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.

Fatigue Damage Map of AZ31B-F Magnesium Alloys under Multiaxial Loading Conditions

In this work, the mechanical behavior of the AZ31B-F magnesium alloy under cyclic loading is analyzed with the goal of contributing to the advancement of its use in the design of AZ31B-F components and structures. To achieve this goal, an experimental program was implemented to evaluate the cyclic response of the AZ31B-F under specific proportional loads with different stress amplitude ratios. Afterwards, regression methods were applied to extend the experimental data to a wide range of proportional loads. As a result, the AZ31B-F damage map, a material property that stablishes the damage scale between normal and shear stresses for finite life loading regimes, was obtained. In addition, a safety factor was developed for the AZ31B-F material when subjected to proportional loading. The achieved results have a direct application in mechanical design of components/structures made of AZ31B-F contributing to its reliability.

Diagnosis of degradation state of the historic building facade through qualitative and quantitative indicators: case study of the Former School of Agronomy Eliseu Maciel

PurposeIn Brazil, the city of Pelotas experienced an economic apex between the end of the nineteenth century and the beginning of the twentieth century, reflecting in the construction of several buildings. The aim of this article is to evaluate the state of degradation of the facades of the Old School of Agronomy Eliseu Maciel, using qualitative and quantitative indicators.Design/methodology/approachThe study was divided into visual and photographic survey, damage map and subsequent application of the Degradation Measurement Method (MMD) and the Element Performance Index (Ip). Taking into consideration the evaluation of the building: through historical research and survey of damages. Finally, the building was framed at a level of degradation.FindingsBy calculating the average damage between the methods – damage map, MMD and Ip – a more faithful representation of the damage was obtained, since the values become balanced. The results show the potential of the use of the methods in the analysis of pathological manifestations in facades.Originality/valueThe originality of this article refers to the use of methods for evaluation of historical buildings. The authors believe that the methods described applied jointly provide the results about the state of degradation through nondestructive and low-cost analyses. The methods of surveying damage to Brazilian heritage are a little researched area. This work will hopefully be engaged by academics and professionals to help establish and promote broad government interest and investments.

Method to Generate Building Damage Maps by Combining Aerial Image Processing and Crowdsourcing

Building damage maps that show the damage status of buildings are an essential information source for various disaster countermeasures, such as evacuation, rescue, and reconstruction. Therefore, they must be generated as quickly as possible. However, to generate a building damage map, it is necessary to collect disaster information and estimate the damage situation over a wide area, which is time consuming. (In this paper, we consider disaster information collection as capturing aerial images.) In recent years, crowdsourcing has been widely used to understand the damage situation. Crowdsourcing achieves large-scale work by dividing it into microtasks that can be solved by anyone and by distributing the microtasks among an unspecified number of workers. We believe that crowdsourcing is suitable for gathering information and assessing damage situations as it can adjust the type and number of workers in a scalable manner and allocate resources according to the size of the disaster. Therefore, crowdsourcing has been used for gathering information and assessing the situation during disaster management. However, usually, the two types of crowdsourcing tasks (i.e., gathering information and assessing the damage) are performed independently; consequently, the collected information is often not utilized effectively. More efficient work can be expected by linking the two crowdsourcing tasks. This paper proposes a framework for efficiently generating a building damage map by combining the two methods of information collection on disaster areas and assessment of disaster situations using aerial image processing. The results of an experiment using a prototype of our proposed framework clarify the range of applications in the collection and assessment crowdsourcing tasks. The experimental results indicate the feasibility of understanding disaster situations using our method. In addition, it is possible to install artificial intelligence workers that can support human workers to estimate the damage situation more quickly.

Earthquake Damage Assessment Based on User Generated Data in Social Networks

Natural disasters have always been one of the threats to human societies. As a result of such crises, many people will be affected, injured, and many financial losses will incur. Large earthquakes often occur suddenly; consequently, crisis management is difficult. Quick identification of affected areas after critical events can help relief workers to provide emergency services more quickly. This paper uses social media text messages to create a damage map. A support vector machine (SVM) machine-learning method was used to identify mentions of damage among social media text messages. The damage map was created based on damage-related tweets. The results showed the SVM classifier accurately identified damage-related messages where the F-score attained 58%, precision attained 56.8%, recall attained 59.25%, and accuracy attained 71.03%. In addition, the temporal pattern of damage and non-damage tweets was investigated on each day and per hour. The results of the temporal analysis showed that most damage-related messages were sent on the day of the earthquake. The results of our research were evaluated by comparing the created damage map with official intensity maps. The findings showed that the damage of the earthquake can be estimated efficiently by our strategy at multispatial units with an overall accuracy of 69.89 at spatial grid unit and Spearman’s rho and Pearson correlation of 0.429 and 0.503, respectively, at the spatial county unit. We used two spatial units in this research to examine the impact of the spatial unit on the accuracy of damage assessment. The damage map created in this research can determine the priority of the relief workers.

Damage-Map Estimation Using UAV Images and Deep Learning Algorithms for Disaster Management System

Estimating the damaged area after a forest fire is important for responding to this natural catastrophe. With the support of aerial remote sensing, typically with unmanned aerial vehicles (UAVs), the aerial imagery of forest-fire areas can be easily obtained; however, retrieving the burnt area from the image is still a challenge. We implemented a new approach for segmenting burnt areas from UAV images using deep learning algorithms. First, the data were collected from a forest fire in Andong, the Republic of Korea, in April 2020. Then, the proposed two-patch-level deep-learning models were implemented. A patch-level 1 network was trained using the UNet++ architecture. The output prediction of this network was used as a position input for the second network, which used UNet. It took the reference position from the first network as its input and refined the results. Finally, the final performance of our proposed method was compared with a state-of-the-art image-segmentation algorithm to prove its robustness. Comparative research on the loss functions was also performed. Our proposed approach demonstrated its effectiveness in extracting burnt areas from UAV images and can contribute to estimating maps showing the areas damaged by forest fires.