Yaming Tang

Loess landslides are major geohazards on the Chinese Loess Plateau, where rainfall and engineering activities commonly trigger slope failures that also drive slope‐to‐gully evolution. This study investigates the ZQ landslide (23 April 2023) in Xiangning County, Shanxi Province, using field surveys, UAV photogrammetry, SBAS‐InSAR, dynamic triaxial tests and finite‐element simulations to clarify deformation, material behaviour, geomorphic evolution and failure mechanisms. The landslide (200 m long, 308 m wide; 6.0 × 10 ⁴ m ² ) developed in a Quaternary loess–palaeosol sequence and showed episodic retrogression, producing three rear scarps. The sliding mass moved from one side of the gully toward the valley bottom, where the deposit partially occupied the gully and induced frontal bulging, showing local adjustment of the gully. SBAS‐InSAR revealed central displacement accumulation with peak line‐of‐sight displacement of −43.3 mm and a mean rate of −41 mm/year; deformation acceleration coincided with rainfall, especially the 71.6 mm total in April 2023. Dynamic triaxial tests show that saturated loess is sensitive to cyclic loading. Higher CSR and loading frequency accelerated plastic‐strain accumulation and dynamic strength degradation, providing laboratory evidence for cyclic‐load–induced weakening. Scenario‐based finite‐element modelling indicated progressive weakening under sustained rainfall. Under the coupled rainfall–vibration scenario, deformation became more toe‐controlled, with a maximum horizontal displacement of 4.28 m, toe uplift of 4.10 m and a factor of safety of 0.773. The development of a connected plastic zone indicates progressive failure and potential reactivation. The results suggest that rainfall‐driven infiltration softening within the loess–palaeosol stratigraphic structure was the main hydrological trigger, whereas mining‐related disturbance may have contributed to failure by promoting crack development, deformation accumulation and cyclic weakening of saturated loess. The landslide evolved through creep and cracking, crack propagation, shear localization, retrogressive sliding, frontal bulging, partial gully blockage and postfailure creep, linking slope failure with local gully evolution.

The earthquake landslide hazard assessment method is mainly based on the traditional Newmark model. However, when the landslide hazard assessment is carried out along the fault zone, the calculated results are often different from the actual situation because the influence of fault effect is not fully considered. Therefore, how to construct a landslide hazard assessment model suitable for the fault zone is a technical problem to be solved by researchers. Taking the Lixian–Luojiabu fault zone in Gansu Province in China as the study area, this paper put forward the concept of fault effect correction coefficient exploringly, systematically studied the relative distance relationship between the landslide and fault zone, and the relative position relationship between landslide and upper and lower sides of the fault zone. The value table of the fault effect correction coefficient along the Lixian–Luojiabu fault zone was established, and the corresponding distribution map of the fault effect correction coefficient was drawn. Based on this, an improved Newmark model for the landslide hazard assessment along the fault zone was constructed. On the basis of systematic analysis of the slope and engineering geological rock group in the study area, the traditional Newmark model and improved Newmark model considering fault effects were used, respectively, to carry out the earthquake landslide hazard assessment under the condition of 10% exceeding probability in 50 years, and the ROC curve and Kappa coefficient methods were used to compare and analyze the evaluation results. The results showed that the AUC value and Kappa coefficient of the danger area obtained by the improved model with the Newmark model were 0.841 and 0.822, respectively, which were significantly higher than the calculated values of the traditional Newmark model, indicating that the model had a good improvement effect. The Newmark improved model, considering the fault effect, fully considered the influence of distance from the fault zone and fault upper and lower side effects, and the research results can provide a new reference for the landslide hazard assessment along the fault zone.

Many popular models have been proposed to study the fractal properties of the pores of porous materials based on mercury intrusion porosimetry (MIP). Howev

Traditional landslide hazard assessment methods generally use the same evaluation model to carry out assessments under different working conditions. Due to the differences in landslide influence factors and model calculation methods considered under different working conditions, the evaluation results obtained using traditional methods are different from those of real-world scenarios. Therefore, research on optimal landslide hazard assessment methods for different working conditions is important for disaster prevention and mitigation in areas along fault zones. Taking the section along the Lixian–Luojiabu fault zone in Gansu province in China as the research area, a landslide hazard assessment was carried out under rainfall and earthquake conditions. A method based on the fractal theory–information coupling model is proposed for the rainfall condition, and a method based on an improved Newmark model considering matric suction is proposed for the earthquake condition. Under the rainfall condition, a landslide hazard assessment was carried out using the information model, the logistic regression model, the fractal theory model, the logistic regression–information coupling model and the fractal theory–information coupling model. Meanwhile, under the earthquake condition, an assessment was carried out using the traditional Newmark model and the improved Newmark model considering matric suction. Finally, the ROC curve and Kappa coefficient were used to test the accuracy of these evaluation models and to determine the optimal model under different working conditions. The results showed that the fractal theory–information coupling model had the largest AUC value and Kappa coefficient value under the rainfall condition (0.856 and 0.807, respectively). The test value of the logistic regression–information coupling model was second, and the values of the other three models were all lower than 0.8. This shows that the evaluation of the fractal theory–information coupling model is better than those of the other models under the rainfall condition. The AUC value and Kappa coefficient of the improved Newmark model under the earthquake condition were 0.805 and 0.794, respectively, which were larger than the test values of the traditional Newmark model. This shows that the evaluation of the proposed model is better than that of the traditional Newmark model under the earthquake condition. These research results provide a reference for landslide hazard assessments in areas with similar characteristics.

Software bug localization aiming to determine the locations needed to be fixed for a bug report is one of the most tedious and effort consuming activities in software debugging. Learning-to-rank (LR) is the state-of-the-art approach proposed by Ye et al. to recommending relevant files for bug localization. Ye et al.'s experimental results show that the LR approach significantly outperforms previous bug localization approaches in terms of "precision" and "accuracy". However, this evaluation does not take into account the influence of the size of the recommended files on the efficiency in detecting bugs. In practice, developers will generally spend more code inspection effort to detect bugs if larger files are recommended. In this paper, we use six large-scale open-source Java projects to evaluate the LR approach in the context of effort-aware bug localization. Our results, surprisingly, show that, when taking into account the code inspection effort to detect bugs, the LR approach is similar to or even worse than the standard VSM (Vector Space Model), a naïve IR-based bug localization approach.