Quantitative Risk Analysis Techniques Research Articles

Risk-Informed, Reliability-Driven Decision Support for Building Basement Systems

A lack of consideration for maintainability at the planning/design stage of a construction project often results in inadequate decision-making and selection of design alternatives, which will likely lead to unproductive operational regimes riddled with frequent defects. This paper attempts to anchor the quantitative risk analysis technique, Bayesian networks, as apt in predicting future operational defects based on the initial design alternatives by looking at specific case studies in building basement systems. Therefore, this paper aims to develop a comprehensive risk-informed, reliability-driven decision-making system. Central to this process is the implementation of a setup wherein a Bayesian Belief Network (BBN) operates as a powerful reliability engine. Concurrently, a risk matrix serves as a vital tool for facilitating risk-informed decision-making. This approach empowers stakeholders to make informed decisions regarding design choices while keeping maintainability at the forefront. It allows for maintainability to be practically used as an effective tool in buildings to lower the lifecycle cost. Furthermore, it is envisaged that this approach can be adopted in the construction industry globally for the prediction and diagnosis of defects, thereby facilitating risk-informed decision support. This study provides significant contributions to the building industry by providing a novel approach to a basement defect analysis, encouraging interdisciplinary collaboration among construction stakeholders, and assisting facility engineers and managers in properly determining maintenance requirements.

Risk likelihood of planned and unplanned cyber-attacks in small business sectors: A cybersecurity concern

Human factors such as planned and unplanned cyber-attacks are a serious threat to any institution. The presence of planned and unplanned actions exposes the state of cybersecurity within the small business sector – leaving them vulnerable to a range of cyber-risks. This study used the AgenaRisk package with Bayesian Network (BN) tools to illustrate the likelihood of risk in planned and unplanned attacks. Adopting the package demonstrates the dependent and independent variables of the human factors, which are planned and unplanned, with their relationships resulting in the ultimate data breach. The work also combined qualitative research with quantitative risk analysis techniques to determine the risk likelihood of planned activities and unplanned employee actions and their behaviors influencing data breaches.
 The work used the judgemental sampling method to select twenty-five (25) research participants who are business owners and Information Technology (IT) managers. An online survey was used to collect data from the selected research participants. Results were analysed using content analysis and interpreted using the package with BN tools, and risk analysis techniques. The results were further discussed, and the study concluded with remarks and future developments.

An index to account for safety and controllability during the design of a chemical process

Inherent safety is well known as the best technique of risk management, but its implementation is a challenge when there is a conflict with other factors, in particular with the process dynamic performance when the application of the minimization principle is considered. In this work, a combined safety-controllability index applicable during the design stage of a process is presented. The index accounts for the process controllability and the risk of the chemical inventory inside the process. The condition number is used to assess controllability, and the quantitative risk analysis technique is carried out to obtain a distance of affectation as a metric for individual risk. The index, based on a combination of normalized values of metrics for controllability and risk, was applied to three case studies dealing with distillation processes that separate flammable and toxic chemicals. The effect of relevant design variables such as column diameter and residence time was analyzed. It is shown how conflicts between safety and controllability exist for different values of those design variables and how a proper controllability-risk compromise can be identified with the use of the proposed index.

A preliminary explore to the forced ventilation on the toxic gas release/dispersion and the hazard mitigation within a petrochemical plant

More than thirty-five years ago, the Bhopal disaster shook the whole world and investigators found out that many people survived just because they turned on the fans in their bedrooms. It was postulated that the forced ventilation played an important role in diluting the toxic gas and saved these people. In order to provide evidence to solve this old mystery, this research employed FLACS software to assess the hazardous degree of a toxic gas (hydrogen sulfide) leakage within a petrochemical process. Series of gas dispersion simulations were performed to actualize the hazardous characteristics and the corresponding risks of the release accident. The study shows that the hazardous level and the hazard range can be greatly influenced when parameters, such as the gas leakage circumstances (atmospheric conditions and wind speed) and the mitigation measures (direction of fans and their speed) are altered.By using explosion-proof fans in different positions and ventilation directions, combined with the natural wind in a certain direction, this research attempts to detect the best combination from various mitigation designs and to compare the influence of fan directions on hazard mitigation. It is also the first time of its kind to simulate the effect of forced ventilation on hazard mitigation within a process plant. The results show that the hazardous level of a toxic release can be effectively alleviated, when the direction of the mechanical ventilation is against the natural wind direction. With the help of the CFD simulation and the quantitative risk analysis technique, different loss prevention strategies can be tested via this method in order to establish a safer working environment.

A systematic review of quantitative risk analysis in construction of mega projects

Abstract Mega projects (MP) require efficient management of risks during their construction. Therefore, it is crucial to identify any possible deviations towards meeting their objectives. Such deviation forced MP to be delivered behind schedule and over budget. According references, MP does not require only qualitative analysis but requires an accurate quantitative analysis based on knowledge and practice. Thus, this paper aims to undergo a systematic review of quantitative analysis literature in construction of worldwide MP, with the utmost aim to improve contractors quantitative risk analysis practices in the presence of uncertainty. A time line was produced which shows the process of quantitative risk analysis in this literature including the past six years from 2013 to 2018. This was followed by a critical analysis in order to account for quantitative risk analysis techniques highlighted throughout literature sources. Furthermore, the paper reviews the literature of worldwide mega projects by which quantitative risk analysis process was practiced. It was observed that Monte Carlo analysis technique has succeeded in supporting project managers in allocating deviations in the objectives of MP. The paper adds value to practitioners using the process of quantitative risk analysis as well as contractors working on construction of MP.

A Quantitative Risk Analysis Method for the High Hazard Mechanical System in Petroleum and Petrochemical Industry

The high hazard mechanical system (HHMS) has three characteristics in the petroleum and petrochemical industry (PPI): high risk, high cost, and high technology requirements. For a HHMS, part, component, and subsystem failures will result in varying degrees and various types of risk consequences, including unexpected downtime, production losses, economic costs, safety accidents, and environmental pollution. Thus, obtaining the quantitative risk level and distribution in a HHMS to control major risk accidents and ensure safe production is of vital importance. However, the structure of the HHMS is more complex than some other systems, making the quantitative risk analysis process more difficult. Additionally, a variety of uncertain risk data hinder the realization of quantitative risk analysis. A few quantitative risk analysis techniques and studies for HHMS exist, especially in the PPI. Therefore, a study on the quantitative risk analysis method for HHMS was completed to obtain the risk level and distribution of high-risk objects. Firstly, Fuzzy Set Theory (FST) was applied to address the uncertain risk data for the occurrence probability (OP) and consequence severity (CS) in the risk analysis process. Secondly, a fuzzy fault tree analysis (FFTA) and a fuzzy event tree analysis (FETA) were used to achieve quantitative risk analysis and calculation. Thirdly, a fuzzy bow-tie model (FBTM) was established to obtain a quantitative risk assessment result according to the analysis results of the FFTA and FETA. Finally, the feasibility and practicability of the method were verified with a case study on the quantitative risk analysis of one reciprocating pump system (RPS). The quantitative risk analysis method for HHMS can provide more accurate and scientific data support for the development of Asset Integrity Management (AIM) systems in the PPI.

Quantitative Risk Analysis and Integration of Value Engineering to Increase the Efficiency of Project Management Case Study: Sattar Khan Commercial Centre - Flour Parking - Tehran

Major organizations and large construction companies in the world are always looking for removing challenges of the project environment through risk areas. Potential risks that sometimes emerge at the beginning, middle and late stages of a project, and in case of improper dealing with it and not getting appropriate response and approach sometimes will lead to failure in the project-based organizations. But providing systemic solutions for transforming risk into opportunity and finding knowledge based on more accurate understanding of qualitative and quantitative risk analysis methods in the early stages of the project is an issue that since the past has attracted risk managers and experts' mind and is considered as the necessity of this research. A distinct use of the two approaches, risk management and value engineering in projects has long been done, therefore the combination and integration of these two techniques is an issue that during recent years has been highly regarded by the researchers and managers. Research framework contains a brief description and introduction of tools and quantitative risk analysis techniques, reasons and benefits of integrating risk management with value engineering and then description of case study. This research has provided a new way for planning and forecasting future performance of the final cost projections. DOI: 10.5901/mjss.2016.v7n4S2p96

Quantitative maritime security assessment: a 2020 vision

Maritime security assessment is moving towards a proactive risk-based regime. This opens the way for security analysts and managers to explore and exploit flexible and advanced risk modelling and decision-making approaches in maritime transport. In this article, following a review of maritime security risk assessment, a generic quantitative security assessment methodology is developed. Novel mathematical models for security risk analysis and management are outlined and integrated to demonstrate their use in the developed framework. Such approaches may be used to facilitate security risk modelling and decision making in situations where conventional quantitative risk analysis techniques cannot be appropriately applied. Finally, recommendations on further exploitation of advances in risk and uncertainty modelling technology are suggested with respect to maritime security risk quantification and management.

Quantitative Risk Analysis of VSAT Communication System

Since today's business environment is dynamic and rapidly change, many surprises and risks are taken into account appear suddenly, leading to block the implementation of engineering projects or may cause a total collapse. A risk management plan has been extensively used to deal with threats facing the activities of any organization. It aims to maximize the probability and consequences of positive events and minimize the probability and consequences of adverse events. This paper presents the advantages of using quantitative risk analysis techniques to describe what risk managers will expect and what is required to be done to enhance the performance of the organization. It also provides better information to support critical decisions which help to estimate the size of contingency reserves for time and cost that would be appropriate for stakeholders. The technique will be used in the assessment is based on Expected Monetary Value which is a simple calculation of a value such as expected target of cost, and a decision tree analysis that is a powerful utility for choosing an option from alternatives.

Evaluation of quantitative risk analysis techniques in selected large construction companies in Nigeria

Purpose The purpose of this paper is to evaluate the adoption of quantitative risk analysis techniques (QRAT) on construction projects in Nigeria with a view to providing better understanding on the state of these techniques.

Environmental project evaluation: IRR-based decision support with a Monte Carlo simulation algorithm

One of the most widely used methods for investment assessment is the internal rate of return (IRR) that is based on the discounted cash flow analysis. Furthermore, the quantitative risk analysis technique is commonly used during the project's initial stage, simply by weighting the financial performance with the incurred risks. Herein, we introduce a decision support algorithm for environmental project evaluation and we focus on its application to solid waste management projects. The proposed algorithm includes some basic steps that should be followed by decision-makers, in order to evaluate a project's options. Specifically, different probability distributions are assigned to all variables that influence the options’ IRRs and Monte Carlo simulation is enforced to compute their expected IRR values. The algorithm presented here can be a useful tool to risk-neutral decision-makers, as it helps them to evaluate an environmental project's options and to select the one with the greatest expected profits.

Risk analysis of stationary dangerous goods railway cars: a case study

Stationary dangerous goods railway cars in sidings are a major source of concern for railroad companies. The potential threat of personal injury and property damage is especially significant when incompatible dangerous goods are involved. Depending on the nature of the dangerous goods, these railway cars may be prone to fires, explosions, and toxic impacts. This paper investigates the threat of stationary dangerous goods railway cars in sidings and reviews quantitative risk analysis techniques in a real case study. The results of this paper can be used to mitigate the risk of stationary dangerous goods cars in sidings through improved operational strategies.

AI-based project risk management process for a kind of manufacturing alliance

In this paper, an artificial intelligence-based project risk management process is proposed to implement quantitative risk management for a kind of manufacturing alliance in the agile manufacturing environment. After the literature review, a multiphase project risk management process is formed consisting of risk analysis, risk evaluation and risk control. Probability Risk Analysis is used as the main quantitative risk analysis technique. Some forms of mathematical programming models are formulated in risk evaluation and risk control phases. Because of the complexity, non-linearity, multiobjective stochastic constraints or interval or fuzzy coefficients of these models, they cannot be solved easily by conventional methods. Genetic algorithms are designed. A real life example is also given and the computational results illustrate the efficiency of the algorithms.

Integrated duration–cost influence network for modelling risk impacts on construction tasks

Quantitative risk analysis is usually ignored in construction risk management, although numerous techniques are available. One of the shortcomings of conventional quantitative risk analysis techniques is that they can only analyse either duration or cost risks. In view of this, an integrated duration–cost influence network that systematically represents the interdependencies among the duration and cost parameters of a construction task has been developed, and mathematical equations have been formulated to represent the dependencies. The generic structure of the influence network can be used for explicitly modelling risk impacts affecting any construction task, and the nature of risks can be investigated in the risk modelling process. In addition, risk impacts modelled can potentially be propagated through the influence network for quantifying the risk‐adjusted task duration and cost simultaneously. Thus, the influence network provides the basis for performing integrated duration–cost risk modelling and simulation in future research.

Repeatability Tests Of A Fire Risk Index Method For Multi-storey Apartment Buildings

Due to advances in building regulations, many timber frame multi-storey apartment buildings have been constructed in the Nordic countries. However, until now, no comprehensive method to estimate fire risks in such buildings has been available to designers. This paper briefly outlines a methodology for developing risk index methods for this purpose and describes the development and testing of such a method, FRIMMAB. It shows how the method has been evaluated against a more complex quantitative risk analysis technique. It describes how a maximum allowable risk index value can be determined when using building codes in the Nordic countries (Sweden, Norway, Finland and Denmark). The paper shows how the method has been tested on 20 different multistorey apartment buildings. Finally, the paper describes how 5 different engineers used the same documentation to evaluate four different buildings, thus allowing a check of the repeatability of the method.

A Strategy for Implementing Risk Analysis

Summary This paper proposes five risk analysis models for analyzing drilling prospects. The models range from a simple, two-outcome analysis (Level 1) to a full Monte Carlo simulation risk model (Level 5) that takes into account all geologic and economic uncertainties. The five levels offer an orderly plan for implementing risk analysis techniques in drilling prospect evaluations. Explorationists can enter the progression at any point, and then gradually expand and enlarge the scope of their evaluation model by following the stepwise progression. Introduction "How do I get started using risk analysis?" "We've been using a two-outcome risk evaluation model: dry hole or a discovery. We would now like to expand our risk assessment procedures. What is the next step for us?" "Is there a simple, first step for using Monte Carlo simulation? " Over the past 10 years petroleum exploration risk analysis has become a popular and topical subject in books and journals. As we extend our search for petroleum to the smaller, harder to find traps we have petroleum to the smaller, harder to find traps we have become increasingly aware of the risks of exploration. Uncertainties about future crude prices, inflation rates, supply and demand forecasts, etc. add to our concerns when evaluating exploratory and development well drilling prospects. Despite this increased awareness of risk and uncertainty, the actual use of quantitative risk analysis techniques vanes considerably. Some use sophisticated Monte Carlo simulation techniques. For many, though, quantification of risk consists only of making a statement about the chance of discovery, such as "we have a one-in-four chance of making a well."In this paper, I outline a systematic progression of five different levels, or models, for analyzing drilling prospects. Level 1 is a basic, two-outcome model prospects. Level 1 is a basic, two-outcome model representing one end of the risk model spectrum. Succeeding levels are increasingly comprehensive, extending finally to a full Monte Carlo simulation analysis in Level 5. This final model represents a state-of-the-art risk evaluation that can account for all uncertainties relating to geologic and economic factors. Five levels are proposed because they offer an orderly plan for implementing risk analysis. You can enter the plan for implementing risk analysis. You can enter the progression at the point of your present analysis method, progression at the point of your present analysis method, and then gradually expand and enlarge the scope of the analysis by following the stepwise progression. If you are just getting started you can begin at Level 1. The five models can be used for evaluating any management strategy for a given prospect such as drilling, farm out, electing to take a back-in option or dry hole contribution, etc. The models are also completely general in the sense they can be used for any type of drilling prospect whether gas, oil, offshore, onshore, exploratory well, or development well. Note, however, that because of the nature and magnitude of the uncertainties involved probably only Levels 4 or 5 will be adequate for offshore and frontier exploration prospects. General Model Characteristics All five risk models outlined here have several common features and assumptions. First, the models are based on the premise that the desired decision-making parameter is an expected value profit measure. "Expected valued" here is used in the sense of the fundamental concept of mathematical expectation-the cornerstone of all formalized strategies for decision making under conditions of uncertainty. An expected value profit is a weighted-average profit, where the weighting factors are the probabilities of occurrence of each possible outcome. Calculation consists of multiplying the profit (or loss) associated with each possible outcome by its respective probability of possible outcome by its respective probability of occurrence. These product terms then are summed algebraically to give the expected value profit (or loss) of the decision strategy. This decision-making parameter is usually given as the expected monetary value profit (EMV) of the option. The decision rule is to accept the decision alternative if its EMV is positive and to reject the alternative if its EMV is negative. For ranking purposes, alternatives that maximize positive EMV are selected. Expected value profit is the only criterion available that allows us to profit is the only criterion available that allows us to incorporate quantitative statements of risk (probabilities) into the evaluation process. For this reason the five risk models are structured to yield an EMV as the final decision-making parameter. For a more in-depth review of the expected value concept, see Chap. 3 of Ref. 1. There are two general types of models proposed. The first three models in the progression are discrete-outcome models, which take account of two or more discrete outcomes occurring. JPT P. 1791