Summary
When creating computational models of engineering structures, it can be very helpful to have insights into the behavior and composition of the physical system. Such insights can be gleaned using data mining techniques, where often unexpected relationships between physical quantities can be made evident.
In the context of an agent-based research project to monitor dams, a number of sensors supply a large amount of data over an extended period of time. This data can naturally be used for data mining purposes to discover new and interesting aspects about the engineering structure. In this paper, an open source data mining tool (Weka) is briefly introduced and presented to show how data mining techniques can be applied in the handling of engineering tasks.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
I. Mittrup and K. Smarsly. Development of a Dam Monitoring System Exemplified by the Ennepe Valley Dam (in German). In VDI, editor, Forum Bauinformatik 2002, volume VDI-Berichte, Reihe 4: Bauingenieurwesen, 181. Verein Deutscher Ingenieure, 9 2002.
K. Smarsly, I. Mittrup, D. Hartmann, and V. Bettzieche. An Agent-based Approach to Dam Monitoring. In The 20th CIB W78 Conference on Information Technology in Construction, 2003, 4 2003.
K. Smarsly, I. Mittrup, D. Hartmann, and V. Bettzieche. Implementation of a Web-based Dam Monitoring System (in German). In Internationales Kolloquium ber Anwendungen der Informatik und Mathematik in Architektur und Bauwesen 2003, 6 2003.
I. H. Witten and E. Frank. Data Mining: Practical machine learning tools with Java implementations. Morgan Kaufmann, San Francisco, 2000.
The University of Waikato. Weka 3-Data Mining with Open Source Machine Learning Software in Java. http://www.cs.waikato.ac.nz/ml/weka.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lehner, K., Mittrup, I., Hartmann, D. (2005). Data Mining Aspects of a Dam Monitoring Project. In: Abraham, A., Dote, Y., Furuhashi, T., Köppen, M., Ohuchi, A., Ohsawa, Y. (eds) Soft Computing as Transdisciplinary Science and Technology. Advances in Soft Computing, vol 29. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-32391-0_79
Download citation
DOI: https://doi.org/10.1007/3-540-32391-0_79
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25055-5
Online ISBN: 978-3-540-32391-4
eBook Packages: EngineeringEngineering (R0)