Geopolymer concrete (GPC) stands as a promising alternative to traditional Portland cement-based concrete in the pursuit of eco-friendly and sustainable construction materials. One of the key properties of GPC is its energy absorption capacity, which denotes the energy absorbed by the material just before failure. However, a standardized method for estimating the energy absorption capacity (EAC) of GPC is lacking, leading most studies to rely on proposed models for conventional concrete to predict this attribute. This study aims to address this gap by developing two machine learning models, artificial neural network (ANN) and random forest (RF), to predict the energy absorption capacity of GPC. The models were trained using data from 90 collected test results. Thirteen input variables were considered, relating to aggregates, alkaline solutions, water content, aluminosilicate material, silica fume, curing conditions, age, compressive strength, fiber type, fiber volume content, notch depth ratio, and maximum size of aggregate. The output variable was the EAC of GPC. The outcomes of the estimation demonstrated that the proposed RF model offered high accuracy and reliability in estimating the energy absorption capacity across both the training and testing sets. Conversely, the ANN model displayed slightly lower accuracy in the testing set. Sensitivity analysis revealed that the notch depth ratio exerted the most significant influence on the energy absorption capacity, while curing temperature emerged as the less critical variable.
Predictive Model for Energy Absorption Capacity of Geopolymer Concrete
Smart Innovation, Systems and Technologies
Congress on Control, Robotics, and Mechatronics ; 2024 ; Warangal, India February 03, 2024 - February 04, 2024
Proceedings of the Second Congress on Control, Robotics, and Mechatronics ; Kapitel : 4 ; 59-67
14.11.2024
9 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
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