Damage risk assessment of a high-rise building against multihazard of earthquake and strong wind with recorded data

Zheng, Xiao-Wei; Li, Hong‐Nan; Yang, Yong; Li, Gang; Huo, Linsheng; Liu, Yang

doi:10.1016/j.engstruct.2019.109697

Cited by 45 publications

(11 citation statements)

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“…The non-linear and linear systems separately and simultaneously assess the injury concerns of an inclined plane exposed to uncontrollable shaking and atmospheric threats, respectively. These systems are divided into three parts: danger showcase; underpinning delicacy examination; and destruction likelihood processing [ 30 ].…”

Section: Literature Reviewmentioning

confidence: 99%

A Novel Blunge Calibration Intelligent Feature Classification Model for the Prediction of Hypothyroid Disease

Devi

Kumar

Brezulianu

et al. 2023

Sensors

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According to the Indian health line report, 12% of the population suffer from abnormal thyroid functioning. The major challenge in this disease is that the existence of hypothyroid may not propagate any noticeable symptoms in its early stages. However, delayed treatment of this disease may lead to several other health problems, such as fertility issues and obesity. Therefore, early treatment is essential for patient survival. The proposed technology could be used for the prediction of hypothyroid disease and its severity during its early stages. Though several classification and regression algorithms are available for the prediction of hypothyroid using clinical information, there exists a gap in knowledge as to whether predicted outcomes may reach a higher accuracy or not. Therefore, the objective of this research is to predict the existence of hypothyroidism with higher accuracy by optimizing the estimator list of the pycaret classifier model. With this overview, a blunge calibration intelligent feature classification model that supports the assessment of the presence of hypothyroidism with high accuracy is proposed. A hypothyroidism dataset containing 3163 patient details with 23 independent and one dependent feature from the University of California Irvine (UCI) machine-learning repository was used for this work. We undertook dataset preprocessing and determined its incomplete values. Exploratory data analysis was performed to analyze all the clinical parameters and the extent to which each feature supports the prediction of hypothyroidism. ANOVA was used to verify the F-statistic values of all attributes that might highly influence the target. Then, hypothyroidism was predicted using various classifier algorithms, and the performance metrics were analyzed. The original dataset was subjected to dimensionality reduction by using regressor and classifier feature-selection algorithms to determine the best subset components for predicting hypothyroidism. The feature-selected subset of the clinical parameters was subjected to various classifier algorithms, and its performance was analyzed. The system was implemented with python in the Spyder editor of Anaconda Navigator IDE. Investigational results show that the Gaussian naive Bayes, AdaBoost classifier, and Ridge classifier maintained the accuracy of 89.5% for the regressor feature-selection methods. The blunge calibration regression model (BCRM) was designed with naive Bayes, AdaBoost, and Ridge as the estimators with accuracy optimization and with soft blending based on the sum of predicted probabilities of classifiers. The proposed BCRM showed 99.5% accuracy in predicting hypothyroidism. The implementation results show that the Kernel SVM, KNeighbor, and Ridge classifier maintained an accuracy of 87.5% for the classifier feature-selection methods. The blunge calibration classifier model (BCCM) was developed with Kernel SVM, KNeighbor, and Ridge as the estimators, with accuracy optimization and with soft blending based on the sum of predicted probabilities of classifiers. The proposed BCCM showed 99.7% accuracy in predicting hypothyroidism. The main contribution of this research is the design of BCCM and BCRM models that were built with accuracy optimization with soft blending based on the sum of predicted probabilities of classifiers. The BCRM and BCCM models uniqueness’s are achieved by updating the estimators list with the effective classifiers and regressors that suit the application at runtime.

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Section: Literature Reviewmentioning

confidence: 99%

A Novel Blunge Calibration Intelligent Feature Classification Model for the Prediction of Hypothyroid Disease

Devi

Kumar

Brezulianu

et al. 2023

Sensors

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“…From the modeling of the structure without TMD in Figure 1, the structure model can be derived into mathematical Equations in Eq. (1).…”

Section: Mathematical Modelling Of Structuresmentioning

confidence: 99%

“…Structural failure is a phenomenon in which the structure's inability to accept disturbance forces is caused by earthquakes or wind loads [1]. The damper can be used as a solution to improve structural performance in reducing vibration due to disturbance forces in the form of wind loads or seismic loads.…”

Section: Introductionmentioning

confidence: 99%

Optimum Design of Tuned Mass Damper Parameters to Reduce Seismic Response on Structure Using Genetic Algorithm

Suryadi

Prasetya²,

Daratha³

et al. 2023

AEJ

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A tuned mass damper (TMD) is a system to improve structural performance in reducing vibration response under seismic loads. This study optimizes TMD parameters using a genetic algorithm. In this study, the structure is modeled as a spring-mass system to obtain mathematical equations. Then, those equations are used to estimate vibration due to seismic loads. A genetic algorithm is used to find optimum parameters which correspond to minimum vibrations. The simulation result shows that the genetic algorithm can find values of parameters such as the ratio of mass, stiffness, and damping values which reduces vibrations on the structure. Based on the tests, it is found that the best combination of genetic algorithm parameters to produce the most optimal fitness value is population size 30, generation size 800, crossover probability 0.5, and mutation probability 0.5. Applying the genetic algorithm shows that the optimal parameter ratio values of TMD to reduce the vibration response are 10% mass, 10% stiffness, and 1% damping of mass, stiffness, and damping in the structure. The result of the vibration response analysis shows that the maximum amplitude value of the main structure without TMD is 0.24259 m reduced to 0.034385 m with the addition of TMD. This shows that the TMD successfully reduces vibrations in the main structure with a percentage of 85.94%. Where the TMD manages to dissipate the energy that should only be received by the structure to the TMD itself.

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“…where EW is the variance computed on the completed data. Impute dataset was generated by applying calculated feature variance from Equation ( 5) to each column of the dataset as shown in Equation (6).…”

Section: Architecture Of Asr-dfcnn Frameworkmentioning

confidence: 99%

Novel ANOVA-Statistic-Reduced Deep Fully Connected Neural Network for the Damage Grade Prediction of Post-Earthquake Buildings

Preethaa

Devi

Rajendran

et al. 2023

Sensors

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Earthquakes are cataclysmic events that can harm structures and human existence. The estimation of seismic damage to buildings remains a challenging task due to several environmental uncertainties. The damage grade categorization of a building takes a significant amount of time and work. The early analysis of the damage rate of concrete building structures is essential for addressing the need to repair and avoid accidents. With this motivation, an ANOVA-Statistic-Reduced Deep Fully Connected Neural Network (ASR-DFCNN) model is proposed that can grade damages accurately by considering significant damage features. A dataset containing 26 attributes from 762,106 damaged buildings was used for the model building. This work focused on analyzing the importance of feature selection and enhancing the accuracy of damage grade categorization. Initially, a dataset without primary feature selection was utilized for damage grade categorization using various machine learning (ML) classifiers, and the performance was recorded. Secondly, ANOVA was applied to the original dataset to eliminate the insignificant attributes for determining the damage grade. The selected features were subjected to 10-component principal component analysis (PCA) to scrutinize the top-ten-ranked significant features that contributed to grading the building damage. The 10-component ANOVA PCA-reduced (ASR) dataset was applied to the classifiers for damage grade prediction. The results showed that the Bagging classifier with the reduced dataset produced the greatest accuracy of 83% among all the classifiers considering an 80:20 ratio of data for the training and testing phases. To enhance the performance of prediction, a deep fully connected convolutional neural network (DFCNN) was implemented with a reduced dataset (ASR). The proposed ASR-DFCNN model was designed with the sequential keras model with four dense layers, with the first three dense layers fitted with the ReLU activation function and the final dense layer fitted with a tanh activation function with a dropout of 0.2. The ASR-DFCNN model was compiled with a NADAM optimizer with the weight decay of L2 regularization. The damage grade categorization performance of the ASR-DFCNN model was compared with that of other ML classifiers using precision, recall, F-Scores, and accuracy values. From the results, it is evident that the ASR-DFCNN model performance was better, with 98% accuracy.

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Damage risk assessment of a high-rise building against multihazard of earthquake and strong wind with recorded data

Cited by 45 publications

References 50 publications

A Novel Blunge Calibration Intelligent Feature Classification Model for the Prediction of Hypothyroid Disease

A Novel Blunge Calibration Intelligent Feature Classification Model for the Prediction of Hypothyroid Disease

Optimum Design of Tuned Mass Damper Parameters to Reduce Seismic Response on Structure Using Genetic Algorithm

Novel ANOVA-Statistic-Reduced Deep Fully Connected Neural Network for the Damage Grade Prediction of Post-Earthquake Buildings

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