Partitioning Ground Motion Uncertainty When Conditioned on Station Data

ABSTRACT Rapid estimation of earthquake ground shaking and proper accounting of associated uncertainties in such estimates when conditioned on strong-motion station data or macroseismic intensity observations are crucial for downstream applications such as ground failure and loss estimation. The U.S. Geological Survey ShakeMap system is called upon to fulfill this objective in light of increased near-real-time access to strong-motion records from around the world. Although the station data provide a direct constraint on shaking estimates at specific locations, these data also heavily influence the uncertainty quantification at other locations. This investigation demonstrates methods to partition the within- (phi) and between-event (tau) uncertainty estimates under the observational constraints, especially when between-event uncertainties are heteroscedastic. The procedure allows the end users of ShakeMap to create separate between- and within-event realizations of ground-motion fields for downstream loss modeling applications in a manner that preserves the structure of the underlying random spatial processes.

Liquefaction hazard assessment and ground failure probability analysis in the Kathmandu Valley of Nepal

During the 2015 Gorkha Earthquake (Mw7.8), extensive soil liquefaction was observed across the Kathmandu Valley. As a densely populated urban settlement, the assessment of liquefaction potential of the valley is crucial especially for ensuring the safety of engineering structures. In this study, we use borehole data including SPT-N values of 410 locations in the valley to assess the susceptibility, hazard, and risk of liquefaction of the valley soil considering three likely-to-recur scenario earthquakes. Some of the existing and frequently used analysis and computation methods are employed for the assessments, and the obtained results are presented in the form of liquefaction hazard maps indicating factor of safety, liquefaction potential index, and probability of ground failure (PG). The assessment results reveal that most of the areas have medium to very high liquefaction susceptibility, and that the central and southern parts of the valley are more susceptible to liquefaction and are at greater risk of liquefaction damage than the northern parts. The assessment outcomes are validated with the field manifestations during the 2015 Gorkha Earthquake. The target SPT-N values (Nimproved) at potentially liquefiable areas are determined using back analysis to ascertain no liquefaction during the aforesaid three scenario earthquakes.

Seismic Soil Characterization to Estimate Site Effects Induced by Near-Fault Earthquakes: The Case Study of Pizzoli (Central Italy) during the Mw 6.7 2 February 1703, Earthquake

Many of the urban settlements in Central Italy are placed nearby active faults and, consequently, the ground motion evaluation and seismic site effects under near-fault earthquakes are noteworthy issues to be investigated. This paper presents the results of site investigations, the seismic site characterization, and the local seismic response for assessing the effects induced by the Mw 6.7 2 February 1703, near-fault earthquake at the Madonna delle Fornaci site (Pizzoli, Central Italy) in which notable ground failure phenomena were observed, as witnessed by several coeval sources. Even though recent papers described these phenomena, the geological characteristics of the site and the failure mechanism have never been assessed through in-situ investigations and numerical modeling. Within a project concerning the assessment of soil liquefaction potential and co-seismic ground failure, deep and shallow continuous core drilling, geophysical investigations and in-hole tests have been carried out. Subsequently, the geotechnical model has been defined and the numerical quantification of the different hypotheses of failure mechanisms has been evaluated. Analyses showed that liquefaction did not occur, and the excess pore water pressure induced by the shaking was not the source of the ground failure. Therefore, it was hypothesized that the sinkhole was likely caused by earthquake-induced gas eruption.

Interpretation of uplift pipeline-soil interaction behaviour using acoustic emission measurements

The objective of this study was to develop quantitative acoustic emission (AE) interpretation for uplift pipeline-soil interaction behaviour, enabling early warning of serviceability and ultimate limit state failures in the field. A series of large-scale uplift experiments was performed on a steel pipe in sand with different burial depths (i.e., stress levels), and varying rates of deformation were imposed. A suite of AE parameters was compared with the pipe force and displacement behaviour. Image-based deformation measurements were used to monitor the soil displacement field. AE generation was proportional to the imposed stress level and pipe displacement rate and related to the evolution of the pipe/soil failure mechanism. Relationships have been quantified between AE generation and stress level (R2 values of 0.99), and between AE generation rate and pipe velocity (R2 values ranging from 0.95 to 0.98), enabling interpretation of accelerating deformation behaviour that accompanies progressive ground failure processes. An example interpretation framework demonstrates how AE parameters can be used to identify the mobilisation of peak uplift resistance and quantify accelerating deformation behaviour during post-peak softening.

Hill Slope Failure During The Development Of Infrastructure Projects In Himalaya: Case Study of Udhampur- Ramban National Highway, Jammu and Kashmir, India

Himalaya is youngest tectonic Active Mountain and northern boundary of India. Various infrastructure projects are under develop in Himalayan region for the better connectivity of peoples and national security of border line. Widening of 295 km National Highway-44 in Jammu and Kashmir is an important developmental project. The highway passes through Outer Himalayan and Higher Himalayan sequences of rocks and is frequently affected by landslides at various places. The paper deals with 43 km Udhampur-Chenani and Nashri- Ramban section of the highway within sedimentary and metamorphic rock sequence. The area is prone to landslides on account of fragile geological, topographic and hydrological conditions. The study area has high rainfall intensity and numerous old landslides zones. During the project feasibility stage, project authority has identified the landslide prone area and suggested precautionary measures but during the construction work various unpredicted landslide and ground sinking events happened which given trouble in project construction cost and project completion time. In this paper, the challenges faced due to landslide and ground failure as failure of old slide adjutant to road construction, agriculture and residential ground failure at higher altitude due to road construction, collapse of high tension towers in cut slopes; its impact on construction activities; are discussed with mitigation measures for unidentified landslide related challenges.

Single line to ground fault detection and location in medium voltage distribution system network based on neural network

<p>The aim of this project was to detect and locate the single ground failure lines that occurs in medium voltage (MV) networks on the transmission lines (TL). Compared with anther faults, single line-to-ground (SLG) is the most frequent. The neural network (NN) algorithm was advanced in order to discover and locate SLG faults. The network is simulated through simulated numerous defects at various locations, as well as changing earth resistance from (or 100 Ω) to TL to gather all of the data. In the electromagnetic transients’ program (EMTP) program software, the existing fault have been measured. In addition, the waves were evaluated by utilize MATLAP's fastfourier-transform to calculate the waves of top three of them, On the MV network are fifty hundred faults are simulated all data in the neural network at MATLAB were trained and examined to improve the NN algorithm according to this data. Comparing all the simulated location faults that have been applied with those all locations detected in the NN algorithm, the overall error between them has been found to be very low and not to exceed 0.7. The Simulink circuit was created from this algorithm and checked in order to predict each failure could occur in the future in the MV network.</p>

Analysis of Soil Liquefaction Potential through Three Field Tests-Based Methods: A Case Study of Babol City

During earthquakes, ground failure is commonly caused by liquefaction. Thus, assessment of soil liquefaction potential in earthquake-prone regions is a crucial step towards reducing earthquake hazard. Since Babol city in Iran country is located in a high seismic area, estimation of soil liquefaction potential is of great importance in this city. For this purpose, in the present research, using field-based methods and geotechnical data (such as unit weight of soil, relative density, SPT number, shear wave velocity and cone tip resistance) of 60 available boreholes in Babol, three liquefaction maps were provided. Finally, one comprehensive liquefaction map was presented for soil of Babol city. The obtained results in this paper are well in line with the previous investigations. Based on the results, the factor of safety in 45% of the study area is less than one (liquefaction occurrence). In addition, the results indicate that since each field-based method requires particular data, applying various field tests is necessary for a more accurate liquefaction assessment.

Fusing Damage Proxy Maps with Geospatial Models for Bayesian Updating of Seismic Ground Failure Estimations: A Case Study in Central Italy

&lt;p&gt;On August 24, 2016, a magnitude-6.2 earthquake in Central Italy resulted in at least 290 deaths, significant ground failure (including landslides and liquefaction), and building damage. After the event, the NASA Advanced Rapid Imaging and Analysis team produced Damage Proxy Maps (DPM) that reflect earthquake-induced surficial changes using synthetic aperture data from the COSMO-SkyMed satellite. However, exact causes of these surface changes, e.g., ground failure, building damage, or other environmental changes, are difficult to directly differentiate from the satellite images alone. For example, changes could reflect building damage, landslides, the co-occurrence of both, or numerous other processes that are not related to the earthquake. Alternatively, existing ground failures models are useful in locating areas of higher likelihoods but suffer from high false alarm rates due to inaccurate or incomplete geospatial proxies and complex physical interdependencies between shaking and specific sites of ground failure. In this work, we present a joint Bayesian updating framework using a causal graph strategy. The Bayesian causal graph models physical interdependencies among ground shaking, ground failures, building damage, and remote sensing observations. Based on the graph, a variational inference approach is designed to jointly update the estimates of ground failure and building damage through fusing traditional geospatial models and the remotely sensed data. As a case study, the DPMs in Central Italy are input to the model for jointly calibrating and updating the probability of ground failure estimations as well as for estimating building damage probabilities. The results showed that by incorporating high-resolution imagery, our model significantly reduces the false alarm rate of ground failure estimates and improves the spatial accuracy and resolution of ground failure and building damage inferences.&lt;/p&gt;