Probability-based Approach Research Articles

Analysis of Service Life of Surface Coated-Concrete Structures Considering Climate Change and Chloride Ingress

Mortar surface coating is widely used as a finishing material to extend the service life and improve chloride ingression resistance of marine concrete structures. This study proposes a model for estimating the service life of surface-coated marine concrete considering climate change. First, the increase of chloride diffusivity due to climate change is considered using Arrhenius’ law. A two-layered chloride ingress model is used to analyze chloride profile. The probability-based approach is used to find the service life of concrete structures. Second, parameter analysis is performed considering the effects of various factors on service life. The influences of thickness and chloride diffusivity of the coating and substrate concrete on service life are highlighted. The reduction of service life due to climate change is clarified based on the regression of results of parameter analysis. For marine concrete with 50 years’ service life, 6% service life reduction occurs because of climate change.

Exact Distribution of Linkage Disequilibrium in the Presence of Mutation, Selection, or Minor Allele Frequency Filtering.

Linkage disequilibrium (LD), often expressed in terms of the squared correlation (r2) between allelic values at two loci, is an important concept in many branches of genetics and genomics. Genetic drift and recombination have opposite effects on LD, and thus r2 will keep changing until the effects of these two forces are counterbalanced. Several approximations have been used to determine the expected value of r2 at equilibrium in the presence or absence of mutation. In this paper, we propose a probability-based approach to compute the exact distribution of allele frequencies at two loci in a finite population at any generation t conditional on the distribution at generation t − 1. As r2 is a function of this distribution of allele frequencies, this approach can be used to examine the distribution of r2 over generations as it approaches equilibrium. The exact distribution of LD from our method is used to describe, quantify, and compare LD at different equilibria, including equilibrium in the absence or presence of mutation, selection, and filtering by minor allele frequency. We also propose a deterministic formula for expected LD in the presence of mutation at equilibrium based on the exact distribution of LD.

Analytical model to estimate the performance of shuttle-based storage and retrieval systems with class-based storage policy

This paper presents a method to determine the performance of shuttle-based storage and retrieval systems (SBS/RS) with tier-captive single-aisle shuttles and class-based storage policy. The use of this approach takes place both in the design process of SBS/RS and in the redesign process of SBS/RS. With this approach, it is possible to evaluate performance improvement by applying a class-based storage policy. Another beneficial scope of application of this approach is the evaluation of the placement of different classes throughout the rack to achieve the maximum throughput. The basis of this calculation method was a continuous-time open-queueing model with limited capacity. The cycle times of lifts and shuttles, as determined by a spatial value approach combined with a probability-based approach to mention the storage policy, could be directly used in the presented method with their time distributions. The data used herein were provided by a European material handling provider. An example was presented to outline how this calculation model can be used for optimizing the already existing SBS/RS via the application of a class-based storage policy. Through the example, depending on the configuration of the policy, applying the class-based storage to an existing SBS/RS would increase the throughput by up to twice the throughput without class-based storage policy.

An approach for a performance calculation of shuttle-based storage and retrieval systems with multiple-deep storage

This paper presents a method to determine the performance of shuttle-based storage and retrieval systems (SBS/RS) with tier captive single-aisle shuttles and multiple-deep storage. The basis of this calculation method is a continuous-time open queueing system with limited capacity. The cycle times of lifts and shuttles, determined by a spatial value approach, can be directly used in the presented method with their time distributions. To take the multiple storage into account, a probability-based approach is applied. The invented approach is validated by a comparison with a discrete event simulation. A European material handling provider had given the data used in this comparison. Finally, an example is presented to outline how this calculation model can be used for designing SBS/RS which fulfil the predefined requirements. The result of this example is that with an increase of the storage depth to a certain value, the throughput increases and the cost decreases up to the same storage depth.

Green remediation of Cd and Hg contaminated soil using humic acid modified montmorillonite: Immobilization performance under accelerated ageing conditions

Solidification/Stabilization (S/S) is an effective way to immobilize toxic metals in contaminated soil. However, utilization of ordinary Portland cement (PC) in this process has raised environmental concerns owing to the high carbon footprint from PC manufacturing and the risk of toxic element leaching in the long term. Hence there is an urgent need to seek for “green” immobilization approaches with long-term stability. In this study, a clay-based material, humic acid modified montmorillonite (HA-Mont) was applied to a Cd and Hg contaminated soil. Field emission scanning electron microscopy combined with energy-dispersive X-ray spectroscopy (FESEM/EDS), N2 adsorption-desorption, Fourier transform infrared (FTIR) spectroscopy and X-ray diffraction (XRD) analyses were performed to investigate the characteristics of this material. Compared to the soil without any treatment, dosage of 5 % HA-Mont could effectively reduce Cd and Hg concentrations by 94.1 % and 93.0 %, respectively and to below the regulatory limits in the TCLP (Toxicity Characteristic Leaching Procedure) leachates. Compared to the soil treated with virgin montmorillonite, HA modification resulted in the reduction of leachate concentrations of Cd and Hg by 69.5 % and 65.9 %, respectively. Long-term immobilization performance of the HA-Mont treatment was examined using a quantitative accelerated ageing method. In order to examine the ageing features, a novel method based on conditional probability was developed, and the reliability of HA-Mont immobilization was found to fit the Weibull model well, as the ageing rate of immobilization effect increased with time. After 120 years of ageing, reliability of both metals could still remain above 0.95. Cd concentration in TCLP leachates at 120th year could still remain below the regulatory limit (294 μg/L vs 1000 μg/L), while Hg concentration reached the regulatory limit of 200 μg/L in 96th year. This is the first attempt developing a green S/S method of Cd and Hg contaminated soil using HA-Mont and examining the long-term ageing characteristics of the stabilized soil using a probability-based approach.

Tractography in the presence of multiple sclerosis lesions

Accurate anatomical localisation of specific white matter tracts and the quantification of their tract-specific microstructural damage in conditions such as multiple sclerosis (MS) can contribute to a better understanding of symptomatology, disease evolution and intervention effects. Diffusion MRI-based tractography is being used increasingly to segment white matter tracts as regions-of-interest for subsequent quantitative analysis. Since MS lesions can interrupt the tractography algorithm’s tract reconstruction, clinical studies frequently resort to atlas-based approaches, which are convenient but ignorant to individual variability in tract size and shape. Here, we revisit the problem of individual tractography in MS, comparing tractography algorithms using: (i) The diffusion tensor framework; (ii) constrained spherical deconvolution (CSD); and (iii) damped Richardson-Lucy (dRL) deconvolution. Firstly, using simulated and in vivo data from 29 MS patients and 19 healthy controls, we show that the three tracking algorithms respond differentially to MS pathology. While the tensor-based approach is unable to deal with crossing fibres, CSD produces spurious streamlines, in particular in tissue with high fibre loss and low diffusion anisotropy. With dRL, streamlines are increasingly interrupted in pathological tissue. Secondly, we demonstrate that despite the effects of lesions on the fibre orientation reconstruction algorithms, fibre tracking algorithms are still able to segment tracts that pass through areas with a high prevalence of lesions. Combining dRL-based tractography with an automated tract segmentation tool on data from 131 MS patients, the cortico-spinal tracts and arcuate fasciculi could be reconstructed in more than 90% of individuals. Comparing tract-specific microstructural parameters (fractional anisotropy, radial diffusivity and magnetisation transfer ratio) in individually segmented tracts to those from a tract probability map, we show that there is no systematic disease-related bias in the individually reconstructed tracts, suggesting that lesions and otherwise damaged parts are not systematically omitted during tractography. Thirdly, we demonstrate modest anatomical correspondence between the individual and tract probability-based approach, with a spatial overlap between 35 and 55%. Correlations between tract-averaged microstructural parameters in individually segmented tracts and the probability-map approach ranged between r=.53 (p<.001) for radial diffusivity in the right cortico-spinal tract and r=.97 (p<.001) for magnetisation transfer ratio in the arcuate fasciculi. Our results show that MS white matter lesions impact fibre orientation reconstructions but this does not appear to hinder the ability to anatomically reconstruct white matter tracts in MS. Individual tract segmentation in MS is feasible on a large scale and could prove a powerful tool for investigating diagnostic and prognostic markers.

Dosing of Continuous Fentanyl Infusions in Obese Children: A Population Pharmacokinetic Analysis.

Differences in fentanyl pharmacokinetics (PK) between obese and nonobese adults have previously been reported; however, the impact of childhood obesity on fentanyl PK is relatively unknown. We developed a population pharmacokinetic (PopPK) model using opportunistically collected samples from a cohort of predominately obese children receiving fentanyl per the standard of care. Using a probability-based approach, we evaluated the ability of different continuous infusion strategies to provide steady-state concentrations (Css ) within an analgesic concentration range (1-3 ng/mL). Fifty-three samples from 32 children were used for PopPK model development. Median (range) age and body weight of study participants were 13 years (2-19years) and 52 kg (16-164 kg), respectively. The majority of children (94%) were obese. A 2-compartment model allometrically scaled by total body weight provided an appropriate fit to the data. Estimated typical clearance was 32.5 L/h (scaled to 70kg). A fixed dose rate infusion of 1µg/kg/h was associated with probabilities between 49% and 58% for achieving Css within target; however, the risk of achieving Css > 3 ng/mL increased with increasing body weight (15% at 16kg vs 43% at 164kg). A proposed model-based infusion strategy maintained consistent probabilities across the examined weight range for achieving Css within (58%) and above (20%) target. Use of an allometric relationship between weight and clearance was appropriate for describing the PK of intravenous fentanyl in our cohort of predominately obese children. Our proposed model-derived continuous infusion strategy maximized the probability of achieving target Css in children of varying weights.

Mapping ilmenite deposit in Pulmudai, Sri Lanka using a hyperspectral imaging-based surface mineral mapping method

Mineral detection using remote sensing techniques is important since it saves the time and effort of carrying out manual land surveys. In this paper a novel algorithm, which can be used to detect ilmenite using hyperspectral image analysis is discussed. To investigate this task, a hyperspectral image obtained from the Earth Observing-1 (EO-1) satellite’s Hyperion sensor was used. In the proposed algorithm, first, principal component analysis (PCA) was used for dimensionality reduction and an Euclidean distance-based method was used to extract the pixels containing soil. Thereafter, lab spectral data of typical ilmenite deposits were considered as the reference and a correlation factor analysis was carried out to determine the soil pixels, which are most likely to contain ilmenite and those most unlikely to contain ilmenite. Using these two sets of pixels, a training set was constructed to apply Fisher’s discriminant analysis (FDA) in order to separate the dataset into two distinct classes – ilmenite and non-ilmenite. Based on the spectral similarity, each pixel of the image was classified under one of these classes. This paper also introduces a probability-based approach to obtain results that are more accurate. A probability density function was designed considering the spatial distribution of the mineral. Thereafter, classification was done considering the probability measure as well. Lab tests performed on the soil samples collected from the locations, which were detected by the algorithm validate that the algorithm is accurate.

Assessment of rock mass erosion in unlined spillways using developed vulnerability and fragility functions

ABSTRACT Hydraulic power can lead to the erosion of rock and cause dams to be at risk of failure. Methods exist to predict the degree of erosion for rock masses in spillways; however, these deterministic approaches are unable to consider the uncertainties of rock mass parameters. We develop a methodology that determines the vulnerability of a rock mass to hydrological erosion, and this approach takes into consideration the uncertainties related to the parameters of the rock mass at the study site. Monte Carlo simulation is used to create a dataset for each class of rock for then developing fragility and vulnerability curves. The effects of each geomechanical parameter on erosion level can be determined by applying this methodology to individual spillway sites. As a result, sensitivity analysis shows that the discontinuity orientation factor is a critical parameter for explaining the erosion of a rock mass; increasing this parameter decreases the vulnerability of the rock mass to erosion. Our approach has an advantage over deterministic methods as the uncertainties of rock mass parameters have been considered. The error associated with the predicted erosion level via our probability-based approach is significantly less than the error obtained via deterministic methods.

A reproducibility probability-based bias-adjustment approach on the specification of non-inferiority margin using historical data

ABSTRACTThe effect of reference treatment over placebo, known as M1, is essential in the development of non-inferiority margin. We proposed a M1 adjustment approach to reduce the selection bias for collected data of historical trials. A quantitative illustration of selection bias of historical data is also defined. Simulation study shows that the proposed approaches would significantly reduce the bias when the proportion of positive studies in historical data is noticeably larger than the power of studies include in historical data. When historical data are constituted by only positive studies, the performance of the proposed method is also appreciable. However, when the proportion of positive studies is close to the power of studies included or the number of studies included is too small, the performance of the proposed approach may not be reliable. A real-data application is also presented. The proposed bias-adjustment approach is a reasonable method to reduce the over-estimate of effect size in the specification of non-inferiority margin. It could also be applied in most non-inferiority margin specification methods or be cooperate used with other bias-adjustment approaches.

Generating a rule-based global gridded tillage dataset

Abstract. Tillage is a central element in agricultural soil management and has direct and indirect effects on processes in the biosphere. Effects of agricultural soil management can be assessed by soil, crop, and ecosystem models, but global assessments are hampered by lack of information on the type of tillage and their spatial distribution. This study describes the generation of a classification of tillage practices and presents the spatially explicit mapping of these crop-specific tillage systems for around the year 2005. Tillage practices differ by the kind of equipment used, soil surface and depth affected, timing, and their purpose within the cropping systems. We classified the broad variety of globally relevant tillage practices into six categories: no-tillage in the context of Conservation Agriculture, traditional annual, traditional rotational, rotational, reduced, and conventional annual tillage. The identified tillage systems were allocated to gridded crop-specific cropland areas with a resolution of 5 arcmin. Allocation rules were based on literature findings and combine area information on crop type, water management regime, field size, water erosion, income, and aridity. We scaled reported national Conservation Agriculture areas down to grid cells via a probability-based approach for 54 countries. We provide area estimates of the six tillage systems aggregated to global and country scale. We found that 8.67 Mkm2 of global cropland area was tilled intensively at least once a year, whereas the remaining 2.65 Mkm2 was tilled less intensely. Further, we identified 4.67 Mkm2 of cropland as an area where Conservation Agriculture could be expanded to under current conditions. The tillage classification enables the parameterization of different soil management practices in various kinds of model simulations. The crop-specific tillage dataset indicates the spatial distribution of soil management practices, which is a prerequisite to assess erosion, carbon sequestration potential, as well as water, and nutrient dynamics of cropland soils. The dynamic definition of the allocation rules and accounting for national statistics, such as the share of Conservation Agriculture per country, also allow for derivation of datasets for historical and future global soil management scenarios. The resulting tillage system dataset and source code are accessible via an open-data repository (DOIs: https://doi.org/10.5880/PIK.2019.009 and https://doi.org/10.5880/PIK.2019.010, Porwollik et al., 2019a, b).

Graph-based Multi-Vehicle Overtaking Strategy for Autonomous Vehicles

Abstract The paper proposes an advanced graph-based optimal solution to overtaking scenarios for autonomous vehicles. The advantage of the method is that it is possible to consider several human-driven vehicles in the environment of autonomous vehicles. There is a probability-based approach in the background of the graph-based route selection optimization, with which the motions of the human-driven vehicles are predicted. The result of the method is the road and the velocity profile of the autonomous vehicle, with which emergencies and even collisions can be avoided. The efficiency of the optimization algorithm is illustrated through a simulation scenario.

Analysis of the relationships between topographic factors and landslide occurrence and their application to landslide susceptibility mapping: a case study of Mingchukur, Uzbekistan

This paper uses a probability-based approach to study the spatial relationships between landslides and their causative factors in the Mingchukur area, Bostanlik districts of Tashkent, Uzbekistan. The approach is based on digital databases and incorporates methods including probability analysis, spatial pattern analysis, and interactive mapping. First, an object-oriented conceptual model for describing landslide events is proposed, and a combined database of landslides and environmental factors is constructed by integrating various databases within a unifying conceptual framework. The frequency ratio probability model and landslide occurrence data are linked for interactive, spatial evaluation of the relationships between landslides and their causative factors. In total, 15 factors were analyzed, divided into topography, hydrology, and geology categories. All analyzed factors were also divided into numerical and categorical types. Numerical factors are continuous and were evaluated according to their R2 values. A landslide susceptibility map was constructed based on conditioning factors and landslide occurrence data using the frequency ratio model. Finally, the map was validated and the accuracy showed the satisfactory value of 83.3%.

A novel approach to transformer fault diagnosis using IDM and naive credal classifier

Transformer fault diagnosis is important for improving the operational reliability of power systems. Despite great efforts to enhance the accuracy of fault diagnosis, the precise detection of transformer faults remains difficult. The major barriers are attributed to the lack of transformer fault records and related data, which may produce unconvincing diagnostic results, and to the fact that multiple latent faults in a transformer are difficult to detect distinctly. In this paper, an imprecise probability-based approach to transformer fault diagnosis by using the imprecise Dirichlet model and naive credal classifier is proposed. Instead of providing a single-valued probability, the approach calculates the interval probability for each possible type of transformer faults to explicitly indicate the uncertainty of the diagnosis caused by the insufficiency of historical fault records. Meanwhile, according to whether an overlap exists among the probability intervals of different types of faults, the proposed approach can output either one explicit fault or multiple probable latent faults that occurred in the transformer. In the proposed approach, the imprecise Dirichlet model (IDM) is used to evaluate the imprecise probabilistic relationships between each type of transformer fault and its corresponding symptom attributes. A naive credal classifier (NCC) is established to integrate the IDM estimation results and calculate the interval-valued probabilities of all types of possible faults. Then, the transformer fault diagnosis results can be obtained by the classification criteria of the NCC. The approach can compensate for the defects of conventional methods, including their inability to perform reliable diagnosis with insufficient fault samples or distinctly indicate the multiple latent transformer faults. The accuracy of diagnosis and the efficiency of transformer maintenance can be significantly improved by implementing the proposed approach. The effectiveness of the approach is verified through case studies.

On Efficient Mining of Frequent Itemsets from Big Uncertain Databases

In the current era of information, communication, and technology the data is being generated at an exponential rate. This provides machine learning and data mining algorithms an opportunity to learn from huge data repositories. However, at the same time, the big data poses many challenges. Data uncertainty being the key concern of the modern data mining systems. This work addresses the problem of extracting frequent itemsets from such large uncertain databases to assist the decision makers in understanding the non-trivial data trends. The usual technique utilized to find frequent itemsets from uncertain databases is known as the Possible Word Semantics (PWS). However, as the database size increases, PWS suffers from performance issues. Therefore, there is a need for efficient frequent pattern mining algorithms. This work presents three techniques to address the issue at hand, namely: 3D linked array-based strategy, connected tree technique, and average probability-based setup with the support of a tree data structure. The objective here is to minimize computational cost by traversing the database only once. The 3D linked array-based solution scans the database only once and stores the support information of the item and its association with other items within the 3D array. For the tree-based method, 1D array is associated with each node of the tree, comprising of support information of the database items and their associations with other items. The average probability-based approach computes the average probability factor and utilizes it to map the uncertain database to a tree. The current proposal addresses attribute uncertainty as well as the tuple uncertainty to map large uncertain databases to the proposed data structures. In addition to introducing the three data structures, this work also presents algorithms to extract frequent itemsets. The proposal is compared with four recent works done in this domain for uncertain data, namely, mining threshold-based (MB) technique, frequent itemsets using nodesets (FIN), prepost + , and uncertain apriori (UApriori). Experiments are performed utilizing four benchmark datasets. The results obtained suggest better performance of the three techniques presented here, while consuming 60% less execution time.

Keeping global warming within 1.5 °C reduces future risk of yield loss in the United States: A probabilistic modeling approach

This study assess the possible outcomes of yield changes in the United States which is responsible for 40% of global maize supply under 1.5 °C and 2 °C global warming scenarios. Instead of providing deterministic estimates, this study introduces a probability-based approach that allow for examination of the associated probability of each outcome, which has great implications for decision-makings. Results show distinct spatial patterns in future yield loss risk associated with temperature rise at the county scale, with highest probability in central and southeastern US, and lowest risk in western US and high production regions such as Iowa. Comparing the estimates under 1.5 °C global warming against that in 2.0 °C warming indicates that keeping global warming within 1.5 °C has great benefits for reducing future yield loss risk. Based on the ensemble mean of 97 climate model simulations, the risk of yield dropping below historical long-term mean is projected to decrease from 81% to 75% for the country as a whole. Such benefit is more evident when considering the risk of yield reduction by 10% and 20%, which is expected to decrease by 25% and 28%, respectively. This suggests that constraining global temperature rise to 1.5 °C has more benefits for reducing extreme yield reductions. Spatially, keeping global warming within 1.5 °C would benefit more in Missouri, South Dakota, Eastern Kansas, Southern Texas and southeastern part of the country than other regions, highlighting the spatially variable benefits of climate mitigation efforts. The analysis framework introduced in this study can also be easily extended to other regions and crops. The results of this study highlight the areas where maize yield is most vulnerable to temperature rise, and the spatially variable benefits for reducing yield loss risk by keeping global warming within 1.5 °C.

Regional co-variability of spatial and temporal soil moisture–precipitation coupling in North Africa: an observational perspective

Abstract. The magnitude and sign of soil moisture–precipitation coupling (SMPC) is investigated using a probability-based approach and 10 years of daily microwave satellite data across North Africa at a 1∘ horizontal scale. Specifically, the co-existence and co-variability of spatial (i.e. using soil moisture gradients) and temporal (i.e. using soil moisture anomaly) soil moisture effects on afternoon rainfall is explored. The analysis shows that in the semi-arid environment of the Sahel, the negative spatial and the negative temporal coupling relationships do not only co-exist, but are also dependent on one another. Hence, if afternoon rain falls over temporally drier soils, it is likely to be surrounded by a wetter environment. Two regions are identified as SMPC “hot spots”. These are the south-western part of the domain (7–15∘ N, 10∘ W–7∘ E), with the most robust negative SMPC signal, and the South Sudanese region (5–13∘ N, 24–34∘ E). The sign and significance of the coupling in the latter region is found to be largely modulated by the presence of wetlands and is susceptible to the number of long-lived propagating convective systems. The presence of wetlands and an irrigated land area is found to account for about 30 % of strong and significant spatial SMPC in the North African domain. This study provides the first insight into regional variability of SMPC in North Africa, and supports the potential relevance of mechanisms associated with enhanced sensible heat flux and mesoscale variability in surface soil moisture for deep convection development.

A data fusion approach for real-time traffic state estimation in urban signalized links

Real-time estimation of the traffic state in urban signalized links is valuable information for modern traffic control and management. In recent years, with the development of in-vehicle and communication technologies, connected vehicle data has been increasingly used in literature and practice. In this work, a novel data fusion approach is proposed for the high-resolution (second-by-second) estimation of queue length, vehicle accumulation, and outflow in urban signalized links. Required data includes input flow from a fixed detector at the upstream end of the link as well as location and speed of the connected vehicles. A probability-based approach is derived to compensate the error associated with low penetration rates while estimating the queue tail location, which renders the proposed methodology more robust to varying penetration rates of connected vehicles. A well-defined nonlinear function based on traffic flow theory is developed to attain the number of vehicles inside the queue based on queue tail location and average speed of connected vehicles. The overall scheme is thoroughly tested and demonstrated in a realistic microscopic simulation environment for three types of links with different penetration rates of connected vehicles. In order to test the efficiency of the proposed methodology in case that data are available at higher sampling times, the estimation procedure is also demonstrated for different time resolutions. The results demonstrate the efficiency and accuracy of the approach for high-resolution estimation, even in the presence of measurement noise.

Vulnerability to failure of cable-stayed bridges for beyond-design basis wind events

Abstract This paper develops a vulnerability assessment framework for cable-stayed bridges based on critical elements identified through a failure analysis. The framework refers to a simple and effective model developed to assess the vulnerability of a bridge failure due to beyond design-basis wind loads. The proposed framework differs from the probability-based approach in that it uses a cable-stayed bridge in operation, not a prototype bridge for fragility assessment, to identify critical elements vulnerable for a bridge failure. Furthermore, this study provides a detailed procedure to calculate the wind loads that account for varying wind speeds. The vulnerability index is defined as the demand-to-capacity ratio determined at threshold wind speeds. By assessing a cable-stayed bridge located in southeastern Georgia in the U.S., it is demonstrated how the proposed framework and analysis model can be successfully used to verify the results, identify critical elements associated with bridge failures for beyond design basis wind speeds, and assess the vulnerability. It is also concluded that a sensitivity analysis of significant variables such as aerodynamic pressure coefficients and dynamic load factor identified through this study is noteworthy, in order to accurately define analytical sensitivity and establish a practical limit on wind speeds for determining if a bridge closure is necessary thorough a vulnerability assessment study.

Crater formation by the rupture of underground natural gas pipelines: A probabilistic-based model

Parallel and crossing pipelines are frequently implemented due to land-use restrictions and their ease of operation and maintenance. Given the proximity of these pipelines and the hazardousness of the substances they transport, an eventual Loss of Containment (LOC) in a parallel/crossing corridor can lead to a domino effect that should be considered in Quantitative Risk Analysis (QRA). For underground pipelines, this LOC is accompanied by a formation of a crater, which can uncover adjacent pipelines triggering a domino effect scenario to take place. This paper aims to develop a model to predict feasible crater dimensions (i.e., width and depth) from a LOC in underground natural gas pipelines using operational and structural parameters. For this purpose, a recent review of 57 underground natural gas pipeline accidents were considered in a probability-based approach once data was processed. This approach initially predicts the Width-to-Depth crater ratio (WD) using a multivariate regression. Then feasible crater dimensions were determined using the regression prediction interval and the width-depth joint probability function, which is approximated with a Gaussian copula. This approach proposes a worst, mean and less severe scenarios to support decision-making processes regarding parallel or crossing underground natural gas pipelines with a LOC. Besides the identification of domino effect scenarios, this information can be used to support pipeline segmentation for risk analysis or even to support Right-of-Way (ROW) definition during pipeline installation.