Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.
Data Intensive Engineering Xosé Manuel Carreira Rodríguezhttp://www.linkedin.com/in/carreira        13th December 2012
BRIEF• In this presentation the possible use of the big  data technologies in the civil engineering is  overviewed.
“…probably indicates that these sectors face strong systemic barriers to increasingproductivity”
We collect enough data.  We need to focus on       1- connecting 2 – identifying patterns3- giving confidence level       ...
Data quality• Processing is cheap and access is easy, the  big problem is data quality.• Considerable research but highly ...
Classic definition of Data Quality• Accuracy  – The data was recorded correctly.• Completeness  – All relevant data was re...
Finding a modern definition• Data quality must  – Reflect the use of the data  – Lead to improvements in processes  – Be m...
What is the problem to solve?• Do you have a bunch of data and want to:  – Estimate an unknown parameter from it?     • Tr...
Case 1: Neural networks for flood• Neural networks modelling of the rainfall-runoff  relationship• No physical model, just...
Case 1: Neural networks for flood• Input: several past rain gauges  and flow gauges• Result: Flow model
Case 1: Neural networks for floodTraining with 1st (larger) set of data
Case 1: Neural networks for floodVerification with 2nd (smaller) set of data
Simulation  sample
How can IT help in maintenance ?• Information Technology has also found applications in  post commission period of the pro...
Case 2: Bridge Management Systems• Double click on the  icon on your desktop  – Introductory screen is    displayed  – Cli...
ConnectingBridgeManagementSystemstoAssetManagement             U.S. Department of Transportation             Federal Highw...
Bridges in the U.S.25% are structurally or functionally deficientaccording to ASCE 140000 120000 100000  80000  60000  400...
Case 2: Bridge Management Systems   Typical BMS Expectations   A tool to evaluate:   •   Bridge condition and serviceabili...
You can run a company from a coffee shop
Why not a lab or a civil infraestructure?
Desktop PCs are idle half the dayDesktop PCs tend to be active   But at night, during most ofduring the workday.          ...
Finally ,          it is argued that IT can readily beused by civil engineers given the lowcapital investment levels requi...
Data Intensive Engineering
Data Intensive Engineering
Data Intensive Engineering
You’ve finished this document.
Download and read it offline.
Upcoming SlideShare
Typologies
Next
Upcoming SlideShare
Typologies
Next
Download to read offline and view in fullscreen.

Share

Data Intensive Engineering

Download to read offline

Data Intensive Engineering - Presentation at the Institution of Civil Engineers, Madrid by Xosé Manuel Carreira

Related Books

Free with a 30 day trial from Scribd

See all

Data Intensive Engineering

  1. 1. Data Intensive Engineering Xosé Manuel Carreira Rodríguezhttp://www.linkedin.com/in/carreira 13th December 2012
  2. 2. BRIEF• In this presentation the possible use of the big data technologies in the civil engineering is overviewed.
  3. 3. “…probably indicates that these sectors face strong systemic barriers to increasingproductivity”
  4. 4. We collect enough data. We need to focus on 1- connecting 2 – identifying patterns3- giving confidence level Multiple data sources: Books Experts in the field Information systems Tests and surveying Data repositories Real time sensors
  5. 5. Data quality• Processing is cheap and access is easy, the big problem is data quality.• Considerable research but highly fragmented
  6. 6. Classic definition of Data Quality• Accuracy – The data was recorded correctly.• Completeness – All relevant data was recorded.• Uniqueness – Entities are recorded once.• Timeliness – The data is kept up to date. • Special problems in federated data: time consistency.• Consistency – The data agrees with itself.
  7. 7. Finding a modern definition• Data quality must – Reflect the use of the data – Lead to improvements in processes – Be measurable• No silver bullets: Use several data quality metrics.
  8. 8. What is the problem to solve?• Do you have a bunch of data and want to: – Estimate an unknown parameter from it? • True rainfall based on radar observations? • Amount of liquid content from in-situ measurements of temperature, pressure, etc? • Regression – Classify what the data correspond to? • A water surge? • A temperature inversion? • A boundary? • Classification• Regression and classification aren’t that different 11
  9. 9. Case 1: Neural networks for flood• Neural networks modelling of the rainfall-runoff relationship• No physical model, just data driven model.• Result: flow forecasting
  10. 10. Case 1: Neural networks for flood• Input: several past rain gauges and flow gauges• Result: Flow model
  11. 11. Case 1: Neural networks for floodTraining with 1st (larger) set of data
  12. 12. Case 1: Neural networks for floodVerification with 2nd (smaller) set of data
  13. 13. Simulation sample
  14. 14. How can IT help in maintenance ?• Information Technology has also found applications in post commission period of the project.• IT can provide easy access to various statistics, drawing & various other data concerning the project.• Self check tools can identify the problems in various systems like fire fighting, air conditioning & can automatically inform concerned service provider.• IT can also help in prompt reporting of problem & its rectification.
  15. 15. Case 2: Bridge Management Systems• Double click on the icon on your desktop – Introductory screen is displayed – Click OK button to continue to the Data collection form Page 18
  16. 16. ConnectingBridgeManagementSystemstoAssetManagement U.S. Department of Transportation Federal Highway Administration
  17. 17. Bridges in the U.S.25% are structurally or functionally deficientaccording to ASCE 140000 120000 100000 80000 60000 40000 20000 0 Pre-1909 10s 20s 30s 40s 50s 60s 70s 80s 90s Bridge Construction by Decade
  18. 18. Case 2: Bridge Management Systems Typical BMS Expectations A tool to evaluate: • Bridge condition and serviceability • Implications of project decisions • Priorities and schedules • Expected budget • Cost of alternative standards • Value of preventive maintenance
  19. 19. You can run a company from a coffee shop
  20. 20. Why not a lab or a civil infraestructure?
  21. 21. Desktop PCs are idle half the dayDesktop PCs tend to be active But at night, during most ofduring the workday. the year, they’re idle. So we’re only getting half their value (or less). 24
  22. 22. Finally , it is argued that IT can readily beused by civil engineers given the lowcapital investment levels required.The “only” requirement is investment ineducation among the civil engineers &recognition of the enormous potentiallying beneath.
  • dhirajdeka5

    May. 16, 2018

Data Intensive Engineering - Presentation at the Institution of Civil Engineers, Madrid by Xosé Manuel Carreira

Views

Total views

3,704

On Slideshare

0

From embeds

0

Number of embeds

2,732

Actions

Downloads

27

Shares

0

Comments

0

Likes

1

×