### Refine

#### Document Type

- Conference Proceeding (41)
- Article (14)

#### Institute

#### Keywords

- Angewandte Mathematik (50)
- Strukturmechanik (48)
- Architektur <Informatik> (4)
- Computerunterstütztes Verfahren (4)
- Angewandte Informatik (2)
- Bruchmechanik (2)
- CAD (2)
- Computer Science Models in Engineering; Multiscale and Multiphysical Models; Scientific Computing (2)
- Baustatik (1)
- Dynamische Belastung (1)

SLang - the Structural Language : Solving Nonlinear and Stochastic Problems in Structural Mechanics
(1997)

Recent developments in structural mechanics indicate an increasing need of numerical methods to deal with stochasticity. This process started with the modeling of loading uncertainties. More recently, also system uncertainty, such as physical or geometrical imperfections are modeled in probabilistic terms. Clearly, this task requires close connenction of structural modeling with probabilistic modeling. Nonlinear effects are essential for a realistic description of the structural behavior. Since modern structural analysis relies quite heavily on the Finite Element Method, it seems to be quite reasonable to base stochastic structural analysis on this method. Commercially available software packages can cover deterministic structural analysis in a very wide range. However, the applicability of these packages to stochastic problems is rather limited. On the other hand, there is a number of highly specialized programs for probabilistic or reliability problems which can be used only in connection with rather simplistic structural models. In principle, there is the possibility to combine both kinds of software in order to achieve the goal. The major difficulty which then arises in practical computation is to define the most suitable way of transferring data between the programs. In order to circumvent these problems, the software package SLang (Structural Language) has been developed. SLang is a command interpreter which acts on a set of relatively complex commands. Each command takes input from and gives output to simple data structures (data objects), such as vectors and matrices. All commands communicate via these data objects which are stored in memory or on disk. The paper will show applications to structural engineering problems, in particular failure analysis of frames and shell structures with random loads and random imperfections. Both geometrical and physical nonlinearities are taken into account.

Dynamic testing for damage assessment as non-destructive method has attracted growing in-terest for systematic inspections and maintenance of civil engineering structures. In this con-text the paper presents the Stochastic Finite Element (SFE) Modeling of the static and dy-namic results of own four point bending experiments with R/C beams. The beams are dam-aged by an increasing load. Between the load levels the dynamic properties are determined. Calculated stiffness loss factors for the displacements and the natural frequencies show differ-ent histories. A FE Model for the beams is developed with a discrete crack formulation. Cor-related random fields are used for structural parameters stiffness and tension strength. The idea is to simulate different crack evolutions. The beams have the same design parameters, but because of the stochastic material properties their undamaged state isn't yet the same. As the structure is loaded a stochastic first crack occurs on the weakest place of the structure. The further crack evolution is also stochastic. These is a great advantage compared with de-terministic formulations. To reduce the computational effort of the Monte Carlo simulation of this nonlinear problem the Latin-Hypercube sampling technique is applied. From the results functions of mean value and standard deviation of displacements and frequencies are calcu-lated. Compared with the experimental results some qualitative phenomena are good de-scribed by the model. Differences occurs especially in the dynamic behavior of the higher load levels. Aim of the investigations is to assess the possibilities of dynamic testing under consideration of effects from stochastic material properties