Sensitivity Analysis of Compressive Strength in CNT-Reinforced Composites: A Comparative Study of Sample-Based, Linearization, and Global Methods

Abstract

Sensitivity analysis (SA) methods determine and quantify how different values of dependent or independent variables affect an output under specific circumstances, such as those represented by a surrogate model. Put differently, sensitivity analyses explore how various sources of uncertainty within a mathematical model collectively impact the model’s overall uncertainty. This study addresses the influence of different parameters—namely, the W/C ratio, CNT type, CNT content, CNT length, CNT diameter, S/C ratio, dispersion method, curing days, and the compressive strength of the control sample (C0) on the compressive strength of carbon nanotube (CNT)-reinforced cementitious nanocomposites as an output. This is achieved by applying four sensitivity analysis methods, including correlation-based indices, Cotter indices, Morris indices, and Borgonovo indices. To implement these four methodologies, a Genetic Programming-based function-finding algorithm known as Gene Expression Programming (GEP) is developed. This algorithm utilizes a collected dataset comprising 326 experimental data points obtained from a comprehensive campaign. Based on the results of the four sensitivity analysis methods, the W/C ratio and the length of CNTs are identified as the most influential input variables across all methods, with CNT type identified in three methods and CNT content in two methods as significant factors affecting compressive strength. Consequently, the W/C ratio, length of CNTs, CNT type, and CNT content are highlighted as the most impactful parameters on the compressive strength of CNT-reinforced cementitious nanocomposites.