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The objective of this study is to create and test an integrated framework for assessment of vulnerability to uncertainty in complex projects. In the proposed framework, construction projects are conceptualized as metanetworks composed of different nodes (i.e., human agents, information, resources, and tasks) and links. The effects of uncertain events are translated into perturbations in the nodes and links of project metanetworks. These uncertainty-induced perturbations are reflected as transformations in a project's topological structure, and thus negatively affect the efficiency of the project's metanetwork. The extent of the variation in the efficiency of a project's metanetwork is used to determine the extent of vulnerability to uncertainty. The application of the proposed framework is shown in an illustrative case study related to a tunneling project. In the case study, various scenarios related to different uncertain events were simulated through the use of dynamic network analysis and Monte Carlo simulation. The illustrative case study demonstrated the application of the proposed framework for predictive assessment and proactive mitigation of vulnerability to uncertainty based on evaluation of dynamic interactions between various entities and networks in construction projects. The proposed framework integrates elements from complex systems, dynamic network analysis, and Monte Carlo simulation approaches, and it provides a novel computational framework for ex-ante evaluation of vulnerability to uncertainty in civil engineering projects. [ABSTRACT FROM AUTHOR]

According to the National Academy of Engineering, innovations such as intelligent transportation systems, alternative fuels, and smart grids are critical to enhancing the resilience and sustainability of infrastructure systems. One key to realizing infrastructure innovations is effective policymaking based on a comprehensive analysis of the system. However, policy analysis in infrastructure systems is difficult because of the existence of complex adaptive behaviors and uncertainties. The objective of this paper is to create an ex-ante analysis framework using Agent-Based simulation to facilitate incorporation of uncertainties and complex adaptive behaviors of stakeholders in the analysis of infrastructure policies. The proposed framework includes three phases: definition, abstraction, and implementation. First, the steps in each phase will be discussed. Then, the application of the framework is demonstrated for the assessment of financial innovation policies for U.S. transportation infrastructure. Using hybrid agent-based/system dynamics techniques, a computational model is created to simulate the micro behaviors of state departments of transportation, private institutional investors, and the public. The results of the model include the visualization of the outcomes of different policies and the identification of the desired policy landscapes. The proposed framework provides policymakers with an integrated methodology through which an infrastructure policy problem is formulated, micro behaviors of the agents are modeled, the policy landscape is created, and the desired scenarios are identified. [ABSTRACT FROM AUTHOR]

Performing highway construction operations during nighttime hours has emerged as a response to traffic congestions caused by daytime lane closures. Work zone conditions at night may be different from those during the day, and nighttime factors that affect project metrics (i.e., safety, quality, and productivity) should be carefully considered during the planning of nighttime projects. Currently, there is no methodology for quantifying the effects of nighttime factors on the productivity of construction operations. The objective of this study therefore is to create such a methodology, specifically for asphalt paving projects, focusing on visibility, personnel fatigue, and glare. The methodology is demonstrated using an example case. First, nighttime qualitative (subjective) factors affecting the productivity of asphalt paving operations are identified. A productivity index (PI) is then estimated to account for these factors. The PI value is subsequently used to modify the baseline productivity simulated by a discrete event model of a paving operation, and is then compared with the actual productivity of the case study project. The analysis indicates that the productivity of the asphalt paving operation in the case study can be predicted within an acceptable accuracy range, implying that the calculated nighttime PI can adequately capture the effects of nighttime factors. Quantification of the effects of nighttime factors could help practitioners understand the extent of these effects on their projects. Because nighttime operations are usually more expensive-attributable to overtime payments, night premium payments, lighting expenses, and costs associated with enhanced traffic control-a better estimate of productivity during the early stages of the project, and accounting for the effects of nighttime factors, could lead to better planning and result in cost savings. [ABSTRACT FROM AUTHOR]