Advances in concrete construction

  • 제14권3호
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  • Pages.185-194
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  • 2022
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  • 2287-5301(pISSN)
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  • 2287-531X(eISSN)
테크노프레스 (Techno-Press)
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EDNN based prediction of strength and durability properties of HPC using fibres & copper slag

  • Gupta, Mohit (Civil Engineering Department, Delhi Technological University, Shahbad Daulatpur) ;
  • Raj, Ritu (Civil Engineering Department, Delhi Technological University, Shahbad Daulatpur) ;
  • Sahu, Anil Kumar (Civil Engineering Department, Delhi Technological University, Shahbad Daulatpur)
  • 투고 : 2021.11.07
  • 심사 : 2022.10.06
  • 발행 : 2022.09.25
⟨ 이전 논문 다음 논문 ⟩

초록

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.

키워드

과제정보

The research described in this paper is supported by Civil engineering laboratory of the Department of Civil Engineering for the availability of the software MATLAB and the library of Delhi Technological University for accessing the research papers.

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