Near-fault impulsive ground-shaking is highly destructive to engineering structures,so its accurate identification ground-shaking is a top priority in the engineering field.However,due to the lack of a comprehensive c...Near-fault impulsive ground-shaking is highly destructive to engineering structures,so its accurate identification ground-shaking is a top priority in the engineering field.However,due to the lack of a comprehensive consideration of the ground-shaking characteristics in traditional methods,the generalization and accuracy of the identification process are low.To address these problems,an impulsive ground-shaking identification method combined with deep learning named PCA-LSTM is proposed.Firstly,ground-shaking characteristics were analyzed and groundshaking the data was annotated using Baker’smethod.Secondly,the Principal Component Analysis(PCA)method was used to extract the most relevant features related to impulsive ground-shaking.Thirdly,a Long Short-Term Memory network(LSTM)was constructed,and the extracted features were used as the input for training.Finally,the identification results for the Artificial Neural Network(ANN),Convolutional Neural Network(CNN),LSTM,and PCA-LSTMmodels were compared and analyzed.The experimental results showed that the proposed method improved the accuracy of pulsed ground-shaking identification by>8.358%and identification speed by>26.168%,compared to other benchmark models ground-shaking.展开更多
In contemporary cities,road collapse is one of the most common disasters.This study proposed a framework for assessing the risk of urban road collapse.The framework first established a risk indicator system that combi...In contemporary cities,road collapse is one of the most common disasters.This study proposed a framework for assessing the risk of urban road collapse.The framework first established a risk indicator system that combined environmental and anthropogenic factors,such as soil type,pipeline,and construction,as well as other indicators.Second,an oversampling technique was used to create the dataset.The framework then constructed and trained a convolutional neural network(CNN)-based model for risk assessment.The experimental results show that the CNN model(accuracy:0.97,average recall:0.91)outperformed other models.The indicator contribution analysis revealed that the distance between the road and the construction site(contribution:0.132)and the size of the construction(contribution:0.144)are the most significant factors contributing to road collapse.According to the natural breaks,a road collapse risk map of Foshan City,Guangdong Province,was created,and the risk level was divided into five categories.Nearly 3%of the roads in the study area are at very high risk,and 6%are at high risk levels,with the high risk roads concentrated in the east and southeast.The risk map produced by this study can be utilized by local authorities and policymakers to help maintain road safety.展开更多
文摘Near-fault impulsive ground-shaking is highly destructive to engineering structures,so its accurate identification ground-shaking is a top priority in the engineering field.However,due to the lack of a comprehensive consideration of the ground-shaking characteristics in traditional methods,the generalization and accuracy of the identification process are low.To address these problems,an impulsive ground-shaking identification method combined with deep learning named PCA-LSTM is proposed.Firstly,ground-shaking characteristics were analyzed and groundshaking the data was annotated using Baker’smethod.Secondly,the Principal Component Analysis(PCA)method was used to extract the most relevant features related to impulsive ground-shaking.Thirdly,a Long Short-Term Memory network(LSTM)was constructed,and the extracted features were used as the input for training.Finally,the identification results for the Artificial Neural Network(ANN),Convolutional Neural Network(CNN),LSTM,and PCA-LSTMmodels were compared and analyzed.The experimental results showed that the proposed method improved the accuracy of pulsed ground-shaking identification by>8.358%and identification speed by>26.168%,compared to other benchmark models ground-shaking.
基金supported by the Guangdong Provincial Key Laboratory of New Construction Technology for Urban Rail Transit Engineering(2017B030302009)。
文摘In contemporary cities,road collapse is one of the most common disasters.This study proposed a framework for assessing the risk of urban road collapse.The framework first established a risk indicator system that combined environmental and anthropogenic factors,such as soil type,pipeline,and construction,as well as other indicators.Second,an oversampling technique was used to create the dataset.The framework then constructed and trained a convolutional neural network(CNN)-based model for risk assessment.The experimental results show that the CNN model(accuracy:0.97,average recall:0.91)outperformed other models.The indicator contribution analysis revealed that the distance between the road and the construction site(contribution:0.132)and the size of the construction(contribution:0.144)are the most significant factors contributing to road collapse.According to the natural breaks,a road collapse risk map of Foshan City,Guangdong Province,was created,and the risk level was divided into five categories.Nearly 3%of the roads in the study area are at very high risk,and 6%are at high risk levels,with the high risk roads concentrated in the east and southeast.The risk map produced by this study can be utilized by local authorities and policymakers to help maintain road safety.
