Lean construction (LC) targets the reduction and eventual removal of non-value adding activities (otherwise known as waste) in construction projects to increase value to stakeholders. The application of LC principles could lead to significant gains in road maintenance projects where the effect of delay in service is profound and the consequences are far-reaching for all project stakeholders. However, classical practices in the infrastructure asset management domain have less embraced lean thinking when planning for maintenance and rehabilitation. This paper presents a novel decision-making model to integrate LC principles in a classical formalized road maintenance planning and scheduling to facilitate the reduction of non-value adding activities. A case study is adopted to validate the proposed approach. Analysis of the case study revealed a reduction in the cost allocation for the selected highway with similar condition levels in comparison with the classical practices. The model could drop non-value activities, limit the gap on lead time variability, and reduce operational complexity as three main principles of lean thinking. Also, the presented approach could assist road agencies in minimizing delays, optimally assign project resources, and select qualified construction contractors more efficiently.

The literature is rich in pavement deterioration modeling; however, classical frameworks for decay predication mostly rely on time series data for deterioration trends while several hidden factors could impact pavement lifespan. Hence, there is a critical need for transportation agencies to develop their own deterioration prediction for decision making instead of borrowing those models developed for other regions. The objective of this research is to illustrate a simple and fast approach for developing distress prediction models for road pavement with only two-time observations. The proposed platform makes agencies capable of developing and updating their own deterioration models with less data dependency through a simplified pavement performance modeling. In the studied case, shorter lifespan is estimated, which could be explained by the number of freeze-thaw cycles. Also, faster pavement condition index (PCI) decay for local segments with less traffic load in comparison to arterial ones is observed. It is suspected that environmental conditions become a critical factor with a larger contribution to pavement deterioration than traffic or type of pavement in this case study.

Metro systems play a crucial role in the movement of millions of passengers worldwide as commuters rely on a fast, reliable, and convenient underground railway for their daily transportation. However, in many cases, the quality of the service that can be delivered, including performance, attractiveness, and customer satisfaction, are constrained by poorly maintained infrastructure. Meanwhile, effective planning to maintain, rehabilitate, replace and expand existing systems must respect technical, social, political, financial, and management constraints. There is a lack of a comprehensive framework for managing metro assets. This is mainly due to the multiplicity of components; the complexity of their interdependencies; common lack of historical data and performance indicators; and unavailability of a unified framework that integrates forecasts of future demand with decision-making systems. The main objective of this research is to review available studies and models for underground rail systems, identify the main strategic-operational planning gaps, and propose critical tasks for a comprehensive asset management framework. The platform developed in this research is suitable for further studies in urban railways such as Rail Rapid Transit (underground and surface), Light Rail Transit, and suburban trains as well as other modes of transport (e.g. traditional buses, Bus-Rapid-Transit).

Urban railways play a critical role in the daily life of citizens. However, extensive deterioration of mostly aged systems complicates the management of this type of infrastructure in coping with increased demand and restricted upgrade and renewal budgets. The main objective of this research is to develop a comprehensive network-level decision-making model for managing urban railway systems to maintain the highest level of performance, reliability, and convenience in the movement of passengers in a metropolitan area. By considering ridership fluctuations and network expansion scenarios, the proposed model integrates the current and expected future usage rates into the decision-making process. The Montreal metro system was considered as a real-world case study. The results indicate that proactive maintenance scheduling can save up to 25% of the cumulative expenditure during a 20-year horizon while improving the overall performance of the system. Comparing the decisions proposed by the model and the government plan for this network presents an opportunity to understand current railway asset management practices. The proposed model is applicable to rail rapid transit (underground and surface), light rail transit, and suburban trains as well as bus rapid transit and guides maintenance, upgrade, and expansion of these systems, targeting improvements in their convenience, reliability, and ridership.

North America’s infrastructure, in general, and stormwater system, as a crucial component of our urban infrastructure, are in dire condition states that require attention and, in some cases, urgent intervention. According to the American Society of Civil Engineering (ASCE), the stormwater system received a condition state “D+”. Even though existing management systems for storm pipes optimize the timing of maintenance and replacement aiming at improving the overall storm pipes’ network condition, they ignored the effect of urban growth and climate change (i.e. intense and frequent rainfall cause flooding) on the pipes’ deterioration. Thus, this paper presents a scheduling decision-making framework that opts at optimizing the pipes’ replacement decisions to increase their resiliency while considering the annual expenditures, urban growth and its’ future impact on the demand. To demonstrate the system’s functionality, it was deployed on the storm pipes of Kindersley in Saskatchewan, Canada. The results showed that $300,000 a year was enough to meet the city’s condition and flow demand–capacity goals, enhance their average condition from 33% to 63% and drop the flow demand–capacity to 64%. Furthermore, replacements’ breakdown analysis indicated that longer analysis periods enhance the system’s capability in capturing the long-term impact of the decisions.

This paper proposes a methodology for measuring and comparing the benefits associated with a change in the decision making method for infrastructure asset management. Three common methods of measuring are reactive approach (also known as worst-first), silos and trade-off optimisation. A case study is used to illustrate the impact of applying different decision making approaches. The case is based on the urban municipality of the Town of Kindersley, Canada, and contains pavements and water main, storm sewer and sanitary sewer pipes. Economic comparisons of (a) the observed levels of service under fixed budgets and (b) the expenditure required to achieve the target levels of service are presented to support the selection of the preferred decision making method and to measure the superiority of one approach over another. Results from the analysis confirmed the expected inferiority of the worst-first method. Applying the trade-off method resulted in the expenditure of 8.83% fewer resources than the use of the silo method. For a yearly budget of C$800 000 (US$590 695) applied to all types of infrastructure, the trade-off resulted in mean condition levels 12.9% higher than those resulting from the silo method. The proposed platform can be applied to other infrastructure using different performance indicators.