The analysis and design of energy geostructures are mainly characterised by the mechanical behaviour of the soil–structure interface in non-isothermal conditions. In this study, direct shear tests are conducted to investigate the shear behaviour of soil and soil–structure interface in the practical temperature range of energy geostructures (i.e., 8–45 °C). The interface in this study is formed of kaolin in contact with concrete specimens with different roughness. Tests are performed on normally consolidated and overconsolidated interfaces following the unloading/reloading paths to better understand the impact of thermal strain on the interface behaviour. The volumetric response of the interface is observed to be highly influenced by the thermal strains experienced during heating/cooling. The soil stress level and the most recent soil stress history are identified as the primary determinants of thermally induced changes in interface shear strength. For normally consolidated interfaces, the temperature increase led to higher adhesion and slightly lower friction angle, whereas higher adhesion and identical friction angles were found for tests conducted on cooled specimens. Temperature does not seem to affect the shear strength of overconsolidated interfaces. Finally, a conceptual understanding of the temperature effect on interface shear behaviour is provided by analysing data from the literature.

Particulate materials, such as sandy soil, are everywhere in nature and form the basis for many engineering applications. The aim of this research is to investigate the particle shape, size, and gradation of sandy soil and how they relate to shear strength, which is an essential characteristic that impacts soil stability and mechanical behaviour. This will be achieved by employing a combination of experimental methodology, which includes the use of a microscope direct shear apparatus, and machine learning techniques, namely multiple linear regression and random forest regression. The experimental findings reveal that angular-shaped sand particles enhance the shear strength characteristics compared to spherical, rounded ones. Similarly, coarser sand particles improve these characteristics compared to finer sand particles, as do well-graded particles when compared to poorly graded ones. The machine learning findings show the validity of both models in predicting shear strength when compared to the experimental results, showing high accuracy. The models are designed to predict shear strength of sand considering six input features: mean particle size, uniformity coefficient, curvature coefficient, dry density, normal stress, and particle regularity. The most important features from both models were identified. In addition, an empirical equation for calculating shear strength was developed through multiple linear regression analysis using the six features.

Unfired bricks offer a sustainable alternative to traditional fired bricks by enabling the large-scale reuse of industrial, construction, and municipal wastes while significantly reducing energy consumption and greenhouse gas emissions. This review contributes to eliminating knowledge fragmentation by systematically organising stabiliser technologies, performance metrics, and sustainability indicators across a wide variety of unfired brick systems. It thus provides a coherent reference framework to support further development and industrial translation. Emphasis is placed on the role of stabilisers—including cement, lime, geopolymers, and microbial or bio-based stabilisers—in improving mechanical strength, moisture resistance, and durability. Performance data are analysed in relation to compressive strength, water absorption, drying shrinkage, thermal conductivity, and resistance to freeze–thaw and wet–dry cycles. The findings indicate that properly stabilised unfired bricks can achieve compressive strengths above 20 MPa and water absorption rates below 10%, with notable improvements in insulation and acoustic properties. Additionally, life-cycle comparisons reveal up to 90% reductions in CO₂ emissions and energy use relative to fired clay bricks. Despite technical and environmental advantages, broader adoption remains limited due to standardisation gaps and market unfamiliarity. The paper concludes by highlighting the importance of hybrid stabiliser systems, targeted certification frameworks, and waste valorisation policies to support the transition toward low-carbon, resource-efficient construction practices.

Alum sludge is a byproduct of water treatment plants, and its use as a soil stabilizer has gained increasing attention due to its economic and environmental benefits. Its application has been shown to improve the strength and stability of soil, making it suitable for various engineering applications. However, to go beyond just measuring the effects of alum sludge as a soil stabilizer, this study investigates the potential of artificial intelligence (AI) methods for predicting the California bearing ratio (CBR) of soils stabilized with alum sludge. Three AI methods, including two black box methods (artificial neural network and support vector machines) and one grey box method (genetic programming), were used to predict CBR, based on a database with nine input parameters. The results demonstrate the effectiveness of AI methods in predicting CBR with good accuracy (R² values ranging from 0.94 to 0.99 and MAE values ranging from 0.30 to 0.51). Moreover, a novel approach, using genetic programming, produced an equation that accurately estimated CBR, incorporating seven inputs. The analysis of parameter sensitivity and importance, revealed that the number of hammer blows for compaction was the most important parameter, while the parameters for maximum dry density of soil and mixture were the least important. This study highlights the potential of AI methods as a useful tool for predicting the performance of alum sludge as a soil stabilizer.

The accurate prediction of the compression index (C_c) is crucial for understanding the settlement behavior of clayey soils, which is a key factor in geotechnical design. Traditional empirical models, while widely used, often fail to generalize across diverse soil conditions due to their reliance on simplified assumptions and regional dependencies. This study proposed a novel hybrid method combining Genetic Programming (GP) and XGBoost methods. A large database (including 385 datasets) of geotechnical properties, including the liquid limit (LL), the plasticity index (PI), the initial void ratio (e₀), and the water content (w), was used. The hybrid GP-XGBoost model achieved remarkable predictive performance, with an R² of 0.966 and 0.927 and mean squared error (MSE) values of 0.001 and 0.001 for training and testing datasets, respectively. The mean absolute error (MAE) was also exceptionally low at 0.030 for training and 0.028 for testing datasets. Comparative analysis showed that the hybrid model outperformed the standalone GP (R² = 0.934, MSE = 0.003) and XGBoost (R² = 0.939, MSE = 0.002) models, as well as traditional empirical methods such as Terzaghi and Peck (R² = 0.149, MSE = 0.090). Key findings highlighted that the initial void ratio and water content are the most influential predictors of C_c, with feature importance scores of 0.55 and 0.27, respectively. The novelty of the proposed method lies in its ability to combine the interpretability of GP with the computational efficiency of XGBoost and results in a robust and adaptable predictive tool. This hybrid approach has the potential to advance geotechnical engineering practices by providing accurate and interpretable models for diverse soil profiles and complex site conditions.