• Earthquakes and Structures
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• v.3 no.2
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• pp.117-140
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• 2012

The proposed research includes a comprehensive study on the seismic vulnerability assessment of typical building types, representative of the structural materials, the seismic codes and the construction techniques of Southern Europe. A damage database is created after the elaboration of the results of the observational data obtained from post-earthquake surveys carried out in the area struck by the September 7, 1999 Athens earthquake, a near field seismic event in an extended urban region. The observational database comprises 180.945 buildings which developed damage of varying degree, type and extent. The dataset is elaborated in order to gather useful information about the structural parameters influence on the seismic vulnerability and their correlation to the type and degree of building damages in near field earthquakes. The damage calibration of the observational data was based on label - damage provided by Earthquake Planning and Protection Organization (EPPO) in Greece and referred to the qualitative characterization for the recording of damage in post-earthquake surveys. Important conclusions are drawn on the parameters that influence the seismic response based on the wide homogeneous database which adds to the reliability of the collected information and reduces the scatter on the produced results.

• Structural Engineering and Mechanics
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• v.92 no.2
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• pp.197-206
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• 2024

In this study, a unified framework that integrates pre- and post-earthquake assessments of buildings was proposed to enhance urban disaster preparedness through the coordination of pre- and post- earthquake efforts. Within this framework, a case study based on the 2023 Kahramanmaraş Earthquake was performed comparing the distribution of seismic risk prioritization for 117 reinforced concrete buildings with their actual damage states observed during post-earthquake field inspections. In order to conduct pre-earthquake evaluation process, street-level images were employed using two different rapid visual screening methods. With the use of generated geospatial database enabling the efficient and reliable transmission of the data between both stages of the assessment procedures, the alignment and validation of pre- and post-earthquake evaluations of the buildings were achieved enhancing the coordination of seismic risk management strategies. By implementing the proposed joint framework in this study, an extensive seismic vulnerability evaluation on an urban scale could be achieved by optimizing the computational demands, cost and time required for the strategic planning activities.

• Earthquakes and Structures
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• v.17 no.4
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• pp.365-372
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• 2019

Post-earthquake crisis management is a key capability for a country to be able to recover after a major seismic event. Instrumental seismic data transmitted and processed in a very short time can contribute to better management of the emergency and can give insights on the earthquake's impact on a specific area. Romania is a country with a high seismic hazard, mostly due to the Vrancea intermediate-depth earthquakes. The elastic acceleration response spectrum of a seismic motion provides important information on the level of maximum acceleration the buildings were subjected to. Based on new data analysis and knowledge advancements, the acceleration elastic response spectrum for horizontal ground components recommended by the Romanian seismic codes has been evolving over the last six decades. This study aims to propose a framework for post-earthquake warning based on code spectrum exceedances. A comprehensive background analysis was undertaken using strong motion data from previous earthquakes corroborated with observational damage, to prove the method's applicability. Moreover, a case-study for two densely populated Romanian cities (Focsani and Bucharest) is presented, using data from a $5.5M_W$ earthquake (October 28, 2018) and considering the evolution of the three generations of code-based spectral levels for the two cities. Data recorded in free-field and in buildings were analyzed and has confirmed that no structural damage occurred within the two cities. For future strong seismic events, this tool can provide useful information on the effect of the earthquake on structures in the most exposed areas.

• Structural Engineering and Mechanics
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• v.46 no.6
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• pp.791-808
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• 2013

The main purpose of this paper is to develop seismic fragility curves for existing reinforced concrete, RC, buildings based on the post earthquake field survey and the seismic performance using capacity design. Existing RC buildings constitute approximately 65% of the total stock in Algiers. This type of buildings, RC, was widely used in the past and chosen as the structural type for the future construction program of more than 2 millions apartments all over Algeria. These buildings, suffered moderate to extensive damage after the 2003 Boumerdes earthquake, on May 21st. The determination of analytical seismic fragility curves for low-rise and mid-rise existing RC buildings was carried out based on the consistent and complete post earthquake survey after that event. The information on the damaged existing RC buildings was investigated and evaluated by experts. Thirty four (34) communes (districts) of fifty seven (57), the most populated and affected by earthquake damage were considered in this study. Utilizing the field observed damage data and the Japanese Seismic Index Methodology, based on the capacity design method. Seismic fragility curves were developed for those buildings with a large number data in order to get a statistically significant sample size. According to the construction period and the code design, four types of existing RC buildings were considered. Buildings designed with pre-code (very poor structural behavior before 1955), Buildings designed with low code (poor structural behavior, between 1955-1981), buildings designed with medium code (moderate structural behavior, between 1981-1999) and buildings designed with high code (good structural behavior, after 1999).

• Earthquakes and Structures
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• v.4 no.3
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• pp.299-324
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• 2013

The current research focuses on the seismic vulnerability assessment of typical Southern Europe buildings, based on processing of a large set of observational damage data. The presented study constitutes a sequel of a previous research. The damage statistics have been enriched and a wider damage database (178578 buildings) is created compared to the one of the first presented paper (73468 buildings) with Damage Probability Matrices (DPMs) after the elaboration of the results from post-earthquake surveys carried out in the area struck by the 7-9-1999 near field Athens earthquake. The dataset comprises buildings which developed damage in several degree, type and extent. Two different parameters are estimated for the description of the seismic demand. After the classification of damaged buildings into structural types they are further categorized according to the level of damage and macroseismic intensity. The relative and the cumulative frequencies of the different damage states, for each structural type and each intensity level, are computed and presented, in terms of damage ratio. Damage Probability Matrices (DPMs) are obtained for typical structural types and they are compared to existing matrices derived from regions with similar building stock and soil conditions. A procedure is presented for the classification of those buildings which initially could not be discriminated into structural types due to restricted information and hence they had been disregarded. New proportional DPMs are developed and a correlation analysis is fulfilled with the existing vulnerability relations.

• Smart Structures and Systems
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• v.31 no.4
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• pp.311-323
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• 2023

Ever since ancient times, earthquakes have been a major threat to the civil infrastructures and the safety of human beings. The majority of casualties in earthquake disasters are caused by the damaged civil infrastructures but not by the earthquake itself. Therefore, the efficient and accurate post-earthquake assessment of the conditions of structural damage has been an urgent need for human society. Traditional ways for post-earthquake structural assessment rely heavily on field investigation by experienced experts, yet, it is inevitably subjective and inefficient. Structural response data are also applied to assess the damage; however, it requires mounted sensor networks in advance and it is not intuitional. As many types of damaged states of structures are visible, computer vision-based post-earthquake structural assessment has attracted great attention among the engineers and scholars. With the development of image acquisition sensors, computing resources and deep learning algorithms, deep learning-based post-earthquake structural assessment has gradually shown potential in dealing with image acquisition and processing tasks. This paper comprehensively reviews the state-of-the-art studies of deep learning-based post-earthquake structural assessment in recent years. The conventional way of image processing and machine learning-based structural assessment are presented briefly. The workflow of the methodology for computer vision and deep learning-based post-earthquake structural assessment was introduced. Then, applications of assessment for multiple civil infrastructures are presented in detail. Finally, the challenges of current studies are summarized for reference in future works to improve the efficiency, robustness and accuracy in this field.

• Structural Engineering and Mechanics
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• v.91 no.5
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• pp.443-457
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• 2024

At present, the traditional concrete surface inspection methods based on artificial vision have the problems of high cost and insecurity, while the computer vision methods rely on artificial selection features in the case of sensitive environmental changes and difficult promotion. In order to solve these problems, this paper introduces deep learning technology in the field of computer vision to achieve automatic feature extraction of structural damage, with excellent detection speed and strong generalization ability. The main contents of this study are as follows: (1) A method based on DeepLabV3+ convolutional neural network model is proposed for surface detection of post-earthquake structural damage, including surface damage such as concrete cracks, spaling and exposed steel bars. The key semantic information is extracted by different backbone networks, and the data sets containing various surface damage are trained, tested and evaluated. The intersection ratios of 54.4%, 44.2%, and 89.9% in the test set demonstrate the network's capability to accurately identify different types of structural surface damages in pixel-level segmentation, highlighting its effectiveness in varied testing scenarios. (2) A semantic segmentation model based on DeepLabV3+ convolutional neural network is proposed for the detection and evaluation of post-earthquake structural components. Using a dataset that includes building structural components and their damage degrees for training, testing, and evaluation, semantic segmentation detection accuracies were recorded at 98.5% and 56.9%. To provide a comprehensive assessment that considers both false positives and false negatives, the Mean Intersection over Union (Mean IoU) was employed as the primary evaluation metric. This choice ensures that the network's performance in detecting and evaluating pixel-level damage in post-earthquake structural components is evaluated uniformly across all experiments. By incorporating deep learning technology, this study not only offers an innovative solution for accurately identifying post-earthquake damage in civil engineering structures but also contributes significantly to empirical research in automated detection and evaluation within the field of structural health monitoring.

• Steel and Composite Structures
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• v.30 no.4
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• pp.351-364
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• 2019

Earthquake and fire are both severe disasters for building structures. Since earthquake-induced damage will weaken the structure and reduce its fire endurance, it is important to investigate the behavior of structure subjected to post-earthquake fire. In this paper, steel-concrete composite beam-to-column joints were tested under fire with pre-damage caused by cyclic loads. Beforehand, three control specimens with no pre-damage were tested to capture the static, cyclic and fire-resistant performance of intact joints. Experimental data including strain, deflection and temperature recorded at several points are presented and analyzed to quantify the influence of cyclic damage on fire resistance. It is indicated that the fire endurance of damaged joints decreased with the increase of damage level, mainly due to faster heating-up rate after cyclic damage. However, cracks induced by cyclic loading in concrete are found to mitigate the concrete spalling at elevated temperatures. Moreover, the relationship between fire resistance and damage degree is revealed from experimental results, which can be applied in fire safety design and is worthwhile for further research.

• 한국방재학회:학술대회논문집
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• 2011.02a
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• pp.12-19
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• 2011

Drawing on its extensive experience with natural disasters, Japan has been dispatching Japan Disaster Relief (JDR) team to disaster-stricken countries to provide specialist assistance in rescue and medical operations. The JDR team has assisted in the wake of disasters including the 2004 Indian Ocean Earthquake and the 2008 Sichuan Earthquake in China. Information about the affected area is essential for a rapid disaster response. However, it can be difficult to gather information on damages in the immediate post-disaster period. To help overcome this problem, we have built on an Earthquake Damage Estimation System. This system makes it possible to produce distributions of the earthquake's seismic intensity and structural damage based on pre-calculated data such as landform and site amplification factors for Peak Ground Velocity, which are estimated from a Digital Elevation Model, as well as population distribution. The estimation result can be shared with the JDR team and with other international organizations through communications satellite or the Internet, enabling more effective rapid relief operations.

• Journal of the Korean Geotechnical Society
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• v.20 no.7
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• pp.15-24
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• 2004

This paper describes a GIS-based assessment of residential building damage caused by the 1994 Northridge earthquake in which the fractions of existing buildings damaged at various percentages of replacement cost are related to a range of seismic parameters. The assessment uses data from safety inspection reports and tax assessor records, both of which were geocoded and linked to seismic parameters derived from strong motion records at 164 different sites. The paper also describes a GIS-based pattern recognition algorithm for identifying locations of most intense building damage. The algorithm provides a framework for rapidly screening remote sensing data and dispatching emerging services.