Structural health monitoring and bridge condition assessment

Thesis (Ph.D.) University of Alaska Fairbanks, 2016This research is mainly in the field of structural identification and model calibration, optimal sensor placement, and structural health monitoring application for large-scale structures. The ultimate goal of this study is to identify the structure behavior and evaluate the health condition by using structural health monitoring system. To achieve this goal, this research firstly established two fiber optic structural health monitoring systems for a two-span truss bridge and a five-span steel girder bridge. Secondly, this research examined the empirical mode decomposition (EMD) method’s application by using the portable accelerometer system for a long steel girder bridge, and identified the accelerometer number requirements for comprehensively record bridge modal frequencies and damping. Thirdly, it developed a multi-direction model updating method which can update the bridge model by using static and dynamic measurement. Finally, this research studied the optimal static strain sensor placement and established a new method for model parameter identification and damage detection.Chapter 1: Introduction -- Chapter 2: Structural Health Monitoring of the Klehini River Bridge -- Chapter 3: Ambient Loading and Modal Parameters for the Chulitna River Bridge -- Chapter 4: Multi-direction Bridge Model Updating using Static and Dynamic Measurement -- Chapter 5: Optimal Static Strain Sensor Placement for Bridge Model Parameter Identification by using Numerical Optimization Method -- Chapter 6: Conclusions and Future Work

Damage identification in structural health monitoring: a brief review from its implementation to the Use of data-driven applications

The damage identification process provides relevant information about the current state of a structure under inspection, and it can be approached from two different points of view. The first approach uses data-driven algorithms, which are usually associated with the collection of data using sensors. Data are subsequently processed and analyzed. The second approach uses models to analyze information about the structure. In the latter case, the overall performance of the approach is associated with the accuracy of the model and the information that is used to define it. Although both approaches are widely used, data-driven algorithms are preferred in most cases because they afford the ability to analyze data acquired from sensors and to provide a real-time solution for decision making; however, these approaches involve high-performance processors due to the high computational cost. As a contribution to the researchers working with data-driven algorithms and applications, this work presents a brief review of data-driven algorithms for damage identification in structural health-monitoring applications. This review covers damage detection, localization, classification, extension, and prognosis, as well as the development of smart structures. The literature is systematically reviewed according to the natural steps of a structural health-monitoring system. This review also includes information on the types of sensors used as well as on the development of data-driven algorithms for damage identification.Peer ReviewedPostprint (published version

A Bayesian Approach to Sensor Placement and System Health Monitoring

System health monitoring and sensor placement are areas of great technical and scientific interest. Prognostics and health management of a complex system require multiple sensors to extract required information from the sensed environment, because no single sensor can obtain all the required information reliably at all times. The increasing costs of aging systems and infrastructures have become a major concern, and system health monitoring techniques can ensure increased safety and reliability of these systems. Similar concerns also exist for newly designed systems. The main objectives of this research were: (1) to find an effective way for optimal functional sensor placement under uncertainty, and (2) to develop a system health monitoring approach with both prognostic and diagnostic capabilities with limited and uncertain information sensing and monitoring points. This dissertation provides a functional/information --based sensor placement methodology for monitoring the health (state of reliability) of a system and utilizes it in a new system health monitoring approach. The developed sensor placement method is based on Bayesian techniques and is capable of functional sensor placement under uncertainty. It takes into account the uncertainty inherent in characteristics of sensors as well. It uses Bayesian networks for modeling and reasoning the uncertainties as well as for updating the state of knowledge for unknowns of interest and utilizes information metrics for sensor placement based on the amount of information each possible sensor placement scenario provides. A new system health monitoring methodology is also developed which is: (1) capable of assessing current state of a system's health and can predict the remaining life of the system (prognosis), and (2) through appropriate data processing and interpretation can point to elements of the system that have or are likely to cause system failure or degradation (diagnosis). It can also be set up as a dynamic monitoring system such that through consecutive time steps, the system sensors perform observations and send data to the Bayesian network for continuous health assessment. The proposed methodology is designed to answer important questions such as how to infer the health of a system based on limited number of monitoring points at certain subsystems (upward propagation); how to infer the health of a subsystem based on knowledge of the health of the main system (downward propagation); and how to infer the health of a subsystem based on knowledge of the health of other subsystems (distributed propagation)

Optimal sensor placement for sewer capacity risk management

2019 Spring.Includes bibliographical references.Complex linear assets, such as those found in transportation and utilities, are vital to economies, and in some cases, to public health. Wastewater collection systems in the United States are vital to both. Yet effective approaches to remediating failures in these systems remains an unresolved shortfall for system operators. This shortfall is evident in the estimated 850 billion gallons of untreated sewage that escapes combined sewer pipes each year (US EPA 2004a) and the estimated 40,000 sanitary sewer overflows and 400,000 backups of untreated sewage into basements (US EPA 2001). Failures in wastewater collection systems can be prevented if they can be detected in time to apply intervention strategies such as pipe maintenance, repair, or rehabilitation. This is the essence of a risk management process. The International Council on Systems Engineering recommends that risks be prioritized as a function of severity and occurrence and that criteria be established for acceptable and unacceptable risks (INCOSE 2007). A significant impediment to applying generally accepted risk models to wastewater collection systems is the difficulty of quantifying risk likelihoods. These difficulties stem from the size and complexity of the systems, the lack of data and statistics characterizing the distribution of risk, the high cost of evaluating even a small number of components, and the lack of methods to quantify risk. This research investigates new methods to assess risk likelihood of failure through a novel approach to placement of sensors in wastewater collection systems. The hypothesis is that iterative movement of water level sensors, directed by a specialized metaheuristic search technique, can improve the efficiency of discovering locations of unacceptable risk. An agent-based simulation is constructed to validate the performance of this technique along with testing its sensitivity to varying environments. The results demonstrated that a multi-phase search strategy, with a varying number of sensors deployed in each phase, could efficiently discover locations of unacceptable risk that could be managed via a perpetual monitoring, analysis, and remediation process. A number of promising well-defined future research opportunities also emerged from the performance of this research

Development of a methodology for the optimal sensor placement to optimize air temperature monitoring in large spaces.

openLa presente tesi descrive lo sviluppo e la validazione di un tool, Sensor Optimization Unit (SOU), per l’ottimizzazione della misura della temperatura dell’aria in grandi ambienti, dove impianti HVAC mantengono condizioni di comfort termico ottimali, tramite un controllo basato sulla misura della temperatura dell’aria all’interno dell’ambiente, trascurando la distribuzione della stessa. Il SOU caratterizza la distribuzione del gradiente orizzontale di temperatura all’interno dell’ambiente attraverso simulazione, misurazioni o un approccio ibrido. Un algoritmo di ottimizzazione, basato su un innovativo indice di performance di misura, definisce il numero minimo e la posizione ottimale di sensori da installare all’interno dell’ambiente, al fine di massimizzare l’accuratezza nella misura della temperatura. L’ottimizzazione continua valutando l’impatto dell’errore di misura sul comfort termico e sui consumi energetici dell’HVAC. La metodologia sviluppata è stata applicata e validata su tre casi di studio reali. L’errato posizionamento di un termostato, all’interno di una piscina indoor, ha generato un valore di incertezza della misura superiore all’accuratezza del sensore stesso per il 42% del periodo preso in considerazione. La soluzione ottima calcolata dal SOU ha ridotto questo il valore al 1.5% del periodo stesso. L’applicazione del SOU in una sala fitness ha confermato come soluzione ottima, calcolata dal tool, tramite l’applicazione dell’indice di performance, coincida con quella calcolata tramite valutazione dell’impatto dell’incertezza di misura sul comfort e consumi dell’HVAC. L’indice di performance di misura è stato applicato ad un ufficio open space, dove il monitoraggio della temperatura avviene tramite una rete di sensori controllati da un BMS. La selezione ottimizzata di soli due, dei sei sensori disponibili, garantisce un’accuratezza della misura all’interno dell’incertezza del sensore stesso.The present PhD thesis summarizes the development and validation of a tool called Sensor Optimization Unit (SOU), meant to be used by HVAC engineers, for the optimization of temperature sensors placement in large spaces, where the HVAC system provides indoor thermal comfort conditions, which involves mostly air temperature control, without taking into account the indoor air temperature distribution inside the space on the monitoring task. The SOU allows approaches based on simulation model, field measurement or calibrated simulation model to characterize the indoor horizontal air temperature distribution. A modular optimization approach based on a novel measurement performance index is proposed, which evaluates the sensor network design, determining the optimal sensors location that provides the maximum measurement accuracy, using the minimum number of sensors. The optimization process evaluates, also, the impact of the measurement deviation on thermal comfort and energy consumption due to HVAC operation. The entire methodology was applied and validated on three different case studies. The incorrect placement of an existing thermostat, inside an indoor swimming pool, showed that the measurement uncertainty was higher than the sensor uncertainty (value from sensor datasheet) for the 42% of the period considered. The optimized sensor network design decreased that period to 1.5% of the overall time. The entire optimization procedure was also applied to a fitness room. The optimal monitoring solution retrieved by application of the measurement performance index was compliance with the one calculated as measurement uncertainty impact on thermal comfort and energy consumption. The measurement performance index was applied to an open space office equipped with a widespread set of temperature sensors controlled by a BMS. The selection of two of the six sensors available, still assuring a measurement accuracy inside the uncertainty of the sensor.INGEGNERIA MECCANICA E GESTIONALE2017-03-11Scuola di Dottorato di Ricerca in Scienze dell'IngegneriaopenSeri, Federic

Development of a methodology for the optimal sensor placement to optimize air temperature monitoring in large spaces.

La presente tesi descrive lo sviluppo e la validazione di un tool, Sensor Optimization Unit (SOU), per l’ottimizzazione della misura della temperatura dell’aria in grandi ambienti, dove impianti HVAC mantengono condizioni di comfort termico ottimali, tramite un controllo basato sulla misura della temperatura dell’aria all’interno dell’ambiente, trascurando la distribuzione della stessa. Il SOU caratterizza la distribuzione del gradiente orizzontale di temperatura all’interno dell’ambiente attraverso simulazione, misurazioni o un approccio ibrido. Un algoritmo di ottimizzazione, basato su un innovativo indice di performance di misura, definisce il numero minimo e la posizione ottimale di sensori da installare all’interno dell’ambiente, al fine di massimizzare l’accuratezza nella misura della temperatura. L’ottimizzazione continua valutando l’impatto dell’errore di misura sul comfort termico e sui consumi energetici dell’HVAC. La metodologia sviluppata è stata applicata e validata su tre casi di studio reali. L’errato posizionamento di un termostato, all’interno di una piscina indoor, ha generato un valore di incertezza della misura superiore all’accuratezza del sensore stesso per il 42% del periodo preso in considerazione. La soluzione ottima calcolata dal SOU ha ridotto questo il valore al 1.5% del periodo stesso. L’applicazione del SOU in una sala fitness ha confermato come soluzione ottima, calcolata dal tool, tramite l’applicazione dell’indice di performance, coincida con quella calcolata tramite valutazione dell’impatto dell’incertezza di misura sul comfort e consumi dell’HVAC. L’indice di performance di misura è stato applicato ad un ufficio open space, dove il monitoraggio della temperatura avviene tramite una rete di sensori controllati da un BMS. La selezione ottimizzata di soli due, dei sei sensori disponibili, garantisce un’accuratezza della misura all’interno dell’incertezza del sensore stesso.The present PhD thesis summarizes the development and validation of a tool called Sensor Optimization Unit (SOU), meant to be used by HVAC engineers, for the optimization of temperature sensors placement in large spaces, where the HVAC system provides indoor thermal comfort conditions, which involves mostly air temperature control, without taking into account the indoor air temperature distribution inside the space on the monitoring task. The SOU allows approaches based on simulation model, field measurement or calibrated simulation model to characterize the indoor horizontal air temperature distribution. A modular optimization approach based on a novel measurement performance index is proposed, which evaluates the sensor network design, determining the optimal sensors location that provides the maximum measurement accuracy, using the minimum number of sensors. The optimization process evaluates, also, the impact of the measurement deviation on thermal comfort and energy consumption due to HVAC operation. The entire methodology was applied and validated on three different case studies. The incorrect placement of an existing thermostat, inside an indoor swimming pool, showed that the measurement uncertainty was higher than the sensor uncertainty (value from sensor datasheet) for the 42% of the period considered. The optimized sensor network design decreased that period to 1.5% of the overall time. The entire optimization procedure was also applied to a fitness room. The optimal monitoring solution retrieved by application of the measurement performance index was compliance with the one calculated as measurement uncertainty impact on thermal comfort and energy consumption. The measurement performance index was applied to an open space office equipped with a widespread set of temperature sensors controlled by a BMS. The selection of two of the six sensors available, still assuring a measurement accuracy inside the uncertainty of the sensor

Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey

Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs