Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Compressive Strength Estimation of Manufactured Sand Concrete Using Hybrid ANN Paradigms Constructed with Meta-heuristic Algorithms
This study implements hybrid machine learning models that utilize six commonly employed meta-heuristic algorithms to predict the compressive strength (CS) of manufactured sand concrete (MSC). Six hybrid artificial neural network (ANN) models were created utilizing multiple meta-heuristic algorithms of different groups. A sum of 275 records were used to determine concrete CS of MSC. The hybrid framework, combining ANN and firefly algorithm, i.e., ANN-FF, shows exceptional accuracy in predicting the CS. During the model development stage, the ANN-FF model achieved R2 = 0.9536 and RMSE = 0.0498. During testing phase, the values of these indices are R2 = 0.9276 and RMSE = 0.0656. The results of the sensitivity analysis demonstrate that the constructed ANN-FF framework effectively estimates the magnitude of the correlation between influential parameters and the CS. The evaluation of outcomes was examined using a variety of tools including Taylor diagram, error matrix, and OBJ criterion. In terms of objective criterion, ANN-FF achieved the best predictive precision. Based on the findings, the constructed ANN-FF can serve as a viable alternative for supporting engineers in civil engineering endeavours. The MATLAB developed ANN-FF model (constructed using eleven distinct influencing parameters) is also attached that can readily be implemented to predict the CS of MSC.
Compressive Strength Estimation of Manufactured Sand Concrete Using Hybrid ANN Paradigms Constructed with Meta-heuristic Algorithms
This study implements hybrid machine learning models that utilize six commonly employed meta-heuristic algorithms to predict the compressive strength (CS) of manufactured sand concrete (MSC). Six hybrid artificial neural network (ANN) models were created utilizing multiple meta-heuristic algorithms of different groups. A sum of 275 records were used to determine concrete CS of MSC. The hybrid framework, combining ANN and firefly algorithm, i.e., ANN-FF, shows exceptional accuracy in predicting the CS. During the model development stage, the ANN-FF model achieved R2 = 0.9536 and RMSE = 0.0498. During testing phase, the values of these indices are R2 = 0.9276 and RMSE = 0.0656. The results of the sensitivity analysis demonstrate that the constructed ANN-FF framework effectively estimates the magnitude of the correlation between influential parameters and the CS. The evaluation of outcomes was examined using a variety of tools including Taylor diagram, error matrix, and OBJ criterion. In terms of objective criterion, ANN-FF achieved the best predictive precision. Based on the findings, the constructed ANN-FF can serve as a viable alternative for supporting engineers in civil engineering endeavours. The MATLAB developed ANN-FF model (constructed using eleven distinct influencing parameters) is also attached that can readily be implemented to predict the CS of MSC.
Compressive Strength Estimation of Manufactured Sand Concrete Using Hybrid ANN Paradigms Constructed with Meta-heuristic Algorithms
Iran J Sci Technol Trans Civ Eng
Bardhan, Abidhan (Autor:in) / Kumar, Sudeep (Autor:in) / Kumar, Avinash (Autor:in) / Suman, Subodh Kumar (Autor:in) / Biswas, Rahul (Autor:in)
01.12.2024
21 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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