Optimal design of flexible pavements using a framework of DAKOTA and MEPDG

Abstract

Pavement design optimisation is an active area of research. Due to a large number of parameters, such as thickness of layers, material properties, climatic conditions, affecting pavement performance, it is usually not feasible to determine an optimal design using a trial and error approach. In order to make the design calculation computationally tractable, the process can be posed as an optimisation problem. Previous investigations in this vein have suffered from the limitations of a specific pavement analysis tool, specific design goals and specific optimisation algorithms. This paper presents a general computational framework, combining Mechanistic-Empirical Pavement Design Guide and Design Analysis Kit for Optimisation and Terascale Applications, to overcome these shortcomings. The framework's promise is demonstrated through its application to a minimum cost pavement design problem using both direct and surrogate-based (SBO) optimisation approaches. The SBO formulation is shown to achieve significant savings in required computational time with a minimal loss of accuracy in the determined optimal design.

Estimation of the depth of frost penetration in both uniform and layered soils in frost-affected regions

“…The formation of frost in pavement structures is of engineering concern because of its potential to cause damage due to differential volume changes (Bilodeau et al 2008;Khan et al 2009;Gaurav et al 2011). In its broadest sense, frost action includes both frost heave and loss of subgrade support during the frost-melt period.…”

Sequential decision making and simulation-optimization for the design of complex engineering systems

“…Although there are many features affecting pavement performance, typical airfield rigid pavement design processes in FAARFIELD use a trial-and-error approach to find optimum slab size (joint spacing), joint stiffness, temperature-induced initial curling, and predefined load location, using a trial-and -error approach. Since it is also not practical to determine the optimum design solution from an exhaustive set of all acceptable designs using this approach (Gaurav et al, 2011), some practical alternatives are needed to expand the airfield rigid pavement design beyond the current restricted approach, making design calculation computationally-tractable, agile, and comprehensive. To achieve this goal, this study proposes a novel approach utilizing Artificial Neural Networks (ANN) along with an optimization technique for airfield rigid pavement design, recognizing that there have been previous studies utilizing predictive models (e.g., ANN) or optimization methods in pavement design and rehabilitation problems to make pavement engineering practices versatile and practical.…”

“…presented a model for reliability cost-based optimization of asphalt pavement structures that considered asphalt-surface fatigue damage and the degradation of granular materials by repetitive loading cycles. They also combined reliability-based design optimization with long-term pavement-maintenance policy Gaurav et al (2011). minimized asphalt-pavement design costs using a surrogate-based optimization approach.…”