Statistically grounded selection and validation of surrogate models for civil-engineering decision support: ANN, Kriging and SVM across three case studies

Abstract High-fidelity numerical models are widely used in civil engineering design and assessment, but their computational cost hinders extensive parametric studies and optimisation. Machine learning-based surrogate models offer a promising alternative by learning fast input–output mappings from limited simulation or experimental data. This work presents a systematic comparative study of three widely used surrogate techniques—artificial neural networks (ANN), Kriging metamodels and support vector machines (SVM)—applied to three representative civil engineering problems: (i) structural optimisation of an overhead travelling crane, (ii) assessment of the liquefaction potential of sandy soils and (iii) back-calculation of flexible pavement layer responses under different loading levels. For each case, a designed set of simulations or measurements is used to train the surrogates, and their predictive performance is evaluated using error-based metrics and computational efficiency indicators. The results provide a clear quantitative picture of model performance: Kriging and SVM consistently achieve the best classification accuracies for small to moderate datasets, with SVM and Kriging reaching top classification accuracies of 99.0% and 98.5%, respectively, whereas ANN performance is highly dependent on data quantity and architecture choice. The cross-domain comparison provides practical guidance on the suitability of each machine learning surrogate family for structural, geotechnical and pavement engineering tasks. The proposed framework can be embedded directly in intelligent decision-support and expert systems for civil engineering, enabling efficient exploration and optimisation of complex design spaces.