Extreme-oriented sensitivity analysis using sparse polynomial chaos expansion: Application to train–track–bridge systems

The use of sensitivity analysis is essential in model development for the purposes of calibration, verification, factor prioritization, and mechanism reduction. While most contributions to sensitivity methods focus on the average model response, this paper proposes a new sensitivity method focusing on the extreme response and structural limit...

Optimal design of rail level crossings and associated transition zones using adaptive surrogate-assisted optimization

Transition zones such as level crossing and bridge approaches are critical links in railway networks due to higher degradation rates and maintenance needs. In this context, parametric optimization has been applied to improve the design in transition zones; however, it requires a more computationally efficient tool to support repetitive...

MitC: Open-source software for construction project control and delay mitigation

Changes in a construction project schedule can impact the project's planned duration, resulting in penalties. A manual trial-and-error probabilistic approach is usually conducted to find an appropriate set of corrective measures to mitigate delays of the overall project. However, this approach does not capture the actual goal-oriented...

Dynamic control for construction project scheduling on-the-run

Construction project management requires dynamic mitigation control to ensure a project's timely completion. Current mitigation approaches are usually performed by an iterative Monte Carlo (MC) analysis which does not reflect (1) the project manager's goal-oriented behavior, (2) contractual project completion performance schemes, and (3)...

Sensitivity method for extreme-based engineering problems

Engineering models are becoming increasingly complex, involving a larger number of variables. As a result, engineers struggle to deeply understand the models and therefore validate and properly use them. Sensitivity analyses (SA) are usually conducted to help with this task. Nonetheless, the knowledge and computational efforts required to...

Combining parallel computing and biased randomization for solving the team orienteering problem in real-time

In smart cities, unmanned aerial vehicles and self-driving vehicles are gaining increased concern. These vehicles might utilize ultra-reliable telecommunication systems, Internet-based technologies, and navigation satellite services to locate their customers and other team vehicles to plan their routes. Furthermore, the team of vehicles...

Mitigation Controller: Adaptive Simulation Approach for Planning Control Measures in Large Construction Projects

Probabilistic Monte Carlo simulations are often used to determine a project's completion time given a required probability level. During project execution, schedule changes negatively affect the probability of meeting the project's completion time. A manual trial and error approach is then conducted to find a set of mitigation measures to...

Multi-system intervention optimization for interdependent infrastructure

The wellbeing of modern societies is dependent upon the functioning of their infrastructure networks. This paper introduces the 3C concept, an integrative multi-system and multi-stakeholder optimization approach for managing infrastructure interventions (e.g., maintenance, renovation, etc.). The proposed approach takes advantage of the...

Interdependent Infrastructure Interventions Optimization: an Integrative Systems Thinking Approach

Despite widespread attention for the governance of infrastructures and improvements of the way infrastructure assets are maintained, the interdependency of infrastructures is not often considered from a systemic maintenance and renovation perspective. In this context, the issue of unforeseen intervention delays is of particular relevance because...