Institute of Engineering Seismology and Earthquake Engineering

Construction of precast reinforced concrete (PRC) industrial halls in seismically active areas has been increasing in recent decades. As connections are one of the most sensitive and vulnerable zones of PRC structures, there is a need to pay special attention to their investigation and modeling in seismic analysis. Knowing that each PRC system is specific and unique, this study aims to evaluate the actual seismic performances of PRC industrial halls built in the AMONT system, which represent a significant portion of the existing industrial building stock in Italy, the Balkans, and Turkey. As there is a lack of published research data on its specific joints, the results of the quasi-static full-scale experiments carried out up to failure on the models of four characteristic connections are presented. Since the implementation of nonlinear dynamic analysis in everyday engineering practice can be demanding, a simplified model of the structure considering the effects of the connections’ stiffness is proposed in this paper. The differences in the roof top displacements between the proposed model and the model with the rigid joints of the analyzed frames are in the range from 16.53% to 66.93%. The values of inter-story drift ratios are larger by 10–100% when the real stiffness of connections is considered, which is above the limit value provided by standard EN 1998-1. These results confirm the necessity of considering the nonlinear behavior and stiffness of connections in precast frame structures when determining displacements, which is particularly important for the verification of the serviceability limit state of structures in seismic regions.

ABSTRACT Reliable ground-motion measurements are essential for seismic hazard assessment and require seismological stations to be installed in free-field conditions, away from structural interferences. However, global network analyses show diverse installation configurations that can affect measurements. Although topographic effects are natural, their influence can extend over several meters, making them closely related to operators’ installation choices. Although summit effects are well studied and known to cause strong amplifications, the impact of cliffs or escarpments remains less explored. In this study, 16 SmartSolo IGU-16HR 3C nodes were placed on either side of a 30-m cliff located in Cephalonia (western Greece), for 4–10 months, recording 307 low-to-moderate-magnitude seismic events. The main results reveal that de-amplifications recorded at the cliff base are greater than amplifications determined at the cliff top, particularly between 5 and 15 Hz. This study shows that amplification and de-amplification are primarily driven by topography, whereas their directional dependence is likely shaped by both topographic and lithologic factors. Furthermore, the anisotropy related to cliff fracturing interacts with the effects of source back azimuth. Amplification and de-amplification can be correctly predicted using the frequency-scaled curvature (FSC) and illuminated FSC proxies, provided that an exponential functional form is used for determining de-amplifications at the base of near-vertical cliffs. Furthermore, the azimuthal dependence observed, inducing strong de-amplifications depending on the direction observed, seems related more to the anisotropy of geologic formations than to the cliff topography itself. These effects should be considered when using data from stations in similar topographic settings. Neglecting them can bias ground-motion models and underestimate earthquake magnitudes, particularly for stations at the base of cliffs. From these considerations, we strongly recommend installing future seismological stations a few tens of meters away from the bases and ridges of even small cliffs or escarpments.

In Greece, almost all accelerometer stations provided accelerometer recordings, more than 400 in total, are characterized by inferred Vs30 values based on combination of surface geology and slope proxy (Stewart et al. 2014). However, only about 15% of them have been characterized by in-situ geophysical and geotechnical methods (invasive or/and non-invasive) were performed at a distance less than 100m from the station. In addition, regarding reference rock stations where shear wave velocity Vs30 is equal or greater than 800m/sec (engineering bedrock), only five (5) of them have been characterized todate, with respective values ranging between 800Vs301183m/s. It is evident that measured site characterization parameters of accelerometer stations in Greece is far from a desired goal, especially regarding those on rock reference sites. In this study multiple/combined non-invasive passive and active seismic techniques are applied in six (6) accelerometer stations throughout Greece, to improve earthquake site characterization metadat of the national accelerometer network, focusing on stations placed on geologic rock conditions. The Vsz (S-wave) and Vpz (P-wave) profiles and thereby Vs30 site class according to the Eurocode-8 are determined. In addition, to form a holistic picture of the site’s characterization, surface geology and topographic properties are provided for the investigated stations. Results of this study aim at contributing on improving site characterization parameters estimated by the Generalized Inversion Technique (source, path, site), as well as in defining Ground Motion Models for rock site conditions.

Abstract Structural damage from earthquakes has been assessed using a variety of methodologies, both statistical and, more recently, utilizing Machine Learning (ML) algorithms. The effectiveness of data-driven procedures, even when applied to extremely time-consuming scenarios and data sets that reflect substantial expertise and research, completely depends on the quality of the underlying data. The performance of the intelligent model can also be impacted by a lack of in-depth knowledge and expertise in using complex machine learning architectures. This can also prevent some crucial hyperparameters from being adjusted, which ultimately reduces the algorithm’s reliability and generalizability. The present research offers a Bayesian-based semi-supervised Automatic Differentiation Variational Inference (ADVI) deep autoencoder for forecasting seismic damage of R/C buildings. It is a state-of-the-art, intelligent technology that automatically converts the variables in the issue into actual coordinate space using an upgraded ADVI technique. Finally, using a brand-new Adaptive Learning Rate Gradient Algorithm (ALRGA), it chooses a technique in this area that is a function of the changed variables and optimizes its parameters. Using the sophisticated ADVI technique to establish a posterior distribution without having an analytical solution is an upgraded version of the semi-supervised learning method. Estimating seismic damage to buildings is accelerated and greatly simplified by the suggested methodology, which eliminates the computational complexity of the analytical methods. By performing Nonlinear Time History Analyses of 3D R/C structures exposed to 65 earthquakes, a realistic dataset for the model evaluation is produced. The system’s strong generalizability and the proposed methodology’s detailed convergence stability reveal that it is a valuable method that can outperform other ML algorithms.

Southeastern Europe faces a significant earthquake threat, endangering lives, property, and infrastructure thus jeopardizing sustainable development. The development of a Rapid Earthquake Damage Assessment System (REDAS) designed to deliver crucial earthquake damage information for scenario planning, real-time response, and bolstering public awareness and preparedness is presented. In doing so, REDAS enhances community resilience and safeguards sustainability. REDAS comprises a Rapid Earthquake Damage Assessment platform (REDA.p), a smartphone application, and an Educational Hub (Edu.Hub). REDA.p provides both scenario-based and near real-time seismic damage evaluation of structures, gas pipelines, and geotechnical failures, based on harmonized Ground Motion Prediction Equations and a comprehensive building taxonomy scheme covering the area under investigation. To assess regional landslide hazards, the Infinite Slope Model and a statistics-based model have been implemented, alongside a statistical model for liquefaction probability assessment. Validated against historical data, REDA.p integrates real-time input from key earthquake monitoring networks in the region, covering cross-border areas as well, while in designated urban zones, the system is enhanced by real-time data from a dense earthquake monitoring network deployed in selected school buildings. The smartphone app and Edu.Hub disseminate critical information, guidelines, and tools to improve public prevention, preparedness, and response capacities, thereby enhancing societal resilience.

The present work offers a comprehensive overview of methods related to condition assessment of bridges through Structural Health Monitoring (SHM) procedures, with a particular interest on aspects of seismic assessment. Established techniques pertaining to different levels of the SHM hierarchy, reflecting increasing detail and complexity, are first outlined. A significant portion of this review work is then devoted to the overview of computational intelligence schemes across various aspects of bridge condition assessment, including sensor placement and health tracking. The paper concludes with illustrative examples of two long-span suspension bridges, in which several instrumentation aspects and assessments of seismic response issues are discussed.

In the current manuscript, a novel software application for rapid damage assessment of RC buildings subjected to earthquake excitation is presented based on artificial neural networks. The software integrates the use of a novel deep learning methodology for rapid damage assessment into modern software development platforms, while the developed graphical user interface promotes the ease of use even from non-experts. The aim is to foster actions both in the pre- and post-earthquake phase. The structure of the source code permits the usage of the application either autonomously as a software tool for rapid visual inspections of buildings prior to or after a strong seismic event or as a component of building information modelling systems in the framework of digitizing building data and properties. The methodology implemented for the estimation of the RC buildings’ damage states is based on the theory and algorithms of pattern recognition problems. The effectiveness of the developed software is successfully tested using an extended, numerically generated database of RC buildings subjected to recorded seismic events.

Environmental conditions resulting from climate change may have diverse impacts on the safety and performance of infrastructure.Bridges, as pivotal structural components of transportation systems, are subject to various natural and environmental factors.Therefore, periodic definition of their dynamic characteristics, such as natural frequencies through experimental measurements, is crucial for promptly and accurately assessing their current state.In this study, experimental analysis is employed for this purpose, involving the measurement of structural responses under ambient conditions.This paper presents an investigation into the environmental effects on the dynamic characteristics of reinforced concrete (RC) frame bridges.The study focuses on two overpasses with similar geometries.Three sets of measurements were conducted for each overpass: in October 2017, March 2020, and May 2022.The identified dynamic characteristics were compared across different time points and correlated with environmental effects.The analysis results indicate that the identified natural frequencies effectively reflect changes in the dynamic characteristics of the overpasses due to environmental effects.A significant difference in identified natural frequencies is observed in the longitudinal direction, while minimal variation occurs in the vertical direction.