An advanced cement-based geocomposite with autonomous sensing and heating capabilities for enhanced intelligent transportation infrastructure

This study introduces an innovative multifunctional cementitious-based geocomposite with self-sensing and selfheating capability, generated for transportation infrastructural health monitoring and traffic recognition. In this route, hybrid graphene nanoplatelets (GNP) and carbon nanotubes (CNT) were uniformly dispersed within a matrix of stabilized sand containing 10% cement. The mechanical and microstructural performances of the composite were examined using various tests. The self-sensing capabilities of the composite were also investigated. In addition, the effects of temperature, moisture content, and loading shape, speed and magnitudes on the strain and stress sensing capability of the specimens were also investigated. The self-sensing performance of this cementitious geocomposite for the asphalt layer strain detection was assessed in a hybrid section including the asphalt mixture layer and smart geocomposite. Moreover, the research also examined the composite's capacity for self-heating, a highly relevant application within the transportation sector. The 0.98 C/s heating rate, 526 mW/C heating power, and 251 C maximum temperature proved the high heating potential of this geocomposite for deicing, self-healing, and temperature sensing applications. The specimens showed a supreme correlation between strain changes and fractional changes in electrical resistance (FCR) values in different loading patterns. Although increasing temperature amplified the piezoresistivity response of the composite, the introduction of humidity had adverse impacts. An appropriate correlation was observed between gauge factor, modulus, and digital image correlation analysis. The piezoresistive response of this geocomposite in the hybrid section including smart geocomposite and asphalt layer, representative of the common structural layers of urban pavements, showed the ability of this geocomposite in terms of strain and stress detection of itself and even in the asphalt layer. This study provides a bright horizon for smart materials development in civil infrastructures, particularly transportation and railways.