{"id":8374,"date":"2025-12-15T19:53:03","date_gmt":"2025-12-15T22:53:03","guid":{"rendered":"https:\/\/vinq.us\/?p=8374"},"modified":"2026-01-03T20:08:58","modified_gmt":"2026-01-03T23:08:58","slug":"analise-probabilistica-estabilidade-pilhas-linguagem-risco","status":"publish","type":"post","link":"https:\/\/vinq.us\/en\/analise-probabilistica-estabilidade-pilhas-linguagem-risco\/","title":{"rendered":"Probabilistic Stability Analysis of Stacks: How to Translate Safety Factor into Risk Language"},"content":{"rendered":"<div data-elementor-type=\"wp-post\" data-elementor-id=\"8374\" class=\"elementor elementor-8374\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-2174579 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"2174579\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-1ed0664\" data-id=\"1ed0664\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-169b545 elementor-widget elementor-widget-text-editor\" data-id=\"169b545\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">In mining operations with large stockpiles, especially in environments with limited space, rugged topography, and heterogeneous materials, the relevant question is no longer &quot;<\/span><i><span style=\"font-weight: 400;\">Does the safety factor meet the requirements?<\/span><\/i><span style=\"font-weight: 400;\">and it became<\/span><i><span style=\"font-weight: 400;\">What is the probability of failure associated with this structure, and is this level of risk acceptable given the consequences involved?<\/span><\/i><span style=\"font-weight: 400;\">.<\/span><\/p><p><span style=\"font-weight: 400;\">This paradigm shift is not just technical, it&#039;s business-related. It redefines how the company prioritizes investments in drainage, monitoring, reinforcement, and closure strategies. Probabilistic studies of tailings piles consistently show that two scenarios with the same average safety factor can present very different orders of magnitude of probability of failure, depending on the variability of the parameters, the quality of the data, and how uncertainties are handled.<\/span><\/p><p><span style=\"font-weight: 400;\">This article discusses how to structure probabilistic stability analyses for fuel piles, how to interpret safety factor distributions, failure probabilities, and reliability indices, and, most importantly, how to translate these results into understandable and actionable risk language for senior management, regulators, and other stakeholders.<\/span><\/p><p>\u00a0<\/p><h2><b>Why is the deterministic safety factor insufficient in batteries?<\/b><\/h2><p><span style=\"font-weight: 400;\">The deterministic safety factor, calculated using a single set of parameters for strength, specific weight, and neutral pressures, will continue to be a central design indicator; however, in isolation, it does not provide the risk insight required today by boards of directors, investors, and regulatory bodies. In high piles, some limitations are especially relevant:<\/span><\/p><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><b>First, the heterogeneity of the material.<\/b><span style=\"font-weight: 400;\">Given the mixture of blocks, soils, fines, oxides, reprocessed materials, and partially saturated zones, adopting a single cohesion-friction angle pair as representative of the entire rock mass is unrealistic. In many cases, the critical failure surface develops in specific zones associated with facies changes or low-resistance interfaces that are not captured by average parameters.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Secondly, hydrological and hydrogeotechnical uncertainty is high.<\/b><span style=\"font-weight: 400;\">On sloping or hillside slopes, the interaction between extreme rainfall, recharge, surface runoff, preferential flow, and internal drainage is complex in space and time. A &quot;representative&quot; water level adopted in the design may be systematically displaced from reality, especially during seasonal transition periods or extreme events, altering the equilibrium condition in a way not captured by deterministic calculations.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Third, there is a false numerical precision.<\/b><span style=\"font-weight: 400;\">When reporting \u201cFS = 1.52\u201d, it implicitly communicates a degree of accuracy that does not exist. In practice, this value is the result of assumptions about parameters, model, geometries, and boundary conditions, each with its own uncertainty intervals. Structures with the same nominal FS can, in fact, operate with very different levels of reliability.<\/span><\/li><\/ul><p><span style=\"font-weight: 400;\">In short, the safety factor is necessary, but not sufficient. It is a point in a problem that is, by nature, distributed. Probabilistic analysis comes in precisely to reveal the structure of this distribution.<\/span><\/p><p>\u00a0<\/p><h2><b>Decision structures: from fixed parameters to random variables<\/b><\/h2><p><span style=\"font-weight: 400;\">The central logic of probabilistic analysis is to explicitly address uncertainty. What was previously a single number becomes modeled as a random variable, with distribution, dispersion, and correlations. In waste rock piles, this applies to parameters such as shear strength, effective cohesion, angle of friction, natural and saturated specific weight, position and variability of the water line, operational overloads, geometries, construction sequences, and even the presence of weak layers or reprocessing zones.<\/span><\/p><p><span style=\"font-weight: 400;\">These variables are represented by probability density functions, for example normal, lognormal or beta distributions, defined based on tests, back-analyses, monitoring data and expert judgment. From there, a performance function is defined, usually of the type G = R minus S, where R represents the &quot;resistance&quot; and S the &quot;demand&quot; and where failure occurs when G is less than or equal to zero, equivalent to FS less than or equal to 1.<\/span><\/p><p><span style=\"font-weight: 400;\">To propagate uncertainties to the safety factor, methods such as Monte Carlo simulation with thousands or tens of thousands of parameter samples are used, as well as first- and second-order reliability methods that allow capturing non-linear responses with lower computational cost. In cases of significant spatial variability, it is possible to employ random fields and an approach of &quot;<\/span><i><span style=\"font-weight: 400;\">random fields<\/span><\/i><span style=\"font-weight: 400;\">&quot;which recognize that the material is not only variable, it is variable in space.&quot;<\/span><\/p><p><span style=\"font-weight: 400;\">The result is a probability distribution of the safety factor or the G performance index. In this way, the question \u201c<\/span><i><span style=\"font-weight: 400;\">What is FS?<\/span><\/i><span style=\"font-weight: 400;\">and then the question is asked<\/span><i><span style=\"font-weight: 400;\">What is the FS distribution, what is its mean, what is its standard deviation, what is the probability of it being below 1.0, and how does this compare to the reliability objectives defined for the structure?<\/span><\/i><span style=\"font-weight: 400;\">.<\/span><\/p><p>\u00a0<\/p><h2><b>Probability of failure and reliability index<\/b><\/h2><p><span style=\"font-weight: 400;\">The crucial step in discussing risk is converting the FS distribution into a probability of failure. This probability, usually represented by Pf, is the area of the FS distribution to the left of 1. In practical terms, it is the fraction of possible scenarios in which the system fails, given the set of uncertainties considered.<\/span><\/p><p><span style=\"font-weight: 400;\">This probability can be expressed on an annual basis, for example Pf equals 10<\/span><span style=\"font-weight: 400;\">-<\/span><span style=\"font-weight: 400;\">\u2074 per year, over a useful life horizon, for example, a cumulative Pf of approximately 10<\/span><span style=\"font-weight: 400;\">-<\/span><span style=\"font-weight: 400;\">\u00b3 in 20 years, or in portfolio terms, for example, \u201cin a hypothetical universe of 10,000 batteries with this level of reliability, approximately one failure is expected over the lifetime of the product\u201d.<\/span><\/p><p><span style=\"font-weight: 400;\">Associated with Pf, the reliability index \u03b2 is defined, which measures, in standard deviation units, the distance between the average state of the system and the fault surface. The larger \u03b2, the smaller Pf. For corporate management, the mathematical detail is less relevant than the interpretation patterns that are built upon these numbers. Some points are central.<\/span><\/p><p><span style=\"font-weight: 400;\">Two projects with the same average safety factor (FS) can have completely different \u03b2 and Pf values if, for example, one is based on poorly investigated parameters with high dispersion, and the other on a dense and consistent database. Investments in geotechnical investigation, advanced testing, and monitoring can increase \u03b2 without necessarily altering the average FS, only reducing uncertainty. In many cases, a structure with a &quot;modest&quot; FS, but with well-controlled uncertainties, is more probabilistically safer than another with a nominally high FS, but supported by optimistic assumptions.<\/span><\/p><p><span style=\"font-weight: 400;\">This logic opens the door to more refined decisions. Instead of simply seeking &quot;higher FS&quot; (financial safety margins), the company begins to evaluate where each real invested generates the greatest reduction in Pf (performing phases), whether in physical reinforcements, drainage, monitoring, or knowledge.<\/span><\/p><p>\u00a0<\/p><h2><b>Acceptable risk levels: connecting probability, consequence, and governance.<\/b><\/h2><p><span style=\"font-weight: 400;\">Establishing acceptable risk levels for piles is essentially a governance decision. Engineering provides quantification, scenarios, and... <\/span><i><span style=\"font-weight: 400;\">trade-offs<\/span><\/i><span style=\"font-weight: 400;\">However, defining what is acceptable depends on risk appetite, regulatory context, social expectations, and ESG commitments.<\/span><\/p><p><span style=\"font-weight: 400;\">In practice, acceptability results from the combination of probability of failure, consequence, and controllability. The same probability may be considered acceptable in a structure with predominantly operational consequences, but unacceptable in a structure whose failure could affect communities, strategic water resources, or critical infrastructure.<\/span><\/p><p><span style=\"font-weight: 400;\">For piles with potentially severe consequences, it is consistent to work with target failure probabilities on the order of 10.<\/span><span style=\"font-weight: 400;\">-<\/span><span style=\"font-weight: 400;\">\u2075 to 10<\/span><span style=\"font-weight: 400;\">-<\/span><span style=\"font-weight: 400;\">\u2074 per year, depending on the benchmark adopted, always connecting these values to robust monitoring plans, emergency preparedness, and system redundancy. For structures with less severe consequences, slightly higher probabilities may be accepted, provided that the decision is documented, communicated, and reviewed periodically in light of new data.<\/span><\/p><p><span style=\"font-weight: 400;\">Without this structured discussion, the company tends to operate under a regime of adjectives, using terms like &quot;safe,&quot; &quot;adequate,&quot; or &quot;conservative&quot; that are not comparable between assets nor do they support consistent investment prioritization decisions. Probabilistic analysis is the bridge between qualitative discourse and quantitative risk governance.<\/span><\/p><p>\u00a0<\/p><h2><b>From technical analysis to risk language: communicating to decide.<\/b><\/h2><p><span style=\"font-weight: 400;\">The incorporation of probabilistic analysis into risk governance is only achieved when the results can be communicated in a clear, honest, and decision-oriented manner. This requires translating technical concepts into risk language.<\/span><\/p><p><span style=\"font-weight: 400;\">A good practice is to start with the practical implication, not the formula. Instead of opening with \u201c<\/span><i><span style=\"font-weight: 400;\">Pf equals 5 times 10<\/span><\/i><i><span style=\"font-weight: 400;\">-<\/span><\/i><i><span style=\"font-weight: 400;\">\u2074 per year<\/span><\/i><span style=\"font-weight: 400;\">It is more effective to say that<\/span><i><span style=\"font-weight: 400;\">Under current conditions, the probability of this pile breaking over 50 years is on the order of X percent.<\/span><\/i><span style=\"font-weight: 400;\">&quot;, then explaining how that number was obtained.<\/span><\/p><p><span style=\"font-weight: 400;\">Another critical dimension is the adoption of probability ranges associated with well-defined qualitative categories. The company can, for example, standardize that events with an annual Pf between 10<\/span><span style=\"font-weight: 400;\">-<\/span><span style=\"font-weight: 400;\">\u2074 and 10<\/span><span style=\"font-weight: 400;\">-<\/span><span style=\"font-weight: 400;\">\u00b3 be classified as \u201c<\/span><i><span style=\"font-weight: 400;\">unlikely<\/span><\/i><span style=\"font-weight: 400;\">\u201d, between 10<\/span><span style=\"font-weight: 400;\">-<\/span><span style=\"font-weight: 400;\">3 and 10<\/span><span style=\"font-weight: 400;\">-<\/span><span style=\"font-weight: 400;\">\u00b2 as \u201c<\/span><i><span style=\"font-weight: 400;\">rare<\/span><\/i><span style=\"font-weight: 400;\">\u201d, between 10<\/span><span style=\"font-weight: 400;\">-<\/span><span style=\"font-weight: 400;\">2 and 10<\/span><span style=\"font-weight: 400;\">-<\/span><span style=\"font-weight: 400;\">\u00b9 as \u201c<\/span><i><span style=\"font-weight: 400;\">possible<\/span><\/i><span style=\"font-weight: 400;\">&quot;Always with clear, consistent, and documented definitions. This prevents different areas from interpreting the same word in divergent ways.&quot;<\/span><\/p><p><span style=\"font-weight: 400;\">Visualization also matters. FS distribution curves, highlighting the region below 1, diagrams that decompose the relative contribution of uncertainties such as water level, resistance, and geometry, risk matrices combining probability and consequence, are tools that help anchor the discussion. The goal is not to impress, but to allow a director, manager, or regulator to quickly understand where the risk lies, what drives it, and what actions are possible.<\/span><\/p><p><span style=\"font-weight: 400;\">Finally, communication needs to connect risk to decision. Each probabilistic outcome should be accompanied by a clear agenda of implications, such as planned volume reduction (height), reinforcement of critical slopes, redesign of drainage systems, expansion of monitoring in sensitive areas, or revision of closure strategies.<\/span><\/p><p>\u00a0<\/p><h2><b>Lessons from probabilistic studies in stacks<\/b><\/h2><p><span style=\"font-weight: 400;\">The experience gained from dealing with high-stakes situations allows us to draw general lessons that are useful for risk management:<\/span><\/p><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><strong>In the first place, <\/strong><span style=\"font-weight: 400;\">Water tends to be the main risk driver.<\/span><span style=\"font-weight: 400;\">In several studies, relatively modest reductions in uncertainty regarding waterline positions and pore pressures, obtained through more efficient drainage, targeted instrumentation campaigns, and more consistent hydrogeological modeling, generate significant decreases in the probability of failure. In many cases, the gains in Pf (probability of failure) per investment associated with knowledge of the water level exceed those obtained by isolated structural reinforcements.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\"><strong>Secondly,<\/strong> <\/span><span style=\"font-weight: 400;\">constructive variability is crucial.<\/span><span style=\"font-weight: 400;\">Differences in procedures between shifts, mining faces, equipment suppliers, and seasonal moisture conditions generate significant internal heterogeneity in the stockpile, with zones of lower relative density, higher fines content, or poorer drainage. Ignoring this variability and working only with average parameters may underestimate the probability of forming critical surfaces on intermediate slopes or berms.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\"><strong>Thirdly,<\/strong> <\/span><span style=\"font-weight: 400;\">Extreme rainfall events can no longer be treated as &quot;distant exceptions.&quot;<\/span><span style=\"font-weight: 400;\">Updated historical data and evidence of climate change indicate that the frequency and intensity of extreme events are changing. Rainfall scenarios previously classified as very remote should be reassessed, both in hydrological and probabilistic terms, with direct impacts on drainage system design and the definition of operational triggers during critical periods.<\/span><\/li><\/ul><p><span style=\"font-weight: 400;\">Finally, sophisticated stability models, by themselves, do not resolve data gaps. The robustness of probabilistic analysis is contingent upon the quality of the geotechnical investigation, the understanding of the water regime, and real-time monitoring. In other words, the software is leverage, but the foundation remains technical data.<\/span><\/p><p>\u00a0<\/p><h2><b>Integrating probabilistic analysis into the risk management system<\/b><\/h2><p><span style=\"font-weight: 400;\">To generate real value, probabilistic analyses cannot be confined to a report appendix. They need to be connected to the company&#039;s risk management system processes, indicators, and decisions.<\/span><\/p><p><span style=\"font-weight: 400;\">This involves defining clear corporate guidelines for target failure probability levels by structure class, based on consequence and criticality; incorporating Pf and \u03b2 into corporate risk matrices, alongside consequence and detection capability metrics; establishing quantitative triggers connecting monitoring data, such as water levels, deformations, and pore pressures, to the probabilistic model, so that certain combinations trigger pre-planned actions, such as reduced operation, intensive inspections, or reinforcement interventions; and periodically reviewing probabilistic analyses in light of new field data, operational changes, structure reforms, or changes in the regulatory context.<\/span><\/p><p><span style=\"font-weight: 400;\">When this happens, the safety factor ceases to be a static number and becomes a living variable, reassessed as the operation evolves. The result is pile management that is more aligned with international best practices in reliability and risk.<\/span><\/p><p>\u00a0<\/p><h2><b>How VinQ supports the journey from \u201cFS\u201d to quantified risk.<\/b><\/h2><p><span style=\"font-weight: 400;\">Migrating from a reliability-focused culture to a risk-oriented culture requires combining geotechnical depth, mastery of reliability methods, and the ability to translate quantitative concepts into business decisions.<\/span><\/p><p><span style=\"font-weight: 400;\">VinQ operates precisely at this intersection. In simpler terms, this means structuring probabilistic stability models aligned with the specific characteristics of the operation, with consistent integration between laboratory tests, retrospective analyses, numerical modeling, and near real-time monitoring data; quantifying failure probabilities and reliability indices compatible with the criticality of the structure, exposure to third parties, and current regulatory frameworks; identifying the main risk drivers and guiding the use of capital, showing where interventions in drainage, reinforcement, further investigation, or monitoring actually reduce Pf; supporting the definition of internal risk acceptability criteria consistent with the ESG strategy and international benchmarks; and developing technical and executive materials that allow for transparent communication of results to management, regulators, investors, and communities.<\/span><\/p><p><span style=\"font-weight: 400;\">By treating the safety factor as part of a broader architecture of reliability, probability of failure, and governance, VinQ helps its clients move away from the reactive discourse of \u201c<\/span><i><span style=\"font-weight: 400;\">we meet the minimum FS<\/span><\/i><span style=\"font-weight: 400;\">and move towards a proactive and sustainable position:<\/span><i><span style=\"font-weight: 400;\">We understand, quantify, and manage the risk of our stockpiles based on data, clear criteria, and decisions aligned with the complete asset lifecycle.<\/span><\/i><span style=\"font-weight: 400;\">.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t<div class=\"elementor-element elementor-element-add6448 e-flex e-con-boxed e-con e-parent\" data-id=\"add6448\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-762964c elementor-widget elementor-widget-text-editor\" data-id=\"762964c\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Authors:<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-898922a e-con-full e-flex e-con e-child\" data-id=\"898922a\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t<div class=\"elementor-element elementor-element-996172c e-con-full e-flex e-con e-child\" data-id=\"996172c\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t<div class=\"elementor-element elementor-element-6fb05cf e-con-full e-flex e-con e-child\" data-id=\"6fb05cf\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t<div class=\"elementor-element elementor-element-5dfc015 e-con-full e-flex e-con e-child\" data-id=\"5dfc015\" 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class=\"elementor-element elementor-element-8a5f12a elementor-widget elementor-widget-text-editor\" data-id=\"8a5f12a\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Bachelor in Mining Engineering (UFMG), Master in Civil Engineering and Management (University of Glasgow), Specialist in Geotechnical Engineering and Project Management.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-13c38cf elementor-widget elementor-widget-text-editor\" data-id=\"13c38cf\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Mining Engineer specializing in geotechnics and project management, an international reference in dams and geotechnical structures applied to 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Azevedo da Silva<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-774f8d2 elementor-widget elementor-widget-text-editor\" data-id=\"774f8d2\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Bachelor in Geology (UFRRJ), Master in Mining Engineering (UFMG) and Specialist in Mineral Resources Engineering.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ed84896 elementor-widget elementor-widget-text-editor\" data-id=\"ed84896\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>A geologist with nearly 20 years of experience in geotechnics, he leads technical projects at VINQ, combining innovation and safety in mining 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416H69V202.2h66.5V416zm-33.2-243c-21.3 0-38.5-17.3-38.5-38.5S80.9 96 102.2 96c21.2 0 38.5 17.3 38.5 38.5 0 21.3-17.2 38.5-38.5 38.5zm282.1 243h-66.4V312c0-24.8-.5-56.7-34.5-56.7-34.6 0-39.9 27-39.9 54.9V416h-66.4V202.2h63.7v29.2h.9c8.9-16.8 30.6-34.5 62.9-34.5 67.2 0 79.7 44.3 79.7 101.9V416z\"><\/path><\/svg>\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t<span class=\"elementor-button-text\">Access Linkedin profile<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>","protected":false},"excerpt":{"rendered":"<p>How probabilistic analyses translate safety factors into probability of failure and support risk decisions in mining stockpiles.<\/p>","protected":false},"author":3,"featured_media":8336,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[20,25],"tags":[45,48,46,47,37],"class_list":["post-8374","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-geotecnia","category-projetos","tag-analise-probabilistica","tag-confiabilidade-estrutural","tag-estabilidade-de-pilhas","tag-fator-de-seguranca","tag-risco-geotecnico"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.2 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>An\u00e1lise Probabil\u00edstica De Estabilidade De Pilhas: Como Traduzir Fator De Seguran\u00e7a Em Linguagem De Risco - VinQ \u00b7 Geotecnia<\/title>\n<meta name=\"description\" content=\"Como an\u00e1lises probabil\u00edsticas traduzem fator de seguran\u00e7a em probabilidade de falha e suportam 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