Fiber-reinforced self-consolidating concrete (FR-SCC) combines the advantageous characteristics of self-compacting concrete with fiber reinforcement, providing a versatile solution for contemporary construction. Howev...
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Fiber-reinforced self-consolidating concrete (FR-SCC) combines the advantageous characteristics of self-compacting concrete with fiber reinforcement, providing a versatile solution for contemporary construction. However, due to its complexity and the scarcity of available data, the strength prediction techniques of FR-SCC are still in their early stages. To get around this limitation, research was done to create an optimal machine learning algorithm for predicting the compressive strength (CS) of FR-SCC. This work aims to precisely forecast the CS of FR-SCC by optimizing the parameters and structure of a levenberg-marquardtbackpropagation Artificial Neural Network (LMBP-ANN) model using K-fold cross-validation. One hundred twenty-three experimental data on FR-SCC from available literature was used to create the dataset. Several validation metrics, including coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE) were employed to validate the models. Essential features that significantly impact the complex behavior of FR-SCC were found and incorporated into the model using multivariate analysis, Pearson correlation chart, and feature selection. The results show that K-fold cross-validation reduced training and testing errors by 22.2% and 18.3%. Consequently, an R2 value of 0.9343 was achieved, which validated the model's accuracy. SHAP analysis was also conducted in order to interpret the contribution of different features to the strength of FR-SCC. The most impactful feature was coarse aggregate, followed by curing age, superplasticizer, fly ash, and fiber content. The current work's findings might aid in precisely predicting the FR-SCC and the ANN network's design optimization procedure.
Biometric systems are becoming important since they provide efficient and more reliable means of human identity verification. Gait Recognition has created much interest in computer vision society over the last few yea...
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ISBN:
(纸本)9781479987924
Biometric systems are becoming important since they provide efficient and more reliable means of human identity verification. Gait Recognition has created much interest in computer vision society over the last few years. In this paper, we have presented a Gait based human identification system using skeleton data acquired by using Microsoft Kinect sensor. The sensor acts as a digital eye which takes the color information as well as depth information through IR sensor. The static and dynamic features of each individual are extracted using the skeleton information. Classification is performed using two different algorithms. First is the levenberg-marquardt back propagation algorithm, second is the correlation algorithm. 90% recognition rate is achieved with correlation algorithm where as for levenberg-marquardt back propagation algorithm proposed system is able to achieve a recognition rate of 94% for 5 persons with fixed Kinect sensor setup.
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