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Civil-Comp Proceedings
ISSN 1759-3433
CCP: 81
PROCEEDINGS OF THE TENTH INTERNATIONAL CONFERENCE ON CIVIL, STRUCTURAL AND ENVIRONMENTAL ENGINEERING COMPUTING
Edited by: B.H.V. Topping
Paper 62

A Process Model for Material Research Applied to Textile Reinforced Concrete

F. Peiffer and R. Chudoba

Chair of Structural Statics and Dynamics, RWTH Aachen University, Germany

Full Bibliographic Reference for this paper
F. Peiffer, R. Chudoba, "A Process Model for Material Research Applied to Textile Reinforced Concrete", in B.H.V. Topping, (Editor), "Proceedings of the Tenth International Conference on Civil, Structural and Environmental Engineering Computing", Civil-Comp Press, Stirlingshire, UK, Paper 62, 2005. doi:10.4203/ccp.81.62
Keywords: object-oriented methods, software engineering, process modeling, material research, experiment planning.

Summary
The research of a new composite material is a complex task with the goal of assessing the mechanical, chemical, economical and technological aspects of the material. The diversity and complexity of the involved research fields calls for a collaboration of specialists coming from various research disciplines. The process of collaborative research is characterized by sequential and parallel planning and elaboration of research tasks in a spatially distributed environment.

Obviously, the coordination of the research process requires effective software support for information sharing in form of data exchange, data analysis and archival storage. Flexible structuring of the data gathered from several sources is a crucial premise for a transparent accumulation of knowledge and, thus, for an efficient research in a long run. At the collaborative research centre "Textile reinforced concrete: the basis for the development of a new material technology" installed in 1998 at Aachen University these requirements led to the development of a technical information system (TIS) [1] serving as a shared blackboard for research data. The system allows participating research groups to store and exchange data. Further it enables users to configure their personal view on the blackboard. Decisions made on behalf of this data can be only as good as the data itself. Thus, data quality is a crucial premise for the success of the project.

Data quality (namely completeness and correctness) can be achieved on two levels: At the class level completeness of the data can be assured for example by marking data fields as mandatory while correctness can be assured by prescribing valid value ranges or text patterns. If a user modifies an object and data is missing or invalid the changes will not be accepted until the object is in a valid state.

A more powerful way to define completeness and correctness is at the process level. Changes and additions to the data pool are usually part of complex long-term processes involving several projects, co-workers and data objects. In contrast to class level definitions we can establish consistency for inter-object dependencies (for example between a material component and the experiments to be conducted). Furthermore, the actual answer to the question whether data is valid or complete usually depends on the state of the underlying process: An advanced experiment may usually be required but it may be omitted if a material component has already failed in the more basic experiments.

It is obvious that we need detailed knowledge of the central research tasks in order to define the consistency constraints. In this paper we focus on the user perspective whereas the methods used in the formalization and implementation of such process have been described in [2]. Once the processes are specified their integration into the TIS provides further advantages:

  • Active support: Whenever the process proceeds the subsequent actions can be prepared and suggested to the responsible users.
  • Transparency: The state of a research process becomes visible and can be observed by all participating researchers and project supervisors.
  • Data quality: Apart from the possible use of process level data conditions, the data completeness can be expected to change for the better since both the active support and the transparency of the research processes prevent the researchers from delaying the data insertions.
  • Acceleration: Due to the same reasons the material development cycles should become shorter because less time is lost due to users not being aware of newly available data.

The processes in the collaborative material research are categorized into (1) experiment planning, (2) evaluation processes, (3) material flow management and (4) material design evolution. As an example covering the experiment planning and evaluation processes [3] we present the investigation of a new yarn. This process is characterized by the collaboration of several co-workers coming from various institutes and disciplines. Further the process takes several months to complete. As such it is a prototype example of the kind of process we plan to support within the TIS.

References
1
R. Chudoba, C. Butenweg, F. Peiffer, "Technical information system for collaborative material research", Advances in Engineering Software, 35, 747-756, 2004. doi:10.1016/j.advengsoft.2004.03.021
2
F. Peiffer, R. Chudoba, "Modelling the Material Research and Design Processes", Advances in Engineering Software, submitted for publication.
3
R. Chudoba, F. Peiffer, K. Meskouris, "Experiment Design and Automated Evaluation Employing Numerical Material Models", Proceedings of 2nd Colloquium on Textile Reinforced Structures, 213-224, 2003.

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