474 research outputs found
Statistical reasoning with set-valued information : Ontic vs. epistemic views
International audienceIn information processing tasks, sets may have a conjunctive or a disjunctive reading. In the conjunctive reading, a set represents an object of interest and its elements are subparts of the object, forming a composite description. In the disjunctive reading, a set contains mutually exclusive elements and refers to the representation of incomplete knowledge. It does not model an actual object or quantity, but partial information about an underlying object or a precise quantity. This distinction between what we call ontic vs. epistemic sets remains valid for fuzzy sets, whose membership functions, in the disjunctive reading are possibility distributions, over deterministic or random values. This paper examines the impact of this distinction in statistics. We show its importance because there is a risk of misusing basic notions and tools, such as conditioning, distance between sets, variance, regression, etc. when data are set-valued. We discuss several examples where the ontic and epistemic points of view yield different approaches to these concepts
A framework for managing global risk factors affecting construction cost performance
Poor cost performance of construction projects has been a major concern for both
contractors and clients. The effective management of risk is thus critical to the success of any construction project and the importance of risk management has grown as projects have become more complex and competition has increased. Contractors have
traditionally used financial mark-ups to cover the risk associated with construction
projects but as competition increases and margins have become tighter they can no longer rely on this strategy and must improve their ability to manage risk. Furthermore, the construction industry has witnessed significant changes particularly in procurement
methods with clients allocating greater risks to contractors.
Evidence shows that there is a gap between existing risk management techniques and
tools, mainly built on normative statistical decision theory, and their practical application
by construction contractors. The main reason behind the lack of use is that risk decision
making within construction organisations is heavily based upon experience, intuition and
judgement and not on mathematical models.
This thesis presents a model for managing global risk factors affecting construction cost
performance of construction projects. The model has been developed using behavioural
decision approach, fuzzy logic technology, and Artificial Intelligence technology. The
methodology adopted to conduct the research involved a thorough literature survey on
risk management, informal and formal discussions with construction practitioners to
assess the extent of the problem, a questionnaire survey to evaluate the importance of
global risk factors and, finally, repertory grid interviews aimed at eliciting relevant
knowledge. There are several approaches to categorising risks permeating construction projects. This
research groups risks into three main categories, namely organisation-specific, global and
Acts of God. It focuses on global risk factors because they are ill-defined, less
understood by contractors and difficult to model, assess and manage although they have
huge impact on cost performance. Generally, contractors, especially in developing
countries, have insufficient experience and knowledge to manage them effectively. The
research identified the following groups of global risk factors as having significant impact
on cost performance: estimator related, project related, fraudulent practices related,
competition related, construction related, economy related and political related factors.
The model was tested for validity through a panel of validators (experts) and crosssectional
cases studies, and the general conclusion was that it could provide valuable
assistance in the management of global risk factors since it is effective, efficient, flexible
and user-friendly. The findings stress the need to depart from traditional approaches and
to explore new directions in order to equip contractors with effective risk management
tools
Advanced system engineering approaches to dynamic modelling of human factors and system safety in sociotechnical systems
Sociotechnical systems (STSs) indicate complex operational processes composed of interactive and dependent social elements, organizational and human activities. This research work seeks to fill some important knowledge gaps in system safety performance and human factors analysis using in STSs. First, an in-depth critical analysis is conducted to explore state-of-the-art findings, needs, gaps, key challenges, and research opportunities in human reliability and factors analysis (HR&FA). Accordingly, a risk model is developed to capture the dynamic nature of different systems failures and integrated them into system safety barriers under uncertainty as per Safety-I paradigm. This is followed by proposing a novel dynamic human-factor risk model tailored for assessing system safety in STSs based on Safety-II concepts. This work is extended to further explore system safety using Performance Shaping Factors (PSFs) by proposing a systematic approach to identify PSFs and quantify their importance level and influence on the performance of sociotechnical systems’ functions. Finally, a systematic review is conducted to provide a holistic profile of HR&FA in complex STSs with a deep focus on revealing the contribution of artificial intelligence and expert systems over HR&FA in complex systems. The findings reveal that proposed models can effectively address critical challenges associated with system safety and human factors quantification. It also trues about uncertainty characterization using the proposed models. Furthermore, the proposed advanced probabilistic model can better model evolving dependencies among system safety performance factors. It revealed the critical safety investment factors among different sociotechnical elements and contributing factors. This helps to effectively allocate safety countermeasures to improve resilience and system safety performance. This research work would help better understand, analyze, and improve the system safety and human factors performance in complex sociotechnical systems
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