Integrated fibre-optic sensor networks as tools for monitoring strain development in bridges during construction

Long-term asset management and maintenance of civil infrastructure relies on having access to reliable performance data in order to inform critical decision-making processes. This paper discusses the development and implementation of a robust, innovative and highly distributed fibre-optic sensor network for use as a bridge monitoring and performance evaluation tool. The main steel girders of a new 26.8 metre span half-through steel railway bridge were each instrumented with 80 fibre Bragg grating (FBG) based sensors (spaced at 1 metre) prior to the casting of the concrete deck. Two major challenges with implementing fibre-optic monitoring systems remain prominent: appropriately compensating for strain changes due to temperature, and designing the system to be sufficiently robust to survive installation and continuous long-term operation. This study addresses these challenges through the implementation of a new temperature compensation sensor cable packaging and the deployment of glass-fibre reinforced strain FBG sensor cables with the aim of improving overall network reliability. The completed system is capable of measuring the dynamic strain of all installed FBG sensors simultaneously at sampling rates of 250 Hz to strain resolutions within ±10 microstrain. Data was collected and initial results are presented for the strain developed within the main girders during the casting and curing of the concrete deck. The sensor readings captured the quasi-distributed profile of strains developed along the main girders due to the casting and curing of the concrete deck and have provided insights into understanding the complex thermal response of the structure. This study demonstrates that integrated structural health monitoring systems installed at the time of construction can provide a complete record of the entire load history of a structure. Performance data of this type is invaluable for understanding the behaviour of composite concrete decks, evaluating future structural capacity, establishing long term monitoring programmes, and allowing performance-based asset management decision making

Distributed machining control and monitoring using smart sensors/actuators

The study of smart sensors and actuators led, during the past few years, to the development of facilities which improve traditional sensors and actuators in a necessary way to automate production systems. In an other context, many studies are carried out aiming at defining a decisional structure for production activity control and the increasing need of reactivity leads to the autonomization of decisional levels close to the operational system. We suggest in this paper to study the natural convergence between these two approaches and we propose an integration architecture dealing with machine tool and machining control that enables the exploitation of distributed smart sensors and actuators in the decisional system

AI and OR in management of operations: history and trends

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested

Big Data and the Internet of Things

Advances in sensing and computing capabilities are making it possible to embed increasing computing power in small devices. This has enabled the sensing devices not just to passively capture data at very high resolution but also to take sophisticated actions in response. Combined with advances in communication, this is resulting in an ecosystem of highly interconnected devices referred to as the Internet of Things - IoT. In conjunction, the advances in machine learning have allowed building models on this ever increasing amounts of data. Consequently, devices all the way from heavy assets such as aircraft engines to wearables such as health monitors can all now not only generate massive amounts of data but can draw back on aggregate analytics to "improve" their performance over time. Big data analytics has been identified as a key enabler for the IoT. In this chapter, we discuss various avenues of the IoT where big data analytics either is already making a significant impact or is on the cusp of doing so. We also discuss social implications and areas of concern.Comment: 33 pages. draft of upcoming book chapter in Japkowicz and Stefanowski (eds.) Big Data Analysis: New algorithms for a new society, Springer Series on Studies in Big Data, to appea