Experimental investigation and predictive modeling of compressive strength and electrical resistivity of graphene nanoplatelets modified concrete

Abstract

Multipurpose cement composites are required for the sustainable development of the construction industry. Incorporation of nanomaterials can produce high-performing and multipurpose cement composites (CC). However, predicting the properties of these composites is challenging due to their complex composite nature and nonlinear behavior. This study investigates the integration of graphene nanoplatelets (GNPs) in concrete to enhance compressive strength and electrical properties. In addition to the experimental study, three machine learning techniques i.e., gene expression programming (GEP), support vector machine (SVM), and random forest regression (RFR), are employed to predict the compressive strength and electrical resistivity of graphene nanoplatelet modified concrete (GrNCC). The experimental results showed that GNP was evenly distributed throughout the matrix, and GrNCC exhibited enhanced compressive strength (CS) and fractional change in resistivity (FCR). GNP concentrations of 0.1% and 0.5% depict strength enhancement of 16.58% and 13.23% respectively. Similarly maximum FCR change of − 13%, and − 12.19% is observed. Moreover, GEP was the most robust ML model with mean R2 values of 0.988 and 0.74 for CS and ER respectively. In addition, SVM has R2 values of 0.93 and 0.71, and RFR shows R2 values of 0.899 and 0.671 for CS and ER respectively. GEP performed 52% and 67.5% better than SVM and RFR for CS, and 34.92% and 35.1% better than SVM and RFR for ER in terms of MAE. Furthermore, sensitivity analysis revealed that graphene content is the most important parameter contributing almost 27.3% and 29.2% for CS and ER respectively, followed by graphene dispersion and water content. Moreover, a graphical user interface (GUI) is made using a model that has been trained to predict the output values based on the input parameters. This simplifies the process and offers a valuable tool for utilizing the model's capabilities in civil engineering.