Pore scale modeling plays a key role in fluid flow through porous media and associated macroscale constitutive relationships. The polyhedral shape and effective local pore size within granular material microstructure are computed in this study by means of the Euclidean Distance Transform (EDT), a local maxima search (non-maximum suppression), and a segmentation process. Various synthetic packed particles are simulated and employed as comparative models during the computation of pore size distribution (PSD). Reconstructed un-sheared and sheared Ottawa 20–30 sand samples are used to compute PSD for non-trivial and non-spherical models.

With no more than a couple of thousand years of experience, humankind has developed some innovative techniques to leverage the subsurface for a variety of beneficial functions. In contrast, nature has had the benefit of several billion years to initially design and subsequently evolve the manner in which flora and fauna practices soil mechanics. This paper uses two examples of biota to compare how nature has evolved its solutions in contrast to what humans have done and identifies enhancements that humans could exploit through a deliberate mimicking of what nature has done (i.e. bioinspiration). In particular, a comparison of selected aspects of ant-soil interaction and root-soil interaction are used to illustrate where significant potential exists in the emerging field of bio-geotechnics. The paper describes the salient characteristics of the framework by which nature designs its technology, and in turn, a methodology by which humans can augment our current design processes.

This research explores the utilization of the Black Marble nighttime light (NTL) product to detect and assess damage caused by hurricanes, tornadoes, and earthquakes. The study first examines average regional NTL trends before and after each disaster, demonstrating that NTL patterns for hurricanes closely align with the features of a resilience curve, unlike those for earthquakes and tornadoes. The relative NTL change ratio is computed using monthly and daily NTL data, effectively reducing variance due to daily fluctuations. Results indicate the robustness of the NTL change ratio in detecting hurricane damage, whereas its performance in earthquake and tornado assessment was inconsistent and inadequate. Furthermore, NTL demonstrates a high performance in identifying hurricane damage in well-lit areas and the potential to detect damage along tornado paths. However, a low correlation between the NTL change ratio and the degree of damage highlights the method’s limitation in quantifying damage. Overall, the study offers a promising, prompt approach for detecting damaged/undamaged areas, with specific relevance to hurricane reconnaissance, and points to avenues for further refinement and investigation.

Limited labeled imagery and tight response windows hinder the accurate damage quantification for post-disaster assessment. The objective of this study is to develop and evaluate a deep learning-based Domain-Adaptive Segmentation (DASeg) workflow to detect post-disaster damage using limited information available shortly after an event. DASeg unifies three Vision Foundation Models in an automatic workflow: fine-tuned DINOv2 supplies attention-based point prompts, fine-tuned Grounding DINO yields open-set box prompts, and a frozen Segment Anything Model (SAM) generates the final masks. In the earthquake-focused case study DASeg-Quake, the pipeline boosts mean Intersection over Union (mIoU) by 9.52% over prior work and 2.10% over state-of-the-art supervised baselines. In a zero-shot setting scenario, DASeg-Quake achieves the mIoU of 75.03% for geo-damage analysis, closely matching expert-level annotations. These results show that DASeg achieves superior workflow enhancement in infrastructure damage segmentation without needing pixel-level annotation, providing a practical solution for early-stage disaster response.