Academic literature on the topic 'SEISMIC SUSCEPTIBILITY'

Abstract As an important part of the substation, the seismic capacity of ultra-high voltage (UHV) electrical equipment is related to the safe and stable operation of the substation and the whole power grid. The UHV gas-insulated transmission lines (GIL) have many structural characteristics, such as long pipeline distance, the large span between supports and the obvious amplification effect of supports. Its natural frequency is close to the predominant frequency of the seismic wave, resulting in its susceptibility to resonance or resonance-like and high seismic susceptibility. In order to analyze the seismic performance of UHV GIL better, the vibration mode decomposition response spectrum method and the dynamic time-history analysis method are used to analyze the seismic response of UHV GIL in this paper. The search for seismic weak positions of GIL is a reference for effectively improving the seismic performance of UHV GIL.

On 8 August 2017 an earthquake (MS7.0) occurred within Jiuzhaigou County, Northern Aba Prefecture, Sichuan Province, China, triggering 4834 landslides with an individual area greater than 7.8 m2 over a more than 400 km2 region. Instead of correlating geological and topographic factors with the coseismic landslide distribution pattern, this study has attempted to reveal the control from seismic landslide susceptibility mapping, which relies on the calculation of critical acceleration values using a simplified Newmark block model. We calculated the average critical acceleration for each cell of the gridded study area (1 km×1 km), which represented the seismic landslide susceptibility of the cell. An index of the potential landslide area generation rate was defined, i.e., the possible landsliding area in each grid cell. In combination with PGA (peak ground acceleration) distribution, we calculated such indexes for each cell to predict the possible landslide hazard under seismic ground shaking. Results show that seismic landslide susceptibility plays an important role in determining the coseismic landslide pattern. The places with high seismic landslide susceptibility tends to host many landslides. Additionally, the areas with high potential landslide area generation rates have high real landslide occurrence rates, consistent with dominant small-medium scale landslides by this earthquake. This approach can aid assessment of seismic landslide hazards at a preliminary stage. Additionally, it forms a foundation for further research, such as the rapid evaluation of post-earthquake landslides and identifying highly impacted areas to help decision makers prioritize disaster relief efforts.

Earthquakes cause a huge number of landslides and alter the regional landslide risk distribution. As a result, after a significant earthquake, the landslide susceptibility maps (LSMs) must be updated. The study goal was to create seismic landslide susceptibility maps containing landslide causative variables which are adaptable to great changes in susceptibility after the Jiuzhaigou earthquake (MS 7.0) and to perform a rapid update of the LSM after the earthquake by means of the distributed scatterer interferometric synthetic aperture radar (DS-InSAR) technique. We selected the territory of Jiuzhaigou County (southwestern China) as the study region. Jiuzhaigou is a world-renowned natural heritage and tourist area of great human and ecological value. For landslide susceptibility mapping, we examined the applicability of three models (logistic regression, support vector machine, and random forest) for landslide susceptibility mapping and offered a strategy for updating seismic landslide susceptibility maps using DS-InSAR. First, using logistic regression, support vector machine, and random forest techniques, susceptibility models of seismic landslides were built for Jiuzhaigou based on twelve contributing variables. Second, we obtained the best model parameters by means of a Bayesian network and network search, while using five-fold cross-validation to validate the optimized model. According to the receiver operating characteristic curve (ROC), the SVM model and RF model had excellent prediction capability and strong robustness over large areas compared with the LR models. Third, the surface deformation in Jiuzhaigou was calculated using DS-InSAR technology, and the deformation data were adopted to update the landslide susceptibility model using the correction matrix. The correction of deformation data resulted in a susceptibility class transition in 4.87 percent of the research region. According to practical examples, this method of correcting LSMs for the continuous monitoring of surface deformation (DS-InSAR) was effective. Finally, we analyze the reasons for the change in the revised LSM and point out the help of ecological restoration in reducing landslide susceptibility. The results show that the integration of InSAR continuous monitoring not only improved the performance of the LSM model but also adapted it to track the evolution of future landslide susceptibility, including seismic and human activities.

Abstract
 
 Indonesia is a country that has a very high level of vulnerability to earthquakes. However, it is very rare for bridges in rural areas or in sub-districts to be evaluated or screened. As a result, many bridges are prone to damage when seismic earthquakes occur, even though bridges are an important infrastructure for the village for the village's economic progress. This research was conducted with the aim of conducting a quick seismic inspection of the bridge and obtaining a priority order for bridge repair using the Seismic Vulnerability Index (SVI) method. The analysis was carried out on the sample, which consisted of 4 bridges located in Pati Regency. The results of the SVI analysis show that of the 4 observed bridges, the Tanjang Bridge has the highest Seismic Vulnerability Index, so it is recommended that this bridge get the first repair priority.
 
 Keywords: earthquake; earthquake prone; bridge; Seismic Susceptibility Index; improvement priority
 
 
 Abstrak 
 
 Indonesia merupakan suatu negara yang mempunyai tingkat kerawanan terhadap gempa bumi yang sangat tinggi. Namun, sangat jarang jembatan-jembatan yang berada di perdesaan atau di kecamatan dievaluasi atau dilakukan screening. Akibatnya, banyak jembatan yang rawan rusak saat terjadi peristiwa gempa bumi seismik padahal jembatan merupakan suatu infrastruktur penting bagi desa untuk kemajuan ekonomi desa tersebut. Penelitian ini dilakukan dengan tujuan untuk melakukan pemeriksaan seismik jembatan secara cepat dan mendapatkan urutan prioritas perbaikan jembatan dengan menggunakan metode Indeks Kerentanan Seismik (SVI). Analisis dilakukan terhadap sampel, yang terdiri atas 4 jembatan yang terletak di Kabupaten Pati. Hasil analisis SVI menunjukkan bahwa dari keempat jembatan yang diamati, Jembatan Tanjang mempunyai Indeks Kerentanan Seismik yang paling tinggi, sehingga jembatan ini direkomendasikan untuk mendapatkan prioritas perbaikan yang pertama.
 
 Kata-kata kunci: gempa bumi; rawan gempa; jembatan; Indeks Kerentanan Seismik; prioritas perbaikan

AbstractThe coupled effect of earthquakes and rainfall is rarely investigated in landslide susceptibility assessments although it could be crucial to predict landslide occurrences. This is even more critical in the context of early warning systems and especially in cases of extreme precipitation regimes in post-seismic conditions, where the rock masses are already damaged due to the ground shaking. Here, we investigate this concept by accounting for the legacy of seismic ground shaking in rainfall-induced landslide (RFIL) scenarios. We do this to identify whether ground shaking plays a role in the susceptibility to post-seismic rainfall-induced landslides and to identify whether this legacy effect persists through time. With this motivation, we use binary logistic regression and examine time series of landslides associated with four earthquakes occurred in Indonesia: 2012 Sulawesi (Mw = 6.3), 2016 Reuleut (Mw = 6.5), 2017 Kasiguncu (Mw = 6.6) and 2018 Palu (Mw = 7.5) earthquakes. The dataset includes one co-seismic and three post-seismic landslide inventories for each earthquake. We use the peak ground acceleration map of the last strongest earthquake in each case as a predisposing factor of landslides representing the effect of ground shaking. We observe that, at least for the study areas under consideration and in a probabilistic context, the earthquake legacy contributes to increase the post-seismic RFIL susceptibility. This positive contribution decays through time. Specifically, we observe that ground motion is a significant predisposing factor controlling the spatial distribution of RFIL in the post-seismic period 110 days after an earthquake. We also show that this effect dissipates within 3 years at most.