Editorial

I hope that our readership had time to relax and spend time with their family and friends over the winter break. I know that our editorial board has been working hard over the last few years bringing down the ‘ahead of print’ (which is now known as Earlycite and can be found at Link to the cited article) backlog and I thank them; and obviously the editorial board is incredibly grateful to all our reviewers for taking the time and effort they provide in the extremely valuable contribution they make throughout the year. Without the voluntary sharing of skills and expertise of our editorial board and our reviewers this journal simply would not exist.In this first issue of Geotechnical Engineering for 2026, we have collated 11 technical papers on subjects ranging from the application of learning models for large datasets, examining consolidation and swelling behaviour, through to pile behaviour. The first two papers both use machine learning, with Yang et al. (2026) from Shandong outlining how they used a deep learning model to delineate their stratigraphy from continuous cone penetration test (CPT) data, while Rahangdale et al. (2026) from Visvesvaraya National Institute of Technology use machine learning to predict seismic amplification factors in India. Yang et al. (2026) introduce a bidirectional head-cohesion long short-term memory (BHC-LSTM) model for stratigraphic classification using continuous CPT data. By leveraging overlapping frames and dual LSTM heads, the model captures directional soil evolution above and below target layers, achieving over 95% accuracy across diverse datasets, while reducing computational overhead compared to conventional Bi-LSTM approaches. This innovation enhances interpretability and efficiency for large-scale geological surveys. Complementing this, Rahangdale et al. (2026) explore seismic site amplification prediction for Indian soils using artificial neural networks (ANN), random forest regression (RFR) and extreme gradient boosting (XGBoost) predictive models on a comprehensive database of 124 sites and 40 ground motions. For those interested in the application of machine learning within geotechnical problems, Raja and Shukla (2022) and Zhu et al. (2025) might also be of interest.This is followed by three papers on settlement and consolidation, starting with Chung et al. (2026) from Busan, who propose a standardised methodology for reconstructing instantaneous consolidation settlement curves in prefabricated vertical drain (PVD)-improved soft clayey ground, demonstrating that the curve-fitting method provided the most reliable reconstruction and back-analysis of settlement, outperforming traditional linear and Asaoka methods. Wu et al. (2026a) investigate the superposition behaviour of ground settlements induced by symmetrical deep excavations in soft soils (Figure 1) through centrifuge tests and finite-element simulations. Complementing these studies, Sun et al. (2026) develop a large-strain, double logarithmic consolidation model for composite foundations reinforced with stone columns, integrating smear effects, well resistance and variable stress distributions. Analytical and numerical solutions confirm that consolidation rates and settlement trends are highly sensitive to initial stress distribution and soil non-linearity defined by the double logarithmic compressive factor (Ic), with linear stress distributions accelerating settlement compared to uniform stress distributions. If these studies pique your interest then I can suggest you also take a look at Long et al. (2022), Wang et al. (2023), Tao et al. (2025), Pugh (2017) and Jamkhaneh et al. (2020).On a similar theme of soil displacement behaviour, Shi et al. (2026) investigate the dilatancy behaviour of saturated Shanghai clays, through laboratory tests on natural and reconstituted specimens’ stress history, natural structure and confining pressure–void ratio states. A novel bounding compression line (BCL) concept integrated with critical state soil mechanics provides a unified interpretation of these behaviours, offering improved predictive capability for deep clay layers. Mahopatra et al. (2026) address pavement deterioration on expansive soils, demonstrating through large-scale model tests that geotextile (GT) and geocell composite (GC) reinforcements (Figure 2) significantly reduce surface heave and improve efficiency under moderate to high swell pressures (400–800 kPa). The optimal configuration of high-tensile geotextile combined with deep geocells achieved up to 81% reduction in heave, enhancing unpaved road performance. Wang et al. (2026a) of Zhejiang explore sustainable reuse of construction or dredged slurry material as a road subgrade material with the use of curing agents. The waste slurry material is made into filter mudcakes through filtering and dewatering, but requires additional intervention to allow it to be suitable for use as an engineering fill. Wang et al. (2026b) found through centrifuge model tests that a composite curing agent consisting of 8% ground granulated blast-furnace slag, 4% quicklime and 1% gypsum was the most effective at improving strength (Uniaxial compressive strength = 2730 kPa), water stability (85%) and California bearing ratio (33.7% at 7 days), supporting the potential of filter mudcakes as an eco-friendly alternative for road subgrade.Finally, moving to the behaviour of piled foundations, it is pertinent that any numerical analysis is sense-checked against laboratory data, or better yet full-scale tests. Wu et al. (2026b) present a finite-element modelling approach for predicting the axial capacity of tapered steel piles in coarse-grained soils, incorporating jacking installation effects. Wang et al. (2026b) of Tongji University investigate the spatial geometry of horizontal soil arching between single-row piles under lateral loads through combined laboratory tests and three-dimensional numerical simulations, and Wagner et al. (2026) examine piled footings in lightly bonded residual soils through full-scale load tests and numerical modelling, showing that combined systems outperform isolated footings or piles by reducing settlements and increasing bearing capacity. Interaction effects reduce individual component efficiency, but correction factors allow reliable capacity prediction, supporting the use of piled footings as cost-effective foundations for low-cost housing in tropical regions.