In recent years, the geopolymer will considerably replace the role of cement in the construction feld. Generally, geopolymers have advantageous characteristics such as minimal shrinkage, minimal creep, and high compressive strength, respectively. In the literature, some of the geopolymer-based concrete is designed which attains low compressive strength and inadequate compressive strength computation. Hence, in this research paper, lightweight geopolymer mortar with base material for the based concrete mix is designed. The base material is considered as fy ash (FA) and alkali-activated slag (AAFS). The main components of lightweight geopolymer mortar are insubstantial burnish aggregate and AAFS binder or alkali-activated FA. The mixed concrete design compressive strength is computed with the Artifcial Intelligence (AI) technique. Here, Adaptive Neuro-Fuzzy Inference Controller (ANFIS) with Salp Swarm Optimization (SSO) is utilized to compute the urging force of the concrete mix. SSO is used to compute the optimal learning rate to fnd out the urging force of the concrete. The preliminary parameter’s potential was inspected with the relations of variant urging force in insubstantial geopolymer mortar. The performance is evaluated by changing the temperature and binder content. The proposed method with an intended concrete mix result illustrates the performance. The proposed method is compared with existing methods of Artifcial Neural Network (ANN).
Talasila Bhavani chowdary is graduated in 2013 from JNTU Kakinada. She completed her post graduation from Acharaya Nagarjuna University in the year 2015. She is a Research Scholar,Koneru lakshmaiah Education Foundation, Department of Civil, and also currently working as Assistant Professor, Vardhaman College of Engineering, Department of Civil, Hyderabad. Her areas of interest include materials and Earthquake Resistant Design of Structures, Fracture Mechanics.
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