Search Results (65)

We review a theoretical and experimental programme with the aim of understanding two intimately related fundamental phenomena in biophysics: (i) the classical analogue of Fröhlich phonon condensation in macromolecules driven out of thermal equilibrium and (ii) the consequent activation of long-range resonant electrodynamic intermolecular forces. Both phenomena are underpinned by explicit Hamiltonian models. The first is derived by applying the time-dependent variational principle (TDVP) to the quantum Wu–Austin model, producing a fully classical Hamiltonian in action-angle variables whose nonlinear rate equations exhibit a nonequilibrium phase transition: the channelling of supplied energy into the lowest-frequency collective mode. The second is grounded in a classical electrodynamic Hamiltonian for two coupled oscillating dipoles whose normal-mode structure predicts long-range (∼$1/r^3$) resonant interactions, absent at thermal equilibrium but activated by out-of-equilibrium collective oscillations. We also discuss a complementary Hamiltonian approach that connects Fröhlich’s rate equations directly to Hamilton’s equations of motion, clarifying the role of bath-mediated nonlinear coupling and the conditions for strong condensation at room temperature. In addition, the TDVP is applied to a Davydov–Holstein–Fröhlich Hamiltonian describing electron–phonon motion along the backbone of a specific DNA sequence and its cognate restriction enzyme, EcoRI: the time-domain Fourier cross-spectrum of the resulting electron currents exhibits a sharp co-resonance peak for the canonical recognition sequence that disappears upon randomisation, providing a sequence-specific electrodynamic signature of DNA–protein recognition. Experimental evidence from THz near-field spectroscopy, fluorescence correlation spectroscopy, and direct observation of protein clustering is reviewed in relation to these theoretical predictions. The results establish a coherent physical picture suggesting that metabolic energy supply can play a role in driving macromolecules into coherently oscillating states that activate selective, distance-reaching electrodynamic forces capable of contributing to the organisation of biochemical reactions in living matter. Full article

Near-fault ground motions influenced by rupture directivity impose seismic demands that differ fundamentally from those associated with conventional far-field earthquakes, particularly in terms of displacement-controlled response. This study presents a performance-based seismic assessment of existing reinforced concrete (RC) buildings subjected to near-fault ground motions in the Sivrice–Pütürge segment of the Malatya–Ovacık Fault Zone, Eastern Anatolia. A probabilistic seismic hazard analysis (PSHA) was performed using NGA-West2 ground-motion prediction equations together with a regionally defined fault model, and the resulting hazard was evaluated within the framework of the Turkish Building Earthquake Code (TBEC-2018). Code-compatible earthquake records were selected and scaled for the DD-2 design earthquake level, while rupture directivity was represented using a literature-based median amplification factor. Nonlinear time-history analyses were subsequently carried out for three existing RC buildings representing low-, mid-, and high-rise structural typologies. Structural performance was evaluated in terms of roof displacement, interstory drift ratio, base shear, and element-level damage states. The maximum roof displacements reached 0.070 m, 0.107 m, and 0.138 m for the low-, mid-, and high-rise buildings, respectively, corresponding to maximum drift ratios of 0.60%, 0.46%, and 0.34%. The results indicate that rupture directivity has only a limited influence on base shear demand but substantially increases displacement-related response quantities and promotes a redistribution of structural damage from predominantly beam-controlled behavior toward increased participation of columns and shear walls, particularly in medium- and high-rise buildings. These findings demonstrate that conventional code-based assessment procedures may underestimate deformation demands in fault-proximal regions and highlight the importance of explicitly considering rupture directivity in the seismic performance assessment of existing reinforced concrete buildings. Full article

Armenia is located within the central segment of the Arabia–Eurasia continental collision zone and is exposed to significant seismic hazard. This study presents an updated probabilistic seismic hazard assessment (PSHA) for Armenia based on an integrated seismotectonic framework incorporating active fault data, paleoseismological evidence, and historical and instrumental seismicity. A hybrid seismic source model was developed by combining fault-based characteristic earthquake sources with distributed background seismicity. Hazard calculations were performed using the OpenQuake engine within a logic-tree framework to account for epistemic uncertainties in earthquake occurrence and ground-motion prediction. Ground motion was estimated using a weighted set of ground motion prediction equations (GMPEs). Peak ground acceleration (PGA) hazard maps were computed for several return periods, with emphasis on the 475-year return period (10% probability of exceedance in 50 years). The results indicate PGA values across Armenia ranging from approximately 0.2 g to 0.5 g, with the highest hazard levels in northwestern Armenia along the Pambak–Sevan–Syunik Fault System. Hazard deaggregation shows that seismic hazard in major Armenian cities is primarily controlled by shallow earthquakes with magnitudes Mw 6.8–7.4 occurring within ~30 km of urban centers. The results provide a scientific basis for seismic hazard assessment, zonation, and earthquake risk mitigation in Armenia. Full article

Mexicali, capital of Baja California, has 1,049,792 inhabitants and lies in a high-seismic-hazard zone in northwestern Mexico, according to CENAPRED, the MDOC-CFE-2015 seismic regionalization, and the ASCE 7-22 “Hazard Toolkit”. This study develops a probabilistic seismic hazard map to estimate peak ground accelerations with a 2% probability of exceedance in 50 years, using the OpenQuake platform. The study area coincides with the 2025 urban development plan polygon for the central population area defined by the Municipal Institute for Research and Urban Planning of Mexicali. The Imperial and Cerro Prieto faults, the Pescaderos–Indiviso fault system, and the Laguna Salada fault were modeled as seismic sources. Four PEER-NGA ground motion prediction equations and regional geophysical and geotechnical data were employed to characterize shear-wave velocity (Vs30). Design response spectra were generated for each grid point for the 21 periods specified in ASCE 7-22. A representative Vs30 of 236 m/s was obtained, and the a, b, and Mc parameters were derived for the seismic catalog. Resulting peak ground accelerations range from 0.842 g to 1.221 g, with a maximum spectral pseudo-acceleration of 2.23 g at 0.30 s. Full article

Marine ports located in regions of moderate seismicity can face high Natech (natural hazard-triggered technological) risk because large inventories of hazardous materials are stored near dense urban populations. This study proposes and applies a Natech risk framework to a representative port on the Saint-Laurence River in Quebec, Canada. Site-specific peak ground accelerations (PGA) are first estimated for 12 earthquake scenarios using regional ground motion prediction equations adjusted for local site conditions. These hazard levels are combined with a damage probability matrix to estimate Hazardous Release Likelihood Index (HRLi) scores for atmospheric steel storage tanks. Offsite consequences are then evaluated to obtain Maximum Distances of Effect (MDEs) for different types of hazardous materials. MDE footprints are intersected with block-level demographic data and complemented by a domino-effect based on inter-tank spacing, yielding a tank-level Natech Risk Index $NR{\mathrm{I}}_{\mathrm{i},\mathrm{s}}$ for each storage tank ($\mathrm{i}$) and seismic scenario ($\mathrm{s}$). These values are then averaged over all tanks to obtain a scenario-level mean Natech Risk Index ($\overline{NRI}$) for each tank substance. Regression equations relating $\overline{NRI}$ to PGA are provided as a practical tool for defining critical intensity thresholds for seismic Natech risk management in marine ports. Full article

This study presents an integrated framework that couples three-dimensional geotechnical ground modeling with a HAZUS-based urban seismic vulnerability assessment for Seoul, Korea. Over 63,000 boreholes, in situ seismic tests, and building inventory records were compiled into a unified relational database following rigorous multi-stage quality control. A multi-parameter N–Vs regression model was calibrated to supplement missing shear-wave velocity (Vs) data, reducing prediction errors by more than 20% relative to conventional empirical equations. Based on the quality-controlled Vs dataset, a high-resolution three-dimensional Vs–ground model was constructed to represent subsurface heterogeneity and associated uncertainty across the metropolitan area. The building inventory, comprising approximately 700,000 structures, was standardized according to the HAZUS structural taxonomy and mapped to Korean seismic design eras, enabling a Seoul-adapted vulnerability assessment in which exposure characterization and seismic demand are localized. Site-specific ground-motion amplification and response spectra derived from the 3D ground model were used to modify the spectral acceleration input to the HAZUS fragility functions. Results reveal pronounced spatial variability in site conditions, with northern mountainous zones corresponding primarily to NEHRP Site Class B, central districts to Class C, and southern alluvial basins to Classes D–E, producing amplification differences of up to 1.7 under identical input spectral accelerations. High-risk zones such as Gangnam, Songpa, and Yeouido exhibit concentrated expected damage where thick alluvial deposits coincide with dense stocks of mid-rise reinforced-concrete buildings. Overall, the study demonstrates that integrating high-resolution 3D geotechnical ground models with HAZUS-based fragility analysis provides a physically consistent and data-driven basis for urban-scale seismic risk assessment and resilience planning. Full article

Inelastic displacement ratios are critical parameters in the seismic design of reinforced concrete (RC) box girder bridges. Existing approaches of displacement prediction, including the Displacement Coefficient Method and the Capacity Spectrum Method, typically rely on simplified single-degree-of-freedom (SDOF) models, which do not fully account for the complex and nonlinear behavior of multi-degree-of-freedom (MDOF) bridge systems. Moreover, the AASHTO Guide Specifications apply the equal displacement rule through the inelastic displacement modification factor R_d, which may underestimate displacement demands for short-period structures. This study evaluates the accuracy of the AASHTO R_d using nonlinear time history analyses of six RC box girder bridge models subjected to 28 recorded ground motions from California. Each ground motion included two orthogonal components applied in the longitudinal and transverse direction. Both elastic and inelastic displacement demands were determined in each direction, and inelastic displacement ratios (C_μ) were computed and compared with AASHTO predictions. A new predictive equation for C_μ was developed to capture response variability. While AASHTO R_d aligns with the average behavior, it fails to provide reliable estimate across the full range of seismic conditions. A comprehensive parametric study was conducted to examine the influence of column boundary condition, column height, superstructure deck width, number of spans, and damping ratio on C_μ. While the elastic and inelastic displacement decreases with an increase in damping ratio, the result shows that C_μ increases with higher damping ratios. Accordingly, a revised amplification factor was proposed to better represent the inelastic displacement demand in MDOF bridge systems. Full article

The frequency content of strong ground motion significantly affects the response of engineered systems under seismic excitation. Among some scalar parameters which exist in the literature, the mean period T_m has proved to be the most efficient. Ground Motion Predictive Equations (GMPEs) are usually developed for ground motion parameters through the calibration of coefficients of predefined functional forms, via linear or nonlinear regression, and based on recorded ground motion data. Such expressions of T_m are rare in the literature. Recently, the use of machine learning (ML) algorithms in earthquake engineering and engineering seismology has increased. The Artificial Neural Network (ANN) is an effective ML algorithm which has already been explored for the development of GMPEs for amplitude-based ground motion parameters. Within the work presented herein, multiple nonlinear regression (NLR)- and ANN-based GMPEs are developed for T_m using the latest strong motion database for shallow earthquakes in Greece. To the author’s knowledge, the implementation of ANN for producing GMPEs for T_m for shallow earthquake events has not been explored. Direct comparison between the NLR- and ANN-based GMPEs is performed, in terms of performance indexes, aleatory uncertainty, and working examples, as well as testing against earthquake events not included in the original dataset. The results reveal that the ANN-based GMPEs are useful in reducing aleatory uncertainty, although care should be taken in their implementation to avoid overfitting issues. Full article

This study proposes the first ground motion prediction equation (GMPE) for the parameter I_Np, an intensity measure based on the spectral shape. A Machine Learning Algorithm based on Support Vector Machines (SVMs) was employed due to its robustness towards outliers, which is a key advantage over ordinary linear regression. I_Np also offers a more robust measure of the ground motion intensity than the traditionally used spectral acceleration at the first mode of vibration of the structure Sa(T₁). The SVM algorithm, configured for regression (SVR), was applied to derive the prediction coefficients of I_Np for diverse vibration periods. Furthermore, the complete dataset was analyzed to develop a unified, generalized expression applicable across all the periods considered. To validate the model’s reliability and its ability to generalize, a cross-validation analysis was performed. The results from this rigorous validation confirm the model’s robustness and demonstrate that its predictive accuracy is not dependent on a specific data split. The numerical results show that the newly developed GMPE reveals high predictive accuracy for periods shorter than 3 s and acceptable accuracy for longer periods. The generalized equation exhibits an acceptable coefficient of determination and Mean Squared Error (MSE) for periods from 0.1 to 5 s. This work not only highlights the powerful potential of machine learning in seismic engineering but also introduces a more sophisticated and effective tool for predicting ground motion intensity. Full article

Earthquakes can severely disrupt power distribution networks, causing extensive outages and disconnection from the transmission grid. This paper proposes a two-stage restoration method tailored for post-earthquake distribution systems. First, earthquake-induced damage is modeled using ground motion prediction equations (GMPEs) and fragility curves, and degraded network topologies are generated by Monte Carlo simulation. Then, a time-domain generator start-up model is developed as a mixed-integer linear program (MILP), incorporating cranking power and radial topology constraints. Further, a prioritized load recovery model is formulated as a mixed-integer second-order cone program (MISOCP), integrating power flow, voltage, and current constraints. Finally, case studies demonstrate the effectiveness and general applicability of the proposed method, confirming its capability to support resilient and adaptive distribution network restoration under various earthquake scenarios. Full article

Post-earthquake fires are typically of great concern for fire protection services, which are expected to be in high demand immediately after a strong earthquake. The post-earthquake functionality of fire stations is necessary after strong earthquakes to reduce potential fire damage and improve emergency services. A reliable assessment of the seismic vulnerability and expected damage for fire stations is therefore a necessary step towards the identification of the most vulnerable structures and the prioritization of seismic retrofit activities. This article presents the development of a methodology for the damage assessment of fire stations based on earthquakes scenarios. The framework is based on four models: seismic hazard, inventory, fragility and impact. The seismic hazard model represents ground shaking in terms of intensity measure at each station using a ground motion prediction equation for Eastern Canada. The inventory model categorizes all the fire stations in building classes based on construction material and seismic code level. The fragility model associates building classes with fragility functions that provide the relationship between intensity measure and expected damage probabilities. The impact model converts damage probabilities into a mean damage state. All Montreal fire stations were selected as case study demonstrations. Simulations were conducted by varying the epicenter location and magnitude for a total number of 345 scenarios. Simplified relationships that correlate the earthquake magnitude and expected damage were developed. The study showed that, for magnitude 6 earthquakes, 45% of stations on average would sustain at least moderate damage. The methodology is particularly useful for emergency planning and prioritization of seismic retrofit activities. Full article

The identification of the most pertinent site parameters to classify soils in terms of their amplification of seismic ground motions is still of prime interest to earthquake engineering and codes. This study investigates many options for improving soil classifications in order to reduce the deviation between “exact” predictions using wave propagation and the method used in seismic codes based on amplification (site) factors. To this end, an exhaustive parametric study is carried out to obtain nonlinear responses of sets of 324 clay and sand columns and to constitute the database for neuronal network methods used to predict the regression equations of the amplification factors in terms of seismic and site parameters. A wide variety of parameters and their combinations are considered in the study, namely, soil depth, shear wave velocity, the stiffness of the underlaying bedrock, and the intensity and frequency content of the seismic excitation. A database of AFs for 324 nonlinear soil profiles of sand and clay under multiple records with different intensities and frequency contents is obtained by wave propagation, where soil nonlinearity is accounted for through the equivalent linear model and an iterative procedure. Then, a Generalized Regression Neural Network (GRNN) is used on the obtained database to determine the most significant parameters affecting the AFs. A second neural network, the Radial Basis Function (RBF) network, is used to develop simple and practical prediction equations. Both the whole period range and specific short-, mid-, and long-period ranges associated with the AFs, F_a, F_v, and F_l, respectively, are considered. The results indicate that the amplification factor of an arbitrary soil profile can be satisfactorily approximated with a limited number of sites and the seismic record parameters (two to six). The best parameter pair is (PGA; resonance frequency, f₀), which leads to a standard deviation reduction of at least 65%. For improved performance, we propose the practical triplet $\left({PGA;V}_{s30};f_0\right)$ with V_s30 being the average shear wave velocity within the upper 30 m of soil below the foundation. Most other relevant results include the fact that the AFs for long periods (F_l) can be significantly higher than those for short or mid periods for soft soils. Finally, it is recommended to further refine this study by including additional soil parameters such as spatial configuration and by adopting more refined soil models. Full article

This study collects all strong motion records from 134 seismic events in the Loess Plateau region of China between 2020 and 2023. The corresponding ground motion attenuation dataset is constructed through data processing and screening, including 1149 soil horizontal strong ground motion records. Based on this dataset, two ground motion attenuation models are developed. Specifically, through magnitude grouping and weighted processing, two-step fitting methods are used to establish prediction equations for the peak ground acceleration (PGA), effective peak acceleration (EPA), and peak ground velocity (PGV) at the horizontal soil layers. These equations can estimate ground motion parameters with magnitudes ranging from M_S 3.0 to M_S 6.5 and epicentral distances between 0 and 100 km. Furthermore, attenuation curves are plotted based on the prediction equations. A comparison between the actual observed values and the predicted curves and an evaluation of the fitting residuals assess our models’ fitting performance. The attenuation characteristics of horizontal ground motion parameters in the Loess Plateau region are systematically summarized. Full article

In this study, we present an analytical tool that can be used to predict the nonlinear dynamic response of ground effect vehicles (GEVs) advancing through sinusoidal head-sea waves. GEVs exhibit a unique instability phenomenon known as porpoising, which is an oscillatory motion along the heave and pitch axes that can cause serious structural damage. The heaving and pitching equations of motion are presented in the form of coupled, forced, and nonlinear Duffing-type equations with cubic nonlinearity. The analytical model developed in this study leverages the Poincaré–Lindstedt perturbation method to express the amplitude and frequency of motion in terms of all physical parameters. The accuracy and reliability of the proposed model were validated through computational fluid dynamics (CFD) simulations based on incompressible unsteady Reynolds-averaged Navier–Stokes (RANS) equations. The results show a strong agreement between the analytical tool and the CFD simulations, with minor discrepancies due to assumptions inherent in the $2D$ simulations, particularly the assumption that seawater only passes beneath the hull, resulting in increased buoyancy forces and reduced damping. This study offers a novel and practical method for predicting the dynamic stability of GEVs under realistic sea conditions, potentially enhancing safety and operational efficiency by mitigating the risks associated with porpoising. Full article

Earthquake environmental effects may significantly contribute to the damage caused by seismic events; similar to ground motion, the environmental effects are globally stronger in the vicinity and decrease moving away from the epicenter or seismogenic source. To date, a single intensity prediction equation (IPE) has been proposed in the Italian Apennines for intensity scale dealings with environmental effects: the Environmental Seismic Intensity (ESI-07). Here, we evaluate the sensitivity of the IPE with respect to input data and methodological choices and we propose IPEs with global validity for crustal normal faults. We show the strong influence of input data on the obtained attenuation investigating the 1980 Irpinia–Basilicata (Southern Italy) earthquake. We exploit a dataset of 26 earthquakes to build an IPE considering the epicentral distance. We also propose an IPE considering the distance from the fault rupture, which is derived from a dataset of 10 earthquakes. The proposed equations are valid for normal faults up to 40 km from the epicenter/fault and may flank other models predicting ground motion or damage to the built environment. Our work thus contributes to the use of the ESI-07 scale for hazard purposes. Full article