Actual Failure Research Articles

Exploring failure evolution of anti-dip slate slope using centrifuge test and discrete element method

Toppling failure commonly occurs in anti-dip slate slopes due to foliation splitting and gravitational deformation. The study uses centrifuge tests and the discrete element method (DEM) to investigate the influence of foliation and existing fractures on the toppling failure evolution of anti-dip slopes. Six centrifuge tests with slate blocks obtained from an actual slope were carried out. Then, a proposed foliation model was implemented in the DEM software 3DEC to simulate the failure evolution of anti-dip slopes. The 3DEC analysis was validated by the actual failure pattern of slopes in centrifuge tests. The results indicate that the toppling failure of the anti-dip slope was initiated by existing fractures rather than the original cohesive foliation. Though the slate foliation is regarded as a weak plane in the rock mass, it retains a higher strength than the existing fracture, so the toppling failure is difficult to initiate from the cohesive foliation. The closer the fracture is to the free surface, the more pronounced the damage. In addition, the simulation indicates that the existing fracture's position also affects the anti-dip slope's failure degree. The fractures on the top propagate more easily than those on the bottom.

Towards more realistic stochastic modelling of leakage in water distribution systems

ABSTRACT A new stochastic model is developed to predict leak types and sizes in water distribution systems. By linking records of actual failures in water networks with new tools for stochastic modelling, the study provides more accurate estimates of network leakage behaviour. District metered areas (DMAs) have been adopted internationally as best practice for monitoring and controlling leakage, with the infrastructure leakage index (ILI) as the main performance indicator. The leakage exponent (N1), which quantifies the relationship between leakage rate and pressure, is a key parameter in describing DMA leakage behaviour. After analysing thousands of stochastic networks for different combinations of pipe material and ILI, the study found that different pipe materials have distinct leakage exponent distributions. Iron and asbestos cement (AC) pipes display N1 values close to 0.5, polyvinyl chloride (PVC) pipes have the highest N1 values, and polyethylene (PE) lies between PVC and iron/AC. The study further showed that current guidelines linking ILI to N1 for different pipe materials depart substantially from the observed results, indicating that these guidelines may need to be updated. The study provides a tool for more realistic modelling of leakage that offers valuable insights into the behaviour of leakage in different pipe materials.

Deep learning-based anomaly detection using one-dimensional convolutional neural networks (1D CNN) in machine centers (MCT) and computer numerical control (CNC) machines

Computer numerical control (CNC) and machine center (MCT) machines are mechanical devices that manipulate different tools using computer programming as inputs. Predicting failures in CNC and MCT machines before their actual failure time is crucial to reduce maintenance costs and increase productivity. This study is centered around a novel deep learning-based model using a 1D convolutional neural network (CNN) for early fault detection in MCT machines. We collected sensor-based data from CNC/MCT machines and applied various preprocessing techniques to prepare the dataset. Our experimental results demonstrate that the 1D-CNN model achieves a higher accuracy of 91.57% compared to traditional machine learning classifiers and other deep learning models, including Random Forest (RF) at 89.71%, multi-layer perceptron (MLP) at 87.45%, XGBoost at 89.67%, logistic regression (LR) at 75.93%, support vector machine (SVM) at 75.96%, K-nearest neighbors (KNN) at 82.93%, decision tree at 88.36%, naïve Bayes at 68.31%, long short-term memory (LSTM) at 90.80%, and a hybrid 1D CNN + LSTM model at 88.51%. Moreover, our proposed 1D CNN model outperformed all other mentioned models in precision, recall, and F-1 scores, with 91.87%, 91.57%, and 91.63%, respectively. These findings highlight the efficacy of the 1D CNN model in providing optimal performance with an MCT machine’s dataset, making it particularly suitable for small manufacturing companies seeking to automate early fault detection and classification in CNC and MCT machines. This approach enhances productivity and aids in proactive maintenance and safety measures, demonstrating its potential to revolutionize the manufacturing industry.

Seismic vulnerability analysis of hospitals and school buildings considering the Gaussian regression algorithm

This study combines the Gaussian regression algorithm to propose a nonlinear regression model that can be used to evaluate the seismic vulnerability of regional hospitals and school buildings. The failure features and disaster mechanisms of hospital and school buildings affected by the Jishishan (JSS) earthquake on December 18, 2023, and the Wenchuan (WC) earthquake on May 12, 2008, were reported. The samples of 252 damaged buildings (37 hospitals and 215 schools) in Dujiangyan city affected by the Wenchuan earthquake for which the author participated in the survey were collected and counted. Hospitals and school buildings were classified according to the types of reinforced concrete (RC) and masonry structures, and earthquake vulnerability probability matrices for hospital and school building clusters were established considering the actual failure probabilities. A total of 2103,213 acceleration records (from the Wenchuan earthquake) monitored by 12 real stations were selected. Using dynamic time history and response spectrum analysis methods, time history and spectrum curves were generated, considering the influence of different seismic directions. Using the developed Gaussian regression model, vulnerability comparison curves and parameter matrices considering multiple regression algorithms were generated. This paper considers the effects of the construction year and number of floors on the seismic vulnerability of hospital and school buildings. Seismic vulnerability curves and failure probability matrices were generated and established on the basis of actual structural failure probabilities. According to regression and vulnerability surface analysis, the evaluation results of the proposed model are highly consistent with actual field observations.

The Construction of a Small-Caliber Barrel Wear Model and a Study of the Barrel Wear Rule

The wear of small-caliber barrels is one of the key factors affecting barrel life. Based on the Archard wear model, a high-temperature pin plate wear experiment was carried out, and wear models of chrome-plated layers and gun barrel materials were established. In addition, a finite element model of the interaction between the bullet and the barrel was established. The movement of the projectile along the barrel was simulated and analyzed, and the force distribution of the spatial geometry structure of the rifling was mastered through simulation. The wear law of the gun barrel along the axial direction was obtained based on the wear model of the chrome-plated layer and gun barrel material. A position 100 mm away from the barrel breech wears very fast; this position is where the cone of the bullet is engraved in the barrel. At the position 150–350 mm away from the barrel breech, the barrel bore wears even faster. The barrel chrome layer is mainly affected by the gunpowder impact and projectile engraving, which is consistent with the actual failure of the coating. When the distance to the barrel breech is 350 m, the wear becomes stable. Through an analysis of the diameter of the barrel, it was found that, when the diameter of the barrel exceeded 12.85 mm, the barrel reached the end of its life.

Temporal forecasting by converting stochastic behaviour into a stable pattern in electric grid

The malfunction variables of power stations are related to the areas of weather, physical structure, control, and load behavior. To predict temporal power failure is difficult due to their unpredictable characteristics. As high accuracy is normally required, the estimation of failures of short-term temporal prediction is highly difficult. This study presents a method for converting stochastic behavior into a stable pattern, which can subsequently be used in a short-term estimator. For this conversion, K-means clustering is employed, followed by long-short-term memory and gated recurrent unit algorithms are used to perform the short-term estimation. The environment, the operation, and the generated signal factors are all simulated using mathematical models. Weather parameters and load samples have been collected as part of a dataset. Monte-Carlo simulation using MATLAB programming has been used to conduct experimental estimation of failures. The estimated failures of the experiment are then compared with the actual system temporal failures and found to be in good match. Therefore, to address the gap in knowledge for any future power grid estimated failures, the achieved results in this paper form good basis for a testbed to estimate any grid future failures.

Determining Quasi-Static Load Carrying Capacity of Composite Sandwich Rotor Blades for Copter-Type Drones

The development of light composite rotor blades with acceptable load carrying capacity is an essential issue to be dealt with in the design of relatively large copter-type drones. In this paper, a method is established to determine the quasi-static blade load carrying capacity which is vital to drone reliability. The proposed method, which provides a systematic procedure to determine blade load carrying capacity, consists of three parts, namely, a procedure to determine the distributed quasi-static blade aerodynamic load via the Blade Element Momentum (BEM) approach, a finite element-based failure analysis method to identify the actual blade failure mode, and an optimization method to determine the actual blade load carrying capacity. The experimental failure characteristics (failure mode, failure thrust, failure location) of two types of composite sandwich rotor blades with different skin lamination arrangements have been used to verify the accuracy of the theoretical results obtained using the proposed load carrying capacity determination method. The skin lamination arrangement for attaining the optimal blade-specific load carrying capacity and the blade incipient rotational speed for safe drone operation has been determined using the proposed method.

MOURNING THE LOSS OF THE IDEAL SELF: SHORT‐TERM WORK WITH A TRANS PATIENT POST‐TRANSITION

AbstractMany individuals who have been through transition struggle to obtain the necessary medical and psychological support. This paper explores the importance of psychological support for post‐transition individuals. In my experience, there is a subgroup of patients who struggle to come to terms with life post‐transition, particularly the losses involved. They remain stuck in the mourning process. There is a loss of fantasies regarding an ideal transition, and the gap between the hoped‐for transition outcomes and the post‐transition reality can be painfully large. In addition, issues that the transition was meant to address remain in some form for some people, and they may also be haunted by misgivings about how the transition occurred. This paper employs a heavily anonymised composite case to illustrate and elaborate on how these issues emerged and were dealt with in the context of a psychotherapeutic process. Working through issues that led to transition and grievances about perceived and actual failures in care from the past allowed the patient to mourn the loss of her pre‐transition image. The patient was able to come to terms with the reality of her transfer from male to trans‐female and her body and life post‐transition and to shift from a preoccupation with the past to move on with her life.

A Review of Training Procedures for Simulated Engine Failure after Take-Off Exercises with Twin-Engine Aircraft under 5700 ft

Engine failure after take-off (or one engine being inoperative) is an exercise conducted as part of multi-engine flight training and on-going competency checking. To prepare pilots to manage a real in-flight emergency, this exercise has traditionally been conducted immediately after take-off. This has led to increased risks of fatal accidents due to the reduced height at which these exercises are typically conducted. Yet, there is variation in the heights stipulated in training procedures published by different stakeholders worldwide. Additionally, the conduct of the exercise has resulted in fatal accidents worldwide. This paper aims to review the previous literature on aviation training and aviation occurrence data to determine what empirical data exists to support the method of conducting simulated engine failures. Peer-reviewed academic publications on aviation training, aviation occurrence databases such as aviation investigation reports, and guidance materials published by aviation authorities on simulated training exercises will be included in this paper. It was found that the previous research on these exercises has focused on the transfer of motion cues or pilot responses to abnormal situations, but did not include specific data comparing pilot performance at different heights above ground level. A review of aviation occurrences found that actual engine failures occurred at higher heights that those used in simulated engine failures. A comparison of the guidance published by aviation authorities identified variations in the minimum altitude published and differing justifications for the minimum height chosen. Future research is needed to compare pilot performance during simulated engine failures to determine the ideal height to conduct the exercise to be representative of an actual engine failure while maintaining safety margins.

Mechanical Properties and Mesoscopic Numerical Simulation of Local Weakening in High-Performance Concrete after 10 Years of Alkali Solution Immersion

The natural environment in the high-altitude regions of Northwest China is extremely harsh, characterized by numerous salt lakes. The high concentrations of chloride salts, sulfates, and alkali metal ions in these areas can induce alkali–silica reactions (ASRs) in concrete. These reactions generate harmful gel within the concrete, causing expansion and cracking, which significantly impacts the durability of concrete structures. This study investigates the evolution of the mechanical properties in high-performance concrete (HPC) under long-term ASR by incorporating different admixtures and varying the equivalent alkali content. A three-dimensional random aggregate mesoscopic model was used to simulate static compression tests under various operational conditions. Non-destructive testing methods were utilized to determine the expansion rate, internal, and surface damage variables of the concrete. The experimental results indicate that the 10-year expansion rate differs from the 1-year rate by approximately 1%, and under long-term ASR mitigation measures, the internal damage in the HPC is minimal, though the surface damage is more severe. As the equivalent alkali content increases, the compressive strength of the concrete cubes decreases, initially rising before falling by 5–15% over time. The HPC with only air-entraining agent added exhibited better mechanical performance than the HPC with both air-entraining and corrosion inhibitors added, with the poorest performance observed in the HPC with only a corrosion inhibitor. A relationship was established between the surface and internal damage variables, with the surface damage initially increasing rapidly before stabilizing as the internal damage rose. Numerical simulations effectively describe the damage behavior of HPC under static uniaxial compression. Comparisons with actual failure morphologies revealed that, in the cube compression tests, crack propagation directly penetrated both coarse and fine aggregates rather than circumventing them. The simulations closely matched the experimental outcomes, demonstrating their accuracy in modeling experiments. This study discusses the compressive mechanical properties of concrete under prolonged ASR through a combination of experimental and simulation approaches. It also delves into the impact of surface damage on the overall mechanical performance and failure modes of concrete. The findings provide experimental and simulation support for the concrete structures in regions with high alkali contents.

Development and Integration of a Digital Twin Model for a Real Hydroelectric Power Plant.

In this study, a digital twin model of a hydroelectric power plant has been created. Models of the entire power plant have been created and malfunction situations of a sensor located after the inlet valve of the plant have been analyzed using a programmable logic controller (PLC). As a feature of the digital twin (DT), the error prediction and prevention function has been studied specifically for the pressure sensor. The accuracy and reliability of the data obtained from the sensor are compared with the data obtained from the DT model. The comparison results are evaluated and erroneous data are identified. In this way, it is determined whether the malfunction occurring in the system is a real malfunction or a malfunction caused by measurement or connection errors. In the case of sensor failure or measurement-related malfunction, this situation is determined through the digital twin-based control mechanism. In the case of actual failure, the system is stopped, but in the case of measurement or connection errors, since the data are calculated by the DT model, the value in the specified region is known and thus there is no need to stop the system. This prevents production loss in the hydroelectric power plant by ensuring the continuity of the system in case of errors.

On the out-of-plane buckling of RHS X-joints – Analytical and numerical approaches

Out-of-plane buckling (OPB) is not widely known failure mechanism in tubular X-joints with equal brace and chord widths (β = b1/b0 = 1.0) but it can occur in the chord member of tubular X-joints axially compressed by the brace members. The OPB consists of distortional and torsional deformations in the chord member, but only the distortional mode is involved in elastic energy of the system. In this work, an analytical formulation for the critical OPB capacity is established by the deformed shape in a beam on elastic foundation (BEF). A theoretical model for the ideal-elastic buckling capacity is computed by considering the in-plane bending of the chord faces and elastic bending of the cross-section as the frame structure. The obtained critical buckling loads are compared with numerical results obtained using linear buckling analyses by finite element (FE) models. The theoretical model provided a reasonable agreement with the numerically obtained eigenvalues with a tendency to slightly underestimate load-carrying capacity. The FE results showed that the OPB of X-joint can be the critical ideal-elastic failure mode in the wide range of various joint geometries and thus be regarded as a competitive failure mechanism for the widely known chord sidewall buckling. However, the results were obtained for the ideal-elastic buckling modes and, eventually, the actual failure mode is highly depended on the existing geometrical imperfections. Nevertheless, experimental tests have also indicated an existence of the OPB mode, and structural designers and analysts of tubular connections should be aware of this potential failure mode.

Multiphase flow and nozzle wear with CFD-DEM in high-pressure abrasive water jet

The issue of wear failure in High-Pressure Abrasive Water Jet (HP-AWJ) nozzles is an unavoidable challenge, and studying methods to enhance and predict the effective lifetime of nozzles is worth deep exploration. This paper employs a CFD-DEM coupling numerical approach to investigate wear phenomena inside the HP-AWJ nozzle, aiming to capture the realistic particle wear and erosion failure issues at the focusing tube region, which is a high-wear area of the HP-AWJ nozzle. Furthermore, the study considers realistic particles and nozzle wall constitutive models, incorporating material properties into the physical model, and employs computer-aided design methods to reflect wear failure conditions at different time intervals in the inner wall of the focusing tube at the nozzle. The results demonstrate that the number of realistic particles and initial inlet velocity has no impact on the particle exit kinetic energy. However, the particle-wall restitution coefficient affects the average particle kinetic energy at the outlet in the AWJ nozzle. The equivalent model of the realistic particles reflects the influence of the particle roundness on particle kinetic energy, acceleration, and stress concentration variations in the nozzle. These variations further affect the particle erosion rate on the nozzle wall and the actual wear failure problems on the wall surface. Finally, by combining the proposed erosion and wear model, a representative erosion profile at the AWJ focusing tube location comparable to experimental results is obtained, and the wear depth of the focusing tube changing with time is also studied. The results and methodologies presented in this paper provide valuable guidance for controlling the effective service lifetime of the AWJ nozzle, improving machining efficiency, and extending the lifespan of the AWJ nozzle.

Risk analysis and assessment of underwater glider mission failure using Dynamic Bayesian Network method

The Autonomous Underwater Glider (AUG), with its extended operational endurance, encounters various uncertain risks during diverse mission tasks. Therefore, conducting dynamic risk analysis is crucial for implementing subsequent decisions and improving equipment safety. This paper combines Fault Tree Analysis (FTA) and Dynamic Bayesian Network (DBN) to develop a customized dynamic risk assessment method for AUG task failure under multiple risk factors. Firstly, preliminary risk factors are identified by FTA using existing experimental data. Next, transformation rules are constructed to map FTA into a basic DBN model. Then, parameters such as conditional probabilities are determined through logic gates and parameter learning, and the proposed model is validated based on axioms. Finally, key risk factors are identified based on actual failure data. The research results indicate that the key risk factors leading to AUG task failure are progressive abnormalities such as biofouling and ocean current disturbance. The method proposed in this paper can achieve dynamic risk assessment, providing strong references for operators in making decisions for subsequent tasks.

Optimization of geopolymer pervious concrete design using multi-phase discrete element modeling

This study aims to optimize mix designs of geopolymer pervious concrete using multi-phase discrete element modeling. Metakaolin based geopolymer concrete was prepared with different mix designs for laboratory testing. A multi-phase discrete element model (DEM) for pervious concrete was developed with randomly generated porous structure and realistic contact models between each component. Contact model parameters were calibrated using orthogonal analysis based on measurements of mechanical strengths and elastic modulus. Results of model validation indicate that the developed numerical model has good reliability to predict mechanical properties of pervious concrete with the influence of porosity. Both actual and simulated pervious concrete specimens present consistent crack propagation patterns and failure behavior. The optimization of mixture design is conducted by considering aggregate gradation and aggregate to paste ratio of geopolymer pervious concrete, which are based on experimental results and post-calibration simulations. The gradation with larger size aggregate is suitable for preparing geopolymer pervious concrete with higher permeability and tensile strength. More importantly, it is feasible to reduce paste content in geopolymer pervious concrete while maintaining similar or even higher mechanical strengths, which reduces the embedded carbon footprint. The study findings demonstrate the beneficial use of numerical modeling to determine the optimized mix design of composite material such as geopolymer pervious concrete.

Investigating the Reliability of an Existing Top Angle and Seat Pad Semi-Rigid Connection System Using Advanced Modelling Techniques

The investigation introduces an advanced model procedure for evaluating the structural reliability of semi-rigid frame connections in existing aged buildings. Specifically, the focus is on the top angle and seat pad (TA–SP) semi-rigid connection, which was not initially considered in the current design standards. The approach employs a plastic hinge model to predict the ultimate strength of the connection and its beam-to-column behaviour. In order to increase computational efficiency, the investigation leverages the nonlinear behaviour of the finite element (FE) model to validate critical parameters. The statistical properties of the existing connection were obtained based on past experimental data, highlighting the weakest elements in the system. The first-order and second-order reliability methods and Monte Carlo simulations were employed to estimate the reliability index. Percentile errors were assessed to understand their impact on higher-order interactions. This new technique of identification quantifies the probability of the system failure interactions. Notably, a 45% lesser error aligns with the target reliability index, while a 114.5% larger error indicates a significant deviation from actual failure probability values. Each methodology introduced adheres to the current standard, and the system reliability analysis provides a vigorous conclusions scheme framework for assessing the existing TA–SP semi-rigid connection.

Bayesian maximum entropy interpolation analysis for rapid assessment of seismic intensity using station and ground motion prediction equations

In this paper, we explored the combination of seismic station data and ground motion prediction equations (GMPE) to predict seismic intensity results by using Bayesian Maximum Entropy (BME) method. The results indicate that: 1) In earthquake analysis in Japan, soft data has predicted higher values of intensity in disaster areas. BME corrected this phenomenon, especially near the epicenter. Meanwhile, for earthquakes in the United States, BME corrected the erroneous prediction of rupture direction using soft data. 2) Compared with other spatial interpolation methods, the profile results of Japan earthquake and Turkey earthquake show that BME is more consistent with ShakeMap results than IDW and Kriging. Moreover, IDW has a low intensity anomaly zone. 3) The BME method overcomes the phenomenon that the strength evaluation results do not match the actual failure situation when the moment magnitude is small. It more accurately delineates the scope of the disaster area and enriches the post-earthquake processing of disaster area information and data. BME has a wide range of applicability, and it can still be effectively used for interpolation analysis when there is only soft data or few sites with data available.

Development of an expert system for assessing failures in oil and gas pipelines due to microbiologically influenced corrosion (MIC)

This study highlights the modeling of an expert system for the classification of internal microbiologically influenced corrosion (MIC) failures related to pipelines in the upstream oil and gas industry. The model is based on artificial neural networks (ANNs) and involves the participation of 15 MIC experts. Each expert evaluated a number of model case studies ranging from MIC- to non-MIC-driven upstream pipeline failures. The model accounts for variations in microbiological testing methods, microbiological sample types, and degradation morphology, among other variables. It also accounts for missing datasets, which is commonly the case in actual failure assessments. The outcome is an expert system model whose outputs are classes (classification ANN) which comprises MIC potential and data confidence. The performances of two approaches are contrasted in this study. One classifies the output in 5 classes, a 5-output classification (5OC) model; the other in 3 classes, a 3-output classification (3OC) model. The 5OC model had an accuracy of 62.0% while the simpler 3OC model had a better accuracy of 74.8%. This modelling exercise has demonstrated that knowledge from experts can be captured in a reasonably effective model to screen for possible MIC failures. It is hoped that this study not only may contribute to a better understanding of the prevalence of MIC in the oil and gas sector, but also may highlight the key areas necessary to improve the diagnosis of MIC failures in the future.

Accurate fault section diagnosis of power systems with a binary adaptive quadratic interpolation learning differential evolution

Fault section diagnosis (FSD) is essential for ensuring the effective operation of power systems. To determine the faulty sections accurately, we proposed an improved binary adaptive quadratic interpolation learning differential evolution called BAQILDE for solving the FSD problem. By comparing the received warning data with the anticipated states of circuit breakers and protective relays, an analytical 0–1 integer programming function is established. To tackle the resultant function accurately, the population in BAQILDE is directly encoded in binary instead of floating-point to facilitate the solving convenience. Besides, three enhanced strategies including adaptive mutation operator, time-varying crossover rate, and dual transformation operator are developed to equilibrate the population diversity and convergence well to strengthen BAQILDE. To evaluate BAQILDE's performance, four test systems were used for verification, including 4-substation power system, IEEE 118 bus system, and two actual failures that occurred in Guangzhou and Jilin power grids, China. The results show that BAQILDE can diagnose various failures within 0.12 s with 100 % success rate and 0 diagnosis error, consuming an average of 32.21 function evaluation times. It outperformed other well-known peer algorithms in success rate, diagnosis error, robustness, convergence, and statistical analysis, which demonstrates its strong competitiveness in solving the FSD problem.

InSAR stacking to detect active landslides and investigate their relation to rainfalls in the Northern Apennines of Italy

Interferometric stacking is a useful technique to detect active slope deformation over vast mountainous area. Compared to the analysis of single interferograms, the stacking approach improves the signal to noise ratio and deformation signals become clearer. In this work, we used stacked interferograms to detect active slow-moving landslides during the years 2015 to 2019 over a 1200 km2 portion of the Northern Apennines of Italy. C-band Sentinel 1 SAR images were used to create short temporal baseline (6 to 24 days) interferograms which limit decorrelation and maximize the range of measurable displacement rates. Then we operated a further selection of interferograms based on coherence and visual inspection, before stacking over multiple time scales (1 month to years). The products of the analysis are ground displacement maps where the presence of residual noise is inversely proportional to the duration of the stack and the deformation signal is clearly recognized. Results show that only a small fraction of the mapped landslide deposits are experiencing deformation that can be detected by differential interferometry. In particular, we identified 118 InSAR deformation signals corresponding to ongoing gravitational slope deformations over the 9916 landslides mapped in the area. Active movements are mostly located on landslides that have undergone catastrophic reactivation in relatively recent times. Annual interferometric stacks proved better suited to detect active slope movements, while <15 % of our deformation signals can only be detected by inspecting monthly stacks. Active slow-moving landslides show variable displacement rates in monthly stacks. Periods of dormancy alternate to accelerations that may lead to actual catastrophic failures or, more often, determine finite periods of sustained slow movement before the displacement rates drop below the detection limit. We compared the evolutionary trends of the phenomena to the occurrence of rainfall events. For this purpose, we use the probability of landslide occurrence which is associated with a rainfall event based on a territorial alert threshold. Results show that, at increasing landslide occurrence probabilities, an increasing fraction of actively deforming landslides can be detected by InSAR.