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Cracking resulting from drying (constrained dehydration) poses a significant challenge in geomaterials, impacting their mechanical performance. To address this problem, extensive efforts have been made to prevent or mitigate the occurrence of cracks, with recent attention focused on the utilisation of biopolymers. This letter investigates the influence of varying concentrations of the xanthan biopolymer on the mechanical response of granular materials, examining both macro and micro scales. The strength changes of the soil were evaluated through desiccation experiments, analysing the appearance and progression of failure on the macro scale. The findings of this study demonstrate that failure (cracking) progression is mitigated and eventually eliminated by increasing the concentration of the additive xanthan. Additionally, capillary experiments were conducted to assess the changes in attraction and the development of capillary bridges on the micro-scale. They indicate that the formation of hydrogel bridges significantly enhances particle attraction, thereby increasing its macro-resistance to cracking. 

Abstract Current earthquake forecasting approaches are mainly based on probabilistic assumptions, as earthquakes seem to occur randomly. Such apparent randomness can however be caused by deterministic chaos, rendering deterministic short‐term forecasts possible. Due to the short historical and instrumental record of earthquakes, chaos detection has proven challenging, but more frequently occurring slow slip events (SSE) are promising candidates to probe for determinism. Here, we characterize the SSE signatures obtained from GNSS position time series in the Hikurangi Subduction Zone (New Zealand) to investigate whether the seemingly random SSE occurrence is governed by chaotic determinism. We find evidence for deterministic chaos for stations recording shallow SSEs, suggesting that short‐term deterministic forecasting of SSEs, similar to weather forecasts, might indeed be possible over timescales of a few weeks. We anticipate that our findings could open the door for next‐generation SSE forecasting, adding new tools to existing probabilistic approaches.