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Internships have been shown to be impactful tools to connect students’ learning in academia with real-world industrial needs. To help the students to get more out of their internship experience, some universities provide a summer internship class in which students do class assignments based on their experiences during their internship. There have been numerous studies on the benefits of internships. However, the benefits of a potential internship class for students in construction management (CM) programs at universities have not yet been investigated. This paper demonstrates the structure of an interview-based internship class and investigates its effectiveness. We have focused on the CM program at the University of Nebraska-Lincoln (UNL) as a case study. We sent online questionnaires to the intern students who took the class, students’ mentors, and the professors who taught the class. The results indicate that despite some challenges to meet the requirements of the class, the students, their mentors, and the professor found the class beneficial. The results of this paper are expected to help CM programs with the establishment and improvement of internship classes in their curriculum. Published version

Critical infrastructures operations significantly affect the triple bottom lines of sustainability. Considering the dynamic nature of infrastructure and its surrounding environment, the interaction between them can dramatically change over time. This results in a deviation from the predictions used in the design and construction of the infrastructure. Therefore, the negative impacts of critical infrastructures on the environment, society, and economy can exacerbate throughout their service life. It is crucial to maintain these impacts within the desired limits. The measures that attempt to perpetuate a facility’s adverse effects on the triple bottom lines of sustainability can be called sustainability maintenance. Regular maintenance operations of infrastructure create an opportunity to integrate sustainability maintenance into preventive, corrective, and periodic maintenances. This chapter discusses four categories of sustainability maintenance of critical infrastructures: (1) minimizing adverse impacts of the infrastructure on people through maintenance, (2) keeping the maintenance operations sustainable, (3) sustainable material allocation throughout the maintenance process, and (4) environmental protection and restoration in maintenance operations. In each category, some of the best practices and methods are discussed.

Shrink of nonrenewable natural resources and the pollution generated by many manufacturing industries have initiated a global determination for pushing the industry toward more sustainable products. Asphalt as the principal element of almost any street and highway pavement is integral in transportation development, which, in turn, is pivotal for sustainable development. On the other hand, the material consumption and pollution generated in the asphalt industry pose significant threats to the environment and, therefore, to sustainability. This chapter reflects some of the sustainability concerns of the asphalt industry and discusses some of the possible solutions to mitigate them. The sustainability considerations are categorized into four phases of asphalt life cycle namely: (1) extraction of materials, (2) processing of raw materials, (3) transportation of asphalt raw materials and products, and (4) reusing and recycling at the end of a pavement’s life. In each phase, best practices to improve and maintain the sustainability of asphalt pavements are discussed. This chapter also discusses sustainable approaches and technologies in the asphalt industry such as warm mixes, permeable asphalt pavements as well as the potentials for improving the mechanical properties of asphalt pavements particularly in terms of resisting heavy load traffic, clogging, and freeze-thaw.

Considering the tremendous losses in the worldwide economy caused by construction delays, it is essential to invest in minimizing the risks of delays. In order to make this happen, two measures should be taken: 1) The roots and fundamental causes of delay should be identified and strategies to mitigate their risks be developed (General remedy). 2) The most significant potential causes of delay in each project should be identified and these causes should be given priority to control (Project-Specific Remedy). The current research invests in both of the measures. To provide the general remedy, causes of delay in the construction industry of the United States is investigated through a national survey responded by the 224 construction experts with an average experience of over 27 years. The results of this study rank the criticality of the thirty main causes of construction delay in the U.S construction industry. The focus of the research is on the project-specific remedy. The research aims at designing a tool, which can prioritize different causes based on their criticality. This is crucial as there is often a large number of potential causes and investing in prevention of all of them is not practical. The designed tool is capable of identifying the most critical causes by assessing its status of the potential causes of delay in three elements of criticality which are: 1) The likelihood of occurrence of the cause, 2) the severity of the cause in creating delays (in case it happens), and 3) the resolvability or likelihood of handling the potential cause before it creates a delay, in case it happens. The three elements of assessment are inserted in a designed tool in Matlab¨, which uses a fuzzy logic system to generate a "risk priority number'. This number is a representative of the riskiness of each potential cause. The next contribution of the research is a model that is capable of predicting the percentage of delay based on the "fuzzy risk priority number". This model uses the output of the aforementioned fuzzy inference system to make a prediction about the percentage of delay. The model was tested by comparing its predictions with actual data (the delay that has actually happened) and has been able to predict the amount of delay with an error of less than 20%.