Data Intensive Engineering
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The document discusses the application of big data technologies in civil engineering, emphasizing the importance of data quality and the need to connect, identify patterns, and ensure confidence in data from various sources. It illustrates the use of neural networks for modeling rainfall-runoff relationships and mentions bridge management systems designed to improve infrastructure maintenance and management. The text highlights that civil engineers can harness these technologies with relatively low capital investment, primarily requiring investment in education to leverage potential benefits.
Data Intensive Engineering
- 1. Data Intensive Engineering Xosé Manuel Carreira Rodríguez http://www.linkedin.com/in/carreira 13th December 2012
- 2. BRIEF • In this presentation the possible use of the big data technologies in the civil engineering is overviewed.
- 6. “…probably indicates that these sectors face strong systemic barriers to increasing productivity”
- 7. We collect enough data. We need to focus on 1- connecting 2 – identifying patterns 3- giving confidence level Multiple data sources: Books Experts in the field Information systems Tests and surveying Data repositories Real time sensors
- 8. Data quality • Processing is cheap and access is easy, the big problem is data quality. • Considerable research but highly fragmented
- 9. 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.
- 10. 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.
- 11. 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
- 12. Case 1: Neural networks for flood • Neural networks modelling of the rainfall-runoff relationship • No physical model, just data driven model. • Result: flow forecasting
- 13. Case 1: Neural networks for flood • Input: several past rain gauges and flow gauges • Result: Flow model
- 14. Case 1: Neural networks for flood Training with 1st (larger) set of data
- 15. Case 1: Neural networks for flood Verification with 2nd (smaller) set of data
- 17. 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.
- 18. 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
- 19. Connecting Bridge Management Systems to Asset Management U.S. Department of Transportation Federal Highway Administration
- 20. Bridges in the U.S. 25% are structurally or functionally deficient according 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
- 21. 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
- 22. You can run a company from a coffee shop
- 23. Why not a lab or a civil infraestructure?
- 24. Desktop PCs are idle half the day Desktop PCs tend to be active But at night, during most of during the workday. the year, they’re idle. So we’re only getting half their value (or less). 24
- 25. Finally , it is argued that IT can readily be used by civil engineers given the low capital investment levels required. The “only” requirement is investment in education among the civil engineers & recognition of the enormous potential lying beneath.