Journal of Infrastructure Systems

The climate in Alaska is changing with temperatures and precipitation increasing, and experts say those changes will continue. The changing climate will, among other kinds of social and economic effects, damage public infrastructure throughout Alaska, adding to maintenance costs and shortening the lifespan of everything from schools to sanitation systems. The question of how much cost impact this change will have and when the costs are expected to occur is the question of significant concern and debate. One approach to answering this question is addressed in this paper. Based on a probabilistic model, the results in this paper estimate how much climate change will add to future costs of public infrastructure in Alaska. Based on a database of public infrastructure throughout Alaska and the estimated replacement costs and lifespans for the various types of infrastructure statewide, the model provides a first analysis of climate impact on infrastructure costs. As an additional refinement of the model, the paper presents two potential adaptation approaches to reduce the impacts of climate change on the adaptation process. Code adaptation and event adaptation are introduced as two potential processes, based on existing responses to natural disasters, that may be employed to adapt to climate change. These adaptation approaches have the potential to reduce climate change costs by 10–45%. This is significant when the total potential cost impact to infrastructure from climate change is projected to add 10–20% to infrastructure costs by 2030 and 10–12% by 2080 under different climate projections and taking design adaptations into account.

In this paper, we use a two-stage stochastic programming model to optimize retrofit decision for highway systems so that damage caused by future earthquakes will be minimized. This work is an example of applying network theories and system optimization approaches to address important problems in the field of critical infrastructure protection. Using a real-world case study based on existing Alameda County highway network settings, we demonstrate potential real-world applications of this research. Comparison between the proposed approach and some commonly used methods in practice indicates significant potential benefit from more rigorous system approaches. Other issues such as value of perfect information, reliability, and robustness are also discussed.

Low-class roads substantially differ from main roads in terms of extensiveness, databank availability, and profitability, which stresses the need for an extraordinarily sophisticated decision-making mechanism for maintenance programs. However, road transportation decision-making administrators in third-world countries such as Iran are occasionally neither well educated in their area of activity nor especially eager to benefit from specialist advice. This usually leads to costly errors in the process of individual decision making. One way to obviate these grave errors could be to incorporate group decision-making techniques into the administrative agenda. The combined Conference-Delphi-analytic hierarchy process (AHP) model employed here to prioritize the low-class roads in Gilan for maintenance, improvement, and upgrading, profoundly incorporates specialist advice and could serve as a counteracting approach in this regard. The preliminary procedures including the Conference and the Delphi survey were employed to determine the list of the specialists for the AHP and the criteria which were then fed into the AHP. The results indicate a significant contrast between conventional individualistic decisions and those made through incorporating systematic specialist comments using our model.

This research examined the environmental impacts due to the construction phase of commercial buildings. Previous building research has often overlooked the construction phase and focused on the material and use phases, discounting the significant environmental impacts of construction. The research was conducted using life-cycle assessment (LCA) methodology, which is a systematic environmental management tool that holistically analyzes and assesses the environmental impacts of a product or process. Life-cycle inventory results focused on particulate matter, global warming potential, ${\text{SO}}_x$ , ${\text{NO}}_x$ , CO, Pb, nonmethane volatile organic compounds, energy usage, and solid and liquid wastes. Results over the entire building life cycle indicate that construction, while not as significant as the use phase, is as important as other life-cycle stages. This research used augmented process-based hybrid LCA to model the construction phase; this modeling approach effectively combined process and input-output (IO) LCA. One contribution was the development of a hybrid LCA model for construction, which can be extended to other sectors, such as building products. Including IO results, especially construction service sectors, is critical in construction LCA modeling. Results of a case study demonstrated that services had the highest level of methane emissions and were a significant contributor to ${\text{CO}}_2$ emissions.

This paper discusses the use of autonomous mowers for application in highway vegetation control. In recent work by these writers, a unique autonomous mower testbed has been developed to prove the efficacy of such an approach. The testbed’s uniqueness comes from its relatively low cost and its new and accurate sensing method. The mowing performance is evaluated based on the consistency of consecutive mowing paths from actual outdoor testing. Then, a hypothetical implementation is used to evaluate the practicality of the system in California Department of Transportation (Caltrans) applications. Results show that autonomous mowers have the potential to significantly reduce Caltrans’ dependence on herbicide, improve worker and public safety, lead to enhanced levels of environmental quality, and decrease the operation’s costs.

Construction vehicles are a source of nonroad mobile air pollutant emissions. Emissions from construction vehicles are typically quantified based on steady-state modal engine dynamometer tests using uninstalled stationary engines. However, these tests do not represent real-world activity. Therefore, there is a need to quantify energy use and air pollutant emissions from construction vehicles based on in-use measurement methods. The purpose of this paper is to outline standard procedures for field data collection for construction vehicles. This methodology is based on second-by-second measurement of in-use activity and air pollutant emissions using a portable emissions measurement system. The field data collection methodology includes the development of a study design, installation and use of instrumentation, and field measurements. After the field data collection, the raw data undergo a quality assurance procedure to check for and correct the synchronization between the engine data and emission data. This procedure also identifies missing data and removes incorrect data. The most significant challenges to data collection include suitable weather, difficult operating conditions, and scheduling data collection sessions. Sample real-world data measurement results for a rubber tire loader are presented. The work presented here can be used as a foundational example for conducting other data collection efforts and analysis of construction vehicle emissions.