Sean M. O'Connor

Abstract: Compressed sensing (CS) is a powerful new data acquisition paradigm that seeks to accurately reconstruct unknown sparse signals from very few (relative to the target signal dimension) random projections. The specific objective of this study is to save wireless sensor energy by using CS to simultaneously reduce data sampling rates, on-board storage requirements, and communication data payloads. For field-deployed low power wireless sensors that are often operated with limited energy sources, reduced communication translates directly into reduced power consumption and improved operational reliability. In this study, acceleration data from a multi-girder steel-concrete deck composite bridge are processed for the extraction of mode shapes. A wireless sensor node previously designed to perform traditional uniform, Nyquist rate sampling is modified to perform asynchronous, effectively sub-Nyquist rate sampling. The sub-Nyquist data are transmitted off-site to a computational server for reconstruction using the CoSaMP matching pursuit recovery algorithm and further processed for extraction of the structure?s mode shapes. The mode shape metric used for reconstruction quality is the modal assurance criterion (MAC), an indicator of the consistency between CS and traditional Nyquist acquired mode shapes. A comprehensive investigation of modal accuracy from a dense set of acceleration response data reveals that MAC values above 0.90 are obtained for the first four modes of a bridge structure when at least 20% of the original signal is sampled using the CS framework. Reduced data collection, storage and communication requirements are found to lead to substantial reductions in the energy requirements of wireless sensor networks at the expense of modal accuracy. Specifically, total energy reductions of 10?60% can be obtained for a sensor network with 10?100 sensor nodes, respectively. The reduced energy requirements of the CS sensor nodes are shown to directly result in improved battery life and communication reliability.

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

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...

Abstract: This paper describes results of an experimental study that used sensing methods for monitoring damage along segmental concrete pipelines resulting from permanent ground displacement across a simulated earthquake fault. The literature contains examples of such damage occurring during actual earthquakes, significantly impacting the functionality of the pipelines. Detecting the location of the damage and the extent of the damage in pipelines can significantly accelerate post-earthquake repair efforts. In this paper, electrical sensing methods, magnetic sensing, and acoustic emission are used to monitor structural damage in a segmental concrete pipeline during a large-scale test. In this test, the segmental concrete pipeline was subjected to a concentrated transverse permanent ground displacements (PGDs). The majority of the damage to the pipe segments was localized at the joints, especially the bell sections while the damage to the spigots was minimal. The damage extended away from the joints in the pipe segments in the immediate vicinity of the fault line. Telescoping (i.e., crushing of the bell-and-spigot) was a primary mode of failure that was observed. The results of this study indicate that electrical sensing methods (including the use of conductive grout), magnetic sensing, and acoustic emission, employed alone or in combination, can detect and quantify the damage in segmental concrete pipelines.

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.

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.

Abstract: Fluid ingress is a primary factor that influences freeze-thaw damage in concrete. This paper discusses the influence of fluid ingress on freeze-thaw damage development. Specifically, this paper examines the influence of entrained air content on the rate of water absorption, the degree of saturation, and the relationship between the saturation level and freeze-thaw damage. The results indicate that whereas air content delays the time it takes for concrete to reach a critical degree of saturation it will not prevent the freeze-thaw damage from occurring. The results of the experiments show that when the degree of saturation exceeds 86–88%, freeze-thaw damage is inevitable with or without entrained air even with very few freeze-thaw cycles.

Abstract: 5.3. Aromatic Ring Formation 1576 5.3.1. One Substituent at C-2 1576 5.3.2. Two Substituents at C-2 and C-3 1577 5.3.3. Two Substituents at C-2 and C-5 1577 5.3.4. Two Substituents at C-3 and C-4 1577 5.3.5. Three Substituents at C-2, C-3, and C-4 1578 5.3.6. Four Substituents at Carbon Atoms 1579 5.3.7. Benzotriazolyl Substituent at the Ring 1579 6. Furan 1580 6.1. Nonaromatic Rings 1580 6.1.1. Tetrahydrofurans 1580 6.1.2. 2,5-Dihydrofurans 1580 6.2. Electrophilic Substitution of the Aromatic Ring 1581 6.3. Aromatic Ring Formation 1582 7. Thiophene 1583 7.1. Nonaromatic Rings 1583 7.2. Electrophilic Substitution of the Aromatic Ring 1583 7.3. Aromatic Ring Formation 1583 8. Five-Membered Rings with Two Heteroatoms 1584 8.1. Pyrazole 1584 8.2. Imidazole 1586 8.3. Isoxazole 1589 8.4. Oxazole 1590 8.5. Thiazole 1592 9. Five-Membered Rings with Three or Four Heteroatoms 1594

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.

Abstract: This paper outlines the development of a large-area sensing skin for damage detection in concrete structures. The developed sensing skin consists of a thin layer of electrically conductive copper paint that is applied to the surface of the concrete. Cracking of the concrete substrate results in the rupture of the sensing skin, decreasing its electrical conductivity locally. The decrease in conductivity is detected with electrical impedance tomography (EIT) imaging. In previous works, electrically based sensing skins have provided only qualitative information on the damage on the substrate surface. In this paper, we study whether quantitative imaging of the damage is possible. We utilize application-specific models and computational methods in the image reconstruction, including a total variation (TV) prior model for the damage and an approximate correction of the modeling errors caused by the inhomogeneity of the painted sensing skin. The developed damage detection method is tested experimentally by applying the sensing skin to polymeric substrates and a reinforced concrete beam under four-point bending. In all test cases, the EIT-based sensing skin provides quantitative information on cracks and/or other damages on the substrate surface: featuring a very low conductivity in the damage locations, and a reliable indication of the lengths and shapes of the cracks. The results strongly support the applicability of the painted EIT-based sensing skin for damage detection in reinforced concrete elements and other substrates.