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Although many studies were carried out to identify the range of risky events in construction projects and the recommended response and precautionary strategies, little has addressed the means of making such decisions in an optimal way. Unfortunately, complex projects are marred with numerous interconnected causes and effects, which make project dynamics rather difficult to understand and control. A research was undertaken to optimize risk treatment in construction projects, where costs and benefits are balanced out at the project level. Study employs ant colony optimization (ACO) and dynamic risk maps (DRM) to achieve the sought research goal. This paper focuses on presenting the mechanics of the research approach via a real life case study. The construction project into consideration was executed by a well-known contractor in the Middle East. The paper first describes the case details. After the identification of project risk events, the risk inter-dependencies were modelled using a DRM, which made use of the professional knowledge of industry professionals and archived project records as well. Furthermore, ACO is utilized for the balanced selection of risk treatment strategies and to help reduce the project's overall risk severity at the minimum cost possible. Paper ends with useful insights into the research approach and outcomes of case study application.
Although many studies were carried out to identify the range of risky events in construction projects and the recommended response and precautionary strategies, little has addressed the means of making such decisions in an optimal way. Unfortunately, complex projects are marred with numerous interconnected causes and effects, which make project dynamics rather difficult to understand and control. A research was undertaken to optimize risk treatment in construction projects, where costs and benefits are balanced out at the project level. Study employs ant colony optimization (ACO) and dynamic risk maps (DRM) to achieve the sought research goal. This paper focuses on presenting the mechanics of the research approach via a real life case study. The construction project into consideration was executed by a well-known contractor in the Middle East. The paper first describes the case details. After the identification of project risk events, the risk inter-dependencies were modelled using a DRM, which made use of the professional knowledge of industry professionals and archived project records as well. Furthermore, ACO is utilized for the balanced selection of risk treatment strategies and to help reduce the project's overall risk severity at the minimum cost possible. Paper ends with useful insights into the research approach and outcomes of case study application.
This paper presents a Qualitative Risk Analysis Framework to identify and prioritize environmental risks encountered in infrastructure projects, which is applied to developing countries. The framework incorporates consensus and quality of experts in the process of evaluating environmental risk events and is composed of (1) Fuzzy Expert System (FES) to determine attributes of experts; (2) Fuzzy Similarity Aggregation Algorithms to aggregate experts' opinions; and (3) three-dimensional prioritization approach to rank the risks, qualitatively. The FES determines an importance weight factor for each expert, based on a set of predetermined qualification attributes. Experts' opinions are aggregated in a linguistic framework, based on the proximity of their opinions on the scale to ensure that their aggregated decision is a result of common agreement. The importance weight factor is combined with the consensus weight factor of each expert in the aggregation process using a scalar modifier and the Euclidean Distance Measure Function is used to determine the linguistic criticality of every environmental risk event. A three-dimensional prioritization approach applies a set of ranking rules to every risk that enables experts to rank and visualize the priority of the risks in a three-dimensional space. The framework contributes to the Construction industry by solving a major problem for project teams in developing countries to qualitatively evaluate environmental risks in a fully supported linguistic framework, using fuzzy logic, which addresses the vagueness and imprecision that exist in the decision-making process. It provides an improvement over other fuzzy qualitative-based models, using FES, instead of relying on an arbitrary assessment of experts' qualifications in aggregating their opinions.
This paper presents a Fuzzy Consensus Qualitative Risk Analysis framework to identify and prioritize risks encountered in real estate projects, which is applied to developing countries. The framework incorporates the consensus and quality of experts in the process of evaluating risk events and is composed of (1) a Fuzzy Expert System (FES)to determine qualification of experts;(2) Fuzzy Similarity Aggregation Algorithms to aggregate experts' opinions; and (3) a threedimensional prioritization approach to rank the risks, qualitatively. Risks are identified through a literature review and interviews with experts who rank the risks in terms of their probability of occurrence, impact and level of detection; each is described using five linguistic terms that are defined by membership functions (MFs) on a 5-point rating scale. The FES determines an importance weight factor for each expert, based on a set of predetermined qualification attributes. Experts' opinions are aggregated in a linguistic framework, based on the proximity of their opinions on the scale to ensure that their aggregated decision is a result of common agreement. The importance weight factor is combined with the consensus weight factor of each expert in the aggregation process using a scalar modifier and the Euclidean Distance Measure Function is used to determine the linguistic criticality of every risk event. A threedimensional prioritization approach applies a set of ranking rules to every risk that enables experts to rank and visualize the priority of the risks in a three-dimensional space. The framework contributes to the Real estate industry by solving a major problem for project teams in developing countries to qualitatively evaluate risks in a fully supported linguistic framework, using fuzzy logic, which addresses the vagueness and imprecision that exist in the decision-making process.
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