STRUREL is one of the most complete collections of software modules for probabilistic modeling in structural engineering. STRUREL offers state-of-the-art numerical methods for structural reliability analysis.

The definition of the probabilistic model is fast and efficient in STRUREL:
The user friendly and intuitive graphical interface (GUI) assists the user in performing the reliability anlysis and in post-processing.
Failure criteria (limit-state functions), for which an explicit analytical expression is available, can be entered directly in the GUI.
However, also advanced limit-state functions (that express failure through e.g. finite element models or other complex models of engineering systems) can be assessed.
For this purpose, interfaces to Python, Matlab, C/C++ and Fortran are readily available.

Additional to that, the user can develop their own interfaces by means of the new Sturel Add-On (SAO) feature.
A SAO can contain limit-state functions, the stochastic model, as well as associated documentation material (in form of PDF or image files).
By means of a SAO, you can conveniently pass your stochastic model to others, and/or easily utilize a stochastic model provided by someone else.

STRUREL is a collection of commercial software programs for structural reliability analysis.
It has been developed and tested for more than 30 years.
Amongst the users of STRUREL are international leading companies in structural, geotechnical and mechanical engineering.

The main package of STRUREL is COMREL-TI for time-invariant component reliability analysis.
Additional to that, four extensions are avialable: COMREL-TV for time-variant component reliability analysis, COSTREL for reliability based optimization,
SYSREL for system reliability analysis and STATREL for statistical data analysis.

COMREL performs time-invariant reliability analysis of individual failure modes based on advanced FORM/SORM methodology. Several algorithms to find the most likely failure point (β-point) are implemented including a gradient free algorithm for non-differentiable failure criteria (state functions). Complementary or alternative computational options are Mean Value First Order (MVFO), Monte Carlo simulation, Adaptive Sampling, Spherical Sampling, several Importance Sampling schemes and Subset Simulation.

FORM/SORM techniques allow to compute a rich set of sensitivity measures showing the impact on reliability of individual basic random variables, of distribution parameters and of other constant parameters. Provided characteristic values are specified partial safety factors for all basic variables are another straightforward result.

COMREL can deal with arbitrary dependence structures in the stochastic model (Rosenblatt, Hermite and Nataf-models). The full set of stochastic models offered by STATREL is supported (44 models at present) and can be input either in parameter form or in terms of the first two moments and additional parameters if necessary. The models can be truncated and new user defined models can be added. You can make distribution parameters dependent on other variables, parameters and even functions. Dependencies can also be described in terms of correlations when this is theoretically admissible. The increased versatility in stochastic modeling certainly is one of the strength of COMREL/SYSREL.

In COMREL several failure criteria can be defined in one job. State functions can be either easily implemented in the Graphical User Interface or called from external programs. State functions can be specified in normal mathematical notation. Names for variables and parameters can be chosen freely and are automatically transferred into the stochastic model and vice versa. Important constants are predefined. Built-in functions include all elementary, trigonometric, hyperbolic, logarithmic and some special functions like the Gaussian distribution function and its inverse, Bessel and Gamma functions. Several alternatives for numerical integration, differentiation and root finding are available as well as comparative operators and test functions. Auxiliary user defined functions and reference functions can be defined.

SYSREL covers system reliability evaluation including event updating. The graphical user interface offers easy and efficient way to define the model. The definition of the stochastic model (basic random variables) is the same as in COMREL with all features to model dependencies. The logical model in SYSREL is connected with the failure criteria and the stochastic model (basic random variables) for a fully interactive control. System modeling includes not only the representation by a (minimal) set of parallel systems in series but also the important case of conditional events (observations, event updating).

For the FORM/SORM methods SYSREL is based on, you have access to several efficient and reliable algorithms searching for the β-point (most likely failure point) with special solution strategies. An alternative computational option is Monte Carlo simulation.

A tool for reliability-oriented statistical analysis of data including simulation and analysis of time series.

STATREL is a program with many special features for statistical reliability-oriented data analysis but also covers standard statistical analyses. It provides a useful set of tools to perform basic descriptive statistics with many graphical facilities. For all models included in other STRUREL modules STATREL performs parameter estimation by different methods, confidence interval and quantile estimation as well as hypothesis testing including tests for sample validity, distribution functions and parameters. Simple analysis of variance and regression is also included. Several Bayesian methods are implemented.

Results are made visible in terms of numerous graphical representations such as histograms, cumulative frequencies, bivariate plots, bar charts, probability paper plots. Import of data from spreadsheet programs like Excel and export of stochastic models to COMREL and SYSREL is possible. Schemes for simulation of random numbers, random vectors and random time series (stationary, non-stationary, Gaussian, Hermite)allow numerical experiments. Different generation techniques such as ARMA or by simple and fast Fourier transforms can be used. Many predefined spectra can be selected and various forms of non-stationarities can be defined.

The implemented features for time series analysis provides rich tools for setting up and testing models. The features include numerous graphical representations of the time series such as histograms, probability paper plots, scatter diagrams, moving averages, Husid functions, mean value crossings and periodograms. Many transformation and filter techniques including trend removals, smoothing, differentiation and integration, amplitude and frequency modulation are provided. Non-parametric spectral estimation and ARMA modeling as well as amplitude and frequency trend estimation are available.