Latin Hypercube Simulation Research Articles

Improving monitoring network design to detect leaks at hazardous facilities: Lessons from a CO2 storage site

Exploring the challenges posed by uncertainties in numerical modeling for hazardous material storage, this study introduces methodologies to improve monitoring networks for detecting subsurface leakages. The proposed approaches were applied to the Korea CO2 Storage Environmental Management (K-COSEM) test site, undergoing calibration, validation and uncertainty analysis through hydraulic and controlled-CO2 release tests. The calibration phase involved inter-well tracer and multi-well pumping tests, leveraging the Parameter ESTimation (PEST) model to determine the aquifer flow and solute transport properties of the K-COSEM site. To tackle uncertainties with limited observation data, we adopted Latin Hypercube simulation. Our uncertainty analysis confirmed model accuracy in simulating observed CO2 breakthrough curves. We also explored a probabilistic method to identify the environmental change point (EnCP) through correlation analysis with the distance from the CO2 injection well, revealing a linear trend and pinpointed potential preferential flow pathways by assessing detection probabilities. Evaluating CO2 detection capabilities was crucial for optimizing monitoring well placement, highlighting strategic well selection based on detection probabilities. This study advances managing uncertainties in hydrogeological modeling, underscoring the importance of sophisticated models in designing monitoring networks for hazardous leak detection in complex subsurface conditions.

Modeling green recycled aggregate concrete using machine learning and variance-based sensitivity analysis

Recycled aggregate concrete (RAC) is commonly used to lessen the environmental effect of concrete building and demolition waste. The compressive strength of the RAC is one of the most critical factors influencing concrete quality. The compressive strength is assessed by a compression test, which takes a large number of materials and is expensive and time-consuming. With the development of novel concrete mixes and applications, academics are obliged to seek accurate models for forecasting mechanical strength. A significant source of difficulty in compressive strength modeling is that there are many mixture components and testing conditions whose variation significantly influences the predicted values. To this end, this study explores the mixture design of sustainable concrete in order to generate eco-friendly concrete mixes. Tests are conducted on 18 different mixtures comprising different proportions of waste tires, plastic, cement, and red brick to experiment with new green RAC mixtures. For the modeling part, the deep residual neural networks (DRNNs) method is first presented to the problem, aided by a database from the literature for a pretraining task. The proposed DRNNs structure uses shortcuts (i.e., residual connections) that bypass some layers in the deep network structure to alleviate the problem of training with high accuracy. The performance of the proposed DRNNs is evaluated using different goodness of fit measures and compared with well-known machine learning tools. The findings showed that the suggested model could provide credible predictions about the desired mechanical parameter, saving the required lab efforts by 40 %. Finally, a variance-based global sensitivity analysis is performed with the Latin hypercube simulation method to help rank/prioritize each mixture component's impact on determining the compressive strength in practice while mitigating the potential misrepresentation of results due to the correlations between the input parameters. The analysis showed that cement and waste contents are the most significant ones in their first and total order effects.

Global sensitivity analysis for studying hot-mix asphalt dynamic modulus parameters

The dynamic modulus (E*) of hot-mix asphalt mixtures is one of the most laborious and time-consuming material parameters to measure in the laboratory. It involves expensive, specialized equipment and expertize that are not readily available in most laboratories. Consequently, several efforts have been devoted to E* prediction models. Unfortunately, developing these prediction models is complex because of the numerous contributory factors and their non-linear influence on E* values. Moreover, such models are not able to prioritize or screen the major factors influencing the E* values. This study presents a new framework for analyzing the dynamic modulus influencing factors by adopting two modeling approaches. First, deep residual neural networks (DRNNs) for non-parametric approaches are used to improve the E* prediction capabilities and derive deep insight into the contributory parameters' effect on the E* value. Second, the well-known Witczak 1–40D prediction equation is used as a representative of the classical statistical modeling approach. In the validation of the models, a comprehensive laboratory database is utilized to account for all significant contributory parameters, such as binder characteristics, volumetric properties, mixture gradation, and testing circumstances parameters. Then, the performance is assessed using typical performance metrics. Lastly, intensive global sensitivity analysis (GSA) is undertaken with the assistance of Latin Hypercube Simulation. Three distinct GSA methods are used to emphasize the influence of each contributory factor on the value of E* in actual practice while reducing the possibility for result distortion owing to correlations between contributory variables. Performance metrics of the DRNNs and the Witczak 1–40D prediction models give the GSA conclusions high credibility. The GSA reveals that, among all possible inputs, the binder content, shear modulus, voids in the mineral aggregates, and temperature are the most significant factors in determining the E* value.

Global sensitivity analysis for seismic performance of shear wall with high-strength steel bars and recycled aggregate concrete

In terms of bearing lateral loads, a reinforced concrete shear wall (SW) is one of the most crucial structural elements in structures. Despite their significance, recent experimental research and post-earthquake surveys have exposed the inadequate safety margins of the SWs. Significant SW components cannot be accurately identified due to the absence of empirical and mechanistic models. This study introduces a novel paradigm for assessing the SW's crucial elements by using two consecutive modeling strategies. Initially, a finite element model is calibrated to mimic one of the laboratory-experimented SW structures. Then, deep residual neural networks (DRNNs) for non-parametric techniques are used to enhance the Abaqus software prediction skills and comprehensively understand the input parameters' influence on the selected SW structure's displacement value. The suggested DRNNs' design uses residual shortcuts (i.e., connections) that skip several levels in the deep network structure in order to address the issue of training with high accuracy. A thorough global sensitivity analysis (GSA) is done using the Latin Hypercube Simulation. Three distinct GSA techniques are used to emphasize the influence of each input variable on the amount of displacement in real-world applications while limiting the risk of result misinterpretation owing to interactions between input variables. In each GSA approach, an effort would be made to rank, filter, or map the input variables. The performance metrics of the DRNNs prediction models lend confidence to the GSA's results. The diameter of steel stirrups is reported to be the most significant component in the SW structure, while SW's displacement is more sensitive to higher and lower values of the lateral load domain.

Analysing Witczak 1-37A, Witczak 1-40D and Modified Hirsch Models for asphalt dynamic modulus prediction using global sensitivity analysis

ABSTRACT The dynamic modulus (∣E*∣) of hot-mix asphalt mixes is one of the most time-consuming and labour-intensive material metrics to evaluate in the laboratory. This study introduces a novel paradigm for assessing the ∣E*∣'s most influential elements by employing widely accepted literature models. Witczak 1-37A, Witczak 1-40D, and Modified Hirsch Models are selected for analysing the asphalt dynamic modulus. First, a thorough laboratory database of Arizona State University is used to account for all major input factors, such as mixture gradation, binder qualities, volumetric properties, and testing conditions parameters, during models' validation. Second, each model's performance is evaluated using standard measures to build confidence levels in the subsequent analysis stage. Finally, with the aid of Latin Hypercube Simulation, a comprehensive global sensitivity analysis (GSA) is performed. Three unique GSA approaches are used; namely, elementary effects, variance-based, and PAWN methods, to highlight the effect of each input variable on the magnitude of ∣E*∣. Different GSA tools are strongly recommended since there is no analytical tool for validating the findings with the complex formulations of the selected mathematical models. The GSA demonstrates that the voids ratio in total mix, binder shear modulus, viscosity, phase angle, and binder quantity are the most significant factors.

Nepal's Sacred Cattle: Profitability Analysis and Policy Implications

This paper analyzes Nepal’s current livestock policy, which is rooted in culture and religion, and the extent to which its design has not kept pace with the continuing transformation of the country’s agricultural landscape. It estimates the impact of the no-slaughtering of cattle policy on dairy producers using Latin hypercube simulations to quantify a baseline and alternative scenario for cattle milk production. The baseline models the current production system, while the alternative scenario models milk production where producers are allowed to slaughter or export cattle. Results indicate that farmers’ profit per liter of milk increases by 19 to 31 percent in the alternative scenario.

Multi-objective robust design optimization (MORDO) of an aeroelastic high-aspect-ratio wing

In this paper, a new approach for multi-objective robust optimization of flutter velocity and maximum displacement of the wing tip are investigated. The wing is under the influence of bending–torsion coupling and its design variables have different levels of uncertainty. In designing and optimizing wings with a high aspect ratio, the optimization process can be done in such a way to increase the flutter velocity, but this can increase the amplitude of the wing tip displacement to a point that leads to the wings damage and structural failure. Therefore, single-objective design optimization may lead to infeasible designs. Thus, for multi-objective optimization, modeling is based on the Euler–Bernoulli cantilever beam model in quasi-steady aerodynamic condition. Using the Galerkin’s techniques, the aeroelastic equations are converted to ODE equations. After validating the results, the system time response is obtained by the numerical solution of the governing equations using 4th Runge–Kutta method and the flutter velocity of the wing is obtained using the theory of eigenvalues. Subsequently, by choosing bending and torsional rigidity and mass per unit wing length as the optimization variables, using Monte Carlo–Latin hypercube (MC-LH) simulation and 4th polynomial chaos expansion (PCE), the effect of uncertainty on these variables is modeled in modeFRONTIER™ software coupled with MATLAB™ and optimization is performed by genetic algorithm. Finally, by plotting the Pareto front, it is observed that with an acceptable increase in flutter velocity, the maximum wing displacement amplitude is reduced as much as possible. The results of the multi-objective robust optimization show more feasible results compared with deterministic optimization.

Assessing responses of hydrological processes to climate change over the southeastern Tibetan Plateau based on resampling of future climate scenarios

With global warming, hydrological regimes in the headwater basins of the Tibetan Plateau (TP) have significantly changed. Investigating the responses of hydrological processes to climate change in TP has become more and more important to make robust strategies for water resources management. However, using just a few GCMs may constrain the uncertainty in assessment of climate impacts. Therefore, a framework is proposed in this study to generate ensemble climate change scenarios and then investigate changes of hydrological processes under climate change in the upper reaches of Yarlung Zangbo River basin (UYZR) and Lancang River basin (ULR). Firstly, the Latin Hypercube Simulation (LHS) is used to generate an ensemble of future climate change scenarios by resampling change factors of meteorological variables from 18 GCMs under emission scenarios RCP2.6 and RCP8.5. The inherent dependence structures of change factors, i.e. the correlations of change factors among 12 months for different meteorological variables, are also considered in ensembles. Secondly, the HBV hydrological model coupled with a degree-day snowmelt model is applied to explore the potential change of runoff in the future period 2041–2070. Results show that: 1) the resampling method is effective and can provide a wide ensemble of climate change scenarios. 2) Precipitation, temperature and potential evapotranspiration in the UYZR and ULR basins are expected to increase under the two scenarios, particularly under RCP8.5. 3) The total runoff also shows a moderately upward trend in two basins, both mainly due to increased precipitation. In the UYZR basin, fast runoff accounts for a larger proportion in total runoff than slow runoff, while in ULR, both almost play the same role in total runoff. Furthermore, snowmelt-induced runoff in both basins would be less and rainfall-induced runoff will probably become more important in the future.

Efficient uncertainty quantification in fully-integrated surface and subsurface hydrologic simulations

Abstract Although high performance computers and advanced numerical methods have made the application of fully-integrated surface and subsurface flow and transport models such as HydroGeoSphere common place, run times for large complex basin models can still be on the order of days to weeks, thus, limiting the usefulness of traditional workhorse algorithms for uncertainty quantification (UQ) such as Latin Hypercube simulation (LHS) or Monte Carlo simulation (MCS), which generally require thousands of simulations to achieve an acceptable level of accuracy. In this paper we investigate non-intrusive polynomial chaos for uncertainty quantification, which in contrast to random sampling methods (e.g., LHS and MCS), represents a model response of interest as a weighted sum of polynomials over the random inputs. Once a chaos expansion has been constructed, approximating the mean, covariance, probability density function, cumulative distribution function, and other common statistics as well as local and global sensitivity measures is straightforward and computationally inexpensive, thus making PCE an attractive UQ method for hydrologic models with long run times. Our polynomial chaos implementation was validated through comparison with analytical solutions as well as solutions obtained via LHS for simple numerical problems. It was then used to quantify parametric uncertainty in a series of numerical problems with increasing complexity, including a two-dimensional fully-saturated, steady flow and transient transport problem with six uncertain parameters and one quantity of interest; a one-dimensional variably-saturated column test involving transient flow and transport, four uncertain parameters, and two quantities of interest at 101 spatial locations and five different times each (1010 total); and a three-dimensional fully-integrated surface and subsurface flow and transport problem for a small test catchment involving seven uncertain parameters and three quantities of interest at 241 different times each. Numerical experiments show that polynomial chaos is an effective and robust method for quantifying uncertainty in fully-integrated hydrologic simulations, which provides a rich set of features and is computationally efficient. Our approach has the potential for significant speedup over existing sampling based methods when the number of uncertain model parameters is modest ( ≤ 20). To our knowledge, this is the first implementation of the algorithm in a comprehensive, fully-integrated, physically-based three-dimensional hydrosystem model.

Seismic Vulnerability Analysis of RC Bridges Based on Kriging Model

ABSTRACTThis paper presents a Kriging model-based method for seismic vulnerability analysis of reinforced concrete (RC) bridges. It aims at reducing the computational effect when the Monte Carlo technique is used for establishing the structural vulnerability curves. The general procedure of the proposed method is put forward firstly. In the procedure, the uncertainties existing in the structures and ground motions are both taken into account, and the uniform design (UD) technique is adopted for generating the random samples. The reliability of the proposed method is demonstrated by the vulnerability analysis of an single degree of freedom (SDOF) system using the Latin hypercube simulation (LHS) method. Vulnerability analysis of an RC bridge system is then carried out using the proposed method. The vulnerability curves of the bridge obtained by the Kriging model-based method are compared with those obtained by the LHS method. Additionally, three simulation schemes adopting different UD tables are employed to investigate the convergence and stability of the proposed method. The results show that the proposed method used for the seismic vulnerability analysis of RC bridges can reduce the computational effort and time to a large extent without much compromise on the accuracy.

Developing a Screening Algorithm for Type II Diabetes Mellitus in the Resource-Limited Setting of Rural Tanzania

BackgroundNoncommunicable diseases are on pace to outnumber infectious disease as the leading cause of death in sub-Saharan Africa, yet many questions remain unanswered with concern toward effective methods of screening for type II diabetes mellitus (DM) in this resource-limited setting. We aim to design a screening algorithm for type II DM that optimizes sensitivity and specificity of identifying individuals with undiagnosed DM, as well as affordability to health systems and individuals. MethodsBaseline demographic and clinical data, including hemoglobin A1c (HbA1c), were collected from 713 participants using probability sampling of the general population. We used these data, along with model parameters obtained from the literature, to mathematically model 8 purposed DM screening algorithms, while optimizing the sensitivity and specificity using Monte Carlo and Latin Hypercube simulation. ResultsAn algorithm that combines risk assessment and measurement of fasting blood glucose was found to be superior for the most resource-limited settings (sensitivity 68%, sensitivity 99% and cost per patient having DM identified as $2.94). Incorporating HbA1c testing improves the sensitivity to 75.62%, but raises the cost per DM case identified to $6.04. The preferred algorithms are heavily biased to diagnose those with more severe cases of DM. ConclusionsUsing basic risk assessment tools and fasting blood sugar testing in lieu of HbA1c testing in resource-limited settings could allow for significantly more feasible DM screening programs with reasonable sensitivity and specificity.

A semi-random field finite element method to predict the maximum eccentric compressive load for masonry prisms

An accurate prediction of the compressive strength of masonry is essential both for the analysis of existing structures and the construction of new masonry buildings. Since experimental material testing of individual masonry components (e.g., masonry unit and mortar joints) often produces highly variable results, this paper presents a numerical modelling based approach to address the associated uncertainty for the prediction of the maximum compressive load of masonry prisms. The method considers a numerical model to be semi-random for a masonry prism by adopting a Latin Hypercube simulation method used in conjunction with a parametric finite element model of the individual masonry prism. The proposed method is applied to two types of masonry prisms (hollow blocks and solid clay bricks), for which experimental testing was conducted as part of the 9th International Masonry Conference held at Guimarães in July 2014. A Class A prediction (presented before the tests were conducted) was generated for the two masonry prisms according to the proposed methodology, and the results were compared to the final experimental testing results. The root mean square deviation of the method for prediction of eccentric compressive strength of both types of prisms differed by only 2.2KN, thereby demonstrating the potential for this probabilistic approach.

Development of Pervious Concrete Pavement Performance Models Using Expert Opinions

Pervious concrete pavement (PCP) is of significant importance in the field of stormwater management in terms of reducing runoff volume. Stormwater managers should initially ensure that PCP adequately performs over time to be able to implement it in a stormwater management system. Performance models are intended to predict the performance of an asset over its service life. To develop a performance model commonly long-term performance data are essential. No performance model has been developed for PCP to date because PCP long-term performance data are rarely available. In such a case, expert knowledge is an alternative method to collect data for developing a performance model. This research aims to develop performance models for PCP for the first time by using an integrated Markov chain technique (combination of homogenous and non-homogenous techniques) through incorporation of expert knowledge. Both deterministic and stochastic approaches are applied to build up Markov models by using expected values and the Latin hypercube simulation technique, respectively. Both approaches provide consistent results although the stochastic Markov model provides more detailed results. Short-term experimental field data are also incorporated to validate the Markov performance models.

Nutritional and toxicological aspects of seafood consumption—An integrated exposure and risk assessment of methylmercury and polyunsaturated fatty acids

Seafood consumption is associated with both risks and beneficial effects to human health. Consequently, an integrated exposure assessment of intake of toxic and nutritious agents in seafood is of importance prior to determination of dietary advisories. We have developed a probabilistic model for the estimation of simultaneous intake of methylmercury (MeHg) and long-chain n-3 polyunsaturated fatty acids (LC-n3 PUFAs) from seafood, to estimate the population proportion at risk for exceeding tolerable MeHg intake and not reaching adequate intake of PUFAs. Seafood consumption data was collected among women of childbearing age using a food frequency questionnaire. A database of mercury and fatty acids concentration in seafood was constructed. A Latin Hypercube simulation was used to calculate the intake of MeHg and LC n-3 PUFAs. Eleven percent of the population exceeded the MeHg reference dose of 0.1 μg/kg bw/day, whereas only 44% reached an adequate PUFA intake. A small proportion (3.7%) exceeded the MeHg reference dose while at the same time did not reach an adequate PUFA intake. Furthermore, we simulated two scenarios in which seafood is consumed according to a general recommendation of three servings per week, whereof one serving of oily seafood. The first scenario included seafood with typically low MeHg concentrations (mean 0.056 and 0.027 μg MeHg/g fish in lean and oily species, respectively), and the second included seafood typically high in MeHg concentrations (mean 0.50 and 0.26 μg MeHg/g fish in lean and oily species, respectively). In the “high”scenario, almost 100% of the population exceeded the reference dose, whereas the corresponding proportion was only 5% in the “low” scenario. Overall, the results stress the importance of communicating species specific seafood consumption advisories for women of childbearing age in general and for pregnant women in particular, while at the same time encourage them to consume more seafood.

Tiotropium's cost-effectiveness for the treatment of COPD: a cost-utility analysis under real-world conditions.

BackgroundTiotropium is reimbursed since March 2004 in Belgium for the treatment of Chronic Obstructive Pulmonary Disease (COPD). Questions however remain on this product's value for money. The purpose of this study is to calculate tiotropium's cost-effectiveness under real-world conditions.MethodsStrengths of both observational and RCT data were combined in a model. A large longitudinal (2002-2006) observational dataset of regular tiotropium users (56 321 patients) was analysed to retrieve the baseline risk for exacerbations and exacerbation-related hospitalisations the year before the first delivery of tiotropium. The relative treatment effect from the UPLIFT (Understanding Potential Long-term Impacts on Function with Tiotropium) trial was then applied to this baseline risk to reflect the effect of tiotropium treatment and calculate the intervention's incremental cost-effectiveness ratio (ICER).ResultsAfter 1000 Latin Hypercube simulations, the incremental benefit expressed as quality-adjusted life years (QALY) gained is on average 0.00048 (95% confidence interval (CI) 0.00009 - 0.00092). In combination with a substantial mean incremental cost of €373 per patient (95% CI 279 - 475), this results in an unfavourable average ICER of €1 244 023 (95% CI 328 571 - 4 712 704) per QALY gained. Results were most sensitive to the treatment effect on hospitalisations. Based on our large observational database, up to 89% of the patients were not hospitalised for COPD in the year before the first tiotropium delivery.ConclusionsThe main cause for tiotropium's unfavourable cost-effectiveness ratio is a combination of a relative high price for tiotropium, a low number of hospitalisations without tiotropium treatment (on average 0.14/year) and a non-significant treatment effect (on average 0.94) with respect to avoiding exacerbation-related hospitalisations. From an economic point of view, a revision of reimbursement modalities (e.g. with a lower price) would be justified and would entail a more efficient use of resources.

Use of Soft Computing Applications to Model Pervious Concrete Pavement Condition in Cold Climates

Development of an adequate performance model based on an appropriate condition index has been a major challenge for engineers particularly in case of a new type of design such as pervious concrete pavement structures (PCPSs) which suffer from limited long term performance data sets especially in colder climates. Soft computing techniques are significantly efficient at dealing with subjective, incomplete, and limited data. This paper proposes three most effective soft computing methods: fuzzy sets, the Latin Hypercube Simulation technique, and the Markov Chain process. A novel comprehensive condition index based on severity, density, and weighting factors of distresses occurring on PCPS has been developed incorporating fuzzy sets. A combination of homogeneous and nonhomogeneous Markov Chain has been applied to develop performance models. Transition probability matrices are presented using probability distribution functions rather than single values. A simulation technique is then used to incorporate the probability distribution function operations to compute the future condition of the pavements. The future performance of the pavements is expressed by both single expected values and suitable probability distribution functions. Ultimately, a probabilistic versus deterministic performance curve is presented.

A two-step approach to investigate the effect of rating curve uncertainty in the Elbe decision support system

For river basin management, the reliability of the rating curves mainly depends on the accuracy and time period of the observed discharge and water level data. In the Elbe decision support system (DSS), the rating curves are combined with the HEC-6 model to investigate the effects of river engineering measures on the Elbe River system. In such situations, the uncertainty originating from the HEC-6 model is of significant importance for the reliability of the rating curves and the corresponding DSS results. This paper proposes a two-step approach to analyze the uncertainty in the rating curves and propagate it into the Elbe DSS: analytic method and Latin Hypercube simulation. Via this approach the uncertainty and sensitivity of model outputs to input parameters are successfully investigated. The results show that the proposed approach is very efficient in investigating the effect of uncertainty and can play an important role in improving decision-making under uncertainty

Limit Analysis of Masonry Structures Accounting for Uncertainties in Constituent Materials Mechanical Properties~!2008-03-30~!2008-04-22~!2008-06-12~!

The uncertainty often observed in experimental strengths of masonry constituent materials makes critical the selection of the appropriate inputs in the finite elements limit analysis of complex masonry buildings, as well as requires modeling the building ultimate load as a random variable. The most direct approach to solve limit analysis problems in presence of random input parameters is the use of extensive Monte Carlo (MC) simulations. Nevertheless, when MC methods are used to estimate the collapse load cumulative distribution of a masonry structure, large scale linear programming problems must be numerically tackled several times, so precluding the practical utilization of large scale MC simulations. To reduce the computational cost of a traditional MC approach, in the present paper direct computer calculations are replaced with inexpensive Response Surface (RS) models. In particular, RS models are utilized for the limit analysis of a masonry structure in- and out-of-plane loaded, assuming input mechanical properties as random parameters. Two different RS models are analyzed, derived respectively from small scale (20 replicates) MC and Latin Hypercube (LH) simulations. The accuracy of the estimated RS models, as well as the good estimations of the collapse load cumulative distributions obtained via polynomial RS models in comparison with large scale MC simulations, show how the proposed approach could be a useful tool in problems of technical interest.

Financial Risk Analysis of Project Finance in Indonesian Toll Roads

If a project is implemented using a project-finance approach, the debt service payment relies solely on the project cash flows and its assets. This paper identifies, quantifies, and evaluates major financial risks associated with project-financed toll road projects in Indonesia. Ordering payments by priority level, subject to cash availability, enables risk to be evaluated from the different perspectives of multiple parties involved. The paper makes use of Latin Hypercube simulations for risk analysis because they deal with problems involving large and complex systems. To better illustrate the concept, a case study is presented. A sensitivity analysis of the impact of delay-in-adjustment risk and of the adoption of a new regulation related to the toll adjustment is performed and discussed. Simulation results show that the project sponsor fares worse as delay-in-adjustment risk increases but that the creditor can fare better, given that the risk level is low or moderate. Output statistics also reveal that ...

Proban – probabilistic analysis

Proban is a general purpose probabilistic analysis program that covers calculation needs for example in structural reliability. Proban calculates probability, distribution, first passage probability, crossing rate, and related sensitivity measures. The program is not restricted to a particular problem, but allows users to define functions on input and to attach function libraries to the program in a DLL (Windows). Supporting tools such as flexible modeling of variables and events, deterministic analysis, distribution fit, parameter study, function libraries and distribution library come with the program. A variety of methods like FORM, SORM, adaptive response surface, nested reliability analysis, (adjusted) Monte–Carlo simulation, stratified simulation, directional simulation, design point simulation, axis orthogonal simulation and Latin Hypercube simulation are implemented. Proban has a graphical user interface, maintains models on a database, records commands on a journal file for possible reread and displays, plots and prints model and results.