Probabilistic Engineering Research Articles

Systematic enhancement of protein crystallization efficiency by bulk lysine-to-arginine (KR) substitution.

Structural genomics consortia established that protein crystallization is the primary obstacle to structure determination using x-ray crystallography. We previously demonstrated that crystallization propensity is systematically related to primary sequence, and we subsequently performed computational analyses showing that arginine is the most overrepresented amino acid in crystal-packing interfaces in the Protein Data Bank. Given the similar physicochemical characteristics of arginine and lysine, we hypothesized that multiple lysine-to-arginine (KR) substitutions should improve crystallization. To test this hypothesis, we developed software that ranks lysine sites in a target protein based on the redundancy-corrected KR substitution frequency in homologs. This software can be run interactively on the worldwide web at https://www.pxengineering.org/. We demonstrate that three unrelated single-domain proteins can tolerate 5-11 KR substitutions with at most minor destabilization, and, for two of these three proteins, the construct with the largest number of KR substitutions exhibits significantly enhanced crystallization propensity. This approach rapidly produced a 1.9 Å crystal structure of a human protein domain refractory to crystallization with its native sequence. Structures from Bulk KR-substituted domains show the engineered arginine residues frequently make hydrogen-bonds across crystal-packing interfaces. We thus demonstrate that Bulk KR substitution represents a rational and efficient method for probabilistic engineering of protein surface properties to improve crystallization.

Computer-supported probabilistic engineering tools assisting fill optimization and the waste reduction of low/intermediate moisture foods supporting vulnerable populations

This work focuses on the shelf-life of moisture-sensitive products, particularly intermediate-moisture (IMFs) and low-moisture foods (LMFs), which can support the needs of vulnerable populations. Deterministic and probabilistic engineering methodologies, i.e., ignoring and considering the variability of parameters used in calculations, respectively, were compared when selecting the initial water activity of product mix components or the packaging materials achieving the shelf-life necessary to reach consumers with safe and high quality products, and when filling packages with the declared net weight. This document presents detailed examples and reviews published computational tools developed to solve these cases. The application of probabilistic engineering and computer supported tools can help developers optimize the product formulation and packaging material selection to reduce food waste by increasing the probability that products will have the shelf-life necessary to be stored and distributed to reach all consumers, and particularly at-risk populations and those affected by natural or man-made disasters. Industrial relevanceUndoubtedly, the need for food waste reduction is now a key responsibility for food producers and consumers. The lack of sufficient food creates social and economic instability, and this is an unfavorable situation for the growth of the food industry. Food waste also increases the cost of products, particularly when retailers and consumers demand a refund for a product that did not meet the shelf-life declared or at least expected for the product. This work reviews computer supported probabilistic engineering tools to reduce the waste of products with a shelf-life controlled by the product formulation and the moisture barrier properties of the packaging material.

Assessment of capital expenditure for fixed-bottom offshore wind farms using probabilistic engineering cost model

The capital expenditure (CAPEX) for the fixed-bottom offshore wind farm is assessed using a probabilistic engineering cost model and the cost reduction scenarios in Japan are analyzed. Firstly, the engineering cost model is described to assess the capital expenditure. A new export cable length model is also proposed considering the landing point distance and the vessel size model is proposed as the function of turbine rated power. The proposed engineering cost model succeeds in explaining the mechanism of the increase and decrease of CAPEX experienced in the UK. The uncertainties of model parameters are identified from the reported data and modeled by the normal distribution function. The workability is predicted using the discrete event simulation. The predicted CAPEX is then compared with the existing 30 fixed-bottom offshore wind farms in the United Kingdom. The predicted mean and standard deviation values of CAPEX show good agreement with the reported ones, while the conventional parametric model underestimates the mean value and cannot predict the standard deviation. Finally, the cost reduction scenarios and their uncertainties of offshore wind farms in Japan are analyzed using the proposed probabilistic engineering cost model. The levelized cost of wind energy reduced from 20.0 JPY/kWh to 17.0 JPY/kWh, 13.6 JPY/kWh and 10.1 JPY/kWh by the reduction of installation days using the specific installation vessel, the turbine enlargement and the improvement of operation and maintenance efficiency. The predicted supply prices for each cost reduction scenario agree well with those reported at the first auction conducted in 2021 in Japan.

Probabilistic defect-based modelling of fatigue strength for incomplete datasets assisted by literature data

Probabilistic defect-tolerant fatigue design protocols have become the leading paradigms in structural engineering. To effectively deal with this problem, El Haddad’s (EH) curves are generally employed for the evaluation of the fatigue endurance limit. Herein, the synergic exploitation of Logistic Regression (LR) and Maximum a Posteriori (MAP) allows for calibrating EH parameters using the sole data from fatigue characterisation and post-mortem fractography. An extensive literature research provided the ground to introduce, when necessary, prior information for some of the more commonly used metallic alloys. Eventually, EH curves are retrieved upon a Monte Carlo simulation to support probabilistic engineering practice.

A performance-based approach to quantify atmospheric river flood risk

Abstract. Atmospheric rivers (ARs) are a class of meteorologic phenomena that cause significant precipitation and flooding on the US West Coast. This work presents a new Performance-based Atmospheric River Risk Analysis (PARRA) framework that adapts existing concepts from probabilistic risk analysis and performance-based engineering for application in the context of AR-driven fluvial flooding. The PARRA framework is a chain of physically based models that link the atmospheric forcings, hydrologic impacts, and economic consequences of AR-driven fluvial flood risk together at consistent “pinch points”. Organizing around these pinch points makes the framework modular, meaning that models between pinch points can be updated without affecting the rest of the model chain, and it produces a probabilistic result that quantifies the uncertainty in the underlying system states. The PARRA framework can produce results beyond analyses of individual scenario events and can look toward prospective assessment of events or system changes that have not been seen in the historic record. The utility of the PARRA framework is demonstrated through a series of analyses in Sonoma County, CA, USA. Individual component models are fitted and validated against a historic catalog of AR events occurring from 1987 to 2019. Comparing simulated results from these component model implementations against observed historic ARs highlights what we can learn about the drivers of extremeness in different flood events by taking a probabilistic perspective. The component models are then run in sequence to generate a first-of-its-kind AR flood loss exceedance curve for Sonoma County. The prospective capabilities of the PARRA framework are presented through the evaluation of a hypothetical mitigation action. Elevating 200 homes, selected based on their proximity to the Russian River, was sufficient to reduce the average annual loss by half. Although expected benefits were minimal for the smallest events in the stochastic record, the larger, more damaging ARs were expected to see loss reductions of approximately USD 50–75 million per event. These results indicate the potential of the PARRA framework to examine other changes to flood hazard, exposure, and vulnerability at the community level.

Structural Fire Risk for Heritage Buildings by the Performance-Based Engineering Format

ABSTRACT This paper discusses and presents an example of fire risk analysis for a case study of an existing heritage building: the Duomo of Modena Cathedral, located in Italy. The main issues related to the application of the fire risk analysis to heritage buildings are discussed in detail, and an ad-hoc procedure is proposed to manage them for the case study. In order to assess the performances of the building in probabilistic terms, the fire risk analysis is conducted by the event-tree method, the fire dynamics are analyzed by numerical thermo-fluid dynamics models, while the structural behavior is investigated by advanced nonlinear thermomechanical finite element models. Results are expressed in terms of risk curves related to a fixed return period, which quantify the occurrence probability of different damage levels induced by fire in the wooden roof of the case study building. This kind of result is fully compatible with probabilistic performance-based engineering frameworks commonly accepted for other hazards (i.e., earthquakes).

A refined subset simulation for the reliability analysis using the subset control variate

The reliability analysis of an engineering system with a small failure probability (Pf) and a complex-geometry performance function is a major challenge in the probabilistic engineering mechanics for which the subset simulation (SS) is a promising addressing algorithm. However, SS is potentially problematic when solving complex problems. This has triggered an interest in improving and reformulating the SS. This paper presents the subset control variate (SCV) technique, a novel approach to reformulate the conventional SS, and provides the statistical properties such as coefficient of variation (c.o.v) of the estimate of Pf using SCV. SCV enhances and generalizes the original SS. It improves the conventional SS formulation and can employ different sampling approaches in the SS to properly find the most probable failure domain with complex and misleading geometry. The proposed method’s capabilities are compared with those of the conventional SS and examined by solving several numerical and practical problems with challenging performance functions. The results, validated by the Monte Carlo Simulation (MCS), show that the SCV improved the SS robustness for solving highly nonlinear problems involving misleading performance functions and its c.o.v is less than that of the original SS for all the numerical examples considered in this study.

Onto2problog: A Probabilistic Ontology-Mediated Querying System using Probabilistic Logic Programming

We present onto2problog, a tool that supports ontology-mediated querying of probabilistic data via probabilistic logic programming engines. Our tool supports conjunctive queries on probabilistic data under ontologies encoded in the description logic mathcal{ELH}^{dr}, thus capturing a large part of the OWL 2 EL profile.

Probabilistic structural fire engineering using incremental fire analysis and cloud analysis

Probabilistic structural fire engineering is an approach to designing structures for fire conditions, which allows the estimation of an annual rate of exceeding various levels of building response. This is done by combining the annual rate of exceeding various fire exposure conditions with probabilistic relationships between structural response and fire severity. One available method that has been used to quantify this relationship is incremental fire analysis (IFA). Another approach which is well-established in earthquake engineering but has so far not been explored in fire engineering is cloud analysis (CA). This paper compares how well both methods estimate structural response under fire conditions and their associated probabilities of exceedance of a specified level of structural response. The paper demonstrates the inner workings of the two methods and their application and also proposes recommendations for their application to structural fire engineering. It is demonstrated with the help of a case study of a composite steel beam exposed to a suite of fires, generated by considering fuel load and ventilation as random variables. CA is found to be computationally less demanding than IFA, yet both approaches produce similar probability of exceedance of various levels of structural response.

CURRENT APPROACH TO ASSET EVALUATION AT THE SEARCH STAGE WITH HIGH UNCERTAINTIES

New approach of complex uncertainties analysis at the exploration stage of the field is considered not only for geological properties but also for reservoir engineering, including the uncertainty of type of hydrocarbons saturation. The method shown in the work is based on the probabilistic method of HIIP estimation which is followed by probabilistic reservoir engineering uncertainties assessment in production profiles calculation.

Bayesian networks for disaggregation of structural reliability

Recent developments in theory and solution algorithms have facilitated the application of Bayesian Probabilistic Networks (BPNs) to probabilistic engineering problems. BPNs’ ability to easily perform backward analysis and parameter updating offers advantage over existing methods. This paper attempts to propose a BPN for reliability assessment of structural systems based on their cut-set representation. A five-layer Bayesian network is proposed which includes random variables, component events, failure paths and failure modes to compute the structural probability of failure. Convenient inclusion of parameter dependency or correlation is an advantage of the proposed method. While in existing methods extensive computation or simplifying assumption are used to account for component and mode dependencies, the proposed method utilizes the inherent ability of BPNs to model the joint probability distributions using factors of independent conditional probabilities. This greatly reduces efforts to include parameter dependencies which may significantly affect the output. Application to a real-world example of 4-storey reinforced concrete structure is presented and optimizations to lower the computation costs are reviewed. Also it is described how disaggregation of structural failure and updated distribution of random variables can be obtained using backward inference without any additional recalculations.

Probabilistic geotechnical engineering analysis based on first order reliability method

Limit equilibrium engineering analysis needs the incorporation of probabilistic approaches for the determination of soil strength statistical measures for deterministic and probabilistic analyses. For the commonly used Mohr–Coulomb strength model, the uncertainty and the characteristic values of cohesion (c) and angle of shearing resistance (φ) determination is not straight­­forward. This paper applies the first order reliability method (FORM) to estimate these values from the direct shear and the typical triaxial tests. The method is verified with test data. Furthermore, the FORM is applied to the planar failure limit equilibrium problem to determine the statistical measures of the safety margin (SM) and safety factor (SF). It is observed that the critical slip surface for the best estimate of the mean (SMm, SFm), for a 5% probability of exceedance (SMp=5%, SFp=5%) and for the characteristic value (SMk, SFk) do not coincide. It is interesting that the maximum probability of having a SM<0 or SF<1 does not correspond to the minimum best estimate of the SM or SF. FORM can be a very useful tool for complete probabilistic analyses. Further­more, probabilistic approaches applied to soil properties estimation can set a framework for the selection of their characteristic values for deterministic analyses.

The Archimedes’ Constant, π Seen by Mechanical Engineers

Probabilistic engineering mechanics is used to relate the value of π with one of the main parameters in fracture mechanics. It proposes an engineering method to obtain the value of it from engineering data without involving any geometrical Euclidian’s circle’s data measurement or analysis. It is the first trial in studying the use of fracture mechanics to determine the value of ratio of circumference and diameter of Euclidean’s circles indirectly, and subsequently evaluate the number of the digits actually needed in fracture mechanics and engineering purposes.

Modeling dynamic responses of heterogeneous seabed with embedded pipeline through multiresolution random field and coupled hydromechanical simulations

In this work, novel multiresolution random field models are integrated with coupled hydromechanical finite element simulations to model dynamic responses of heterogeneous seabed with embedded pipeline subjected to nonlinear wave-induced loadings. The heterogeneous properties of the seabed are characterized and modeled as spatially correlated random variables. A conditional sequential simulation technique is implemented and multiresolution random field realizations of heterogeneous properties are generated. The effects of spatially correlated seabed properties on the characteristic hydromechanical responses are analyzed in details through validated hydromechanical finite element simulations. Statistics and associated uncertainties of system response are obtained with recourse to Monte Carlo simulations. The relations between the inherent heterogeneities and uncertainties in the system responses are derived. Findings of this study are shown to have important implications for probabilistic engineering designs.

Bridging the I&amp;M and design optimization communities

Note from Melanie Ooi: For this month's issue, we have one of last year's Graduate Fellowship Awards recipients, Mr. Arvind Rajan, to provide a fresh graduate's perspective on the future of instrumentation and measurement. Our future development lies in the hands of our student and graduate members, and therefore, it is crucial that we seek to understand the world of instruments and measurements from their eyes. He is currently a Monash University scholar pursuing his Ph.D. in the field of uncertainty evaluation, reliability analysis, and probabilistic engineering design.

Inactivation model and risk-analysis design for apple juice processing by high-pressure CO2.

Sigmoidal microbial survival curves are observed in high-pressure carbon dioxide (HPCD) pasteurization treatments. The objectives of this study were to use the Gompertz primary model to describe the inactivation in apple juice of the pathogen Escherichia coli CGMCC1.90 and to apply probabilistic engineering to select HPCD treatments meeting at least 5 log10 reductions (SV≥5) at 95% confidence. This required secondary models for the temperature (T, °C) and pressure (P, MPa) dependence of the Gompertz model parameters. The expressions [Formula: see text] and [Formula: see text] selected using goodness-of-fit measures and assessments based on Akaike and Bayesian information criteria were consistent with proposed mechanistic models for HPCD bactericidal effects. Monte Carlo simulations accounting for the variability and uncertainty of the parameter b and c estimates were used to predict SV values for a given time, temperature and CO2 pressure combination and desired confidence boundary. A similar approach used to estimate process times meeting SV≥5 at 95% confidence for a given temperature and CO2 pressure combination, showed that HPCD processes met this requirement only for relatively long processing times, i.e., 35-124min in the experimental range of 32-42°C and 10-30MPa. Therefore, further HPCD research is required to reduce processing time.

Deriving environmental contours from highest density regions

Environmental contours are an established method in probabilistic engineering design, especially in ocean engineering. The contours help engineers to select the environmental states which are appropriate for structural design calculations. Defining an environmental contour means enclosing a region in the variable space which corresponds to a certain return period. However, there are multiple definitions of environmental contours for a given return period as well as different methods to compute a contour. Here, we analyze the established approaches and present a new concept which we call highest density contour (HDC). We define this environmental contour to enclose the highest density region (HDR) of a given probability density. This region occupies the smallest possible volume in the variable space among all regions with the same included probability, which is advantageous for engineering design. We perform the calculations using a numerical grid to discretize the original variable space into a finite number of grid cells. Each cell's probability is estimated and used for numerical integration. The proposed method can be applied to any number of dimensions, i.e. number of different variables in the joint probability model. To put the highest density contour method in context, we compare it to the established inverse first-order reliability method (IFORM) and show that for common probability distributions the two methods yield similarly shaped contours. In multimodal probability distributions, however, where IFORM leads to contours which are difficult to interpret, the presented method still generates clearly defined contours.

Distributions and Causation Probabilities of Multiple-Run-Rules and Their Applications in System Reliability, Quality Control, and Start-Up Tests

Statistical decision rules have been used successfully in various engineering fields for many years: system reliability, quality control, start-up tests, and switching rules. Many of these decision rules are made of multiple-run-rules with their statistical distributions and characteristics that remain largely unknown. This paper shows that, given multiple-run-rules, there exists a single run-rule defined by a compound pattern which is equivalent to the multiple-run-rules and its waiting time is finite Markov chain imbeddable. Their distributions and causation probabilities can be obtained through the finite Markov chain imbedding (FMCI) technique which provides ways to obtain theoretical and numerical results for many probabilistic engineering systems problems. Two examples of multiple-run-rules, one in system reliability and the other in quality control, are provided to demonstrate the imbedding method and theoretical results.

Variability of dynamic responses of frequency dependent visco-elastic sandwich beams with material and physical properties modeled by spatial random fields

Probabilistic engineering mechanics aims to take into account uncertainties of input parameters of models and evaluating their impact onto the model responses. For frequency dependent visco-elastic sandwich structures, used in many industrial applications such as for vibration damping, the computation of the stochastic response can lead to a prohibitive computational cost. In this paper, an efficient methodology is proposed to analyze the output variability by a stochastic method based on fast Monte Carlo Simulation using a specific formulation obtained with the modal stability assumption. Geometrical and mechanical properties of a three-layered sandwich beam with a visco-elastic core made of 3M ISD112 damping polymer are represented by random fields to model the spatial variability. The validation of the formulation is studied, then the variability of the frequency response is evaluated for moderate and high variability levels. The benefits of the approach are also discussed.

Cost-benefit evaluation of self-centring concentrically braced frames considering uncertainties

Self-centring concentrically braced frame (SC-CBF) systems have been developed to reduce post-earthquake damages in braced frames. However, due to special details required by the SC-CBF system, the construction cost of an SC-CBF is expected to be higher than that of a conventional CBF. In this study, the seismic performance and economic effectiveness of two prototype buildings utilising SC-CBFs are assessed and compared with buildings utilising conventional CBFs by evaluating the annual probabilities of exceeding various damage levels, expected annual losses, life cycle costs (under seismic hazard) and economic benefit of using SC-CBFs considering prevailing uncertainties. The results of this study show that the SC-CBF buildings have lower drift-related losses but higher acceleration-related losses. The SC-CBF is found to be beneficial for the 6-storey configuration, but not for the 10-storey configuration. For the 6-storey buildings studied here, if the construction cost of the SC-CBF is assumed to be twice that of the CBF, the pay-off time is expected to be 12 to 21 years, with a probability of 68%, considering the uncertainties in the demand, capacity, loss parameters and initial construction costs. Finally, appropriate probabilistic engineering demand parameter model formulation is critical for generating accurate loss analysis results.