Search

Data-driven models utilizing powerful artificial intelligence (AI) algorithms have been implemented over the past two decades in different fields of simulation-based engineering science. Most numerical procedures involve processing data sets developed from physical or numerical experiments to create closed-form formulae to predict the corresponding systems' mechanical response. Efficient AI methodologies that will allow the development and use of accurate predictive models for solving computational intensive engineering problems remain an open issue. In this research work, high-performance machine learning (ML) algorithms are proposed for modeling structural mechanics-related problems, which are implemented in parallel and distributed computing environments to address extremely computationally demanding problems. Four machine learning algorithms are proposed in this work and their performance is investigated in three different structural engineering problems. According to the parametric investigation of the prediction accuracy, the extreme gradient boosting with extended hyper-parameter optimization (XGBoost-HYT-CV) was found to be more efficient regarding the generalization errors deriving a 4.54% residual error for all test cases considered. Furthermore, a comprehensive statistical analysis of the residual errors and a sensitivity analysis of the predictors concerning the target variable are reported. Overall, the proposed models were found to outperform the existing ML methods, where in one case the residual error was decreased by 3-fold. Furthermore, the proposed algorithms demonstrated the generic characteristic of the proposed ML framework for structural mechanics problems. [ABSTRACT FROM AUTHOR]

Heat transfer through the soil is important in shallow geothermal applications, in plant growth as well as in the surface energy balance. In this work, an analytical solution for a conduction–convection equation that models heat transfer is presented for non-homogeneous soil. The main assumption underlying the solution is that the thermal diffusivity of soil is piecewise-constant. A method to determine the depth-dependent thermal diffusivity and the water flux density through the porous medium based on temperature measurements is also developed and applied to field data from a site in Ioannina, Greece. The thermal diffusivity was found to increase in the layer below the surface and decrease for larger depths, while convection through the porous medium was found to be present in wet conditions and to account for about 10% of the heat flux in terms of the annual variability of temperature. Finally, the capability of the novel method in capturing the spatio-temporal variability of soil temperature is compared to three commonly used algorithms: the amplitude, the phase, and the conduction–convection algorithm. The novel method is able to reduce the root-mean-square error for the predicted variability of temperature at all depths by an order of magnitude compared to the other three algorithms. [ABSTRACT FROM AUTHOR]

Optimization algorithms appear in the core calculations of numerous Artificial Intelligence (AI) and Machine Learning methods and Engineering and Business applications. Following recent works on AI's theoretical deficiencies, a rigour context for the optimization problem of a black-box objective function is developed. The algorithm stems directly from the theory of probability, instead of presumed inspiration. Thus the convergence properties of the proposed methodology are inherently stable. In particular, the proposed optimizer utilizes an algorithmic implementation of the n-dimensional inverse transform sampling as a search strategy. No control parameters are required to be tuned, and the trade-off among exploration and exploitation is, by definition, satisfied. A theoretical proof is provided, concluding that when falling into the proposed framework, either directly or incidentally, any optimization algorithm converges. The numerical experiments verify the theoretical results on the efficacy of the algorithm apropos reaching the sought optimum. [ABSTRACT FROM AUTHOR]