Data Model for Large-Scale Structural Experiments

Abstract

Large-scale laboratory experiments are carried out to evaluate and improve the performance of structural members, connections, and assemblies. The information related to these large-scale structural laboratory experiments, such as pseudo-dynamic tests and hybrid pseudo-dynamic tests, is often complicated and stored in various documents, drawings, photos, and other computer-based files. A data model is needed to organize this information and the related data from the structural experiments, so that this information and data can be accessed, shared, and used efficiently. This paper describes a data model for large-scale structural laboratory experiments developed at the Real-Time Multi-Directional (RTMD) testing facility at the ATLSS Center at Lehigh University. The RTMD facility is an equipment site within the George E. Brown, Jr. Network for Earthquake Engineering Simulation (NEES). The data model is called the Lehigh Model. For typical structural experiments, the Lehigh Model has a class hierarchy consisting of the project, experimental task, test condition, and test classes to organize and represent information about structural laboratory experiments. For hybrid pseudo-dynamic tests, the model also has the hybrid experimental task class and related classes to represent the communications among the simulation coordinator at a site, the analytical substructures at one or more sites, and the physical substructures at one or more sites. The test condition class provides more detailed information on the setup of a structural experiment including the geometries, materials, and locations of the components of the specimen. An application of the classes of the Lehigh Model is presented using steel moment connection tests as an example.

Keywords:

• Structural Test
• Pseudo-dynamic Test
• Data Model
• Information Model

Acknowledgments

This paper is based upon work supported by the National Science Foundation under Grant No. CMS-0402490 (NEES Consortium Operation), within the George E. Brown, Jr. Network for Earthquake Engineering Simulation (NEES) program. The first author was supported by a research grant from Academic Research Foundation of Hankyong National University (2005) for a scholarly exchange program. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.

Notes

Oregon State University and Network Alliance for Computational Science and Engineering [2003] “NEES Database and Metadata Structure, Version 1.3,” white paper, Network for Earthquake Engineering Simulation.