Advanced Economic Issues

ECMM159 CWK1
Do what you can, the aim is to show you what real world data look like and how to handle.
Part A -75 marks

On 6th January we retrieved monitoring data from Wolf Rock lighthouse, covering the period January 2019 to January 2020. The raw data are saved in directories for most days, each containing between one and four subdirectories of raw data files e.g.
wolf rock 2019 data/2019-01-09/1001
each containing a set of 10-minute tdms files created by the National Instruments cRIO logger e.g.
cRIO_Data_190109_124506.tdms
These tdms files have been merged for you into single MATLAB files for each directory e.g.
wolf rock 2019 data/2019-01-09/1001/*.tdms -> WOLF_2019-01-09_1001_DSA.mat
The script that did this is proc_wolf_2019_everything.m

There may not be data for every day, for other days there may not be complete data, and even when data are complete all or part of it may be unusable. The data files are time series for x-direction accelerometer and y-direction accelerometer (units are ‘g’)

Your job is to examine the data, discount (and perhaps explain) unusable data and periods with no data, identify clear strong vibration events and correlate them with weather conditions (mainly to do with sea state, tide etc.). This will all be presented in a 10-page report, see marking scheme below.

Days of data are missing due to mainly problems with power supply to the monitoring system.

Out of the complete and usable data you should find days with strong response due to breaking wave impacts. These occur with high tides and strong winds, and after a long period of stormy weather with strong sea states. You could look up historic weather in the neighborhood e.g. here is for St Just Cornwall:

https://www.meteoblue.com/en/weather/historyclimate/weatherarchive/st-just_united-kingdom_11001762?fcstlength=1m&year=2018&month=1

Other sources of weather data can be checked; if you can find out anything about wave heights anywhere near, that is more likely (I think) to correlate.

From ‘interesting’ data present clear examples of impulsive response due to wave impact and estimate the maximum (initial) velocity of the impulse. This value can be used to infer the modal impulse (a function of duration, force and impact location that characterize the breaking wave). You probably worked out that all the action is in the second mode (or mode pair: there is a pair with horizontally orthogonal alignments for each mode (shape) in vertical direction.

Other events are due to human interference when Trinity House maintenance crews visit, for example for example when the helicopter (EC135) is on the helideck the rotors generate strong vibrations. We also found the sensor had been physically moved a few cm, and in fact rotated by about 10º. It would be good (but very challenging) to know when this rotation occurred because we need to identify the direction of wave-induced response knowing accelerometer orientation.

For the report use the template provided. Do not write more than 10 pages. This simulates real world conditions where your bosses (and ultimately you) want information provided in compact form.

Marking criteria:
Clear writing with good grammar and correct English. A good style helps. 7.5 marks
Effective use of plots to convey information. 20 marks
Clear explanation of your processing procedure, the approach you adopted. 10 marks
Half the marks will go to your findings, discussion and conclusions, i.e. your observations and feasible explanations based on the data, what you have found that is interesting and useful and its significance. Remember, conclusions are not a discussion, they are what you conclude. 37.5 marks

 
Part B -25 marks
You will have experimented with the wide range of data available from Humber Bridge monitoring.
Your job: Pick an interesting day of 1Hz -sampled Humber data and write a four-page report (same template, but shorter) describing what you observe and the significance in terms of serviceability and safety of the bridge.
The data are in 16 channels, resampled (‘fused’) at 1Hz. Some data are high-pass filtered -to leave dynamic component, some low-pass filtered -e.g. to leave mostly quasi-static component
Some data e.g. like extensions, GPS, inclinometer are not always present.
Here are the ‘labels’ and units.
Refer to Humber_Bridge_new_SHM_system_user_guide.pdf for details of sensor channels.
‘GPS eastings (E+W)/2; mm’; %1000*(GPSH000WE+GPSH000EE)/2
‘GPS heights (E+W)/2; mm’; %1000*(GPSH000WH+GPSH000EH)/2
‘Delta GPS heights (W-E); mm’; %1000*(GPSH000WH-GPSH000EH)
‘GPS northings (W+E)/2; mm’; %1000*(GPSH000WN+GPSH000EN/2)
‘GPS heights (E+W)/2; mm HPF>0.1Hz’; %filter(1000*(GPSH000WH+GPSH000EH)/2)
‘ROTATION BExt(W-E)+HExt(W-E); mm LPF<0.1Hz'; %1000*(EXB077WD-EXB077ED+EXH077WD-EXH077ED) 'VERT acc (E+W)/2; mm/sec^2 LPF <0.2Hz'; %9800*(ACCH000WV+ACCH000WV)/2 'TILT Etrans acc -> height diff at 22m; mm<0.04Hz)';%filter(22000*ACCH000ET) '->NORTH BExt(W+E)/2-HExt(W+E)/2; mm LPF<0.1Hz';%1000*(EXB077WD+EXB077ED-EXH077WD-EXH077ED)/2 'wind; m/sec^2'; %WNDH000MS 'LAT transverse E acc; mm/sec^2 LPF <0.1Hz';%filter(9800*ACCH000ET) 'SHORT BExt(W+E)/2+HExt(W+E)/2; mm LPF<0.1Hz';%1000*(EXB077WD+EXB077ED+EXH077WD+EXH077ED)/2 'INCW (inc to w diff at 28m); mm'; %-28*INH000CDN 'INCN (inc to n diff at 28m); mm HPF>0.005Hz’; %filter(INH000CDW)
‘VERT W acc mm/s^2’; %9800*ACCH000WV
‘VERT E acc mm/s^2’; %9800*ACCH000EV
Same criteria as for Part A, but marks divided by 3 (to total 25).