A scalable adaptive sampling approach for surrogate modeling of rigid pavements using machine learning

Rigid pavement design is a high-dimensional optimization problem, involving several variables and design considerations. The existing machine learning (ML) design models are either low-dimensional or less accurate than computational simulations. Surrogate modeling is a powerful tool for approximating the results of high-fidelity computational simulations, which is commonly used to approximate the results of these simulations by sampling their solutions to train models. However, conventional and adaptive sampling methods face the challenge of "curse of dimensionality", due to the exponential increase in required sample size and computations with the number of variables. To overcome this, we propose a scalable adaptive sampling (SAS) method that uses random samples for testing the model's performance but generates new training samples as a subset of a full factorial design of experiments (DoE). The factorial level increases with each iteration, allowing the algorithm to sample training data at a progressively finer scale, and updating the ML models each time adaptively. The proposed method was tested on several 2D benchmark examples, as well as practical pavement design problems. Our results demonstrate that the proposed technique can develop accurate surrogate models for both low- and high-dimensional inference spaces. For a 4D inference space, the surrogate models derived from the proposed method had an order of magnitude lower error than those derived from "one-shot" conventional sampling with the same sample size. For a 6D inference space, the sample size required by the proposed method was only 5 % of that required by conventional sampling for comparable performance.