• Advances in concrete construction
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• 제14권3호
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• pp.185-194
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• 2022

For producing cement and concrete, the construction field has been encouraged by the usage of industrial soil waste (or) secondary materials since it decreases the utilization of natural resources. Simultaneously, for ensuring the quality, the analyses of the strength along with durability properties of that sort of cement and concrete are required. The prediction of strength along with other properties of High-Performance Concrete (HPC) by optimization and machine learning algorithms are focused by already available research methods. However, an error and accuracy issue are possessed. Therefore, the Enhanced Deep Neural Network (EDNN) based strength along with durability prediction of HPC was utilized by this research method. Initially, the data is gathered in the proposed work. Then, the data's pre-processing is done by the elimination of missing data along with normalization. Next, from the pre-processed data, the features are extracted. Hence, the data input to the EDNN algorithm which predicts the strength along with durability properties of the specific mixing input designs. Using the Switched Multi-Objective Jellyfish Optimization (SMOJO) algorithm, the weight value is initialized in the EDNN. The Gaussian radial function is utilized as the activation function. The proposed EDNN's performance is examined with the already available algorithms in the experimental analysis. Based on the RMSE, MAE, MAPE, and R² metrics, the performance of the proposed EDNN is compared to the existing DNN, CNN, ANN, and SVM methods. Further, according to the metrices, the proposed EDNN performs better. Moreover, the effectiveness of proposed EDNN is examined based on the accuracy, precision, recall, and F-Measure metrics. With the already-existing algorithms i.e., JO, GWO, PSO, and GA, the fitness for the proposed SMOJO algorithm is also examined. The proposed SMOJO algorithm achieves a higher fitness value than the already available algorithm.

• International Journal of Naval Architecture and Ocean Engineering
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• 제9권5호
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• pp.499-508
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• 2017

This paper uses optimization techniques to obtain bow hull form of a 66,000 DWT bulk carrier in calm water and in waves. Parametric modification functions of SAC and section shape of DLWL are used for hull form variation. Multi-objective functions are applied to minimize the wave-making resistance in calm water and added resistance in regular head wave of ${\lambda}/L=0.5$. WAVIS version 1.3 is used to obtain wave-making resistance. The modified Fujii and Takahashi's formula is applied to obtain the added resistance in short wave. The PSO algorithm is employed for the optimization technique. The resistance and motion characteristics in calm water and regular and irregular head waves of the three hull forms are compared. It has been shown that the optimal brings 13.2% reduction in the wave-making resistance and 13.8% reduction in the added resistance at ${\lambda}/L=0.5$; and the mean added resistance reduces by 9.5% at sea state 5.