Relevant bibliographies by topics / Infrastructure health monitoring

Academic literature on the topic 'Infrastructure health monitoring'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Infrastructure health monitoring"

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Brownjohn, J. M. W. "Structural health monitoring of civil infrastructure." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 365, no. 1851 (December 13, 2006): 589–622. http://dx.doi.org/10.1098/rsta.2006.1925.

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Structural health monitoring (SHM) is a term increasingly used in the last decade to describe a range of systems implemented on full-scale civil infrastructures and whose purposes are to assist and inform operators about continued ‘fitness for purpose’ of structures under gradual or sudden changes to their state, to learn about either or both of the load and response mechanisms. Arguably, various forms of SHM have been employed in civil infrastructure for at least half a century, but it is only in the last decade or two that computer-based systems are being designed for the purpose of assisting owners/operators of ageing infrastructure with timely information for their continued safe and economic operation. This paper describes the motivations for and recent history of SHM applications to various forms of civil infrastructure and provides case studies on specific types of structure. It ends with a discussion of the present state-of-the-art and future developments in terms of instrumentation, data acquisition, communication systems and data mining and presentation procedures for diagnosis of infrastructural ‘health’.
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Aktan, A. E., F. N. Catbas, K. A. Grimmelsman, and C. J. Tsikos. "Issues in Infrastructure Health Monitoring for Management." Journal of Engineering Mechanics 126, no. 7 (July 2000): 711–24. http://dx.doi.org/10.1061/(asce)0733-9399(2000)126:7(711).

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Aggelis, Dimitrios G., Ninel Alver, and Hwa Kian Chai. "Health Monitoring of Civil Infrastructure and Materials." Scientific World Journal 2014 (2014): 1–2. http://dx.doi.org/10.1155/2014/435238.

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Chang, Peter C., Alison Flatau, and S. C. Liu. "Review Paper: Health Monitoring of Civil Infrastructure." Structural Health Monitoring: An International Journal 2, no. 3 (September 2003): 257–67. http://dx.doi.org/10.1177/1475921703036169.

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Miyamoto, Ayaho. "IT-based Bridge Health Monitoring for Sustainable Infrastructure." IABSE Symposium Report 96, no. 20 (January 1, 2009): 22–29. http://dx.doi.org/10.2749/222137809796067704.

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Miyamoto, Ayaho. "IT-based Bridge Health Monitoring for Sustainable Infrastructure." IABSE Symposium Report 96, no. 5 (January 1, 2009): 231–38. http://dx.doi.org/10.2749/222137809796088594.

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Zhang, Yinan, Lei Yuan, Zhi Zhou, and Amardeep Kaur. "Sensors for Structural Health Monitoring in Civil Infrastructure." Journal of Sensors 2018 (June 3, 2018): 1–2. http://dx.doi.org/10.1155/2018/6023059.

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Olund, Josh, and John DeWolf. "Passive Structural Health Monitoring of Connecticut’s Bridge Infrastructure." Journal of Infrastructure Systems 13, no. 4 (December 2007): 330–39. http://dx.doi.org/10.1061/(asce)1076-0342(2007)13:4(330).

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Feng, M. Q. "Application of structural health monitoring in civil infrastructure." Smart Structures and Systems 5, no. 4 (July 25, 2009): 469–82. http://dx.doi.org/10.12989/sss.2009.5.4.469.

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Altabey, Wael A., and Mohammad Noori. "Artificial-Intelligence-Based Methods for Structural Health Monitoring." Applied Sciences 12, no. 24 (December 12, 2022): 12726. http://dx.doi.org/10.3390/app122412726.

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Intelligent and resilient infrastructure and smart cities make up a rapidly emerging field that is redefining the future of urban development and ways of preserving the existing infrastructure against natural hazards...
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Dissertations / Theses on the topic "Infrastructure health monitoring"

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Morris, Scott Edward Washer Glenn A. "Remote health monitoring for asset management." Diss., Columbia, Mo. : University of Missouri--Columbia, 2009. http://hdl.handle.net/10355/6556.

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The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file. Title from PDF of title page (University of Missouri--Columbia, viewed on November 19, 2009). Thesis advisor: Dr. Glenn Washer. Includes bibliographical references.
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Tonelli, Daniel. "Management of Civil Infrastructure based on Structural Health Monitoring." Doctoral thesis, Università degli studi di Trento, 2020. http://hdl.handle.net/11572/272315.

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The interest in structural health monitoring (SHM) has grown considerably in the past half century, due to an explosive growth in the availability of new sensors, the development of powerful data analysis techniques, and the increasing number of civil infrastructure that are approaching or exceeding their initial design life. In SHM, we acquire observation on the behavior of a structure to understand its condition state, based on which we decide how to manage it properly. However, this optimistic view of SHM is in contrast with what happen in real life: infrastructure operators are typically skeptical about the capacity of monitoring to support decisions, and instead of following the suggestions provided by SHM, they often act based on their experience or common sense. The reason is that at present it is not fully clear how in practice to make decisions based on monitoring observation. To fill this gap between theory and practice, I propose to consider SHM as a logical process of making decision based on observation consisting of two steps: judgment, in which the condition state of structures is inferred based on SHM data, and decision, in which the optimal action is identified based on a rational and economic principle. From this perspective, a monitoring system should provide information that can improe he managers knoledge on he srcral condiion sae enough to allow them to make better decision on the structure management. Therefore, in designing a monitoring system, the design target must be the accuracy in the knowledge of structural state achieved analyzing the observations provided by it. However, when an engineer designs a monitoring system, the approach is often heuristic, with performance evaluation based on experience or common sense rather than on quantitative analysis. For this reason, I propose a performance-based monitoring system design, which is a quantitative method for the calculation of the expected performance of a monitoring solution a pre-posteriori and for checking it effectiveness in the design phase. It is based on the calculation of the monitoring capacity and the monitoring demand the counterparts of structural capacity and demand in the semi-probabilistic structural design, and like in structural design, the solution is satisfactory if the capacity is equal or better than the demand. The choice in whether to invest a limited budget on a monitoring system or in a retrofit is another critical choice for infrastructure managers: a retrofit work can increase the capacity and the safety of a structure, while sensors do not change the capacity, nor reduce the loads. Recently, the SHM-community has acknowledged that the benefit of installing a monitoring system can be properly quantified using the concept of Value of Information (VoI). A typical assumption in the VoI estimation is that a single decision-maker is in charge for decisions on both the investment in SHM for a structure, and its management based on SHM data. However, this process is usually more complex in the real world, with more individuals involved in the decision chain. Therefore, I formalize a rational method for quantifying the conditional value of information when two different actors are involved in the decision chain: the manager, who operate the structure based on monitoring data; and the owner, who chooses whether to install the monitoring system or not, before having access to these data. The results are particularly interested, showing that under appropriate conditions, the owner may be willing to pay to prevent the manager to use the monitoring system. Application to case studies are presented for all the research contribution presented in this doctoral thesis.
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Bhesania, Alpaben. "Wireless data acquisition system for health monitoring of civil infrastructure /." Available to subscribers only, 2005. http://proquest.umi.com/pqdweb?did=1079666551&sid=17&Fmt=2&clientId=1509&RQT=309&VName=PQD.

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de, Battista Nicholas. "Wireless technology and data analytics for structural health monitoring of civil infrastructure." Thesis, University of Sheffield, 2013. http://etheses.whiterose.ac.uk/6161/.

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The aim of this research was to investigate how wireless technology, combined with data analytics, can be used for effective structural health monitoring (SHM) of civil infrastructure. Two main applications were investigated, for which wireless sensor networks (WSNs) were integrated into complete SHM solutions: (1) long-term quasi-static monitoring on a suspension bridge, and (2) temporary monitoring of pedestrian bridge vibration. In the first application, a commercial off-the-shelf WSN was used to acquire and transmit data from extensometers measuring the longitudinal deck displacement of the Tamar Bridge in the UK. Six months of displacement data were analysed in conjunction with the ambient and structural temperature data acquired from a separate monitoring system on the bridge. Empirical models were fitted to relate the deck displacement to various combinations of temperatures. Comparisons of each model’s prediction accuracy showed that the practice of estimating a suspension bridge deck’s thermal expansion based solely on the air temperature is overly simplistic. The deck displacement was predicted more accurately by considering instead the temperatures of the deck itself and of the underlying structure. In preparation for the second application, a number of indoor tests and short-term deployments on full-scale structures were carried out using an existing prototype WSN, to assess its suitability for vibration monitoring. Subsequently, an embedded data processing method was developed by adapting various signal processing techniques and combining them in sequence. The method was then programmed on the WSN, which was integrated into autonomous SHM systems deployed to monitor two in-service, multi-span pedestrian bridges in Singapore for two weeks. The wireless sensor nodes periodically acquired ambient vibration response data and processed them in a decentralised manner to extract and transmit useful results pertaining to the bridges’ response and modal properties. These results showed that the dynamic properties of the bridges were not affected significantly by the diurnal usage pattern or by the vibration amplitude. The maximum vibration levels recorded on both bridges were found to be within the limits recommended in design guides. Wireless technology has the potential to make SHM viable for a much broader range of civil structures than it is at the moment. While some WSNs are readily applicable for quasi-static monitoring, considerable development and system integration effort are required to use existing wireless technology in a reliable SHM system for dynamic monitoring.
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Mmekwa, Keamogetswe Antoinette. "Assessing the economic value of using structural health monitoring systems on South African bridges by studying the Ermelo-Richards Bay Freight Railway line." Master's thesis, University of Cape Town, 2017. http://hdl.handle.net/11427/27480.

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There is a need for appropriate tools and techniques to undertake the vast task of sound repair, maintenance and rehabilitation of concrete infrastructure which is deemed to be deteriorating at unacceptable rates. Low economic growth predictions lead to limited budgets and a deferring of maintenance. The use of technology could be used to extend the useful life of concrete structures. Structural Health Monitoring Systems (SHMS) can be used to monitor structural integrity and the information obtained from these systems can be used in detecting overloading (on bridges for instance) and to alert asset managers of any due maintenance. Büyüköztürk (2007) argues that conventional methods of inspecting the condition of bridges are generally subjective and that this does not give a true reflection of the state of the structure. The objective of this study is to determine the economic value of using SHMS on South African bridges as opposed to conventional bridge inspection methods. The detailed study was conducted on railway bridges on the Transnet Freight Rail (TFR) Ermelo - Richards Bay coal route to assess the contribution that a commodities line such as this one makes to the South African economy. This study makes use of data from Transnet to establish economic value. It is recommended that the results and recommendations be used for a more detailed study into the value of SHMS to be extrapolated for use on other bridges (e.g. road bridges).
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Neves, Cláudia. "Structural Health Monitoring of Bridges : Model-free damage detection method using Machine Learning." Licentiate thesis, KTH, Bro- och stålbyggnad, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-205616.

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This is probably the most appropriate time for the development of robust and reliable structural damage detection systems as aging civil engineering structures, such as bridges, are being used past their life expectancy and beyond their original design loads. Often, when a significant damage to the structure is discovered, the deterioration has already progressed far and required repair is substantial. This is both expensive and has negative impact on the environment and traffic during replacement. For the exposed reasons the demand for efficient Structural Health Monitoring techniques is currently extremely high. This licentiate thesis presents a two-stage model-free damage detection approach based on Machine Learning. The method is applied to data gathered in a numerical experiment using a three-dimensional finite element model of a railway bridge. The initial step in this study consists in collecting the structural dynamic response that is simulated during the passage of a train, considering the bridge in both healthy and damaged conditions. The first stage of the proposed algorithm consists in the design and unsupervised training of Artificial Neural Networks that, provided with input composed of measured accelerations in previous instants, are capable of predicting future output acceleration. In the second stage the prediction errors are used to fit a Gaussian Process that enables to perform a statistical analysis of the distribution of errors. Subsequently, the concept of Damage Index is introduced and the probabilities associated with false diagnosis are studied. Following the former steps Receiver Operating Characteristic curves are generated and the threshold of the detection system can be adjusted according to the trade-off between errors. Lastly, using the Bayes’ Theorem, a simplified method for the calculation of the expected cost of the strategy is proposed and exemplified.

QC 20170420

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Le, Guillarme Jonathan, and Jakob Lindstam. "Implementering av Structural Health Monitoring : SHM - system för detektering och övervakning av vanligt förekommande skador på betongbroar." Thesis, KTH, Byggteknik och design, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-259696.

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Sverige har som många länder runt om i världen en åldrande infrastruktur och behovet av underhåll stiger. I en artikel i Svenska Dagbladet från 21/9–2018 redovisar analys- och teknikkonsultföretaget WSP en grov uppskattning att 300 miljarder kronor behöver investeras för att rusta upp existerande infrastruktur. Efter upprustningen skulle det krävas ca 25 miljarder kronor årligen för att utföra löpande tillståndsbedömning och underhåll av Sveriges väg- och järnvägsnät. Idag används inspektioner för att bedöma broars tillstånd. Det finns tre olika inspektionstyper; huvudinspektion, allmäninspektion och särskild inspektion. Structural Health Monitoring (SHM) är en teknik som globalt används mer och mer som en metod vid tillståndsbedömning av broar. SHM siktar mot att i realtid utföra automatisk bedömning av hela konstruktionens och de enskilda skadornas tillstånd. SHM använder sig av sensorer placerade på kritiska positioner för att samla in mätdata som jämförs med i förtid definierade gränsvärden. I Sverige används SHM sparsamt idag där kunskapen ligger på akademisk nivå och det råder en osäkerhet om hur SHM ska implementeras i praktiken. Genom att implementera SHM, kan skador upptäckas i ett tidigt skede och minimeras genom att snabbt utföra reparationer på konstruktionen innan skadorna blivit kritiska. Studien syftar till att producera en rapport som kan användas som en guide för hur SHM kan implementeras samt visa på hur SHM-systemen har implementerats på tidigare projekt. Rapporten skall ge läsaren en helhetsbild över hur tillståndsbedömning går till idag, vilka skador som är vanligt förekommande samt hur SHM kan användas som ett verktyg vid tillståndsbedömning. Rapporten syftar också till att ge en övergripande förklaring på svenska av SHM-tekniken och vad som behövs för implementering. Öppna ostrukturerade intervjuer genomfördes med forskare inom SHM och skador på betongkonstruktioner samt sakkunniga inom tillståndsbedömning. Intervjuerna användes som utgångspunkt för vidare studier av vanligt förekommande skador och SHM-tekniker. I litteraturstudien användes olika vetenskapliga databaser såsom Diva och ScienceDirect, samt KTH:s bibliotek för att få fram tidigare material om betongskador på broar, deras skademekanismer och om SHM-tekniken. Vidare hämtades information från ett antal doktorsavhandlingar, artiklar och tidigare examensarbeten. Genom litteraturstudie och intervjuer med sakkunniga kom studien fram till att armeringskorrosion och sprickor i betongen är skador som är vanligt förekommande i betongbroar. Skadornas skademekanismer beskrivs i rapporten. Studien identifierade tre olika system som kan användas för detektering och övervakning av armeringskorrosion och sprickor, de systemen är akustisk emission, SOFO-system (SOFO är en förkortning för Surveillance d'Ouvrage par Fibers Optics) och MuST-system (MuST är ett kommersiellt namn). Rapporten ger exempel på hur systemen kan användas för detektering och övervakning av skadorna genom att presentera fyra fallstudier där systemen har använts. Två av fallstudierna presenterar hur akustisk emission har implementeras och två av fallstudierna presenterar hur SOFO-systemet har implementerats. Studien kunde inte identifiera någon fallstudie för MuST-systemet och hur systemet har implementerats. Slutsatsen av studien är att SHM kan användas som ett komplement vid tillståndsbedömningar men man kan inte utföra automatiserade tillståndsbedömningar idag.
Like many countries around the world, Sweden has an aging infrastructure and the need for maintenance is increasing. In an article in Svenska Dagbladet from 21 / 9–2018, the analysis and engineering consulting company WSP reports a rough estimate that SEK 300 billion needs to be invested to upgrade existing infrastructure. After the upgrade, it would require approximately SEK 25 billion annually to carry out ongoing condition assessment and maintenance of Sweden's road and railway networks. Today, inspections are used to assess the condition of bridges. There are three types of inspection; main inspection, general inspection and special inspection. Structural Health Monitoring (SHM) is a technology that is globally more used as a method for condition assessment of bridges. SHM aims to perform automatic assessment of the state of the entire construction and individual damages in real time. SHM uses sensors placed at critical positions to collect measurement data compared to pre-defined limit values. In Sweden, SHM is used sparingly today, where knowledge is at an academic level and there is uncertainty about how SHM should be implemented in practice. By implementing SHM, damage can be detected at an early stage and minimized by quickly performing repairs on the design before the damage becomes critical. The study aims to produce a report that can be used as a guide on how SHM can be implemented and show how the SHM systems have been implemented on previous projects. The report should give the reader an overall picture of how condition assessment is performed today, which damages are common and how SHM can be used as a tool when assessing the condition. The report also aims to provide an overall explanation in Swedish of the SHM technology and what is needed for implementation. Open unstructured interviews were conducted with researchers within SHM and damages to concrete structures as well as experts in condition assessment. The interviews were used as a starting point for further studies of commonly occurring damages and SHM techniques. In the literature study, various scientific databases were used, such as Diva and ScienceDirect, as well as KTH's library to obtain earlier material on concrete damage to bridges, its damage mechanisms and about SHM technology. Furthermore, information was obtained from a number of doctoral dissertations, articles and previous degree projects. Through literature study and interviews with experts, the study concluded that reinforcement corrosion and cracks in the concrete are damages that are commonly found in concrete bridges. The damage mechanisms are described in the report. The study identified three different systems that can be used for detection and monitoring of reinforcement corrosion and cracks, those systems are acoustic emission, SOFO system (SOFO is an abbreviation for Surveillance d'Ouvrage pair of Fiber's Optics) and MuST system (MuST is a commercial name). The report gives examples of how the systems can be used for the detection and monitoring of the damages by presenting four case studies where the systems have been used. Two of the case studies present how acoustic emission has been implemented and two of the case studies present how the SOFO-system has been implemented. The study could not identify any case study for the MuST-system and how the system was implemented. The conclusion of the study is that SHM can be used as a supplement to condition assessments but cannot for the time being used for performing automated condition assessments today.
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Ruffels, Aaron. "Model-Free Damage Detection for a Small-Scale Steel Bridge." Thesis, KTH, Bro- och stålbyggnad, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-232363.

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Around the world bridges are ageing. In Europe approximately two thirds of all railway bridges are over 50 years old. As these structures age, it becomes increasingly important that they are properly maintained. If damage remains undetected this can lead to premature replacement which can have major financial and environmental costs. It is also imperative that bridges are kept safe for the people using them. Thus, it is necessary for damage to be detected as early as possible. This research investigates an unsupervised, model-free damage detection method which could be implemented for continuous structural health monitoring. The method was based on past research by Gonzalez and Karoumi (2015), Neves et al. (2017) and Chalouhi et al. (2017). An artificial neural network (ANN) was trained on accelerations from the healthy structural state. Damage sensitive features were defined as the root mean squared errors between the measured data and the ANN predictions. A baseline healthy state could then be established by presenting the trained ANN with more healthy data. Thereafter, new data could be compared with this reference state. Outliers from the reference data were taken as an indication of damage. Two outlier detection methods were used: Mahalanobis distance and the Kolmogorov-Smirnov test. A model steel bridge with a span of 5 m, width of 1 m and height of approximately 1.7 m was used to study the damage detection method. The use of an experimental model allowed damaged to be freely introduced to the structure. The structure was excited with a 12.7 kg rolling mass at a speed of approximately 2.1 m/s (corresponding to a 20.4 ton axle load moving at 47.8 km/h in full scale). Seven accelerometers were placed on the structure and their locations were determined using an optimal sensor placement algorithm. The objectives of the research were to: identify a number of single damage cases, distinguish between gradual damage cases and identify the location of damage. The proposed method showed promising results and most damage cases were detected by the algorithm. Sensor density and the method of excitation were found to impact the detection of damage. By training the ANN to predict correlations between accelerometers the sensor closest to the damage could be detected, thus successfully localising the damage. Finally, a gradual damage case was investigated. There was a general increase in the damage index for greater damage however, this did not progress smoothly and one case of ‘greater’ damage showed a decrease in the damage index.
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Lee, Sangwook. "Digital color image processing system for civil infrastructure health assessment and monitoring steel bridge coating case." 2005. http://docs.lib.purdue.edu/dissertations/AAI3191509/.

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Thesis (Ph. D.)--Purdue University, 2004.
Includes vita. Includes bibliographical references (leaves 154-160). Also available online via the Purdue University website (http://docs.lib.purdue.edu/).
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Jang, Jinwoo. "Development of Data Analytics and Modeling Tools for Civil Infrastructure Condition Monitoring Applications." Thesis, 2016. https://doi.org/10.7916/D82N52HN.

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This dissertation focuses on the development of data analytics approaches to two distinct important condition monitoring applications in civil infrastructure: structural health monitoring and road surface monitoring. In the first part, measured vibration responses of a major long-span bridge are used to identify its modal properties. Variations in natural frequencies over a daily cycle have been observed with measured data, which are probably due to environmental effects such as temperature and traffic. With a focus on understanding the relationships between natural frequencies and temperatures, a controlled simulation-based study is conducted with the use of a full-scale finite element (FE) model and four regression models. In addition to the temperature effect study, the identified modal properties and the FE model are used to explore both deterministic and probabilistic model updating approaches. In the deterministic approach (sensitivity-based model updating), the regularization technique is applied to deal with a trade-off between natural frequency and mode shape agreements. Specific nonlinear constraints on mode shape agreements are suggested here. Their capabilities to adjust mode shape agreements are validated with the FE model. To the best of the author's knowledge, the sensitivity-based clustering technique, which enables one to determine efficient updating parameters based on a sensitivity analysis, has not previously been applied to any civil structure. Therefore, this technique is adapted and applied to a full-scale bridge model for the first time to highlight its capability and robustness to select physically meaningful updating parameters based on the sensitivity of natural frequencies with respect to both mass and stiffness-related physical parameters. Efficient and physically meaningful updating parameters are determined by the sensitivity-based clustering technique, resulting in an updated model that has a better agreement with measured data sets. When it comes to the probabilistic approach, the application of Bayesian model updating to large-scale civil structures based on real data is very rare and challenging due to the high level of uncertainties associated with the complexity of a large-scale model and variations in natural frequencies and mode shapes identified from real measured data. In this dissertation, the full-scale FE model is updated via the Bayesian model updating framework in an effort to explore the applicability of Bayesian model updating to a more complex and realistic problem. Uncertainties of updating parameters, uncertainty reductions due to information provided by data sets, and uncertainty propagations to modal properties of the FE model are estimated based on generated posterior samples. In the second part of this dissertation, a new innovative framework is developed to collect pavement distress data via multiple vehicles. Vehicle vibration responses are used to detect isolated pavement distress and rough road surfaces. GPS positioning data are used to localize identified road conditions. A real-time local data logging algorithm is developed to increase the efficiency of data logging in each vehicle client. Supervised machine learning algorithms are implemented to classify measured dynamic responses into three categories. Since data are collected from multiple vehicles, the trajectory clustering algorithm is introduced to integrate various trajectories to provide a compact format of information about road surface conditions. The suggested framework is tested and evaluated in real road networks.
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Books on the topic "Infrastructure health monitoring"

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International Conference on Structural Health Monitoring and Intelligent Infrastructure (1st 2003 Tokyo, Japan). Structural health monitoring and intelligent infrastructure: Proceedings of the First International Conference on Structural Health Monitoring and Intelligent Infrastructure, 13-15 November 2003, Tokyo, Japan. Lisse: A. A. Balkema, 2003.

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Ettouney, Mohammed. Infrastructure health in civil engineering: Applications and management. Boca Raton: CRC Press, 2012.

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Reauthorization of the Beaches Environmental Assessment and Coastal Health Act: Hearing before the Subcommittee on Water Resources and Environment of the Committee on Transportation and Infrastructure, House of Representatives, One Hundred Tenth Congress, first session, July 12, 2007. Washington: U.S. G.P.O., 2007.

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Structural Health Monitoring of Civil Infrastructure Systems. Elsevier Science & Technology, 2009.

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Karbhari, Vistasp M., and F. Ansari. Structural Health Monitoring of Civil Infrastructure Systems. Elsevier Science & Technology, 2009.

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Karbhari, Vistasp M., and Farhad Ansari. Structural health monitoring of civil infrastructure systems. Woodhead Publishing Limited, 2009. http://dx.doi.org/10.1533/9781845696825.

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Structural Health Monitoring Of Civil Infrastructure Systems. CRC Press, 2009.

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Ou, Jinping. Structural Health Monitoring and Intelligent Infrastructure (Volume 2). Taylor & Francis, 2005.

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Ou, Jinping. Structural Health Monitoring and Intelligent Infrastructure (Volume 1). Taylor & Francis, 2005.

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Ettouney, Mohammed M., and Sreenivas Alampalli. Infrastructure Health in Civil Engineering. Taylor & Francis Group, 2019.

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Book chapters on the topic "Infrastructure health monitoring"

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Jeong, Seongwoon, Rui Hou, Jerome P. Lynch, and Kincho H. Law. "Structural-Infrastructure Health Monitoring." In Cyber-Physical Systems in the Built Environment, 215–35. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-41560-0_12.

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Collins, Frank, and Frédéric Blin. "Health monitoring and intervention strategies." In Ageing of Infrastructure, 103–29. Boca Raton : Taylor & Francis, a CRC title, part of the Taylor & Francis imprint, a member of the Taylor & Francis Group, the academic division of T&F Informa, plc, [2018]: CRC Press, 2018. http://dx.doi.org/10.1201/9780429455704-7.

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Varshney, Upkar. "Health Monitoring using Infrastructure-oriented." In Pervasive Healthcare Computing, 165–87. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-1-4419-0215-3_8.

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Vanderzee, Peter J., and Frank B. Wingate. "Structural Health Monitoring for Bridges." In Infrastructure Reporting and Asset Management, 178–82. Reston, VA: American Society of Civil Engineers, 2008. http://dx.doi.org/10.1061/9780784409589.ch25.

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Budillon, Alessandra, Giampaolo Ferraioli, Angel Caroline Johnsy, Vito Pascazio, and Gilda Schirinzi. "Infrastructure Health Monitoring Using SAR Tomography." In Lecture Notes in Civil Engineering, 257–70. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-74258-4_18.

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Mehta, Neha, and Archana Chaudhary. "Infrastructure and System of Telemedicine and Remote Health Monitoring." In TELe-Health, 13–28. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-99457-0_2.

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Zhang, Lihai, Nilupa Herath, Babar Nasim Khan Raja, Shilun Chen, Saeed Miramini, and Colin Duffield. "Automation in Structural Health Monitoring of Transport Infrastructure." In Automating Cities, 141–72. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8670-5_6.

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Chen, Jaime, Manuel Díaz, Bartolomé Rubio, and José M. Troya. "RAISE: RAIlway Infrastructure Health Monitoring Using Wireless SEnsor Networks." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 143–57. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-04166-7_10.

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Ha, Dae Woong, Jun Su Park, Jong Moon Kim, and Hyo Seon Park. "Structural Health Monitoring of Infrastructure Using Wireless Sensor System." In Ad Hoc Networks, 228–34. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-13329-4_20.

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Hızal, Çağlayan, and Engin Aktas¸. "Structural Health Monitoring-Integrated Reliability Assessment of Engineering Structures." In Reliability-Based Analysis and Design of Structures and Infrastructure, 117–28. Boca Raton: CRC Press, 2021. http://dx.doi.org/10.1201/9781003194613-9.

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Conference papers on the topic "Infrastructure health monitoring"

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HUSTON, DRYVER, TIAN XIA, DYLAN BURNS, DAN ORFEO, YU ZHANG, and CONNIE OU. "Mapping, Assessing and Monitoring Urban Underground Infrastructure." In Structural Health Monitoring 2017. Lancaster, PA: DEStech Publications, Inc., 2017. http://dx.doi.org/10.12783/shm2017/13873.

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KAYA, YAVUZ, and CARLOS VENTURA. "British Columbia Smart Infrastructure Monitoring System (BCSIMS)." In Structural Health Monitoring 2015. Destech Publications, 2015. http://dx.doi.org/10.12783/shm2015/200.

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Aktan, A. Emin, Fikret N. Catbas, Kirk A. Grimmelsman, and Constantine J. Tsikos. "Health monitoring for infrastructure management." In 1999 Symposium on Smart Structures and Materials, edited by S. C. Liu. SPIE, 1999. http://dx.doi.org/10.1117/12.348663.

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KHALOO, ALI, and DAVID LATTANZI. "Integrating 3D Computer Vision and Robotic Infrastructure Inspection." In Structural Health Monitoring 2017. Lancaster, PA: DEStech Publications, Inc., 2017. http://dx.doi.org/10.12783/shm2017/14231.

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PHILLIPS, STEPHEN, NICHOLAS CHARRON, EVAN MCLAUGHLIN, and SRIRAM NARASIMHAN. "Infrastructure Mapping and Inspection using Mobile Ground Robotics." In Structural Health Monitoring 2019. Lancaster, PA: DEStech Publications, Inc., 2019. http://dx.doi.org/10.12783/shm2019/32469.

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THOMS, ALEXANDER, GABRIEL EARLE, NICHOLAS CHARRON, SVEN MALAMA, and SRIRAM NARASIMHAN. "COMBINED LIDAR AND SONAR MAPPING FOR PARTIALLY SUBMERGED INFRASTRUCTURE." In Structural Health Monitoring 2021. Destech Publications, Inc., 2022. http://dx.doi.org/10.12783/shm2021/36336.

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Advances in robotic mapping, planning, and perception have spurred applications-based robotics research in the domain of infrastructure inspection and preservation. Though a significant portion of this research has centered around the use of unmanned aerial, ground, and underwater vehicles, research in the use of unmanned surface vehicles (USVs) is limited. USVs present a unique opportunity to capture combined maps above and below water, which is essential for the inspection of waterspanning bridges, harbors, dams, and levees. In this paper, we investigate the use of USVs for infrastructure inspection by outfitting a USV platform with a multibeam sonar, horizontally and vertically mounted lidars, several ruggedized RGB cameras, and a high-rate inertial measurement unit (IMU). By time-synchronizing all sensors, we are able to fuse information collected from lidar, camera, and IMU units via tightly-coupled lidar-visual-inertial (LVI) simultaneous mapping and localization (SLAM). We validate our methodology by collecting sensory data of an abandoned quarry and by generating a combined 3D point cloud map using lidar data, multibeam sonar data, and maximum a posteriori trajectory from the LVI SLAM approach. Experiments validate the performance of the proposed USV system, highlighting challenges in extrinsic calibration of non-overlapping sensors, sonar denoising, and refined inter-keyframe pose estimation for key-frame based SLAM approaches.
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LI, SHUO, and MATTEO POZZI. "Parametric Analysis of Value of Information for Monitoring Infrastructure Components." In Structural Health Monitoring 2017. Lancaster, PA: DEStech Publications, Inc., 2017. http://dx.doi.org/10.12783/shm2017/14001.

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HUSTON, DRYVER, TIAN XIA, MAURICIO PEREIRA, DYLAN BURNS, and DANIEL ORFEO. "Subsurface Infrastructure Asset Monitoring with Geophysical Sensors and Augmented Reality." In Structural Health Monitoring 2019. Lancaster, PA: DEStech Publications, Inc., 2019. http://dx.doi.org/10.12783/shm2019/32344.

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FORSTNER, ERNST, PETER FURTNER, and ALBRECHT KARLUSCH. "Automated Infrastructure Inspection Based on Digital Twins and Machine Learning." In Structural Health Monitoring 2019. Lancaster, PA: DEStech Publications, Inc., 2019. http://dx.doi.org/10.12783/shm2019/32345.

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JEONG, SEONGWOON, YILAN ZHANG, JEROME LYNCH, HOON SOHN, and KINCHO LAW. "A NoSQL-based Data Management Infrastructure for Bridge Monitoring Database." In Structural Health Monitoring 2015. Destech Publications, 2015. http://dx.doi.org/10.12783/shm2015/196.

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Reports on the topic "Infrastructure health monitoring"

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Harvey, Dustin Yewell, Eric Brian Flynn, Stuart Glynn Taylor, Charles Reed Farrar, Octavio Jr Ramos, and Kelly Lynn Parker. SHMTools: Structural Health Monitoring Software for Aerospace, Civil, and Mechanical Infrastructure. Office of Scientific and Technical Information (OSTI), April 2015. http://dx.doi.org/10.2172/1178315.

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Roach, Dennis Patrick, David Villegas Jauregui, and Andrew Nicholas Daumueller. Development of a structural health monitoring system for the life assessment of critical transportation infrastructure. Office of Scientific and Technical Information (OSTI), February 2012. http://dx.doi.org/10.2172/1035338.

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Roach, Dennis Patrick, Waylon Anthony Delong, Scott White, Esteban Yepez, Kirk A. Rackow, and Earl David, Jr Reedy. Use of composite materials, health monitoring and self-healing concepts to refurbish our civil and military infrastructure. Office of Scientific and Technical Information (OSTI), September 2007. http://dx.doi.org/10.2172/920441.

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Carpenter, Jo. Monitoring Inclusion in Crises. Institute of Development Studies, September 2022. http://dx.doi.org/10.19088/k4d.2022.103.

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This Briefing Note responds to key challenges articulated by Foreign, Commonwealth & Development (FCDO) staff in monitoring how, and the extent to which, programming, policy and humanitarian interventions in crisis contexts support inclusion. It provides an overview of how to monitor inclusion, focusing on ongoing monitoring during the implementation of interventions. However, there is some crossover with evaluation and learning processes, especially in complex crisis contexts. The information provided is relevant to people working within and across a range of sectors that seek to address the diverse needs that emerge during crises, including social protection; climate resilience and food security; health; water, sanitation and hygiene (WASH); education; livelihoods; infrastructure and economic growth; mental health and psychosocial support; protection; and governance or peacebuilding initiatives.
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Land Disputes and Stalled Investments in India. Rights and Resources Initiative, November 2016. http://dx.doi.org/10.53892/nhew6671.

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India’s ambitious development agenda involves facilitating investment for economic growth, infrastructure development, and social progress. Yet, thousands of investment projects have been stalled to date, raising red flags for the health of the country’s financial regulatory systems, public sector banks, and investment community. While official reasons given for stalled projects remain opaque, deep contestation leading to conflict on public (and private) lands must be better understood as a substantive risk to investments. An improved understanding of the actual causes of stalled projects will not only help investors, financial institutions and regulators make better decisions, but also inform public policies regarding communities’ property rights and provide a path to more inclusive development. This new analysis—initiated by the Rights and Resources Initiative and the Bharti Institute of Public Policy, Indian School of Business—seeks to provide evidence-based insight into this complex subject. It aims to inform policy discussions and interventions that can mitigate the current situation. The study is part of a larger geo-spatial analytical platform being developed by the Bharti Institute of Public Policy. This brief is based on the interim findings of the ongoing study, which are significant enough to be shared widely and considered in proposed policy interventions. The main source of data on stalled projects in India is the CapEx database from the Center for Monitoring Indian Economy (CMIE).
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