Probabilistic Evaluation Research Articles

Capacity and failure modes of restrained hollow core slabs taking into account compressive membrane action

AbstractThe formation of compressive membrane action in restrained precast concrete hollow core slabs is investigated, and how it affects the load bearing capacity and failure mode under excessive loading. To this end, attention is oriented to a recently completed experimental campaign specifically aimed at quantifying this phenomenon in real‐scale hollow core slabs. The outcomes of this experimental campaign are then analyzed using a validated finite element model, to further study the effects of compressive membrane action on the structural behavior of hollow core slabs. From the numerical evaluations, it can be concluded that the ultimate capacity and failure mode of restrained hollow core slabs strongly depend on the restraint conditions, as well as the span‐to‐depth ratio. The results also indicate that in some cases the failure is caused by crushing of concrete in the top flange, which is typically not observed in case of simply supported boundary conditions. Additionally, the influence of the inherent uncertainties associated with material properties and geometry on the ultimate capacity and failure mode of restrained hollow core slabs is investigated. Probabilistic model evaluations are performed for a wide range of boundary conditions. The results indicate a very high influence of the uncertainty on the concrete tensile strength on the membrane action in precast hollow core slabs.

Machine Learning and Case-Based Reasoning for Real-Time Onboard Prediction of the Survivability of Ships

The subject of damaged stability has greatly profited from the development of new tools and techniques in recent history. Specifically, the increased computational power and the probabilistic approach have transformed the subject, increasing accuracy and fidelity, hence allowing for a universal application and the inclusion of the most probable scenarios. Currently, all ships are evaluated for their stability and are expected to survive the dangers they will most likely face. However, further advancements in simulations have made it possible to further increase the fidelity and accuracy of simulated casualties. Multiple time domain and, to a lesser extent, Computational Fluid dynamics (CFD) solutions have been suggested as the next “evolutionary” step for damage stability. However, while those techniques are demonstrably more accurate, the computational power to utilize them for the task of probabilistic evaluation is not there yet. In this paper, the authors present a novel approach that aims to serve as a stopgap measure for introducing the time domain simulations in the existing framework. Specifically, the methodology presented serves the purpose of a fast decision support tool which is able to provide information regarding the ongoing casualty utilizing prior knowledge gained from simulations. This work was needed and developed for the purposes of the EU-funded project SafePASS.

An alternative approach to capture uncertainties embedded in the estimation of social cost of carbon

AbstractThe continuous increase in global surface temperature, which has been triggered by the increase in atmospheric CO2 concentration from anthropogenic activity, results in damage not only in the short term but also in the long term. This damage, called the social cost of carbon (SCC), has been widely estimated using integrated assessment models (IAMs). A large range of estimated SCC values have been observed because of uncertainties in IAMs' parameters. This study provides a comprehensive review of these uncertainties after dividing IAM modules into four categories: climate sensitivity, damage function, discount rate, and regional–sectoral validation. The review was conducted by comparing key ideas considered by various IAMs: socioeconomic conditions in relation to projected CO2 emissions, estimation of the atmospheric concentration of CO2, estimation of total radiative forcing, parameters of the temperature function, parameters of the damage function, and discount rate value. In addition, this study presents an alternative approach to capture the uncertainties embedded in the SCC estimation, using a machine learning approach. This enables a probabilistic evaluation of a specific level of SCC and improves our comprehension of the implication of the calculated SCC using IAMs. This alternative approach provides a basis for further study of SCC.This article is categorized under: Climate and Environment > Net Zero Planning and Decarbonization

Probabilistic evaluation of plan quality for time-dependent anatomical deformations in head and neck cancer patients

PurposeIn addition to patient set-up uncertainties, anatomical deformations, e.g., weight loss, lead to time-dependent differences between the planned and delivered dose in a radiotherapy course that currently cannot easily be predicted. The aim of this study was to create time-varying prediction models to describe both the average and residual anatomical deformations. MethodsWeekly population-based principal component analysis models were generated from on-treatment cone-beam CT scans (CBCTs) of 30 head and neck cancer patients, with additional data of 35 patients used as a validation cohort. We simulated treatment courses accounting for a) anatomical deformations, b) set-up uncertainties and c) a combination of both. The dosimetric effects of the simulated deformations were compared to a direct dose accumulation based on deformable registration of the CBCT data. ResultsSet-up uncertainties were seen to have a larger effect on the organ at risk (OAR) doses than anatomical deformations for all OARs except the larynx and the primary CTV. Distributions from simulation results were in good agreement with those of the accumulated dose. ConclusionsWe present a novel method of modelling time-varying organ deformations in head and neck cancer. The effect on the OAR doses from these deformations are smaller than the effect of set-up uncertainties for most OARs. These models can, for instance, be used to predict which patients could benefit from adaptive radiotherapy, prior to commencing treatment.

Three‐dimensional simulation on spatiotemporal variation of salinity in reclaimed saline soil and yield reduction risk assessment

AbstractAmelioration of saline soil to grow crops could increase food production and improve food security. However, the long‐term effects of different methods for ameliorating saline soil still need to be evaluated. In this study (2014–2021), the salinity variations of saline soils under different amelioration methods (T1: salt isolation by a stone layer + cropping; T2: subsurface drainage + cropping; T3: 5‐year cropping; T4: 3‐year cropping; CK: bare land) were monitored using electromagnetic induction. Besides, the long‐term effects of different amelioration methods and yield reduction risk were assessed using the three‐dimensional sequential Gaussian simulation (3D‐SGS). The 3D‐SGS results showed that soil salinity increased during abandonment (2014–2015) and then decreased during reclamation (2016–2021). Salt accumulated in the 0–40 cm soil layer in T3, T4, and CK treatments, while it accumulated in the 40–80 cm soil layer in T1 and T2 treatments. The proportion of high‐risk areas in T1, T2, T3, and T4 treatments reduced after reclamation, but soil salinity only slightly reduced in T3 and T4 treatments, showing a high risk of yield reduction. Besides, the time‐specific regression model had the highest prediction accuracy for soil salinity. Therefore, probabilistic evaluation based on 3D‐SGS is an effective method to evaluate the effectiveness of saline soil amelioration methods, and could help decision‐makers formulate scientific saline soil amelioration plans.

Implementation and validation of swept density reflectometry for integrated data analysis at ASDEX Upgrade

In the tokamak ASDEX Upgrade, Integrated Data Analysis (IDA) is used to infer plasma quantities, such as electron density, using heterogeneous data sources. Essential is forward modeling from the parameter space into the data space with physically reasonable models for probabilistic evaluation. This paper presents a new forward model for O-mode profile reflectometry, a necessary prerequisite for Bayesian inference and inclusion in IDA. An efficient forward model based on the analytic solution for a piece-wise linear density description allows IDA to overcome problems associated with the established determination of cut-off locations via Abel inversion and Bottollier-Curtet’s method. Instead of using a hard-coded initialization for densities below the first measured cut-off density, other diagnostics, such as the lithium beam, are used to analyze the shape of the initial part of the profile. Error propagation from the measured data, and other uncertain sources, to the uncertainties in the density profile and also its gradient is an intrinsic property of the probabilistic approach, which benefits from the joint analysis. Missing or ambiguous data do not prevent the profile evaluation, but only increase the uncertainty for densities in the affected range. Density profiles together with their uncertainties are determined by the joint analysis of complementary diagnostics, with the newly added reflectometry closing a gap in the outer core region. A stand-alone inversion based on the new forward model, including uncertainty quantification, is introduced, optionally providing an n(R) profile with uncertainties and a gradient. This method is a candidate for real-time analysis, providing error bars.

Доверие к данным при пополнении онтологий и графов знаний

The problem of assessing trust in the information extracted from textual sources to populate ontologies or knowledge graphs is considered. For a unit of information or a fact, the minimum knowledge about an instance of the subject area, expressed by a single RDF triplet, is taken. The paper provides a description of a probabilistic trust evaluation model based on Markov random processes. When assessing, the model is built on the basis of available information about sources, taking into account previously extracted data. A method for assessing the credibility of information with parallel weighting of sources is also provided. The proposed approach is in demand when the quality of the data sources is unknown or unavailable. As part of testing the model, sets of numerical data of various sizes were automatically generated, experiments were carried out to weigh the sources and assess trust in the information extracted from them. It was shown that in most cases the weights of the sources calculated on the basis of the proposed model are the greater, the smaller the average deviation of the information they provide from the true one, and the confidence in facts increases with decreasing distance to the true data. Comparison with data aggregation models is made. In most cases, the aggregation based on the trust score showed the smallest average deviation from the true data among the considered models. The obtained results show that the proposed model is effective in comparison with other similar models and can be used in problems of assessing trust in facts represented by real numbers.

Probabilistic seismic performance assessment of nuclear containment structure considering statistical uncertainty

Statistical uncertainty is inherently involved in probabilistic seismic performance assessment of containment structure due to heavy computational efforts of nonlinear dynamic analysis. Aiming at the statistical uncertainty involved in seismic fragility and seismic risk evaluation of containment structure, this study proposes a method for quantifying the statistical uncertainty in probabilistic seismic performance assessment of the containment structure. Taking a three story reinforced concrete frame as an example, effectiveness and efficiency of the proposed method for confidence interval estimation of seismic fragility curve is verified. Then, the proposed method is applied to fragility analysis and seismic risk evaluation of the containment structure. Roof drift of the containment structure under different ground motion intensity levels is discussed. Confidence interval for seismic fragility curve and seismic risk of the containment structure considering statistical uncertainty is quantified by the proposed method. The results highlighted the necessity for quantifying the impact of statistical uncertainty involved in probabilistic seismic performance evaluation of the containment structure.

A probabilistic framework of creep life assessment of structural components at elevated temperatures

Current creep life assessment methods of components at elevated temperatures are mainly based on deterministic analysis strategies, which could not achieve the goal of probabilistic evaluation on creep failure. Based on this, a probabilistic framework of creep life assessment for components at elevated temperatures was provided. A method of determining distribution characteristics of material parameters was provided by randomly selecting a group of results at each stress level. Monte Carlo simulation was combined with finite element analysis technology to capture the distribution characteristic of creep rupture life of one typical structural component. Effect of standard derivation of material parameters on creep reliability assessment was discussed. Comparisons between probabilistic and deterministic creep design methods were made. Results indicated that the probabilistic analysis strategy can calculate the specific value of failure probability at various loading conditions, not two values of failure probability (i.e. 100% and 0%) by deterministic analysis strategy. The effect of standard derivation on mean values of effective stress and creep rupture life of the component is dependent on distribution characteristics of material parameters and related variables. A small standard derivation reduces the data scatter of effective stress and creep rupture life of the component.

A Non-local Fokker-Planck Equation with Application to Probabilistic Evaluation of Sediment Replenishment Projects

A Non-local Fokker-Planck Equation with Application to Probabilistic Evaluation of Sediment Replenishment Projects

Fragility curve development of highway bridges using probabilistic evaluation (case study: Tehran City)

Seismic fragility analysis of bridges can be used to identify the probability of different damage states of bridges under earthquake hazards. This study aims to generate the fragility curve of highway bridges in Tehran, Iran, where these curves are not provided yet. To derive new fragility curves, the fragility curve of highway bridges from studies all around the world has been classified into different categories, based on their structural designs, bridge materials, country, and their analytical methods. A decision-tree analysis has been used to determine new fragility curves. The accuracy of these new curves was then assessed using a case study bridge and incremental dynamic analysis. The assessment analysis indicates that newly developed fragility curves for Tehran city have reasonable accuracy. The average difference between the derived fragility curve by the curves determined using Incremental Dynamic Analysis was acceptable. These new fragility curves helped to identify bridges throughout the Tehran city highways with higher seismic vulnerability. These can also be used for risk assessment and reducing seismic risk studies and future urban planning.

Spatial‐temporal response capability probabilistic evaluation method of electric vehicle aggregator based on trip characteristics modelling

AbstractAccurately evaluating the response capability of electric vehicles (EVs) is very important for an EV aggregator (EVA) to formulate reasonable bidding strategies in ancillary service markets. Most of the methods proposed in the existing literature only evaluate EVs’ temporal response capability while ignoring their spatial distribution. In addition, the evaluation results provided by the existing methods are typically deterministic, which fails to characterize the uncertainty of EV trip. The above two issues pose high risk of economic loss for the EVA. To this end, a probabilistic evaluation method of spatial‐temporal response capability for EVA is proposed in this paper. The gravity model is adopted to calculate a spatial transfer probability matrix describing EV owners’ trip characteristics between different areas at each time in a region which is divided into several different areas according to the evaluation requirements. Then, the trip chains of EVs are modelled based on the spatial transfer probability matrix, and the states of charge (SOCs) of EVs are tracked in the process. The spatial‐temporal response capability of EVA is evaluated based on charging–discharging states and states of charge of EVs, and the probabilistic evaluation results of response capability are obtained by multiple Monte Carlo simulations. The effectiveness of the proposed method is verified on a real dataset from San Francisco, CA, USA.

Influence of Near Fault Earthquakes with Forward Directivity and Fling Step on Seismic Response of Steel Box-Girder Bridge

The existing bridge seismic design guidelines that rely on the ground acceleration in the far fault zone,ignore the potential impact of near fault forward directivity and fling-step effects on the bridge structures. In the current study probabilistic seismic damage evaluation of a continuous four-span box girder bridge under the impact of near-fault forward directivity and fling step effect is studied employing the fragility analysis. The incremental dynamic analysis is used to construct the fragility curves which shows a range of damage states from minor to collapse for the different damage metrics and for the considered peak ground acceleration varying between 0.1g and 1.2g. Damage metrics such displacement pier ductility, rotational pier ductility and displacement of girder are used to develop the fragility curves and the probabilistic seismic damage model. To evaluate the bridge vulnerability, a probabilistic seismic damage assessment is performed using an ensemble of forward directivity and an ensemble of fling-step comprising permanent ground offset. The suggested probability-based earthquake damage framework is anticipated to be a well-versed model able to estimate the seismic damages to the continuous box girder bridges while taking into account the variation of near fault earthquakes. The findings show that, even at low PGA values the forward directivity and the fling-step ground motions represent a significant risk to the bridge.

Probabilistic evaluation of chloride-induced corrosion effects on design parameters of RC beams

The chloride-induced corrosion in reinforced concrete (RC) structures could decrease their service life. In this regard, studying the effects of corrosion on the designing parameters could prevent these costly expenses. The presence of uncertainties in the corrosion process requires probabilistic models to be applied. Therefore, in this study, Monte Carlo simulations have been used to investigate the effects of corrosion in RC elements. To do so, the remained area of reinforcements, changes in yield stress, ductility and flexural capacity of RC beam sections under propagating corrosion were studied. These changes were applied to a singly reinforced beam model and its behavior in the flexural limit state was investigated. Results have indicated that statistical dispersion in remained area of rebars and their yield stress increases over time. As a result of a decrease in yield stress, the balanced steel ratio in cross-section increases. Moreover, the ductility of the beam decreases as corrosion propagates. Reliability analysis of the beam in different conditions emphasized the deteriorating effect of reducing concrete cover, increasing the w/c ratio and using reinforcements with smaller diameters or lower yield stress. Furthermore, reliability analysis of corroded RC beams can effectively be used in planning inspections of existing structures.

Probabilistic of in Situ Seismic Soil Liquefaction Potential Based on CPT-Data in Central Jakarta, Indonesia

Jakarta City located at the northern part of Java Island has a potential liquefaction since there are many soil layers as a qualified as liquefaction susceptible, such as: silty sand and sand. The effects of liquefaction occurrence on saturated silty sand and sand during earthquakes can have potential to damage structure. A case history using CPT-data randomly taken from Central Jakarta region was compiled and back analyzed to investigated probability of liquefaction. The present study focuses on the probabilistic evaluation of liquefaction potential of this region, considering silty sand and sand layers related to CPT soundings. Silty sand was found as a thin layer or a lens between silty clay, silt and clay which was classified as a soft soil layer, and the sand layer is mostly found as a hard bottom layer. Normalization of tip CPT resistance (qc) corrected by an effective overburden stress was not only used to thick layer of silty sand and sand, but also it was applied to determine liquefaction potential existed as the “thin” layers. The silty sand and sand layers have a probability of liquefaction (PL) between 5 to 50% for magnitudes and distances of 500 years with PGA less than 0.42 gal.

Probabilistic evaluation of the axis distance’s influence on the flexural strength deterioration of reinforced concrete beams under ISO 834 fire

The distance from the nearest concrete exposed surface to the centroidal axis of main longitudinal steel re-inforcing bars, so-called axis distance, plays a critical role in ensuring the safety of reinforced concrete (RC) structures under fire, as it helps the rebars not being directly exposed to heating in a fire incident. However, a large axis distance value could reduce the effective height as well as the beam’s flexural strength at ambient condition. In order to determine the appropriate values of axis distance, this article developes a data-driven method for predicting the flexural strength deterioration (FSD) of RC beams under ISO 834 standard fire based on the material and geometrical inputs. This method consists of two main stages: (i) Establishing a theoretical/experimental database by collecting experimental data from the literature; and (ii) Engineering a probabilis-tic model based on the Bayesian Neural Network. The results obtained show that the proposed approach is a practical tool that is capable of performing quick and reasonably accurate analysis such as degradation curves of FSD against exposure time. In addition, the uncertainty related to the prediction results is also evaluated, providing useful information for structural fire engineers to achieve conservative designs.

Probabilistic Evaluation Method of Transformer Neutral Direct Current Distribution in Urban Power Grid Caused by DC Metro Stray Current

With the coupling of the DC metro network and urban power grid, the DC metro stray currents flowing into transformers have caused serious DC bias. There is, therefore, a need to evaluate transformer neutral direct currents caused by the DC metro stray current. To achieve this, an evaluation model considering the coupling of DC metro network and urban power grid is proposed. In the modeling principle, metro lines are coupled with grounding grids of transfer stations. The DC metro system and the urban power grid are coupled with the soil and grounding systems. The train operations on DC metro lines are simulated using the Monte Carlo method. And, considering the conditions of one-way conduction devices (OWCDs) and over-voltage protection devices (OVPDs), transformer neutral direct currents are calculated. Moreover, two probability indexes are proposed to evaluate distributions of transformer neutral direct currents. The applicability of the proposed model is verified by comparing the calculation results with test data of the transformer neutral direct current. Using the evaluation model, transformer neutral direct currents are evaluated. The result indicates that substation supplying power to the DC metro network has a large probability of a high-level neutral direct current.

Short-term probabilistic forecasting models using Beta distributions for photovoltaic plants

This article presents original probabilistic forecasting models for day-ahead hourly energy generation forecasts for a photovoltaic (PV) plant, based on a semi-parametric approach using three deterministic forecasts. Input information of these new models consists of data of hourly weather forecasts obtained from a Numerical Weather Prediction model and variables related to the sun position for future instants. The proposed models were satisfactorily applied to the case study of a real-life PV plant in Portugal. Probabilistic benchmark models were also applied to the same case study and their forecasting results compared with the ones of the proposed models. The computer results obtained with these proposed models achieve better point and probabilistic forecasting evaluation indexes values than the ones obtained with the benchmark models.

Interpretation of well logs and core data via Bayesian inversion

Estimating in situ petrophysical and compositional properties of rocks (e.g., porosity, mineralogy, and fluid saturation) from well logs and core measurements is critical for the evaluation of subsurface fluid resources. Traditional multimineral analysis of well logs is susceptible to abnormal borehole and geometric conditions, such as shoulder-bed effects and tool/borehole-related biases. Moreover, traditional multimineral analysis rarely includes uncertainty quantification, much less assessments of the impact of measurement noise, borehole conditions, and/or inaccurate rock-physics models (RPMs) on estimated properties. Probabilistic formation evaluation with credible-interval estimations can improve the outcome of mineral and fluid evaluations from well logs by explicitly incorporating the effects of measurement biases and a priori compositional and petrophysical constraints in the estimation. A probabilistic workflow is developed to estimate petrophysical/compositional properties from well logs and available core data. The workflow consists of two sequential steps: borehole measurements (e.g., density, resistivity, and gamma ray) are first converted into a layer-by-layer earth model, which consists of piecewise constant property values, with associated uncertainty estimations. The estimated earth model is tool/borehole independent, allowing for a common baseline to effectively compare properties among neighboring wells. Next, the estimated earth-model physical properties are used to derive petrophysical properties (e.g., volumetric concentration of fluid and solid rock constituents) via petrophysical joint inversion. In each step, Bayesian inversion is implemented with Markov chain Monte Carlo sampling in contrast to deterministic algorithms used by traditional multimineral methods. The developed method has the following advantages over traditional approaches: (1) it propagates uncertainty from well logs to petrophysical properties by assimilating measurement noise, tool/borehole-related biases, and RPM errors and (2) it enables the implementation of a priori knowledge per rock class to constrain the petrophysical estimation, effectively integrating field-specific core/regional geologic data. Examples of thinly laminated and organic shale formations successfully verify the reliability, efficiency, and accuracy of our estimation method.

Frequency Regulation of Electric Vehicle Aggregator Considering User Requirements with Limited Data Collection

High penetration of renewable energy in the power grid causes serious frequency deviations. Large-scale integrations of electric vehicles (EVs) in the power grid provide considerable vehicle-to-grid potential for frequency regulation. Existing frequency regulation strategies with aggregated EVs realize accurate power control that relies on complete information interaction between the EV aggregator and individual EVs. However, the data collection for all EV parameters is not applicable due to privacy protection and the limited communication environment. Considering the limited data collection from grid-connected EVs, this paper provides a novel frequency regulation strategy and tends to address the uncertain influence from EV users’ charging requirements, the EV aggregator’s power regulation, and the frequency regulation performance. Firstly, considering the influence of the limited data collection by EVs on the users’ requirement of traveling and regulation preference, a probabilistic evaluation model for the available regulation capacity of the EV aggregator and the probabilistic control method for EVs are developed. Then, a frequency regulation strategy with error correction control and progressive regulation recovery is developed to simultaneously guarantee the system frequency regulation performance and the regulation requirements of EV users. Finally, case studies are carried out to validate the effectiveness of frequency regulation strategy for decreasing the uncertain influence from the limited data collection, ensuring the EV users’ requirements, and improving the system frequency stability.