Roadmap on measurement technologies for next generation structural health monitoring systems

Structural health monitoring (SHM) is the automation of the condition assessment process of an engineered system. When applied to geometrically large components or structures, such as those found in civil and aerospace infrastructure and systems, a critical challenge is in designing the sensing solution that could yield actionable information. This is a difficult task to conduct cost-effectively, because of the large surfaces under consideration and the localized nature of typical defects and damages. There have been significant research efforts in empowering conventional measurement technologies for applications to SHM in order to improve performance of the condition assessment process. Yet, the field implementation of these SHM solutions is still in its infancy, attributable to various economic and technical challenges. The objective of this Roadmap publication is to discuss modern measurement technologies that were developed for SHM purposes, along with their associated challenges and opportunities, and to provide a path to research and development efforts that could yield impactful field applications. The Roadmap is organized into four sections: distributed embedded sensing systems, distributed surface sensing systems, multifunctional materials, and remote sensing. Recognizing that many measurement technologies may overlap between sections, we define distributed sensing solutions as those that involve or imply the utilization of numbers of sensors geometrically organized within (embedded) or over (surface) the monitored component or system. Multi-functional materials are sensing solutions that combine multiple capabilities, for example those also serving structural functions. Remote sensing are solutions that are contactless, for example cell phones, drones, and satellites. It also includes the notion of remotely controlled robots

Machine learning algorithms for monitoring pavement performance

ABSTRACT: This work introduces the need to develop competitive, low-cost and applicable technologies to real roads to detect the asphalt condition by means of Machine Learning (ML) algorithms. Specifically, the most recent studies are described according to the data collection methods: images, ground penetrating radar (GPR), laser and optic fiber. The main models that are presented for such state-of-the-art studies are Support Vector Machine, Random Forest, Naïve Bayes, Artificial neural networks or Convolutional Neural Networks. For these analyses, the methodology, type of problem, data source, computational resources, discussion and future research are highlighted. Open data sources, programming frameworks, model comparisons and data collection technologies are illustrated to allow the research community to initiate future investigation. There is indeed research on ML-based pavement evaluation but there is not a widely used applicability by pavement management entities yet, so it is mandatory to work on the refinement of models and data collection methods

Optical sensing for nondestructive structural evaluation and additive manufacturing process monitoring

Sensing is a significant engineering science which quantify parameters from the physical world and discover the physics running behind the measurement process. Optical sensing makes use of electromagnetic waves from infrared to ultraviolet on the light spectrum as a medium to measure variables, such as position, temperature and strain. Image sensing and fiber sensing are two of the most widely applied optical sensing methods in industries and daily life. They have been studied by the academia for decades, due to their immunity to electromagnetic interference and ease of installation. This dissertation introduced the research on the intelligent and flexible metrology methodologies for real-time structure and process monitoring based on optical sensing. The works focused on two major topics: 1) structural health monitoring for compact heat exchanger (CHE), and 2) bimetallic additive manufacturing process monitoring. For the structural health test, a novel online sensing method capable of detecting internal cracks for Compact Heat Exchanger (CHE) was designed and developed through optical fiber sensor based strain measurement. A crack diagnosis model was built to evaluate crack positions based on limited sampling data in mechanical structure. The model established a physical basis to correlate crack position and distributed strain variation that can be detected by the optical fiber sensors. A physical model quantifying the strain transfer from the sensor embedded mechanical structure to the fiber sensor was built to describe the performance of the sensors at different working conditions. A good match has been observed in the comparison of the data from experimental tests and analytical models, with an average relative error 2.4%. Finally, an experimental platform was designed and setup to validate introduced nondestructive test method. The experimental results showed that strain variations can be detected by optical fiber sensors when crack presented in CHE during elastic deformation, plastic deformation and crack growth process. For bimetallic additive manufacturing process monitoring, an in-situ sensing method for measuring material composition in the printed alloy was modeled and developed based on infrared imaging. The method takes the size of temperature contours surrounding the heated spots during additive manufacturing process as an indicator of the material composition variation. The relationship between material composition and dimensions of the temperature contour was analytically modeled based on Fourier’s law of thermal conduction. The thermal images acquisition by IR camera were processed through a series of designed algorithms to extract geometrical features such as the length and width of the contours, which showed consistent trend through the theoretical analysis. The extracted features and actual weight percentage of copper in the alloy were further used to train an Artificial Neuron Network (ANN) model. The results showed that the accuracy of 94% was achieved when using the trained ANN model to estimate the composition of alloy from the thermal image data. The analytical/numerical models, simulations, experiments, and data analysis included in this thesis were expected to provide solid support for testing the research hypotheses and developing new hardware/software in advanced manufacturing systems

Roadmap on measurement technologies for next generation structural health monitoring systems

Structural health monitoring (SHM) is the automation of the condition assessment process of an engineered system. When applied to geometrically large components or structures, such as those found in civil and aerospace infrastructure and systems, a critical challenge is in designing the sensing solution that could yield actionable information. This is a difficult task to conduct cost-effectively, because of the large surfaces under consideration and the localized nature of typical defects and damages. There have been significant research efforts in empowering conventional measurement technologies for applications to SHM in order to improve performance of the condition assessment process. Yet, the field implementation of these SHM solutions is still in its infancy, attributable to various economic and technical challenges. The objective of this Roadmap publication is to discuss modern measurement technologies that were developed for SHM purposes, along with their associated challenges and opportunities, and to provide a path to research and development efforts that could yield impactful field applications. The Roadmap is organized into four sections: distributed embedded sensing systems, distributed surface sensing systems, multifunctional materials, and remote sensing. Recognizing that many measurement technologies may overlap between sections, we define distributed sensing solutions as those that involve or imply the utilization of numbers of sensors geometrically organized within (embedded) or over (surface) the monitored component or system. Multi-functional materials are sensing solutions that combine multiple capabilities, for example those also serving structural functions. Remote sensing are solutions that are contactless, for example cell phones, drones, and satellites. It also includes the notion of remotely controlled robots

Highly-sensitive measurements with chirped- pulse phasesensitive OTDR

Distributed optical fiber sensing is currently a very predominant research field, which perceives optical fibers as the potential nervous system of the Earth. Optical fibers are understood as continuous densely-packed sensing arrays, able of retrieving physical quantities from the environment of the fiber. Some of the most prominent distributed sensing implementations nowadays rely on performing interferometric measurements using the Rayleigh backscattered light, resorting to a technique called Phase-sensitive Optical Time-Domain Reflectometry (CP-ϕOTDR). A variant to this technique has been recently proposed in 2016, known as Chirped-Pulse Phase-Sensitive OTDR, which allowed to overcome most of the limitations of traditional ϕOTDR implementations while retaining a simple setup, yielding remarkably high sensitivities. In this thesis, we aim to optimize the stability and performance of chirped-pulse ϕOTDR systems over long-term measurements, and develop novel paradigm changing applications benefiting from the high sensitivity provided by the technique. We reach a mK-scale long-term stability in ϕOTDR systems, and perform highly sensitive strain, temperature, and refractive index measurements, demonstrating new photonic applications such as distributed bolometry, electro-optical reflectometry, or distributed underwater seismology. We discuss how these applications might be able of increasing the efficiency in the energy field, paving the way towards the development of self-diagnosable grids (smart-grids), and also of revolutionizing next-generation seismological networks, allowing to overcome some of the greatest limitations faced in modern seismology today.Distributed optical fiber sensing is currently a very predominant research field, which perceives optical fibers as the potential nervous system of the Earth. Optical fibers are understood as continuous densely-packed sensing arrays, able of retrieving physical quantities from the environment of the fiber. Some of the most prominent distributed sensing implementations nowadays rely on performing interferometric measurements using the Rayleigh backscattered light, resorting to a technique called Phase-sensitive Optical Time-Domain Reflectometry (φOTDR). A variant to this technique has been recently proposed in 2016, known as Chirped-Pulse Phase-Sensitive OTDR, which allowed to overcome most of the limitations of traditional φOTDR implementations while retaining a simple setup, yielding remarkably high sensitivities. In this thesis, we aim to optimize the stability and performance of chirped-pulse φOTDR systems over long-term measurements, and develop novel paradigm changing applications benefiting from the high sensitivity provided by the technique. We reach a mK-scale long-term stability in φOTDR systems, and perform highly sensitive strain, temperature and refractive index measurements, demonstrating new photonic applications such as distributed bolometry, electro-optical reflectometry, or distributed underwater seismology. We discuss how these applications might be able of increasing the efficiency in the energy field, paving the way towards the development of self-diagnosable grids (smart-grids), and also of revolutionizing nextgeneration seismological networks, allowing to overcome some of the greatest limitations faced in modern seismology today. We finally conclude and summarize the objectives achieved in this thesis, commenting on the potential of the novel applications shown, and proposing future lines of research based on the results

Deep neural mobile networking

The next generation of mobile networks is set to become increasingly complex, as these struggle to accommodate tremendous data traffic demands generated by ever-more connected devices that have diverse performance requirements in terms of throughput, latency, and reliability. This makes monitoring and managing the multitude of network elements intractable with existing tools and impractical for traditional machine learning algorithms that rely on hand-crafted feature engineering. In this context, embedding machine intelligence into mobile networks becomes necessary, as this enables systematic mining of valuable information from mobile big data and automatically uncovering correlations that would otherwise have been too difficult to extract by human experts. In particular, deep learning based solutions can automatically extract features from raw data, without human expertise. The performance of artificial intelligence (AI) has achieved in other domains draws unprecedented interest from both academia and industry in employing deep learning approaches to address technical challenges in mobile networks. This thesis attacks important problems in the mobile networking area from various perspectives by harnessing recent advances in deep neural networks. As a preamble, we bridge the gap between deep learning and mobile networking by presenting a survey on the crossovers between the two areas. Secondly, we design dedicated deep learning architectures to forecast mobile traffic consumption at city scale. In particular, we tailor our deep neural network models to different mobile traffic data structures (i.e. data originating from urban grids and geospatial point-cloud antenna deployments) to deliver precise prediction. Next, we propose a mobile traffic super resolution (MTSR) technique to achieve coarse-to-fine grain transformations on mobile traffic measurements using generative adversarial network architectures. This can provide insightful knowledge to mobile operators about mobile traffic distribution, while effectively reducing the data post-processing overhead. Subsequently, the mobile traffic decomposition (MTD) technique is proposed to break the aggregated mobile traffic measurements into service-level time series, by using a deep learning based framework. With MTD, mobile operators can perform more efficient resource allocation for network slicing (i.e, the logical partitioning of physical infrastructure) and alleviate the privacy concerns that come with the extensive use of deep packet inspection. Finally, we study the robustness of network specific deep anomaly detectors with a realistic black-box threat model and propose reliable solutions for defending against attacks that seek to subvert existing network deep learning based intrusion detection systems (NIDS). Lastly, based on the results obtained, we identify important research directions that are worth pursuing in the future, including (i) serving deep learning with massive high-quality data (ii) deep learning for spatio-temporal mobile data mining (iii) deep learning for geometric mobile data mining (iv) deep unsupervised learning in mobile networks, and (v) deep reinforcement learning for mobile network control. Overall, this thesis demonstrates that deep learning can underpin powerful tools that address data-driven problems in the mobile networking domain. With such intelligence, future mobile networks can be monitored and managed more effectively and thus higher user quality of experience can be guaranteed

A SEASAT report. Volume 1: Program summary

The program background and experiment objectives are summarized, and a description of the organization and interfaces of the project are provided. The mission plan and history are also included as well as user activities and a brief description of the data system. A financial and manpower summary and preliminary results of the mission are also included

Innovations and advances in structural engineering: Honoring the career of Yozo Fujino

This special issue of Smart Structures and Systems (SSS) is dedicated to Dr. Yozo Fujino to celebrate his outstanding and innovative contributions to structural engineering during his career. The papers in this issue present a wide range of recent results on bridge dynamics, wind and earthquake effects on structures, health monitoring, and passive/active control technology. This collection of papers also provides a glimpse into the broad nature of Dr. Fujino’s interests. Prof. Fujino is an internationally recognized leader who has been an inspiration to industrial and academic scientists and engineers for over 30 years. During his brilliant academic career, Prof. Fujino has made and continues to make fundamental contributions to dynamics, control and monitoring of bridges considering both wind actions and earthquakes loading. In addition, he has consulted on over 30 signature bridge projects including Akashi Kaikyo Bridge in Japan, Millennium Bridge (vibration control) in UK and Stonecutters Bridge in Hong Kong, demonstrating his recognition not only for his research achievements, but also for his practical knowledge and experience in bridge engineering. In addition to his numerous contributions to science and engineering, Dr. Fujino is a dedicated and passionate teacher and professor, inspiring young scientists and engineers to advance their knowledge and experiences. Dr. Fujino is currently a Distinguished Professor of Advanced Sciences at Yokohama National University (YNU) in Japan. He is also jointly appointed as a Program Director (Policy Adviser) for the Council for Science, Technology and Innovation, Cabinet Office, Japanese Government. Prior to joining YNU, he served for more than 30 years as a Professor of Civil Engineering and the head of the Bridge and Structures Laboratory at The University of Tokyo. On behalf of all the contributors to this special issue, we would like to sincerely congratulate Dr. Yozo Fujino on a truly amazing career and wish him good health, happiness, and many more contributions to structural engineering in the years to come.Ope

Time Localization of Abrupt Changes in Cutting Process using Hilbert Huang Transform

Cutting process is extremely dynamical process influenced by different phenomena such as chip formation, dynamical responses and condition of machining system elements. Different phenomena in cutting zone have signatures in different frequency bands in signal acquired during process monitoring. The time localization of signal’s frequency content is very important. An emerging technique for simultaneous analysis of the signal in time and frequency domain that can be used for time localization of frequency is Hilbert Huang Transform (HHT). It is based on empirical mode decomposition (EMD) of the signal into intrinsic mode functions (IMFs) as simple oscillatory modes. IMFs obtained using EMD can be processed using Hilbert Transform and instantaneous frequency of the signal can be computed. This paper gives a methodology for time localization of cutting process stop during intermittent turning. Cutting process stop leads to abrupt changes in acquired signal correlated to certain frequency band. The frequency band related to abrupt changes is localized in time using HHT. The potentials and limitations of HHT application in machining process monitoring are shown