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Environmental regulations can greatly affect the operation of new and existing infrastructure projects. In fact, the economic performance of infrastructure projects is closely related to environmental regulations whose landscape is extremely dynamic. To assist infrastructure project sectors in better coping with environmental regulatory changes, this study proposes guidelines for the robust adaptation to environmental regulations through information collected from an exploratory research process. As part of these guidelines, a process map model was developed that details the different stages in the environmental regulation development process, including prerulemaking, proposed rule development and publication, commenting period, final rule development and publication, and post-promulgation. Five adaptation subprocesses (i.e., regulatory horizon scanning, stakeholder engagement, risk and opportunity assessment, response strategy development, and compliance implementation) are examined to help regulated entities better adapt to regulatory changes across different regulatory stages. This information has the potential to provide companies with a structured stage-by-stage approach for dealing with uncertainties arising from environmental regulation changes. [ABSTRACT FROM AUTHOR]

In the last decade, the state departments of transportation in the US have experienced an increase in their construction projects, while the level of their in-house inspection staff and resources has either remained the same or declined. Previous studies have found that one strategy that may reduce the inspection workload is prioritizing construction activities for inspection. However, reducing the number of inspections also has its risks, such as functional failures and reduced design life. Thus, available inspection resources should be allocated to the activities with significant risk consequences if inspection is reduced. The objective of this paper is to develop a risk-based inspection protocol to facilitate efficient allocation of available inspection resources to minimize the risks associated with reduced inspection. First, the risk consequences associated with reduced inspection are identified for various construction activities linked to transportation projects. Based on data collected from 23 state departments of transportation, 58 engineers and inspectors from the Indiana Department of Transportation and 20 inspection consultants in the Midwest, the subjective perceived probabilities associated with the occurrence of each risk consequence are encoded by using fuzzy analysis, from which the risk impacts due to reduced inspection are obtained. The construction activities are prioritized based on the risk impacts associated with reduced inspection into five priority levels. The greater the risk impacts are as a result of reduced inspection, the higher the priority would be for inspection of that activity. Thus, the proposed protocol can be used for resource allocation based on risk impacts. The proposed list of prioritized construction activities can assist project and program managers in state departments of transportation in better allocating their limited inspection resources while reducing the risks due to reduced inspection. [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]

The selection of an appropriate project delivery system that suits all project and owner needs is one of the key decisions to a successful project. Therefore, this decision should be made based on thorough analysis. In this paper, a fuzzy multiattribute decision-making (FMADM) model is developed. The model accounts for uncertainties and imprecision in the decision space as well as fuzziness in the nature of the decision attributes. The model utilizes fuzzy decision-making approach in order to evaluate the membership function corresponding to the utility of each project delivery alternative. Project delivery system alternatives are ranked using fuzzy technique for order preference by similarity to ideal solution (TOPSIS) method based on their utility membership functions and by evaluating the distance of each project delivery alternative from fuzzy ideal solutions. In the TOPSIS method, alternatives are ranked based on their closeness coefficient (CC). In addition, the risk attitude of the decision maker is considered in the model by using derived utility membership functions corresponding to the risk attitude of the decision maker. The model is applied to a petrochemical project as a case study. In the case study, the model outcome that ranked Turnkey system as the best system conforms to the lessons learned by the decision maker from several past projects. Moreover, sensitivity analysis is done in the case study. The results show the significant value of the FMADM model for selecting appropriate project delivery system for projects. [ABSTRACT FROM AUTHOR]