Response Spectra-Based Post-Earthquake Rapid Structural Damage Estimation Approach Aided with Remote Sensing Data: 2020 Samos Earthquake

Effective post-event emergency management contributes substantially to communities’ earthquake resilience, and one of the most crucial actions following an earthquake is building damage assessment. On-site inspections are dangerous, expensive, and time-consuming. Remote sensing techniques have shown great potential in localizing the most damaged regions and thus guiding aid and rescue operations in recent earthquakes. Furthermore, to prevent post-earthquake casualties, heavily damaged, unsafe buildings must be identified immediately since in most earthquakes, strong aftershocks can cause such buildings to collapse. The potential of the response spectrum concept for being associated with satellite-based remote sensing data for post-earthquake structural damage estimation was investigated in this study. In this respect, a response spectra-based post-earthquake structural damage estimation method aided by satellite-based remote sensing data was proposed to classify the buildings after an earthquake by prioritizing them based on their expected damage levels, in order to speed up the damage assessment process of critical buildings that can cause casualties in a possible strong aftershock. A case study application was implemented in the Bayrakli region in Izmir, Turkey, the most affected area by the Samos earthquake, on 30 October 2020. The damage estimations made in this research were compared with the in situ damage assessment reports prepared by the Republic of Turkey Ministry of Environment and Urbanization experts. According to the accuracy assessment results, the sensitivity of the method is high (91%), and the necessary time spent by the in situ damage assessment teams to detect the critical buildings would have been significantly reduced for the study area.

Cavitation erosion damage of self-fluxing NiCrSiB hardfacings deposited by oxy-acetylene powder welding

This paper comparatively investigates the cavitation erosion damage of two self-fluxing NiCrSiB hardfacings deposited via the oxy-acetylene powder welding method. Examinations were conducted according to the procedure given by ASTM G32 standard. In order to research cavitation erosion (CE), the vibratory apparatus was employed. The cavitation damaged surfaces were inspected using a scanning electron microscope, optical microscope and surface profilometer. The hardness of the A-NiCrSiB hardfacing equals 908HV while that of C-NiCrSiB amounts to 399HV. The research showed that the CE resistance of C-NiCrSiB is higher than that of A-NiCrSiB. The results demonstrate that in the case of multiphase materials, like the NiCrSiB hardfacings, hardness cannot be the key factor for cavitation erosion damage estimation whereas it is strongly subjected to material microstructure. In order to qualitatively recognise the cavitation erosion damage of the NiCrSiB self-fluxing hardfacings at a given exposure time, the following factors should be respected: physical and mechanical properties, material microstructure and also material loss and eroded surface morphology, both stated at specific testing time. The general idea for the cavitation erosion damage estimation of the NiCrSiB oxy-acetylene welds was presented.

Stiffness Modification-Based Bayesian Finite Element Model Updating to Solve Coupling Effect of Structural Parameters: Formulations

The Bayesian model updating approach (BMUA) benefits from identifying the most probable values of structural parameters and providing uncertainty quantification. However, the traditional BMUA is often used to update stiffness only with the assumption of well-known mass, which allows unidentifiable cases induced by the coupling effect of mass and stiffness to be circumvented and may not be optimal for structures experiencing damages in both mass and stiffness. In this paper, the new BMUA tailored to estimating both mass and stiffness is presented by using two measurement states (original and modified systems). A new eigenequation with a stiffness-modified system is formulated to address the coupling effect of mass and stiffness. The posterior function is treated using an asymptotic approximation method, giving the new objective functions with stiffness modification. Analytical formulations of modal parameters and structural parameters are then derived by a linear optimization method. In addition, the covariance matrix of uncertain parameters is determined by the inverse of the Hessian matrix of the objective function. The performance of the proposed BMUA is evaluated through two numerical examples in this study; a probabilistic damage estimation is also implemented. The results show the proposed BMUA is superior to the traditional one in mass and stiffness updating.

Damage Estimation using Shock Zones: A case study of Amphan tropical cyclone

The tropical cyclone affects millions of people living in the coastal regions. The changing climate has led to an increased intensity and frequency of cyclones, therefore increasing the damage caused to people, the environment, and property. The Bay of Bengal is most prone to tropical cyclones, which affects Bangladesh and the eastern coastal region of India due to geographical proximity. Hence, a comprehensive understanding of the inundation damage and intensity caused becomes essential to focus the relief efforts on the affected districts. This study identified the shock zone and assessed the inundation associated damage caused by recent cyclone Amphan in the area of Bangladesh and West Bengal in India. The shock zonation was based on the track of cyclones, cyclone wind speed zones, elevation, wind impact potentiality, and agricultural population area. The identification of the affected area was done using integrated Landsat and SAR data, and economic damage cost was assessed using the Asian Development Bank’s (ADB) Unit price approach. The total people affected due to inundation are 2.4 million in India and 1.4 million in Bangladesh and the damage totaled up to 5.4 million USD. The results of this study can be used by concerned authorities to identify the shock zones and be used for rapid assessment of the damages.

Earthquake Damage Assessment in Three Spatial Scale Using Naive Bayes, SVM, and Deep Learning Algorithms

Earthquakes lead to enormous harm to life and assets. The ability to quickly assess damage across a vast area is crucial for effective disaster response. In recent years, social networks have demonstrated a lot of capability for improving situational awareness and identifying impacted areas. In this regard, this study proposed an approach that applied social media data for the earthquake damage assessment at the county, city, and 10 × 10 km grids scale using Naive Bayes, support vector machine (SVM), and deep learning classification algorithms. In this study, classification was evaluated using accuracy, precision, recall, and F-score metrics. Then, for understanding the message propagation behavior in the study area, temporal analysis based on classified messages was performed. In addition, variability of spatial topic concentration in three classification algorithms after the earthquake was examined using location quotation (LQ). A damage map based on the results of the classification of the three algorithms into three scales was created. For validation, confusion matrix metrics, Spearman’s rho, Pearson correlation, and Kendall’s tau were used. In this study, binary classification and multi-class classification have been done. Binary classification was used to classify messages into two classes of damage and non-damage so that their results could finally be used to estimate the earthquake damage. Multi-class classification was used to categorize messages to increase post-crisis situational awareness. In the binary classification, the SVM algorithm performed better in all the indices, gaining 71.22% accuracy, 81.22 F-measure, 79.08% accuracy, 85.62% precision, and 0.634 Kappa. In the multi-class classification, the SVM algorithm performed better in all the indices, gaining 90.25% accuracy, 88.58% F-measure, 84.34% accuracy, 93.26% precision, and 0.825 Kappa. Based on the results of the temporal analysis, most of the damage-related messages were reported on the day of the earthquake and decreased in the following days. Most of the messages related to infrastructure damages and injured, dead, and missing people were reported on the day of the earthquake. In addition, results of LQ indicated Napa as a center of the earthquake as the concentration of damage-related messages in all algorithms were based there. This indicates that our approach has been able to identify the damage well and has considered the earthquake center one of the most affected counties. The findings of the damage estimation showed that going away from the epicenter lowered the amount of damage. Based on the result of the validation of the estimated damage map with official data, the SVM performed better for damage estimation, followed by deep learning. In addition, at the county scale, algorithms showed better performance with Spearman’s rho of 0.8205, Pearson correlation of 0.5217, and Kendall’s tau of 0.6666.