An Earthquake Fatalities Assessment Method Based on Feature Importance with Deep Learning and Random Forest Models

Jia, Hanxi; Jun, Lin; Liu, Jinlong

doi:10.3390/su11102727

Cited by 15 publications

(12 citation statements)

References 29 publications

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“…The decision tree intelligent discriminant analysis method is a kind of classification regression tree algorithm (45), which is widely used in medical diagnosis (46), biological analysis (47), deep learning (48) and optical application analysis (49), and also has many applications in data comparative analysis (50) and classification importance analysis (51). In the field of engineering, the decision tree is mainly used in equipment improvement (52), geotechnical engineering special new analysis ( 53), building damage assessment (54), and engineering modeling (55).…”

Section: Introductionmentioning

confidence: 99%

Effect of Occupational Health and Safety Training for Chinese Construction Workers Based on the CHAID Decision Tree

Cao¹,

Chen

Cao

2021

Front. Public Health

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Background: Occupational health and safety (OHS) training is an important way to prevent construction safety risks. However, the effectiveness of OHS training in China is questionable. In this study, the CHAID (chi-squared automatic interaction detection) decision tree, chi-square analysis, and correlation analysis were used to explore the main, secondary, weak, unrelated, and expectation factors affecting the effectiveness of training. It is the first to put forward the “five-factor method” of training effectiveness. It is found that training effectiveness is positively correlated with job responsibilities, OHS training, and job satisfaction. It is also significantly related to job certificate, training time, training method, and working time. However, the effectiveness of training has nothing to do with personal age, marital status, educational level, job type, and whether or not they have experienced industrial accidents. And the workers on site expect the enterprise to provide security and opportunities such as physical safety, training and learning, and future career development. The results show that OHS system training should be strengthened in the construction industry, and classified training should be carried out according to post responsibility, training methods, job satisfaction, and working hours.

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Section: Introductionmentioning

confidence: 99%

Effect of Occupational Health and Safety Training for Chinese Construction Workers Based on the CHAID Decision Tree

Cao¹,

Chen

Cao

2021

Front. Public Health

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“…In practical applications, if a bridge in an earthquake has some factors that cannot be obtained due to some reasons, such as age, data loss, and other human factors, the corresponding influencing factors can be deleted and the model can be modified in a short time. e RF model performs better than the other ensemble algorithms [17]. e reason for this may be that it works better with categorical features than the other methods.…”

Section: Discussionmentioning

confidence: 96%

“…However, no studies have used ensemble methods to assess the feature importance in the seismic risk area. One of the most widely used ensemble learning techniques is the RF method, which has the best overall performance compared to other algorithms, such as AdaBoost, logistic regression, and Classification and Regression Tree (CART) [17].…”

Section: Introductionmentioning

confidence: 99%

Bridge Seismic Damage Assessment Model Applying Artificial Neural Networks and the Random Forest Algorithm

Jia

Jun

Liu

2020

Advances in Civil Engineering

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Earthquakes cause significant damage to bridges, which have a very strategic location in transportation services. The destruction of a bridge will seriously hinder emergency rescue. Rapid assessment of bridge seismic damage can help relevant departments to make judgments quickly after earthquakes and save rescue time. This paper proposed a rapid assessment method for bridge seismic damage based on the random forest algorithm (RF) and artificial neural networks (ANN). This method evaluated the relative importance of each uncertain influencing factor of the seismic damage to the girder bridges and arch bridges, respectively. The input variables of the ANN model were the factors with higher importance value, and the output variables were damage states. The data of the Wenchuan earthquake were used as a testing set and a training set, and the data of the Tangshan earthquake were used as a validation set. The bridges under serious and complete damage states are not accessible after earthquakes and should be overhauled and reinforced before earthquakes. The results demonstrate that the proposed approach has good performance for assessing the damage states of the two bridges. It is robust enough to extend and improve emergency decisions, to save time for rescue work, and to help with bridge construction.

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“…Most of the current methods are field surveys. Similarly, secondary disasters also have such problems, and it is unreasonable to use only numerical simulations to predict them [18]. In areas with more developed economies, the more developed the construction technology, the higher the level of safety of the corresponding roads.…”

Section: Discussion and Future Workmentioning

confidence: 99%

“…One of the best ways to choose the factors that influence road damage is to assess the importance of them [16]. e method of ensemble learning algorithms has been proved to have a good performance in the analysis of feature importance [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Seismic Damage Rapid Assessment of Road Networks considering Individual Road Damage State and Reliability of Road Networks in Emergency Conditions

Liu

Jia

Jun

et al. 2020

Advances in Civil Engineering

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Road networks are one of the vital components of a transportation system that influence the traffic capacity and disaster losses after the earthquakes. The road network reliability is crucial for the postearthquake emergency decision-making. In this study, a method is proposed to assess the seismic damage of road networks considering individual road damage state and reliability of road networks in emergency conditions. First, we assess the importance of the factors that affect seismic road damage using the AdaBoost algorithm. In addition, artificial neural networks are used to evaluate the damage state of an individual road based on the factors that are selected with higher importance. Then, the improved estimation for the reliability of road networks is adopted to assess the damage of road networks. Last, the method is demonstrated using the road networks in Karamay, China.

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An Earthquake Fatalities Assessment Method Based on Feature Importance with Deep Learning and Random Forest Models

Cited by 15 publications

References 29 publications

Effect of Occupational Health and Safety Training for Chinese Construction Workers Based on the CHAID Decision Tree

Effect of Occupational Health and Safety Training for Chinese Construction Workers Based on the CHAID Decision Tree

Bridge Seismic Damage Assessment Model Applying Artificial Neural Networks and the Random Forest Algorithm

Seismic Damage Rapid Assessment of Road Networks considering Individual Road Damage State and Reliability of Road Networks in Emergency Conditions

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