Impact of aleatoric, stochastic and epistemic uncertainties on project cost contingency reserves

Producción CientíficaIn construction projects, contingency reserves have traditionally been estimated based on a percentage of the total project cost, which is arbitrary and, thus, unreliable in practical cases. Monte Carlo simulation provides a more reliable estimation. However, works on this topic have focused exclusively on the effects of aleatoric uncertainty, but ignored the impacts of other uncertainty types. In this paper, we present a method to quantitatively determine project cost contingency reserves based on Monte Carlo Simulation that considers the impact of not only aleatoric uncertainty, but also of the effects of other uncertainty kinds (stochastic, epistemic) on the total project cost. The proposed method has been validated with a real-case construction project in Spain. The obtained results demonstrate that the approach will be helpful for construction Project Managers because the obtained cost contingency reserves are consistent with the actual uncertainty type that affects the risks identified in their projects.Junta de Castilla y Leon (grant VA180P20

Uncertainty in life cycle costing for long-range infrastructure. Part I: leveling the playing field to address uncertainties

Purpose Life cycle costing (LCC) is a state-of-the-art method to analyze investment decisions in infrastructure projects. However, uncertainties inherent in long-term planning question the credibility of LCC results. Previous research has not systematically linked sources and methods to address this uncertainty. Part I of this series develops a framework to collect and categorize different sources of uncertainty and addressing methods. This systematization is a prerequisite to further analyze the suitability of methods and levels the playing field for part II. Methods Past reviews have dealt with selected issues of uncertainty in LCC. However, none has systematically collected uncertainties and linked methods to address them. No comprehensive categorization has been published to date. Part I addresses these two research gaps by conducting a systematic literature review. In a rigorous four-step approach, we first scrutinized major databases. Second, we performed a practical and methodological screening to identify in total 115 relevant publications, mostly case studies. Third, we applied content analysis using MAXQDA. Fourth, we illustrated results and concluded upon the research gaps. Results and discussion We identified 33 sources of uncertainty and 24 addressing methods. Sources of uncertainties were categorized according to (i) its origin, i.e., parameter, model, and scenario uncertainty and (ii) the nature of uncertainty, i.e., aleatoric or epistemic uncertainty. The methods to address uncertainties were classified into deterministic, probabilistic, possibilistic, and other methods. With regard to sources of uncertainties, lack of data and data quality was analyzed most often. Most uncertainties having been discussed were located in the use stage. With regard to methods, sensitivity analyses were applied most widely, while more complex methods such as Bayesian models were used less frequently. Data availability and the individual expertise of LCC practitioner foremost influence the selection of methods. Conclusions This article complements existing research by providing a thorough systematization of uncertainties in LCC. However, an unambiguous categorization of uncertainties is difficult and overlapping occurs. Such a systemizing approach is nevertheless necessary for further analyses and levels the playing field for readers not yet familiar with the topic. Part I concludes the following: First, an investigation about which methods are best suited to address a certain type of uncertainty is still outstanding. Second, an analysis of types of uncertainty that have been insufficiently addressed in previous LCC cases is still missing. Part II will focus on these research gaps

Uncertainty Assessment in High-Risk Environments Using Probability, Evidence Theory and Expert Judgment Elicitation

The level of uncertainty in advanced system design is assessed by comparing the results of expert judgment elicitation to probability and evidence theory. This research shows how one type of monotone measure, namely Dempster-Shafer Theory of Evidence can expand the framework of uncertainty to provide decision makers a more robust solution space. The issues imbedded in this research are focused on how the relevant predictive uncertainty produced by similar action is measured. This methodology uses the established approach from traditional probability theory and Dempster-Shafer evidence theory to combine two classes of uncertainty, aleatory and epistemic. Probability theory provides the mathematical structure traditionally used in the representation of aleatory uncertainty. The uncertainty in analysis outcomes is represented by probability distributions and typically summarized as Complimentary Cumulative Distribution Functions (CCDFs). The main components of this research are probability of X in the probability theory compared to mx in evidence theory. Using this comparison, an epistemic model is developed to obtain the upper “CCPF - Complimentary Cumulative Plausibility Function” limits and the lower “CCBF - Complimentary Cumulative Belief Function” limits compared to the traditional probability function. A conceptual design for the Thermal Protection System (TPS) of future Crew Exploration Vehicles (CEV) is used as an initial test case. A questionnaire is tailored to elicit judgment from experts in high-risk environments. Based on description and characteristics, the answers of the questionnaire produces information, that serves as qualitative semantics used for the evidence theory functions. The computational mechanism provides a heuristic approach for the compilation and presentation of the results. A follow-up evaluation serves as validation of the findings and provides useful information in terms of consistency and adoptability to other domains. The results of this methodology provide a useful and practical approach in conceptual design to aid the decision maker in assessing the level of uncertainty of the experts. The methodology presented is well-suited for decision makers that encompass similar conceptual design instruments

On the project risk baseline: Integrating aleatory uncertainty into project scheduling

Producción CientíficaObtaining a viable schedule baseline that meets all project constraints is one of the main issues for project managers. The literature on this topic focuses mainly on methods to obtain schedules that meet resource restrictions and, more recently, financial limitations. The methods provide different viable schedules for the same project, and the solutions with the shortest duration are considered the best-known schedule for that project. However, no tools currently select which schedule best performs in project risk terms. To bridge this gap, this paper aims to propose a method for selecting the project schedule with the highest probability of meeting the deadline of several alternative schedules with the same duration. To do so, we propose integrating aleatory uncertainty into project scheduling by quantifying the risk of several execution alternatives for the same project. The proposed method, tested with a well-known repository for schedule benchmarking, can be applied to any project type to help managers to select the project schedules from several alternatives with the same duration, but the lowest risk

Analysis, Perception and Aspects of Risk Management in the Construction Sector of Pakistan

Management of risk is very significant in the construction sector of Pakistan. Firstly, risk and uncertainty are defined and described in detail. Risk management and risk management process is also defined, described and explained. This risk and risk management provided necessary details and background for this study. A questionnaire survey was conducted for collection of data and information about risk management in Pakistan. Interviews were also conducted for the deeper investigation, study and analysis of the particular specific aspects of risk management in construction sector of Pakistan. Data analysis was done on the basis of data, information, ideas and views regarding risk management from the results of questionnaire survey and interviews. In data analysis, we discussed the aspects of risk management, documentation analysis, research trustworthiness, contracting types and the role of collaborative relationships such as relational contracting and joint risk management. This analysis and discussion provided the important concepts of better and effective risk management. Keywords: Construction Project, Risk Management, Questionnaire Survey, Interview, Analysis

Uncertainty Characterization in the Design of Hydraulic Structures Profiles Using Genetic Algorithm And Fuzzy Logic

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchiv

Uncertainty in Quantitative Risk Analysis - Characterisation and Methods of Treatment

The fundamental problems related to uncertainty in quantitative risk analyses, used in decision making in safety-related issues (for instance, in land use planning and licensing procedures for hazardous establishments and activities) are presented and discussed, together with the different types of uncertainty that are introduced in the various stages of an analysis. A survey of methods for the practical treatment of uncertainty, with emphasis on the kind of information that is needed for the different methods, and the kind of results they produce, is also presented. Furthermore, a thorough discussion of the arguments for and against each of the methods is given, and of different levels of treatment based on the problem under consideration. Recommendations for future research and standardisation efforts are proposed

Risk-informed decision-making in the presence of epistemic uncertainty

International audienceAn important issue in risk analysis is the distinction between epistemic and aleatory uncertainties. In this paper, the use of distinct representation formats for aleatory and epistemic uncertainties is advocated, the latter being modelled by sets of possible values. Modern uncertainty theories based on convex sets of probabilities are known to be instrumental for hybrid representations where aleatory and epistemic components of uncertainty remain distinct. Simple uncertainty representation techniques based on fuzzy intervals and p-boxes are used in practice. This paper outlines a risk analysis methodology from elicitation of knowledge about parameters to decision. It proposes an elicitation methodology where the chosen representation format depends on the nature and the amount of available information. Uncertainty propagation methods then blend Monte-Carlo simulation and interval analysis techniques. Nevertheless, results provided by these techniques, often in terms of probability intervals, may be too complex to interpret for a decision-maker and we therefore propose to compute a unique indicator of the likelihood of risk, called confidence index. It explicitly accounts for the decision-maker's attitude in the face of ambiguity. This step takes place at the end of the risk analysis process, when no further collection of evidence is possible that might reduce the ambiguity due to epistemic uncertainty. This last feature stands in contrast with the Bayesian methodology, where epistemic uncertainties on input parameters are modelled by single subjective probabilities at the beginning of the risk analysis process

Cost engineering for manufacturing: current and future research

The article aims to identify the scientific challenges and point out future research directions on Cost Engineering. The research areas covered in this article include Design Cost; Manufacturing Cost; Operating Cost; Life Cycle Cost; Risk and Uncertainty management and Affordability Engineering. Collected information at the Academic Forum on Cost Engineering held at Cranfield University in 2008 and further literature review findings are presented. The forum set the scope of the Cost Engineering research, a brainstorming was held on the forum and literatures were further reviewed to understand the current and future practices in cost engineering. The main benefits of the article include coverage of the current research on cost engineering from different perspectives and the future research areas on Cost Engineering