Project risk analysis must be implemented using a systematic approach where project size, data availability and project team requirements are promptly taken into account. However, complex projects are marred with numerous interconnected causes and effects, which make project dynamics rather difficult to understand and control. One approach to overcome this hardship and provide a facility to understand and visualize such dependencies is risk mapping. With emphasis on the pipeline construction sector in the Middle East, the research at hand aims first to identify the most critical risk factors in the denoted sector and then to develop a dynamic risk map (DRM) for it. N2 Diagrams were employed to construct the interdependency relationships of the DRM. The DRM can be utilized in calculating the significance of project risks via posterior probabilities. In this context, the cross impact analysis (CIA) method is proposed as an appropriate computational and reasoning tool. The CIA method is simply a technique designed to predict chances of future events by capturing the interactions among a set of variables. From the DRM it is possible to envisage not only the ultimate effect of a risk on the project but also the incremental steps leading to it. This makes it possible to evaluate the effect of potential risk factors for unlimited project scenarios.
-Efforts undertaken in identifying, analyzing and assessing project risks are only made good use of when proper risk treatment strategies are decided upon and pursued. Based on the criteria established by senior management, the risk management plan goes about defining how each risk is to be handled. There are options to that end, including acceptance, avoidance, transfer and mitigation. Whilst these strategies are known to all in the industry, the decision-making process is far from easy. A research was undertaken to optimize risk treatment in construction projects, where both costs and benefits are balanced out at the project level. The paper particularly introduces Ant Colony Optimization (ACO) as a capable algorithm for the balanced selection of risk treatment strategies; that is to reduce the overall risk severity in a project at the minimum cost possible. ACO resembles the real life behavior of ants in their intelligent and guided search for food. The research is being applied in the pipeline construction sector and made use of professional knowledge and project records from a big construction company in the Middle East. The paper further presents an example project to demonstrate how ACO explores the risk treatment alternatives in a project and chooses the optimal set of strategies in such context.
The reinforced concrete assets such as bridges, tunnels, administration buildings, schools and the residential buildings are important factors in the Egyptian economy. Several researches have been made in the field of concrete defects and methods of repair. Concrete can deteriorate for a variety of reasons such as the corrosion of steel bars, design errors, construction errors and the environmental conditions. Concrete structures' deterioration is often the result of a combination of deterioration factors. Factors that contribute to the deterioration of the reinforced concrete structures can be classified into five categories as follows: (1) Design factors such as the code requirements, the structural system, and the foundation design; (2) construction factors such as the mix design and the quality control; (3) protection factors such as the thermal insulation and the water proofing; (4) material factors such as the aggregate and the cement; and (5) the applied loads factors such as the chemicals and the earthquakes. This paper provides a model for prioritizing the concrete structures' repair works. Past researches have been reviewed to gather the main factors that contribute to the deterioration of concrete structures. Interviews with senior engineers have been conducted during the development of the proposed model. A numerical example has been presented to demonstrate the proposed model.
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