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.

The massive number of infrastructure intervention activities occurring in cities leads to detrimental social, environmental, and economic impacts on the community. Thus, integrating the asset intervention activities is required to minimize the community disruption and maintain an acceptable level of service throughout the assets’ lifecycle. This paper presents an integrated multiobjective asset management system for the road and water infrastructure that enables asset managers to trade off intervention alternatives and compare the outcomes of both conventional and integrated asset management systems. The multiobjective framework considers (1) physical state, (2) lifecycle costs, (3) user costs, and (4) replacement value. It revolves through three core models: (1) a database model that contains detailed asset inventory for the road and water networks; (2) key performance indicator (KPI) computational models for assessing the impact of the intervention plan on the predefined set of KPIs; and (3) an optimization model that relies on a combination of metaheuristics, dynamic programming, and goal optimization to schedule the intervention activities throughout the planning horizon. The system is applied to road and water networks in Kelowna, British Columbia, and the results show 33 and 50% savings in lifecycle costs and user costs, respectively. Moreover, it shows the potential ability to scale the framework to include other infrastructure such as sewer, electricity, gas, and telecom, provided that the information can be shared among these entities.

Municipalities are experiencing high inefficiency and financial burden imposed by their underperforming aging infrastructure. One-third of Canada’s municipal infrastructure is in fair, poor, and failing condition states, increasing the risk of service disruption and leaving decision-makers with no choice but to undertake immediate intervention. Furthermore, the massive number of intervention activities occurring in cities leads to detrimental social, environmental, and economic impacts on the community. Thus, coordinating municipal infrastructure maintenance is progressively becoming of paramount importance to cope with those tough challenges and reduce service disruptions, repair cost, and time. This research presents an asset management solution to ensure proper expenditure utilization while maintaining infrastructure performance. The framework revolves around three core models: (1) a central database that contains detailed asset inventory for the infrastructure systems; (2) multidimensional computational models to assess the performance of the intervention plan through the duration and cost savings as well as the asset condition state; and (3) a trilevel multiobjective goal optimization model that relies on a combination of metaheuristic rules, goal optimization, and genetic algorithms to plan the corridor interventions across the planning horizon. To demonstrate the functionality of the framework, the system was applied to a $9\text{-}\mathrm{km}$ stretch of roads, water, and sewer networks from the city of Montreal. The preliminary results of the coordinated intervention scenario displayed an overall improvement of 8%, broken down to 11% improvement in the network condition state, 7% financial savings, and 6% temporal savings, as opposed to the conventional (uncoordinated) intervention scenario. Furthermore, the coordinated intervention scenario proved to be more efficient in more than 70% of the corridors under study, revealing less number of visits to the same corridor and causing less service disruption to the surrounding community, as opposed to the conventional intervention scenario.

Performance-based road maintenance contracts (PBRMCs) are an effective mechanism to transfer risk and responsibility of road maintenance activities to the private sector. Proper structuring of performance indicators and the extent of penalties and incentives in the contract has a significant effect on the overall cost and level of service (LOS) provided to the public. This research develops a series of mathematical optimization models and a computational tool that allows road agencies and contractors to better structure the following contractual conditions in a performance-based contract (PBC) for road maintenance: (1) types of performance indicators to be considered; (2) their threshold levels; and (3) the appropriate levels of penalties and incentives. The availability of such models and tools will allow road agencies and contractors that are unfamiliar with PBRMCs to make informed decisions on their approach to contractual risk allocation. A case study of a major highway in Egypt is presented to demonstrate how slight changes in performance thresholds and contractual penalties affect the overall lifecycle costs (LCC) and eventual road performance. The results demonstrate that: (1) small variations in KPI thresholds can have a significant effect on the overall lifecycle costs and eventual contract price; and (2) the addition of penalties and incentives can be an effective mechanism to ensure contractors adhere to performance requirements in PBRMCs.

Keeping asphalt-surfaced highways and roads in satisfactory condition is a major challenge that departments of transportation strive to overcome. According to a recent assessment of roads in both Canada and the United States, road surfaces are in a critical condition state. Accordingly, an accurate condition-rating system is needed to simulate the actual effect of climate, traffic, and operational factors on asphalt-surfaced highways. This research develops a comprehensive condition-rating model that incorporates a wide range of possible factors impacting flexible-pavement performance. Data are collected through questionnaires from experts and from various pavement tests in order to assess condition using multiattribute utility theory (MAUT). The developed model prioritizes the identified factors, with transverse cracking amount having the highest impact, 24.52%, in determining flexible-pavement condition. The model is validated through several stages showing 94% accuracy compared with actual results. Finally, it is applied to a case study and the results are compared with widely used pavement indicators for which the model showed a similar pattern. The developed model will facilitate the selection of maintenance and rehabilitation strategies for road networks.

I.A.A.R.C. - International Association for Automation and Robotics in Construction Civil Engineering