Editorial: Artificial intelligence (AI) applications in ground improvement

This collection of articles aims to provide a pioneering introduction in this journal to the use of artificial intelligence in ground improvement research and applications, coupled with experimental testing and computational methods. Using optimisation algorithms, probabilistic modelling, and machine learning (ML), they advance prediction of settlement, strength, and failure behaviour across soil–cement systems, fibre-reinforced composites, jet-grouting, alkali-activated binders, and embankments.The paper by Nima et al. (2025) evaluates the performance of soil–cement (SC) columns in improving the seismic stability of soft soils through combined experimental and numerical analyses. The study demonstrates that SC columns substantially reduce settlement under both rigid and ductile overburdens and that a grey wolf optimisation framework, supported by artificial neural networks, can reliably identify optimal design parameters. While the proposed model provides a comprehensive tool for seismic soil improvement, further field validation is recommended to enhance its practical applicability.Collico et al. (2025) present a probabilistic Bayesian framework for predicting unconfined compressive strength (UCS) and diameter properties of jet-grouted columns during preliminary design. Drawing on a regional data set of soil and system parameters, the method combines local and most-similar site information to improve prediction accuracy under data-limited conditions. The approach offers a more reliable alternative to conventional empirical or theoretical correlations, supporting cost-effective and informed decision-making in early project phases, although further data enrichment is needed to enhance prediction robustness.Martins et al. (2025) apply a novel design of experiments (DOE) methodology to evaluate fibre-reinforced cement-stabilised soils, enabling prediction of both strength and failure mode while quantifying the influence of key parameters. Results show that binder content is the dominant factor governing UCS, while polypropylene fibres outperform sisal in improving ductility. The brittleness index was validated as a reliable criterion for distinguishing failure modes, and the DOE model demonstrated high accuracy with reduced testing effort, offering a robust framework for predictive analysis, though further validation across wider soil, binder, and fibre conditions is recommended.Tinoco et al. (2025) investigate the use of ML to predict the UCS of soils stabilised with one-part alkali-activated binders, offering a sustainable alternative to Portland cement. Despite a limited data set, random forest, neural networks, and support vector machines achieved high predictive accuracy, with water and soil content identified as the most influential parameters. The study demonstrates the potential of ML as a pre-design tool to optimise soil stabilisation, reduce reliance on laboratory testing, and support sustainable construction, while underscoring the need for broader data sets and further model validation.Jones et al. (2025) apply Bayesian updating to assess embankment performance on soft ground, comparing models with different soil layering and random variables. Results show that while both four- and nine-layer models can reproduce monitoring data, the nine-layer model yields more realistic posterior parameters, especially when only surface settlement data are available. Incorporating magnetic extensometer and piezometer data significantly improves prediction quality, underscoring the importance of high-quality field monitoring, while highlighting that computational efficiency depends more on hardware and software optimisation than on model simplification.These contributions reveal an emerging direction in ground improvement, where artificial intelligence takes a central role, enhanced by experimental studies and computational modelling. Together, they point to smarter, data-driven design tools that enhance sustainability and bridge laboratory insights with field performance, although broader data sets and large-scale validation are still needed.