Sensing concrete has the capability to sense its condition and environmental changes, including stress (or force), strain (or deformation), crack, damage, temperature, and humidity, by incorporating functional fillers. Sensing concrete has recently attracted major research interest aiming to produce smart infrastructures with elegantly integrated health monitoring abilities. In addition to having highly improved mechanical properties, sensing concrete has multifunctional properties, such as improved ductility, durability, resistance to impact, and, most importantly, self-health monitoring due to its electrical conductivity capability, allowing damage detection without the need for an external grid of sensors. This tutorial will provide an overview of sensing concrete, with attention to its principles, properties, and applications. It concludes with an outline of some future opportunities and challenges in the application of sensing concrete in the construction industry.

Structural health monitoring (SHM) is the automation of the condition assessment process of an engineered system. When applied to geometrically large components or structures, such as those found in civil and aerospace infrastructure and systems, a critical challenge is in designing the sensing solution that could yield actionable information. This is a difficult task to conduct cost-effectively, because of the large surfaces under consideration and the localized nature of typical defects and damages. There have been significant research efforts in empowering conventional measurement technologies for applications to SHM in order to improve performance of the condition assessment process. Yet, the field implementation of these SHM solutions is still in its infancy, attributable to various economic and technical challenges. The objective of this Roadmap publication is to discuss modern measurement technologies that were developed for SHM purposes, along with their associated challenges and opportunities, and to provide a path to research and development efforts that could yield impactful field applications. The Roadmap is organized into four sections: distributed embedded sensing systems, distributed surface sensing systems, multifunctional materials, and remote sensing. Recognizing that many measurement technologies may overlap between sections, we define distributed sensing solutions as those that involve or imply the utilization of numbers of sensors geometrically organized within (embedded) or over (surface) the monitored component or system. Multi-functional materials are sensing solutions that combine multiple capabilities, for example those also serving structural functions. Remote sensing are solutions that are contactless, for example cell phones, drones, and satellites. It also includes the notion of remotely controlled robots.

Recently self-sensing cementitious composite has demonstrated its strong potentiality for structural health monitoring of civil infrastructures because of its low-cost, long-term stability and compatibility with concrete structures. In this paper, we propose novel hybrid nanocarbon materials engineered cement-based sensors (HNCSs) with high-sensitivity, which are fabricated with self-sensing cementitious composites containing electrostatic self-assembled CNT/NCB composite fillers. The mechanical property and sensing performance of the HNCSs are pre-characterized under static and dynamic compressive loadings. The HNCSs are then integrated into a five-story building model via custom-made clamps to verify the feasibility for dynamic response measurements. Results show that the developed sensors have satisfactory mechanical property and excellent pressure-sensitive reproducibility and stability. With clamps holding on the building model, the HNCSs perform satisfactorily under sinusoidal excitations in the frequency range from 2 to 40 Hz. In addition, the modal frequencies and their changes of the building model caused by ‘damage’ simulated through adding additional masses identified by the HNCSs are favorably consistent with the counterparts acquired by accelerometers and strain gauges, indicating that the developed HNCSs have great potential for structural modal identification and damage detection applications.

: Self-sensing cementitious composite (SSCC) has been viewed as a promising sensing technology for structural health monitoring and traffic detection on account of its high sensitivity, low cost, long-term stability and compatibility with concrete structures. However, temperature variation effects in the electrical resistance measurements would impede the potential application of SSCC. It is therefore of great significance to understand the temperature effects on the piezoresistive performance of SSCC and eliminate such effects. In this study, temperature effects on the electrical and piezoresistive properties of SSCCs with different contents of carbon nanotube/nano carbon black (CNT/NCB) composite fillers are investigated under varying temperatures ranging from −20 °C to 60 °C and under concurrent temperature and loading variations. Experimental results show that an increase in CNT/NCB composite filler content can decrease the activation energy of SSCC and facilitate the transport of the charge carriers, thus attenuating the sensitivity of SSCC to temperature. Temperature variation has no effect on the piezoresistive repeatability of SSCC due to the stable overall distribution of conductive network in SSCC. However, temperature rise can reduce the piezoresistive sensitivity of SSCC. Aiming to diminish the effect of temperature on the piezoresistive property of SSCC, the SSCC responses to simultaneous temperature and loading excitations are then treated using a Bayesian blind source separation (BSS) method to reconstruct two independent sources. Regardless of the CNT/NCB composite filler content, the reconstructed source in relation to temperature variation always has a high correlation with the measured temperature, indicating that the proposed Bayesian BSS method can well extract and separate the electrical resistance variation induced by temperature variation from that induced by simultaneous temperature and loading excitations.