Explore the vast range of titles available.

The present study aims at developing a prediction model for ground motion intensity measures using the artificial neural network (ANN) technique for active shallow crustal earthquakes in India. The database for the study consists of 659 ground motion records collected from 138 earthquakes recorded by various seismic networks in the study region. Owing to the lack of near-field data, we have added 116 records from seven earthquakes over a distance < 30 km and M > 6 from the NGA database. The developed model predicts 21 ground motion parameters (GMPs) in both horizontal and vertical directions, with input predictor variables of magnitude (M), hypocentral distance (R), site condition (S), and flag for the region (f). A multi-layer perceptron (MLP), with a total of 276 unknowns, constitutes the architecture of the model. The residuals associated with the GMPs are analyzed in detail to aid in hazard calculations. In addition, a comparison of the developed model with global relations is performed. Further, the model is demonstrated by performing seismic hazard analysis for GMPs for 2% and 10% probability of exceedance in 50 years. The ANN model is a first version and has to be improved as more strong motion data becomes available for the region. The developed ground motion model must be combined along with other global models in seismic hazard analysis.

Landslides are a major cause of earthquake damage, and the ability to anticipate seismically triggered landslide displacement is critical for seismic hazard assessment. The necessity for efficient measures for preventing and minimizing the damage caused by co-seismic landslides has prompted the development of innovative approaches for assessing areas exposed to seismically induced landslides at a regional scale. Uttarakhand is highly seismically active, and major geological formations of this region are heavily jointed or fractured. Landslides are common in this area, and the risk of earthquake-induced landslides is particularly significant due to the region's strong seismicity. The present study incorporated a combination of a probabilistic approach and modified Newmark’s method to obtain seismically induced landslide susceptibility maps. Firstly, a well-established probabilistic seismic hazard assessment method was utilized to calculate the probability of occurrence for various levels of earthquake shaking in terms of Arias intensity for different time intervals. Then by using an empirical equation based on Newmark’s displacement model, the slope strength demand was evaluated. The resulting slope strength demand values represent the minimal value of resistance required by a slope to maintain the probability of triggering an earthquake-induced landslide below a predetermined threshold. Finally, the spatial distribution of slope strength demand was compared with in-situ critical acceleration values computed using a modified Newmark method to determine slope failure probability. The obtained map presents a detailed demarcation of areas that will be affected by co-seismic landslide hazards in the future.

Strong motion sensor networks deployed in metropolitan areas are able to provide valuable information for civil protection Decision Support Systems (DSSs) aiming to mitigate seismic risk and earthquake social-economic impact. To this direction, such a network is installed and real-time operated in Chania (Crete Island, Greece), city located in the vicinity of the seismically active south front of the Hellenic Subduction Zone. A blend of both traditional and advanced analysis techniques and interpretation methods of strong ground motion data are presented, studying indicative cases of Chania shaking due to earthquakes in the last couple years. The orientation independent spectral acceleration as well as the spatial distribution of the strong ground motion parameters such as the Peak Ground Acceleration (PGA), Peak Ground Velocity (PGV), Peak Ground Displacement (PGD) and Arias Ιntensity observed at the urban area of Chania are presented with the use of a Geographic Information System (GIS) environment. The results point to the importance of the strong ground motion networks as they can provide valuable information on earthquake hazards prior to and after detrimental seismic events to feed rapid systems supporting civil protection decisions for prevention and emergency response.

We perform a spectral decomposition of the Fourier amplitude spectra disseminated along with the Engineering Strong Motion (ESM) flat file for Europe and Middle East. We apply a non-parametric inversion schema to isolate source, propagation and site effects, introducing a regionalization for the attenuation model into three domains. The obtained propagation and source components of the model are parametrized in terms of geometrical spreading, quality factor, seismic moment, and corner frequency assuming a ω2 source model. The non-parametric spectral attenuation values show a faster decay for earthquakes in Italy than in the other regions. Once described in terms of geometrical spreading and frequency-dependent quality factor, slopes and breakpoint locations of the piece-wise linear model for the geometrical spreading show regional variations, confirming that the non-parametric models capture the effects of crustal heterogeneities and differences in the anelastic attenuation. Since they are derived in the framework of a single inversion, the source spectra of the largest events which have occurred in Europe in the last decades can be directly compared and the scaling of the extracted source parameters evaluated. The Brune stress drop varies over about 2 orders of magnitude (the 5th, 50th and 95th percentiles of the ∆σ distribution are 0.76, 2.94, and 13.07 MPa, respectively), with large events having larger stress drops. In particular, the 5th, 50th and 95th percentiles for M > 5.5 are 2.87, 6.02, and 23.5 MPa, respectively whereas, for M < 5.5, the same percentiles are 0.73, 2.84, and 12.43 MPa. If compared to the residual distributions associated to a ground motion prediction equation previously derived using the same Fourier amplitude spectra, the source parameter and the empirical site amplification effects correlate well with the inter-event and inter-station residuals, respectively. Finally, we calibrated both non-parametric and parametric attenuation models for estimating the stress drop from the ratio between Arias intensity and significant duration. The results confirm that computing the Arias stress drop is a suitable approach for complementing the seismic moment with information controlling the source radiation at high frequencies for rapid response applications.

Probabilistic modelling is gaining increased attention in the field of assessing the landslide hazard due to the ability to account for the spatial and temporal uncertainties related to the variability of geological, hydrological, geotechnical, seismological and geomorphological parameters. In this study, a seismic landslide hazard assessment was carried out for Uttarakhand state, located in the Indian Himalayan region. A methodology was developed to model the parametric uncertainties incorporated in the modified Newmark slope stability analysis model, which considers the rock joint shear strength properties in permanent displacement computation. The uncertainties related to input parameters were taken into account by utilizing statistical distributions to represent these parameters. On a pixel-by-pixel basis, several probability density functions were simulated using the Monte Carlo method, and the simulation results were retained throughout the computation process. As a result, there were no constraints on the mathematical complexity or symmetry of the underlying distributions when casting the derived quantities into probabilistic hazard maps. The hazard map showed the probability of exceedance of seismic slope displacement beyond a threshold value of 5 cm. High probability values were observed in the Middle and Greater Himalayas, emphasizing the likelihood of a large number of earthquake-induced landslides in this region. Finally, the results were validated using the landslide inventory of the 1999 Chamoli earthquake. The prepared seismic landslide hazard map will give infrastructural planners and local authorities a tool for evaluating the risk associated with a seismic landslide for land use planning and taking appropriate mitigation measures to reduce the losses.

In the field of ground motion simulation, the stochastic site-based methodology relies on the existing database of ground shaking. Based on these methodologies, several properties of seismic signals are used to simulate seismic waves. These parameters could be evaluated either parametrically via linear or nonlinear regression techniques or non-parametrically via sophisticated machine-learning algorithms. Nonetheless, parametric models, which consist of a particular mathematical formulation, can be a source of large bias. In this study, machine learning techniques are employed to develop predictive models for two main input parameters of a stochastic site-based ground motion model: Arias intensity and significant duration, which control the time variation of the simulated ground shakings. The Arias intensity, defined by the integral of the square of the acceleration time series, and the significant duration, which is related to the strong shaking phase of an earthquake, are also of particular interest in structural and geotechnical engineering fields. For this purpose, the random forest approach is employed to develop prediction models for the Italian database. To guarantee the prediction accuracy of the models also for unseen future data, only 80 percent of the data is used for training, and the rest is reserved for testing the trained model. The model hyperparameters are tuned to control bias and variance trade-offs by k-fold cross-validation. For each model, a set of hyperparameters is selected, and a possible range is given. Then, a Bayesian optimization technique is implemented to find the best set of these hyperparameters among the given range. All these models provided promising results compared to the prior models in the literature.

This study presents empirical ground-motion models (GMMs) for estimating Arias intensity (I A ), cumulative absolute velocity (CAV) and significant ground-motion duration (D 5–75 and D 5–95 ), calibrated on Iranian strong motion database. The dataset consists of 1749 (with two horizontal components) acceleration motion time-series originated from 566 events with moment magnitude (M w ) 3–7.5 range and recorded at 338 stations in the distances range up to 200 km. Common functional forms were adopted for all four models to facilitate easy comparison of derived model parameters and model predictions. Residual distributions and their unbiased variation with predictor variables M w , hypocentral distance (R hypo ), time-averaged shear-wave velocity in the top 30 m (V S30 ) indicated robustness of the derived models. This study also examines residual correlations between different pairs of ground-motion intensity measures (GMIMs). The correlations were analysed separately for between-event ( δ B e ) and within-event ( δ W S es ) component of the residuals. The correlation of δ B e between: (1) I A and the two duration measures (D 5–75 and D 5–95 ), (2) CAV and the two duration measures were found depending upon the event magnitude (strongest for M w > 6). Similarly, the correlation of δ W S es between: (1) I A and the two duration measures, (2) CAV and the two duration measures were observed depending upon source-to-site distance (strongest for R hypo < 50 km). Furthermore, a relatively stronger negative correlation of δ W S es was observed between CAV and station-specific attenuation parameter (κ 0 ) (mainly at softer soil sites) in comparison to that between I A and κ 0 .

To explore the cumulative deformation effect of the dynamic response of a tunnel crossing the hauling sliding surface under earthquakes, the shaking table test was conducted in this study. Combined with the numerical calculations, this study proposed magnification of the Arias intensity (MIa) to characterize the overall local deformation damage of the tunnel lining in terms of the deformation characteristics, frequency domain, and energy. Using the time‐domain analysis method, the plastic effect coefficient (PEC) was proposed to characterize the degree of plastic deformation, and the applicability of the seismic cumulative failure effect (SCFE) was discussed. The results show that the low‐frequency component (f1 and f2 ≤ 10 Hz) and the high‐frequency component (f3 and f4 > 10 Hz) acceleration mainly cause global and local deformation of the tunnel lining. The local deformation caused by the high‐frequency wave has an important effect on the seismic damage of the lining. The physical meaning of PEC is more clearly defined than that of the residual strain, and the SCFE of the tunnel lining can also be defined. The SCFE of the tunnel lining includes the elastic deformation effect stage (<0.15g), the elastic–plastic deformation effect stage (0.15g–0.30g), and the plastic deformation effect stage (0.30g–0.40g). This study can provide valuable theoretical and technical support for the construction of traffic tunnels in high‐intensity earthquake areas. The shaking table test of a tunnel crossing the hauling sliding surface is carried out. The MIa evaluation index considering the natural frequency characteristics of earthquakes is proposed. The plastic effect coefficient evaluation index considering the plastic effect of the lining under an earthquake is proposed. Highlights The shaking table test of a tunnel crossing the hauling sliding surface is carried out. The MIa evaluation index considering the natural frequency characteristics of earthquakes is proposed. The plastic effect coefficient evaluation index considering the plastic effect of the lining under an earthquake is proposed.

Arias intensity is an essential ground motion measure correlating with the potential for earthquake-induced landslides. The Sichuan–Yunnan region, which is primarily mountainous, is a high incidence region of earthquake-induced landslides in China. However, there is no available attenuation relationship for this intensity measure due to the backward construction of the stations. In this study, we developed a region-specific Arias intensity attenuation relationship using the China Strong-Motion Networks Center (CSMNC) database which was established in 2008. We recommend this relationship be applied in the Sichuan–Yunnan region for moment magnitudes ranging between 4.2 and 7.0, distances ranging between 0 and 300 km and with Vs30 (the average shear-wave velocity in the upper 30 m of a soil profile) ranging between 128 and 760 m/s. The current study finds that this relationship’s intra-event, inter-event, and total standard deviations are greater than for other regions. This is likely caused by the complicated seismotectonic activities, nonlinear site effects, error from inferring Vs30, basin effects, etc. However, this relationship has the best performance in fitting and predicting the data from the Sichuan–Yunnan region.

In this study, the Beibu earth fissure site in the northeastern part of Weihe Basin, which contains four nearly parallel earth fissures, was studied. A long straight microtremor measuring line, containing 49 measuring points across four earth fissures, was established to investigate the dynamic response of this site using Fourier spectrum, response acceleration spectrum, Arias intensity, and HVSR analyses. The main results are as follows: (1) The fundamental frequencies of 44 measuring points obtained from HVSR analysis are concentrated within 1.67 Hz–2.25 Hz, and the existence of the earth fissures has little effect on the fundamental frequency changes. (2) There is an amplification effect near a single earth fissure. The dynamic responses are large at the measuring points near the earth fissure, and the values decrease with increasing distance from the earth fissure. In areas between two adjacent earth fissures, these values decrease and are even lower than those in sites without amplification effects. (3) In this earth fissure site, the general area (or less affected area) and affected areas were delineated based on the amplification effect. In engineering applications, construction design should avoid these affected areas and existing structures should be reinforced to satisfy the seismic fortification requirements.