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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]

The ASCE Body of Knowledge and the ABET Engineering Accreditation Commission have emphasized a broad set of competencies required by 21st century civil engineers. In addition to competencies that are traditionally technical, these organizations recognize that professional competencies such as leadership, the ability to design sustainable systems, effective communication, and interdisciplinary teamwork are among the many skills required by engineers of the future. To develop and enhance all of these proficiencies within the undergraduate curriculum, there is a need to take an approach that interconnects all of these outcomes throughout the curriculum, or a "networked" approach. This paper discusses service learning as a potential approach for facilitating the broad set of foundational, technical, and professional learning outcomes cited by ASCE and ABET, by investigating two case studies within the Engineering Projects in Community Service program at Purdue University. The case studies are two service-learning projects from one large, multidisciplinary, vertically integrated student design team. One project focuses on sustainable and energy efficient housing for Habitat for Humanity (HFH) in Indiana, whereas the other project focuses on affordable houses for HFH in Haiti. The current analysis of these cases suggest that service-learning projects can provide a unique learning experience for the students to develop and synthesize the broad set of foundational, technical, and professional skills by integrating learning outcomes [ABSTRACT FROM AUTHOR]

There is an urgent need for policies to close the existing financing gap for civil infrastructure. However, identification of the desired scenarios for the closure of the financing gap is complex because there are many factors that affect investment in infrastructure. Thus, comprehensive models are required to (1) simulate the impacts of policies, and (2) identify the highly likely scenarios for desired policy outcomes. The objective of this paper is to create a simulation model for ex-ante analysis of financing policies in highway transportation infrastructure in the United States. Using the agent-based technique, this model simulates the microbehaviors of state Departments of Transportation, private institutional investors, and the public. Using the output of the simulation model, financing landscapes of the U.S. transportation infrastructure are developed. The simulated landscape is shown to be helpful in identifying the highly likely scenarios leading to a high level of investment in highway transportation infrastructure under the existing infrastructure investment structures and budget constraints in the United States. The study presented in this paper is novel with respect to (1) the application of the system-of-systems modeling in the analysis of transportation infrastructure financing policies, (2) identification of the desired policy scenarios and their likelihoods for closure of the financing gap in the presence of uncertainties and adaptive behaviors, and (3) simulation and visualization of the impacts of financing policies at the state and national levels. The model promotes a data-driven policy analysis and provides policymakers with a tool to simultaneously account for the impacts of several factors and uncertainties. [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]

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]

This paper proposed and tested an integrated textual–visual–geo framework to enhance social sensing techniques in smart city digital twins in the context of disasters. Effective and efficient disaster response and recovery require reliable situational awareness regarding infrastructure disruptions and their societal impacts. Due to the rapid unfolding and evolution of events in disasters and emergencies, typical data sensing techniques (such as remote sensing and satellite images) are not sufficient to gain reliable situational awareness about disruptions that affect communities at a local scale. Social sensing enables gathering and analyzing massive user-generated data from various sources (social media, in particular) to monitor unfolding of localized events such as infrastructure disruptions and community needs. To advance social sensing methods and their integration into digital twins of cities, this study proposes an integrated framework for detecting infrastructure disruptions based on three information elements embedded in social media content: images, texts, and geo-maps. The framework consists of three main methods: a graph-based approach for detecting critical tweets, an image-ranking algorithm for selecting important images, and a kernel density estimate for estimating the geographical scales of the disruptions. The application of the proposed framework was demonstrated in a case study of water release from flood control reservoirs in Houston during Hurricane Harvey in 2017. The findings illustrate the capabilities of the proposed framework for capturing the critical situational information and interpreting the results for situational awareness and disruption response. The proposed framework can enhance integration of social sensing elements into smart city digital twins in the context of disasters. Accordingly, the proposed framework can improve the ability of community members, volunteer responders, residents, and other stakeholders in coping with built environment disruptions in disasters. [ABSTRACT FROM AUTHOR]

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]

Infrastructure projects experience different regulatory impacts during permitting, operations, and decommissioning. However, no framework is in place to provide a foundation for infrastructure operators and administrators to take advantage of the opportunities (e.g., gain competitive advantage) and cope with the risks (e.g., risk of noncompliance) related to regulatory requirements. This study aimed to identify the existing methodologies and emerging practices to proactively identify, evaluate, and respond to the impact of regulatory changes. To this end, an exploratory research approach was adopted, through which multiple field studies were conducted with interviewees from various functions and units of regulated entities as well as from regulatory agencies. Accordingly, this study established a future-proofing framework that enables companies operating in infrastructure sectors to more proactively respond to and comply with regulations during their projects. In general, the regulatory future-proofing framework encompasses two main components: (1) core subprocesses, which distinguish job sharing and specific responsibilities in dealing with regulatory changes; and (2) influencing factors, which represent overarching areas that make the subprocesses work more effectively. The framework was validated by experts and would enable companies to more effectively and proactively prepare for regulatory adaptation and response in infrastructure projects. [ABSTRACT FROM AUTHOR]

A lack of consistent, reliable, and objective metrics and indicators for project controls poses major challenges for effectively measuring project progress and performance in the construction industry. Often, project managers are misled in their perceptions of project performance until the project nears its end. This lack of accurate project progress and performance information is a major issue that causes performance shortcomings and resource shortfalls. This study investigated critical factors for improving the reliability of project control systems throughout the construction project life cycle. Through conducting a survey and 10 in-depth case studies, this research identified 15 critical reliability improvement factors and 85 indicators with specific application timing and milestones. This robust framework for improving the reliability of project controls throughout the life cycle of a project was then validated by a panel of experts. The results of this study address an important gap related to the understanding of the key factors and indicators that contribute to the reliability of project control metrics and methods. Findings of this research may provide construction industry stakeholders with a tool to significantly improve the reliability of project progress and performance assessment practices. [ABSTRACT FROM AUTHOR]