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Table 1 - Subset of the TD associated to each SHM sensor (machine-understandable format)
Type
Property

Name

Description

acceleromenter_sample

Last 3-axial accelerometer measurement

Property

accelerometer_vector

Last 3-axial accelerometer vector of samples

Property

accelerometer_threshold

Accelerometer threshold for event detection

Action

start/stop

Activate/deactivate the sensor monitoring

Event

onOverThresholdEvent

Trigger the event when the accelerometer sample is greater than the threshold value

Data Management Layer
In [13], we proposed the WoT Store, a novel SW platform supporting the dynamic management of Web Things on generic
WoT environments. The platform has been installed on a private cloud and customized for the SHM domain by enabling
the following functionalities: (1) device discovery, i.e., it is possible to monitor the Things/sensors available in the current
WoT deployment; (2) device interaction, i.e., it is possible to interact with each Thing through a Web dashboard, e.g., reading
or setting a sensor property; and (3) service management, i.e., it
is possible to execute external SW modules that store and process data produced by each Thing/sensor. Regarding point
(2), we highlight the extendibility of the WoT-SHM platform:
since the Web dashboard is dynamically generated by reading
the TD of the registered Things, the insertion of new sensors
is automatically supported and does not require any manual
configuration. Dealing with point (3), three SW modules were
designed for the SHM data storage, processing and visualization purpose. The storage module issues periodic queries to
each available SHM sensor/Thing (the up-to-date list is provided by the device discovery) and saves the measurements
on a distributed database implemented in InfluxDB (https://
www.influxdata.com). The query period is configurable according to the requirements of the monitoring application. The
visualization module, instead, enables to plot the stored timeseries of each sensor/Thing on the Grafana (https://grafana.
com) tool. Finally, the data analytics module (currently under
development) will implement machine-learning and signalprocessing techniques for structural risk assessment, anomaly
detection and remaining life-cycle prediction.

Validation and Discussion
The proposed SHM architecture will be extensively validated
by the MAC4PRO project [4] for the monitoring and predictive
maintenance of industrial sites and civil engineering structures. Here, the preliminary project's results are reported,
concerning the monitoring of a metallic frame structure
located at the research labs of the Department of Civil Engineering of the University of Bologna, Italy. More specifically,
the facility consists of a high-rise five-story frame composed of
five identical cubic modules with nominal height of 1 m. This
structure was instrumented with a double chain of six accelerometers fixed in correspondence of the junction elements.
The rationale behind the selection of one out of two GW units
concerns the idea to minimize the total electrical consumption
24	

while exploiting the beneficial multi-drop capabilities of the
SAN. Indeed, the practical limit about the total number of
connected sensors per GW is dictated by the power budget admitted by the chosen GW-to-EC connection bus.
During this experimental campaign, a USB 2.0 cable with
a nominal power output of 500 mA has been employed. As a
result, taking into consideration the power drawn by the GW
itself and that associated to the sensor node (which amounts
to 8 mA and 40.8 mA respectively), a network density of 12
nodes simultaneously connected is achievable. Furthermore, a
favorable deployment strategy was followed to halve the electrical load seen by the GW device, concurrently allowing the
torsional modes, which are expected to characterize the dynamic response of this structure, to be reconstructed. The final
installation plan is sketched in Fig. 3 where the two clusters
of sensors have been differentiated with red (cluster 1, label
C1) and green (cluster 2, label C2) colors, while the GW unit
is identified by the gray rectangle drawn at the mid-span of
one bar on the third floor. Noteworthy, the geometrical rigidity of the elements imposes quite a stiffened dynamic behavior.
Thus, a sampling frequency fs = 833 Hz was selected (among the
available ones) to extend the spectral analysis in a frequency
range compatible with the high-order modes of vibration,
which are more suited for damage detection. Time series were
acquired continuously with a fixed batch size of 2000 samples
for each DoF.

Offline Data Retrieval
A sample dataset collected at point C1.3 after a one-shot knocking excitation of the frame (hammer shaking at point K along
the y direction, shown in Fig. 3) is displayed in Fig. 4a on top of
the relative frequency content (Fig. 4b). These cloud data were
accessed from a host PC remotely connected via the HTTP port
and retrieved for further off-line processing.
The observed accelerations/angular velocities are coherent
with the adopted spatial reference system, since the bending
mechanism forces the structure to vibrate along the vertical
axis, hence favoring highly lateral displacements while minimizing the vertical and rotational ones. As such, a richer
and sharpner frequency distribution is expected along the x
and y directions, a prediction which is proven by the denser
and more localized number of harmonics appearing in the
Ax / Wx and Ay / Wy spectra. Conversely, a flatter frequency profile characterizes the Az / Wz response lying on the latitudinal
plane (Fig. 4b). Moreover, the structural complexity causes the

IEEE Instrumentation & Measurement Magazine	

December 2020


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