Yilan Zhang

Bio: Yilan Zhang is an academic researcher from University of Michigan. The author has contributed to research in topics: Wireless sensor network & Structural health monitoring. The author has an hindex of 8, co-authored 19 publications receiving 265 citations.

Long-term testing of a vibration harvesting system for the structural health monitoring of bridges

Abstract: Advances in energy harvesting systems are needed to power wireless sensors for structural health monitoring. Research on developing a harvesting system that converts the low frequency, non-periodic, and low-acceleration vibrations present on bridges is continued and significantly extended in this work. The mechanics of the harvester were optimized to increase its robustness and lifetime, power electronics were added, and the complete system was installed on the New Carquinez suspension bridge in California. The complete results and analysis are presented in this study. The power management circuit is added to rectify and boost the low AC output of the harvester and convert it into a usable DC voltage. The harvester design is further enhanced to significantly improve performance and robustness. During short-term on-bridge testing, the system was able to charge a 10 μF capacitor to 2 V DC, and the average harvester output power ranges from 1.6 to 5.0 μW, depending on the location on the bridge, a 10× improvement over previous results. A long-term test of the harvesting system has been conducted, during which the performance of the system was monitored remotely using a wireless sensor network. The system improvements described in this study enabled continuous operation in the harsh bridge environment for 13 months starting April 30, 2012 and constitute a major milestone in the development of miniaturized motion harvesters. Finally, the system was retrieved and analyzed to understand and verify the cause of observed long-term performance changes.

Long-term performance assessment of the Telegraph Road Bridge using a permanent wireless monitoring system and automated statistical process control analytics

Abstract: The purpose of this study is to advance wireless sensing technology for permanent installation in operational highway bridges for long-term automated health assessment The work advances the design of a solar-powered wireless sensor network architecture that can be permanently deployed in harsh winter climates where limited solar energy and cold temperatures are normal operational conditions To demonstrate the performance of the solar-powered wireless sensor network, it is installed on the multi-steel girder bridge carrying northbound I-275 traffic over Telegraph Road (Monroe, Michigan) in 2011; a unique design feature of the bridge is the use of pin and hanger connections to support the bridge main span A dense network of strain gauges, accelerometers and thermometers are installed to acquire bridge responses of interest to the bridge manager including responses that would be affected by long-term bridge deterioration The wireless monitoring system collects sensor data on a daily schedule and

Long-Term Modal Analysis of Wireless Structural Monitoring Data from a Suspension Bridge under Varying Environmental and Operational Conditions: System Design and Automated Modal Analysis

Abstract: Structural monitoring systems installed on cable-supported bridges have the potential to generate large data repositories from which a deeper understanding of bridge behavior can be obtaine...

SenStore: A Scalable Cyberinfrastructure Platform for Implementation of Data-to-Decision Frameworks for Infrastructure Health Management

Abstract: Structural monitoring systems are an objective and quantitative-based management tool that have been developed to assist structure owners with their diagnostic and prognostic decision making processes. As sensing technologies mature, the deployment of permanent sensing arrays in structures is becoming more popular, resulting in increasing volumes of sensing data. Comprehensive data-management systems are needed to host and curate heterogeneous data sets associated with structural asset management including structural information (design and inspection information) and massive amounts of sensing data. More importantly, the data-management system must also provide a functional but secure means of allowing software clients to perform analytics on the data contained in the system. In this paper, a scalable and secure cyberinfrastructure platform termed SenStore is introduced for the management and automated analysis of sensing data. SenStore includes a hybrid database architecture that maximizes query...

A NoSQL data management infrastructure for bridge monitoring

Abstract: Advances in sensor technologies have led to the instrumentation of sensor networks for bridge monitoring and management. For a dense sensor network, enormous amount of sensor data are collected. The data need to be managed, processed, and interpreted. Data management issues are of prime importance for a bridge management system. This paper describes a data management infrastructure for bridge monitoring applications. Specifically, NoSQL database systems such as MongoDB and Apache Cassandra are employed to handle time-series data as well the unstructured bridge information model data. Standard XML-based modeling languages such as OpenBrIM and SensorML are adopted to manage semantically meaningful data and to support interoperability. Data interoperability and integration among different components of a bridge monitoring system that includes on-site computers, a central server, local computing platforms, and mobile devices are illustrated. The data management framework is demonstrated using the data collected from the wireless sensor network installed on the Telegraph Road Bridge, Monroe, MI.

High-Performance Piezoelectric Energy Harvesters and Their Applications

Abstract: Energy harvesting holds great potential to achieve long-lifespan self-powered operations of wireless sensor networks, wearable devices, and medical implants, and thus has attracted substantial interest from both academia and industry. This paper presents a comprehensive review of piezoelectric energy-harvesting techniques developed in the last decade. The piezoelectric effect has been widely adopted to convert mechanical energy to electricity, due to its high energy conversion efficiency, ease of implementation, and miniaturization. From the viewpoint of applications, we are most concerned about whether an energy harvester can generate sufficient power under a variable excitation. Therefore, here we concentrate on methodologies leading to high power output and broad operational bandwidth. Different designs, nonlinear methods, optimization techniques, and harvesting materials are reviewed and discussed in depth. Furthermore, we identify four promising applications: shoes, pacemakers, tire pressure monitoring systems, and bridge and building monitoring. We review new high-performance energy harvesters proposed for each application.

Big Data in the construction industry

Abstract: Existing works for Big Data Analytics/Engineering in the construction industry are discussed.It is highlighted that the adoption of Big Data is still at nascent stageOpportunities to employ Big Data technologies in construction sub-domains are highlighted.Future works for Big Data technologies are presented.Pitfalls of Big Data technologies in the construction industry are also pointed out. The ability to process large amounts of data and to extract useful insights from data has revolutionised society. This phenomenon-dubbed as Big Data-has applications for a wide assortment of industries, including the construction industry. The construction industry already deals with large volumes of heterogeneous data; which is expected to increase exponentially as technologies such as sensor networks and the Internet of Things are commoditised. In this paper, we present a detailed survey of the literature, investigating the application of Big Data techniques in the construction industry. We reviewed related works published in the databases of American Association of Civil Engineers (ASCE), Institute of Electrical and Electronics Engineers (IEEE), Association of Computing Machinery (ACM), and Elsevier Science Direct Digital Library. While the application of data analytics in the construction industry is not new, the adoption of Big Data technologies in this industry remains at a nascent stage and lags the broad uptake of these technologies in other fields. To the best of our knowledge, there is currently no comprehensive survey of Big Data techniques in the context of the construction industry. This paper fills the void and presents a wide-ranging interdisciplinary review of literature of fields such as statistics, data mining and warehousing, machine learning, and Big Data Analytics in the context of the construction industry. We discuss the current state of adoption of Big Data in the construction industry and discuss the future potential of such technologies across the multiple domain-specific sub-areas of the construction industry. We also propose open issues and directions for future work along with potential pitfalls associated with Big Data adoption in the industry.

Building Information Modeling (BIM) for transportation infrastructure – Literature review, applications, challenges, and recommendations

Abstract: Transportation infrastructure is a critical component to a nation’s economy, security, and wellbeing. In order to keep up with the rising population, there is a great need for more efficient and cost-effective technologies and techniques to not only repair the infrastructure, but also to advance and expand the transportation infrastructure to sustain the growing population. Building Information Modeling (BIM) has been widely adopted in the building industry, and its established methods and technologies show enormous potential in benefiting the transportation industry. The purpose of this paper is to present a literature review and critical analysis of BIM for transportation infrastructure. A total of 189 publications in the area of BIM for transportation infrastructure were reviewed, including journal articles, conference proceedings, and published reports. Additionally, schemas and file formats from 9 main categories and 34 areas related to transportation infrastructure were reviewed. An application was developed to collect, store, and analyze the publications. Various algorithms were developed and implemented to help in the automation and analysis of the review. The goal of this paper is to provide a comprehensive, up-to-date literature review and critical analysis of research areas regarding BIM for transportation infrastructure to further facilitate research and applications in this domain. Based on the results of the analysis, current topics and trends, applications and uses, emerging technologies, benefits, challenges and limitations, research gaps, and future needs are discussed. Significantly, the contribution of this paper is providing the foundation of current research, gaps, and emerging technologies needed to facilitate further research and applications for both academia and industry stakeholders to develop more efficient and cost-effective techniques necessary to repair, advance, and expand the transportation infrastructure. Furthermore, the results show that the use of BIM for transportation infrastructure has been increasing, although the research has mainly been focusing on roads, highways, and bridges. The results also reveal a major need for a standard neutral exchange format and schema to promote interoperability. Most importantly, the continuing collaboration between academia and industry is required to mitigate most challenges and to realize the full potential of BIM for transportation infrastructure.

Recent Developments on Wireless Sensor Networks Technology for Bridge Health Monitoring

Abstract: Structural health monitoring (SHM) systems have shown great potential to sense the responses of a bridge system, diagnose the current structural conditions, predict the expected future performance, provide information for maintenance, and validate design hypotheses. Wireless sensor networks (WSNs) that have the benefits of reducing implementation costs of SHM systems as well as improving data processing efficiency become an attractive alternative to traditional tethered sensor systems. This paper introduces recent technology developments in the field of bridge health monitoring using WSNs. As a special application of WSNs, the requirements and characteristics of WSNs when used for bridge health monitoring are firstly briefly discussed. Then, the state of the art in WSNs-based bridge health monitoring systems is reviewed including wireless sensor, network topology, data processing technology, power management, and time synchronization. Following that, the performance validations and applications of WSNs in bridge health monitoring through scale models and field deployment are presented. Finally, some existing problems and promising research efforts for promoting applications of WSNs technology in bridge health monitoring throughout the world are explored.

Big Data for transportation and mobility: recent advances, trends and challenges

Abstract: Big Data is an emerging paradigm and has currently become a strong attractor of global interest, specially within the transportation industry. The combination of disruptive technologies and new concepts such as the Smart City upgrades the transport data life cycle. In this context, Big Data is considered as a new pledge for the transportation industry to effectively manage all data this sector required for providing safer, cleaner and more efficient transport means, as well as for users to personalize their transport experience. However, Big Data comes along with its own set of technological challenges, stemming from the multiple and heterogeneous transportation/mobility application scenarios. In this survey we analyze the latest research efforts revolving on Big Data for the transportation and mobility industry, its applications, baselines scenarios, fields and use case such as routing, planning, infrastructure monitoring, network design, among others. This analysis will be done strictly from the Big Data perspective, focusing on those contributions gravitating on techniques, tools and methods for modeling, processing, analyzing and visualizing transport and mobility Big Data. From the literature review a set of trends and challenges is extracted so as to provide researchers with an insightful outlook on the field of transport and mobility.