Wireless sensor networks have attracted great attention for applications in structural health monitoring due to their ease of use, flexibility of deployment, and cost-effectiveness. This paper presents a software framework for WiFi-based wireless sensor networks composed of low-cost mass market single-board computers. A number of specific system-level software components were developed to enable robust data acquisition, data processing, sensor network communication, and timing with a focus on structural health monitoring (SHM) applications. The framework was validated on Raspberry Pi computers, and its performance was studied in detail. The paper presents several characteristics of the measurement quality such as sampling accuracy and time synchronization and discusses the specific limitations of the system. The implementation includes a complementary smartphone application that is utilized for data acquisition, visualization, and analysis. A prototypical implementation further demonstrates the feasibility of integrating smartphones as data acquisition nodes into the network, utilizing their internal sensors. The measurement system was employed in several monitoring campaigns, three of which are documented in detail. The suitability of the system is evaluated based on comparisons of target quantities with reference measurements. The results indicate that the presented system can robustly achieve a measurement performance commensurate with that required in many typical SHM tasks such as modal identification. As such, it represents a cost-effective alternative to more traditional monitoring solutions.
<p>A physics-informed machine learning model, in the form of a multi-output Gaussian process, is formulated using the Euler-Bernoulli beam equation. Given appropriate datasets, the model can be used to regress the analytical value of the structure’s bending stiffness, interpolate responses, and make probabilistic inferences on latent physical quantities. The developed model is applied on a numerically simulated cantilever beam, where the regressed bending stiffness is evaluated and the influence measurement noise on the prediction quality is investigated. Further, the regressed probabilistic stiffness distribution is used in a structural health monitoring context, where the Mahalanobis distance is employed to reason about the possible location and extent of damage in the structural system. To validate the developed framework, an experiment is conducted and measured heterogeneous datasets are used to update the assumed analytical structural model.</p>
We regret to report the passing away of Allan Murray Goodwin, one of Canada's foremost Precambrian geologists at Toronto. He was Chairman of the Archaean Geochemistry Group founded by the GSC and NSERC. After graduation from Queens University, Kingston (B.Sc. and M.Sc.) and the University of Wisconsin (Ph.D.), Dr. Goodwin joined the geological branch of the Ontario Department of Mines and worked for a number of years in the Archaean greenstone belts of Northern Ontario. He later became Professor of Geology in the University of Toronto. Prof. Goodwin was elected Honorary Fellow of hte Geological Society of India in 1975 in recognition of his meritorious work on the Archaean greenstone belts of the world. The citation of the Royal Society of Canada gives further details: "Professor Goodwin's work on Archaean greenstone belts has been outstanding as a result of its breadth of
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