Prediction of Permeability Coefficient k in Sandy Soils Using ANN

How scanning probe microscopy can be supported by artificial intelligence and quantum computing?

The impact of Artificial Intelligence (AI) is rapidly expanding, revolutionizing both science and society. It is applied to practically all areas of life, science, and technology, including materials science, which continuously requires novel tools for effective materials characterization. One of the widely used techniques is scanning probe microscopy (SPM). SPM has fundamentally changed materials engineering, biology, and chemistry by providing tools for atomic-precision surface mapping. Despite its many advantages, it also has some drawbacks, such as long scanning times or the possibility of damaging soft-surface materials. In this paper, we focus on the potential for supporting SPM-based measurements, with an emphasis on the application of AI-based algorithms, especially Machine Learning-based algorithms, as well as quantum computing (QC). It has been found that AI can be helpful in automating experimental processes in routine operations, algorithmically searching for optimal sample regions, and elucidating structure-property relationships. Thus, it contributes to increasing the efficiency and accuracy of optical nanoscopy scanning probes. Moreover, the combination of AI-based algorithms and QC may have enormous potential to enhance the practical application of SPM. The limitations of the AI-QC-based approach were also discussed. Finally, we outline a research path for improving AI-QC-powered SPM. RESEARCH HIGHLIGHTS: Artificial intelligence and quantum computing as support for scanning probe microscopy. The analysis indicates a research gap in the field of scanning probe microscopy. The research aims to shed light into ai-qc-powered scanning probe microscopy.

Analysis on Response of a Single Pile Subjected to Tension Load Considering Excavation Effects

With the development of urbanization, numerous excavations are carried out to facilitate the development of underground space. As a support for tunnel structures, uplift piles are often installed prior to tunnel excavation. The excavation inevitably causes disturbance to the soil below the excavation surface, changing the soil’s mechanical behavior and stress state significantly. However, there is still a lack of a method to evaluate the change in pile capacity due to excavation. This paper proposes a semi-analytical approach for estimating the change in load-settlement behavior of an uplift pile considering the effects of excavation. A hyperbolic model was adopted to simulate the nonlinear interaction of the pile–soil interface. The nonlinear shear-induced soil displacement outside the pile–soil interface is introduced to obtain a more realistic load-displacement behavior of the uplift pile. An effective iterative program was implemented based on the proposed semi-analytical approach. The comparisons between the results from the proposed methods, well-documented field tests, centrifuge tests, and other analytical methods showed that the proposed approach is suitable for analyzing an uplift pile considering excavation effects. A parametric study was conducted to investigate the effects of main soil properties on the pile capacity loss caused by excavation. The results showed that the soil friction angle and the ratio of the excavation depth to the pile effective length have a great influence on the loss in pile uplift capacity caused by excavation. However, the loss of pile uplift capacity caused by excavation is not affected by the soil’s shear modulus or Poisson’s ratio.