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This study uses a data-driven approach to address the complexities associated with research focused multi-sleeve Cone Penetration Test (CPT) devices, particularly focusing on the multi-friction attachment (MFA) and multi-piezo-friction attachment (MPFA) CPT devices. Hindered by time-consuming assembly and susceptibility to sensor stream losses due to extensive electronic components, these advanced devices demand optimization to transform from research devices to practice-suitable devices. This study aims at optimizing the design of the multi-sleeve CPT devices using machine learning, with soil type classification performance as the primary metric for device configuration effectiveness. The research scope centers not on using machine learning for soil classification but on refining the design of multi-sleeve CPT devices. A two-phase data-driven approach is adopted, testing various feature combinations across eight machine learning models. The first phase involves identifying the most suitable model for the dataset, followed by a refinement of features to balance sensor number minimization and soil classification accuracy. The result is a proposed configuration for a multi-sleeve CPT device, simplifying the original design while maintaining robustness, thereby enhancing cost-efficiency and operational effectiveness in geotechnical practice. This work sheds light on how the integration of machine learning can guide the design optimization of geotechnical instruments. [ABSTRACT FROM AUTHOR]

Quantification of particle shape features to characterize granular materials remains an open problem till date, owing to the complexity involved in obtaining the geometrical parameters necessary to adequately compute the shape components (sphericity, roundness and roughness). A new computational method based on image analysis and filter techniques is proposed in this paper to overcome this difficulty. In this method, operations are performed on binary images of particles obtained from raster images (collection of pixels) by the process of image segmentation. The boundary of particles captured in 2D images consist of micro, meso and macro scale features on which filter techniques are applied to remove the micro level features for the quantification of particle roughness and to obtain a roughness free boundary. A robust algorithm is then written and implemented in MATLAB to obtain the complete geometry of the particle boundary (free from roughness features) and to identify the precise corner and non-corner regions along the boundary. This information is used to quantify the roundness (as per Wadell in J Geol 40:443-451, 1932) and sphericity of particles. The proposed methodology to measure roundness and sphericity is compared against standard visual charts provided by earlier researchers. Finally, the methodology is demonstrated on real soil particles falling across a wide range of sizes, shapes and mineralogical compositions. Also, an idea to comprehend the kinematics of particle motion based on its concavo-convex features is discussed with two proposed novel descriptors and a visual classification chart. [ABSTRACT FROM AUTHOR]

The interplay between pore body and throat geometries in particulate material microstructure governs the accurate analysis of macro-scale constitutive relationships, fluid flow properties, and deformation response of sheared assemblies, among several others. However, classical pore models generally assume simplified pore and throat geometries, primarily due to the historical lack of tools necessary to adequately characterize the complex pore space architecture. This paper presents a novel geometrical-based approach to discretizing the pore space of particulate microstructure into a three-dimensional network of pore bodies interconnected by throats without assumptions about their geometries. Post-discretization, microstructural parameters like sizes and shapes of pore bodies and throats, sharpness of pore throats, and geometrical tortuosity of the media are extracted and analyzed. The method is validated against idealized packed structures and demonstrated on synthetic particle systems and digitally reconstructed sand microstructures. Furthermore, the ability of the method to capture shear-induced localized changes in sands is demonstrated. [Display omitted] • An operator-independent algorithm is presented for 3D pore space characterization. • Pore bodies and throats are segmented based on the pore space architecture geometry. • The algorithm has no predefined shape assumptions as necessary in competing methods. • The method is applicable to microstructures with any particle shape or gradation. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]