Izvorni znanstveni članak
https://doi.org/10.1080/00051144.2024.2399319
Smart material selection strategies for sustainable and cost-effective high-performance concrete production using deep learning
T. Seethalakshmi
; Department of Civil Engineering, Government College of Engineering, Tirunelveli, India
M. Murugan
; Department of Civil Engineering, Government College of Engineering, Tirunelveli, India
P. Maria Antony Sebastin Vimalan
; Department of Civil Engineering, PSN College of Engineering and Technology, Tirunelveli, India
*
* Dopisni autor.
Sažetak
The creation of high-performing concrete (HPC) is greatly influenced by the selection of materials, with cost and sustainability factors playing a bigger part in contemporary building techniques. To overcome these limitations, we developed a Multi-Objective Ant Colony Adaptive Dense Convolutional Neural Network (MOAC-ADenseNet) with 5-K-Fold cross validation, a
dependable and precise forecasting model for the cost-effective selection of HPC material. First,
we collect a concrete material dataset for evaluating the suggested method. MOAC-ADenseNet
utilized Dense convolutional neural networks and ant colony optimization for complex material
data analysis, which makes it easier to choose expensive and sustainable materials for highperformance concrete manufacturing operations. The experimental findings of the suggested
approach are evaluated for the relative measure such as Pearson’s Linear Correlation Coefficient
(R) is 0.93, the Root Mean Square Error (RMSE) is 91.38, Mean Absolute Error (MAE) of 58.15, and
Mean Absolute Percentage Error (MAPE) is 8.79. The outcomes demonstrated that the material cost of HPC was correctly predicted by the MOAC-ADenseNet. The actual measured value
and the MOAC-ADenseNet model predictions, following 5-K-fold cross-validation and input feature improvement, shows its effectiveness. A The MOAC-ADenseNet approach provides feasible
method for enhancing material selection in HPC manufacturing accomplishing sustainability and
cost-effectiveness goals.
Ključne riječi
Concrete material; high-performing concrete (HPC); multi-objective ant colony based adaptive dense convolutional neural networks (MOAC-ADenseNet)
Hrčak ID:
326344
URI
Datum izdavanja:
10.9.2024.
Posjeta: 0 *