r - The Hong Kong Polytechnic University

e carried out once per maintenance contract period (which is a 6-year period at current stage). The first
measurement, which had been carried out two weeks before the opening of the bridge to public traffics (24
December 2009), was composed of four loading types, i.e. (i) longitudinal line load – a line load of nine 5-axle
trucks (each of ~41 tons) with a loaded length of ~145m in a traffic lane, (ii) uniformly distributed load – a
pattern load of 3 x 3 5-axle trucks in three traffic lanes, (iii) transverse line load – a transverse line load of eight
5-axle trucks distributed across the 6 traffic lanes and 2 hard shoulders, and (iv) moving load – influence line
testing of one or two 5-axle trucks travelling on traffic lanes. The first three loading types are static load trials
and form the basic load-patterns and loading-positions used in the simulation (by super-position of appropriate
measured results in each load test) of adverse load-effects under traffic jams with much heavy goods vehicles.
The loading centers of these three loading types were set at 1/4, 1/2 and 3/4 of main span. The last loading type
was used to calibrate/update the analyzed influence lines at instrumented points. These influence lines will form
the basis to estimate the stress ranges for fatigue damage monitoring. The results of this monitoring are also
used to calibrate/update the static computational features of the finite element bridge model. The
calibrated/updated model will form the basis to compute the stress ranges at non-instrumented locations for
fatigue assessment. The flow diagram of the software tools for global static features monitoring is shown in
Figure 20.
I. Customized Software Tools for Global Dynamic Features Monitoring [Ref. 15, 16, 18, 19, 21 and 28]
The global dynamic features monitoring, which is a fundamental monitoring requirement in SHMS for
long-span bridge with steel deck structure, is the extraction of the modal frequencies and modal vectors (or
modes shapes) of the global bridge structural system by the approach of operational modal (or ambient vibration)
analysis. The extracted global dynamic characteristics will be used to calibrate/update the 3D global finite
element bridge model. The calibrated/updated bridge model will be used to estimate the responses at locations
with and/or without sensory systems.
For global dynamic features monitoring, with the consideration of saving equipment cost, both fixed and
removal types of accelerometers are deployed, and they are installed in the two towers (i.e., at tower-top, 2/3
and 1/3 height of tower above deck-level), in the steel deck at main span (i.e., at 1/8 main span spacing) and in
the concrete deck at two side-spans (i.e., at mid-span between two side-span piers). All accelerometers installed
are removal except those accelerometers at the five fixed locations (i.e., two tower-tops and 3 deck-levels at 1/4,
1/2 and 3/4 main span). The fixed accelerometers are mainly for instant modal frequencies monitoring. All
accelerometers used are tri-axial servo-type accelerometers, and each is made of three uni-axial QA 700 Q-Flex
accelerometers from Honeywell. The total number of accelerometers deployed is 58 nos., of which 48 nos. are
removal (which are also used for stay forces monitoring) and 10 nos. (including the 2 nos. at tower-based for
seismic loads monitoring) are fixed. The sampling rate of the accelerometer is devised to be reconfigurable in 3
rates, i.e, 50 Hz (default), 100 Hz (under ambient vibration measurements and typhoons), and 1000 Hz (under
load-trials or special events).
For global dynamic features monitoring, the tri-axial accelerometers inside the deck section should be installed
in accordance with Figure 21. The distance between the two tri-axial accelerometers should be kept constant and
the accelerometers (both removal-type and fixed type) installed along the bridge-deck alignment should be kept
as a continuous line at the same horizontal plane and be parallel to the centroid of the bridge-deck section. The
measured time-series acceleration data are converted to four types of processed time-series acceleration data, i.e.,
vertical deck acceleration, lateral deck acceleration, torsional deck acceleration and longitudinal deck
acceleration, as shown in formulas given in Figure 21. Similarly, the arrangement of accelerometers and
processing of acceleration data for tower-sections are given in Figure 22, which results in three types of
time-series acceleration data, i.e., longitudinal tower acceleration, lateral tower acceleration and torsional tower
acceleration. Customized software tools are devised to extract the seven types of predominating vibration mode
shapes from the above mentioned seven types of processed time-series acceleration data by power spectrum
analysis (for frequencies extraction), linear spectrum analysis and curve fitting analysis (for mode shapes
determination). The Fast Fourier Transform (FFT) is used to transform the processed time-series acceleration
data into power spectrum and linear spectrum. The FFT, due to its intrinsic deficiency of spectrum leakage for
random signals, is applied together with Hanning Window technique (which is used to minimize the spectrum
leakage induced by FFT) and 50% data overlap processing (which is used to improve the accuracy of spectral
estimation or overcoming the attenuation of useful data by the Hanning Window on two ends of each data
block/frame).
The seven types of vibration mode shapes required for extraction are: (i) vertical flexural deck modes, (ii) lateral
flexural deck modes, (iii) longitudinal flexural deck modes, (iv) torsional flexural deck modes, (v) lateral
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