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Glacier collapses can occur due to shear failure at the ice-rich debris–rock interface (IDRI). To examine the shear behavior of IDRI, shear tests were conducted on artificial IDRI specimens with varying ice contents (40%, 65%, and 90%), normal stresses (150, 250, 350, 450, and 550 kPa), and temperatures (−1, −3, −5, −7, and −9 °C). Our findings reveal that temperature has the most significant impact on both peak and residual shear strength, followed by normal stress and ice content. As the temperature increases from −9 to −1 °C, the peak and residual shear stress decreased by 62.5%–78%. Notably, for IDRI with the lowest ice content (40%), the residual shear stress is highly influenced by normal stress. We have developed an improved Mohr–Coulomb strength criterion of IDRI in which the cohesion and internal friction angle are determined by ice content and temperature. Furthermore, we propose a novel constitutive model, based on the disturbed state concept, to describe the shear behavior of IDRI. This model combines a spring model and a hyperbolic model. We also discuss the mechanisms through which ice content and temperature influence the shear deformation modes and shear strength of IDRI. [ABSTRACT FROM AUTHOR]

Ref.: Can. Geotech. J. 61(6): 1051–1072 (2024). https://doi.org/10.1139/cgj-2023-0375In the originally published article, the funding information published was incorrect. The correct statement is below, and the original article is corrected.Funding informationThis work is supported by the National Natural Science Foundation of China (Nos. U23A2047, 42277187, 42272331, and 42007276) and the Natural Science Foundation of Hebei Province (No. D2021202001).By Da Huang; Qiujie Meng; Yixiang Song; Dongming Gu; Duofeng Cen and Zhu ZhongReported by Author; Author; Author; Author; Author; Author [Extracted from the article]

Purpose: In order to improve the reconstruction accuracy of magnetic induction tomography (MIT) and achieve fast imaging especially in the detection of cerebral hemorrhage, artificial intelligence algorithms are proposed to improve the accuracy of MIT inverse problem. Methods: According to the standard geometric data of human head, a three‐dimensional (3D) head model containing four layer tissues is established for brain image reconstruction of MIT. Four deep learning (DL) networks, including restricted Boltzmann machine (RBM), deep belief network (DBN), stacked autoencoder (SAE), and denoising autoencoder (DAE), are used to solve the nonlinear reconstruction problem of MIT, and the reconstruction results of DL networks and back‐projection algorithm are compared. Finally, in order to verify the practical value of DL algorithms, the phantom experiment is carried out with MIT detection system. Results: Using the nonlinear data learning ability of DL algorithms, the rapid and high‐precision imaging of cerebral hemorrhage can be realized. Compared with the back‐projection algorithm, the DL improves the artifact and the accuracy of the reconstruction image. The location and volume of bleeding can be reconstructed and the prediction time reaches 20 ms. Moreover, the anti‐noise performance of the networks can reach 20 dB. Conclusions: The DL can effectively improve the reconstruction accuracy and prediction speed of the image when it is applied to the reconstruction of cerebral hemorrhage in MIT. This feasibility study MIT to be a potential technology for brain diseases to fully meet the needs of accurate, rapid, and low‐cost clinical diagnosis and continuous monitoring. [ABSTRACT FROM AUTHOR]