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TesiproV

The goal of TesiproV is to provide a flexible framework for probabilistic reliability analysis of structural systems. It implements several state-of-the-art algorithms (FORM, SORM, MVFOSM and Monte-Carlo methods) and supports parametric studies through reference classes such as SYS_PROB, SYS_PARAM, and SYS_LSF.

Installation

The stable release will be available on CRAN soon. Until then you can install directly from our GitLab server:

# install.packages("remotes")
remotes::install_gitlab("ls-massivbau/tesiprov", host = "gitlab.tu-dortmund.de")

You can install the development version of TesiproV via the gitLab Server of the TU Dortmund University: https://gitlab.tu-dortmund.de/ls-massivbau/tesiprov

If you need any permissions, please contact the project authors (see below).

Parallel computing configuration

TesiproV automatically configures parallel execution using the parallel and future packages. By default up to 4 cores are used for outer parallelisation (ProbMachines), while inner Monte-Carlo simulations manage their own threads. You can change this behaviour with environment variables before loading the package:

Sys.setenv(TesiproV.max_workers = "8") # allow up to 8 workers
Sys.setenv(TesiproV.future = "multisession") # choose backend explicitly
library(TesiproV)

For details see the help page of .onLoad() or your system’s documentation.

Example

This is a basic example which shows how to define a limit-state function and run a FORM analysis:

library("TesiproV")
Var1 <- PROB_BASEVAR(Id = 1, Name = "X1", DistributionType = "norm", Mean = 0.25, Sd = 1) # kN/m²
Var2 <- PROB_BASEVAR(Id = 2, Name = "X2", DistributionType = "norm", Mean = 0.25, Sd = 1) # m

lsf <- SYS_LSF(vars = list(Var1, Var2), name = "UQLab 2d_hat")
lsf$func <- function(X1, X2) {
  return(20 - (X1 - X2)^2 - 8 * (X1 + X2 - 4)^3)
}

form <- PROB_MACHINE(name = "FORM Rack.-Fieß.", fCall = "FORM")

ps <- SYS_PROB(
  sys_input = list(lsf),
  probMachines = list(form)
)
ps$runMachines()

ps$beta_single
#>                     UQLab 2d_hat
#> FORM Rack.-Fieß. 3.4345652859862

Parametric study example

A simple sweep over different mean values can be performed with PARAM_BASEVAR and SYS_PARAM:

pvar <- PARAM_BASEVAR(
  Name = "E_mod",
  DistributionType = "norm",
  ParamType = "Mean",
  ParamValues = c(30e3, 35e3, 40e3)
)

lsf_param <- SYS_LSF(vars = list(pvar), name = "ParamStudy")
lsf_param$func <- function(E_mod) {
  E_mod / 1000 - 30
}

machine_form <- PROB_MACHINE(name = "FORM", fCall = "FORM")

ps_param <- SYS_PARAM(
  sys_input = list(lsf_param),
  probMachines = list(machine_form)
)
ps_param$runMachines()
print(ps_param$beta_params)

Recent changes

Version 0.9.5 introduces major refactoring:

• Improved consistency between probabilistic objects (SYS_PROB, SYS_PARAM, …).
• Corrected implementation of the Gumbel distribution in $prepare().
• Enhanced error handling in $check() for limit-state functions.
• Automatic parallel plan configuration via .onLoad() with capped worker pool (default 4 cores).

For a complete changelog see NEWS.md.

Further information

For more, check out the vignette! There are plenty more demonstrations and detailed explanations for each object class.

You can open it directly in R:

vignette("TesiproV")
#> Warning: vignette 'TesiproV' not found

Contact & Citation

If you use TesiproV for research or teaching, please cite it as:

Nille-Hauf K., Lux T., Feiri T., Ricker M. (2026): TesiproV — Probabilistic Reliability Analysis Framework. TU Dortmund University.

For questions or bug reports please contact massivbau.ab@tu-dortmund.de.

License

TesiproV is licensed under the MIT License.
You are free to use, modify and redistribute the software provided that the original copyright notice and license terms are retained.

© 2021-2026 Konstantin Nille-Hauf, Til Lux, Tania Feiri, Marcus Ricker — Hochschule Biberach / TU Dortmund University.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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