In the study of the seepage characteristics of layered rock slope under rainfall conditions, the majority of previous research has considered the hydraulic conduction to be isotropic, or only considered the anisotropy ratio of the hydraulic conductivity, ignoring the anisotropy angle. In the current study, a layered rock slope in the Pulang region was selected as an example. Then, based on the fitting parameters of the Van Genuchten model, pore water pressure sensitivity analyses of the layered rock slope were carried out. The anisotropy ratio and anisotropy angle were used to analyze the sensitivity of the seepage and stability of the layered rock slopes. The results show that as the anisotropy angle of hydraulic conductivity of layered rock slope decreased, the maximum volume water content of surface (MWCS) of layered rock slope gradually increased. Additionally, as the anisotropy ratio decreased and the anisotropy angle increased, the rising heights of the groundwater (RHG) of layered rock slope gradually increased. When the hydraulic conduction of layered rock slope was considered isotropic, the factor of safety (FS) tended to be overestimated. As the anisotropy ratio decreased and the anisotropy angle increased, the factor of safety (FS) of layered rock slope decreased. Prevention should be the objective for rock slopes with larger dip angles in the bedding plane in the Pulang region. This study provides feasible schemes for the evaluation of the seepage and stability of layered rock slopes in Pulang region of southwestern China.
Along with the development of computer science and communication techniques, tunnel monitoring has been digital and intellectualized gradually. In recent years, traditional tunnel monitoring systems have been becoming powerless to deal with huge amounts of data as the number of tunnels and monitoring sensors surge. In this study, an innovative and comprehensive intelligent tunnel monitoring system was designed based on micro-service architecture. Various function modules were distributed to multiple service instances to get the computing pressure decentralized. The system offered powerful massive data processing support, flexibility, and extensibility. For a certain tunnel, the system could acquire the monitoring data in real-time using wireless sensors deployed on its surface. When data reached the early warning threshold, the system would immediately transmit the warning message to users. Besides, an app was developed to offer support for wireless communication between the monitoring apparatus and watchers. The application of the Anping case, one of the monitoring targets, proved the high efficiency of data transmitting and effective emergency response of the developed system. The results indicated that the microservice architecture could facilitate algorithm development, decentralize calculation pressure, and promote the service capacity by decoupling components and distributing loads.
Using multi-source monitoring data to model and predict the displacement behavior of landslides is of great significance for the judgment and decision-making of future landslide risks. This research proposes a landslide displacement prediction model that combines Variational Mode Decomposition (VMD) and the Long and Short-Term Time-Series Network (LSTNet). The bootstrap algorithm is then used to estimate the Prediction Intervals (PIs) to quantify the uncertainty of the proposed model. First, the cumulative displacements are decomposed into trend displacement, periodic displacement, and random displacement using the VMD with the minimum sample entropy constraint. The feature factors are also decomposed into high-frequency components and low-frequency components. Second, this study uses an improved polynomial function fitting method combining the time window and threshold to predict trend displacement and uses feature factors obtained by grey relational analysis to train the LSTNet networks and predict periodic and random displacements. Finally, the predicted trend, periodic, and random displacement are summed to the predicted cumulative displacement, while the bootstrap algorithm is used to evaluate the PIs of the proposed model at different confidence levels. The proposed model was verified and evaluated by the case of the Baishuihe landslide in the Three Gorges reservoir area of China. The case results show that the proposed model has better point prediction accuracy than the three baseline models of LSSVR, BP, and LSTM, and the reliability and quality of the PIs constructed at 90%, 95%, and 99% confidence levels are also better than those of the baseline models.
Although electrical resistivity tomography (ERT) may gather the internal resistivity information from a landslide area in a large-scale, low-cost, and non-invasive manner compared to point-based sensor monitoring technology, the indirect resistivity information obtained cannot directly evaluate the landslide’s current mechanical status, such as stress, strength, etc. Based on ERT monitoring data, a framework for quantitatively and directly evaluating the evolution of the factor of safety (FOS) of landslides during rainfall is proposed. The framework first inverts ERT observation data using the inexact Gauss–Newton method based on multiple constraints to obtain a more realistic resistivity distribution, then calculates the saturation distribution using Archie’s equation, and finally calculates the FOS of landslides using the finite element strength reduction method. Twelve sets of numerical experiments were designed and carried out based on the synthetic data of a theoretical model. The experimental results show that the proposed framework is valid and reliable under various arrays, apparent resistivity noise, and uncertainty in the water-electric correlation curve, with the Dipole-Dipole array outperforming the others in terms of accuracy, sensitivity, and anti-noise capability. The proposed framework is significant in improving ERT monitoring and early warning capabilities for rainfall-induced landslides.
Numerical simulation has emerged as a powerful technique for landslide failure mechanism analysis and accurate stability assessment. However, due to the bias of simplified numerical models and the uncertainty of geomechanical parameters, simulation results often differ greatly from the actual situation. Therefore, in order to ensure the accuracy and rationality of numerical simulation results, and to improve landslide hazard warning capability, techniques and methods such as displacement back-analysis, machine learning, and numerical simulation are combined to create a novel landslide warning method based on DBA-LSTM (displacement back-analysis based on long short-term memory networks), and a numerical simulation algorithm is proposed, i.e., the DBA-LSTM algorithm is used to invert the equivalent physical and mechanical parameters of the numerical model, and the modified numerical model is used for stability analysis and failure simulation. Taking the Shangtan landslide as an example, the deformation mechanism of the landslide was analyzed based on the field monitoring data, and subsequently, the superiority of the DBA-LSTM algorithm was verified by comparing it with DBA-BPNN (displacement back-analysis based on back-propagation neural network); finally, the stability of the landslide was analyzed and evaluated a posteriori using the warning threshold calculated by the proposed method. The analytical results show that the displacement back-analysis based on the machine learning (DBA-ML) algorithm can achieve more than 95% accuracy, and the deep learning algorithm exemplified by LSTM had higher accuracy compared to the classical BPNN algorithm, meaning that it can be used to further improve the existing intelligent inversion theory and method. The proposed method calculates the landslide’s factor of safety (FOS) before the accelerated deformation to be 1.38 and predicts that the landslide is in a metastable state after accelerated deformation rather than in failure. Compared to traditional empirical warning models, our method can avoid false warnings and can provide a new reference for research on landslide hazard warnings.
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