacing over the Internet and grasping data through about their well being status using the wearable gadgets gave in the unit utilizing a raspberry pi microcontroller which would have the option to record the patient's pulse, circulatory strain. The framework will be brilliant to imply the patient's relatives and their primary care physician about the patient's present well being status and full clinical data if any health-related crisis emerges. The gathered data can be utilized to investigate and anticipate ongoing issues or different disorders, such as coronary episodes in the fundamental stage using the information mining strategies that will likewise give the methodology favorable to dynamic.

Global Positioning System (GPS) has been successfully used to measure displacements of oscillating flexible civil engineering structures such as long suspension bridges and high-rise buildings, and to derive their modal frequencies, usually up to 1 Hz, but there is evidence that these limits can be exceeded using high frequency GPS receivers. Based on systematic experiments in computer controlled oscillations with one- and three-degrees of freedom we investigated the potential of GPS, first to record higher oscillation frequencies, at least up to 4 Hz at the minimum resolution level of this instrument for kinematic applications (⩾5 mm), and second, to identify more than one dominant frequency. Data were processed using least squares-based spectral analysis and wavelet techniques which permit to analyze entire time series, even those of too short duration or those characterized by gaps, in both the frequency and the time domain.The ability of GPS to accurately measure frequencies of oscillations of relatively rigid (modal frequencies 1–4 Hz) civil engineering structures is demonstrated in the cases of two bridges.The outcome of this study is that GPS is suitable for the identification of dynamic characteristics of even relatively rigid (modal frequencies up to 4 Hz) civil engineering structures excited by various loads (wind, traffic, earthquakes, etc.) if displacements are above the uncertainty level of the method (⩾5 mm). Structural health monitoring of a wide range of structures appears therefore a promising field of application of GPS.