This study develops an integrated model of the main contextual factors that affect sustainable delivery of megaprojects in Iran. The inputs to the model are based on a comprehensive literature review affecting the “triple bottom line” of sustainability, as measured in economic, environmental, and social costs. Innovation diffusion theory and extralogical laws of imitation inform the theoretical points of departure. The model inputs were customized for the context of Iran, and a structural equation model was developed, using data collected from 101 survey questionnaires. The findings identify a wide range of factors that directly impact megaproject sustainability, and these may be used by policymakers and practitioners within the Iranian construction industry to better manage sustainability outcomes. Principally, megaprojects would benefit from (1) the generation of sustainability awareness at the evaluation phase, (2) tackling corruption at the preparation phase, and (3) consolidating responsible project management practices at the usage phase. Moreover, these lessons may be more broadly applicable across the entire construction supply chain in Iran, as well as being transferable to other developing countries.

Maintenance cost optimization and performance prediction of bridge structures have become important challenges in bridge management systems. The performance of bridge structures should be carefully monitored, especially in severe climatic conditions. The objective of this study is to develop a rational method that predicts the most cost-effective intervention schedule for bridges, where the structural safety is maintained with the minimum possible lifecycle cost. The framework functions through (1) a central database that contains the asset inventory along with the maintenance actions list, (2) a biquadratic system reliability–based deterioration model, (3) an intervention effect model that simulates the effect of undertaking various intervention scenarios on the bridge superstructure performance, (4) a financial model that computes the lifecycle costs throughout the planning horizon, and (5) an optimization model that utilizes a genetic algorithms engine to compare the different intervention scenarios and selects the most cost-effective one. This method is applied to a simply supported bridge superstructure case study, designed in accordance with Canadian highway bridge design standards. The results indicate that undertaking less costly minor repair actions may considerably reduce the lifecycle costs as a result of decreasing the number of costly major interventions. The optimum scenario resulted in an equivalent uniform annual cost of US$8,277 per year, which shows 4.5 times cost saving as compared with the conventional scenario where only major repairs are performed. This innovative combination of reliability analysis, nonlinear finite-element modeling, and genetic algorithms optimization supports asset managers in long-term planning and ensures undertaking rational and objective decisions.