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Special Issue "Sensors, Big Data Analytics and Modeling for Infrastructure Monitoring and Maintenance"
Deadline for manuscript submissions: 30 September 2020.
Interests: online Bayesian system identification; Kalman filtering; particle filtering; Machine Learning; Big Data Analytics; infrastructure monitoring and maintenance
Interests: MEMS; structural sensors; Kalman filtering
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Structural health monitoring (SHM) of civil structures and infrastructure aims at detecting any potential damage, which might lead to an irreversible reduction of their stiffness and strength characteristics and, possibly, to catastrophic events. Since changes in the mechanical properties cannot be directly and explicitly sensed, SHM systems must perform data analytics on measurements in order to indirectly identify damage. In this regard, SHM is the process of autonomous analysis of sensor data from operating structures and infrastructure systems to extract information used for the decision-making process. While in other fields SHM has already transitioned to industrial applications, when dealing with civil infrastructures its application to operating systems is somehow still pending. Several obstacles impede commercialization of SHM in structural systems, including the state-of-the-art and practice in sensing and the various sources of uncertainties.
The goal of the present Special Issue is to collect contributions in the disciplines of physical sensors, computer science, and engineering, to serve as a forum for researchers in the field of sensor technologies and sensing strategies and to foster the development of real-time SHM of real-life structures. Experimental and theoretical works are both welcome, with the aim of providing a fresh account of methods to move towards the design of robust and resilient smart sensing strategies, and to extract information from the raw data acquired by pervasive sensor networks. Critical reviews and surveys of the state of the art and practice are also encouraged.
In this regard, the following category of contributions are welcome:
- Sensor-oriented contributions, including wireless sensor networks, multi-functional materials, energy harvesting for SHM, MEMS sensors, and Internet of Things approaches;
- Big Data analytics, including machine learning and statistical approaches, emerging strategies for sensor fusion, AI-based data mining, and cloud/edge/fog computing for infrastructure maintenance;
- Model-based Big Data analytics methods, such as Kalman filtering, particle filtering, and similar time-series analysis for online and real-time damage detection;
- Computational modeling approaches for infrastructure simulation, stochastic, and deterministic optimization, response prediction, force prediction;
- High-performance computing frameworks, including parallel processing and reduced order modeling.
The editors hope that the multidisciplinary nature of this Special Issue could provide readers with a grasp of cutting-edge research in all vital spokes of SHM.
Dr. Saeed Eftekhar Azam
Prof. Dr. Stefano Mariani
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- SHM, structural health monitoring
- damage detection
- sensor networks
- data mining
- Big Data Analytics
- Machine Learning