An Experimental Study on Static and Dynamic Strain Sensitivity of Embeddable Smart Concrete Sensors Doped with Carbon Nanotubes for SHM of Large Structures

Smart Cementitious Sensors with Nano-, Micro-, and Hybrid-Modified Reinforcement: Mechanical and Electrical Properties

The current paper presents the results of an experimental study of carbon nano-, micro-, and hybrid-modified cementitious mortar to evaluate mechanical performance, energy absorption, electrical conductivity, and piezoresistive sensibility. Three amounts of single-walled carbon nanotubes (SWCNTs), namely 0.05 wt.%, 0.1 wt.%, 0.2 wt.%, and 0.3 wt.% of the cement mass, were used to prepare nano-modified cement-based specimens. In the microscale modification, 0.05 wt.%, 0.5 wt.%, 1.0 wt.% carbon fibers (CFs) were incorporated in the matrix. The hybrid-modified cementitious specimens were enhanced by adding optimized amounts of CFs and SWCNTs. The smartness of modified mortars, indicated by their piezoresistive behavior, was investigated by measuring the changes in electrical resistivity. The effective parameters that enhance the composites' mechanical and electrical performance are the different concentrations of reinforcement and the synergistic effect between the types of reinforcement used in the hybrid structure. Results reveal that all the strengthening types improved flexural strength, toughness, and electrical conductivity by about an order of magnitude compared to the reference specimens. Specifically, the hybrid-modified mortars presented a marginal reduction of 1.5% in compressive strength and an increase in flexural strength of 21%. The hybrid-modified mortar absorbed the most energy, 1509%, 921%, and 544% more than the reference mortar, nano-modified mortar, and micro-modified mortar, respectively. The change rate of impedance, capacitance, and resistivity in piezoresistive 28-day hybrid mortars improved the tree ratios by 289%, 324%, and 576%, respectively, for nano-modified mortars and by 64%, 93%, and 234%, respectively, for micro-modified mortars.

Smart Infrastructure Monitoring through Self-Sensing Composite Sensors and Systems: A Study on Smart Concrete Sensors with Varying Carbon-Based Filler

Structural Health Monitoring allows an automated performance assessment of buildings and infrastructures, both during their service lives and after critical events, such as earthquakes or landslides. The strength of this technology is in the diffuse nature of the sensing outputs that can be achieved for a full-scale structure. Traditional sensors adopted for monitoring purposes possess peculiar drawbacks related to placement and maintenance issues. Smart construction materials, which are able to monitor their states of strain and stress, represent a possible solution to these issues, increasing the durability and reliability of the monitoring system through embedding or the bulk fabrication of smart structures. The potentialities of such novel sensors and systems are based on their reliability and flexibility. Indeed, due to their peculiar characteristics, they can combine mechanical and sensing properties. We present a study on the optimization and the characterization of construction materials doped with different types of fillers for developing a novel class of sensors able to correlate variations of external strains to variations of electrical signals. This paper presents the results of an experimental investigation of composite samples at small and medium scales, made of cementitious materials with carbon-based inclusions. Different from a previous work by the authors, different carbon-based filler composite sensors are first compared at a small cubic sample scale and then tailored for larger plate specimens. Possible applications are in the strain/stress monitoring, damage detection, and load monitoring of concrete buildings and infrastructures.

Application of Artificial Neural Networks for Prediction of Mechanical Properties of CNT/CNF Reinforced Concrete

Prominence of concrete is characterized by its high mechanical properties and durability, combined with multifunctionality and aesthetic appeal. Development of alternative eco-friendly or multipurpose materials has conditioned improvements in concrete mix design to optimize concrete production speed and price, as well as carbon footprint. Artificial neural networks represent a new and efficient tool in achieving optimal concrete mixtures according to its intended function. This paper addresses concrete mix design and the application of artificial neural networks (ANNs) for self-sensing concrete. The authors review concrete mix design methods and the development of ANNs for prediction of properties for various types of concrete. Furthermore, the authors present developments and applications of ANNs for prediction of compressive strength and flexural strength of carbon nanotubes/carbon nanofibers (CNT/CNF) reinforced concrete using experimental results for the learning process. The goal is to bring the ANN approach closer to a variety of concrete researchers and possibly propose the implementation of ANNs in the civil engineering practice.

Recent Advancements in Carbon Nano-Infused Cementitious Composites

A rising demand for efficient functional materials brings forth research challenges regarding improvements in existing materials. Carbon infused cementitious composites, regardless of being an important research topic worldwide, still present many questions concerning their functionality and properties. The paper aims to highlight the most important materials used for cementitious composites, their properties, and their uses while also including the most relevant of the latest research in that area.

A Multichannel Strain Measurement Technique for Nanomodified Smart Cement-Based Sensors in Reinforced Concrete Structures

Nanomodified smart cement-based sensors are an emerging self-sensing technology for the structural health monitoring (SHM) of reinforced concrete (RC) structures. To date, several literature works demonstrated their strain-sensing capabilities, which make them suited for damage detection and localization. Despite the most recent technological improvements, a tailored measurement technique allowing feasible field implementations of smart cement-based sensors to concrete structures is still missing. In this regard, this paper proposes a multichannel measurement technique for retrieving strains from smart cement-based sensors embedded in RC structures using a distributed biphasic input. The experiments performed for its validation include the investigation on an RC beam with seven embedded sensors subjected to different types of static loading and a long-term monitoring application on an RC plate. Results demonstrate that the proposed technique is effective for retrieving time-stable simultaneous strain measurements from smart cement-based sensors, as well as for aiding the identification of the changes in their electrical outputs due to the influence of environmental effects variable over time. Accordingly, the proposed multichannel strain measurement technique represents a promising approach for performing feasible field implementations of smart cement-based sensors to concrete structures.

Development of a Novel Multifunctional Cementitious-Based Geocomposite by the Contribution of CNT and GNP

In this study, a self-sensing cementitious stabilized sand (CSS) was developed by the incorporation of hybrid carbon nanotubes (CNTs) and graphene nanoplatelets (GNPs) based on the piezoresistivity principle. For this purpose, different concentrations of CNTs and GNPs (1:1) were dispersed into the CSS, and specimens were fabricated using the standard compaction method with optimum moisture. The mechanical and microstructural, durability, and piezoresistivity performances, of CSS were investigated by various tests after 28 days of hydration. The results showed that the incorporation of 0.1%, 0.17%, and 0.24% CNT/GNP into the stabilized sand with 10% cement caused an increase in UCS of about 65%, 31%, and 14%, respectively, compared to plain CSS. An excessive increase in the CNM concentration beyond 0.24% to 0.34% reduced the UCS by around 13%. The addition of 0.1% CNMs as the optimum concentration increased the maximum dry density of the CSS as well as leading to optimum moisture reduction. Reinforcing CSS with the optimum concentration of CNT/GNP improved the hydration rate and durability of the specimens against severe climatic cycles, including freeze–thaw and wetting–drying. The addition of 0.1%, 0.17%, 0.24%, and 0.34% CNMs into the CSS resulted in gauge factors of about 123, 139, 151, and 173, respectively. However, the Raman and X-ray analysis showed the negative impacts of harsh climatic cycles on the electrical properties of the CNT/GNP and sensitivity of nano intruded CSS.

Self-Sensing Well Cement

Chemical reactions with reservoir fluids and geology related in-situ stress changes may cause damages to cement sealing material in plugged and abandoned oil, gas and CO₂ wells. To avoid leakages, a legitimate monitoring technique is needed that could allow for early warning in case such damages occur. In this paper, we test the utility of oil and gas well cement with a conductive filler in sensing stress changes. To this end, we have measured the resistance response of Portland G—oil and gas well cement with carbon nanofibers (CNF) to axial load during uniaxial compressive strength test. Simultaneously, the microseismicity data were collected. The resistance of the nanocomposite was measured using two-point method in the direction of loading. The resistance changes were correlated with acoustic emission events. A total of four different material response regions were distinguished and the resistivity and acoustic emission changes in these regions were described. Our results suggest that the two complementary methods, i.e., acoustic emission and resistance measurements, can be used for sensing stress state in materials including well cement/CNF composites. The results suggest that the well cement/CNF composites can be a good candidate material to be used as a transducer sensing changes in stress state in, e.g., well plugs up to material failure.

Multifunctional Cement Mortars Enhanced with Graphene Nanoplatelets and Carbon Nanotubes

Recent findings have brought forward the potential of carbon nano-species, especially nanotubes and graphene, to impart exceptional multifunctional potential to cement, offering simultaneous enhancement of mechanical, fracture mechanical and electrical properties. While available knowledge on the topic is still limited, there is a complete absence of direct comparisons of the potential of the nano-species to improve strength and toughness and provide multifunctionality to the mortars. The study offers a comprehensive overview of these potentials, for mortars modified with pure graphene nanoplatelets and carbon nanotubes at consistent, directly comparable, concentrations up to 1.2 wt.%. Testing included flexure under pure bending moments, axial compression, electrical resistivity measurements and fracture tests under three point bending configuration; the latter were also independently assessed by acoustic emission. Differences in documented properties and optimal concentrations associated with improved mechanical performance were directly compared and rationalized in terms of nanospecies morphology. Dramatic, statistically consistent improvements in fracture behavior, up to 10-fold of control values, were documented for specific nanofiller concentrations, indicating an excellent potential of the material system for contemporary smart construction applications. An exceptionally favorable comparison of acoustic emission and fracture energy data confirmed that the non-destructive technique can independently assess the fracture performance of mortars with exceptional precision.

Self-Sensing Properties of Green Alkali-Activated Binders with Carbon-Based Nanoinclusions

This paper is aimed at investigating the self-sensing properties of Portland-free alkali-activated binders doped with carbon-based nanofillers. Four different inclusions (carbon nanotubes, carbon nanofibers, carbon black and graphene nanoplatelets) were added into the matrix in the same amount. The physical and electromechanical properties were analyzed. The self-sensing capabilities of the samples were tested by applying a square wave voltage signal and measuring the variation of electrical resistance during cyclical compression tests. The results showed that the presence of nano-inclusions enhanced the sensing behavior of the materials, especially regarding the linearity and the hysteresis performances. Such results appear promising for the application of such novel and innovative nano-modified composites in the field of monitoring structures and infrastructures.

Smart Graphite-Cement Composite for Roadway-Integrated Weigh-In-Motion Sensing

Smart multifunctional composites exhibit enhanced physical and mechanical properties and can provide structures with new capabilities. The authors have recently initiated a research program aimed at developing new strain-sensing pavement materials enabling roadway-integrated weigh-in motion (WIM) sensing. The goal is to achieve an accurate WIM for infrastructure monitoring at lower costs and with enhanced durability compared to off-the-shelf solutions. Previous work was devoted to formulating a signal processing algorithm for estimating the axle number and weights, along with the vehicle speed based on the outputs of a piezoresistive pavement material deployed within a bridge deck. This work proposes and characterizes a suitable low-cost and highly scalable cement-based composite with strain-sensing capabilities and sufficient sensitivity to meet WIM signal requirements. Graphite cement-based smart composites are presented, and their electromechanical properties are investigated in view of their application to WIM. These composites are engineered for scalability owing to the ease of dispersion of the graphite powder in the cement matrix, and can thus be used to build smart sections of road pavements. The research presented in this paper consists of electromechanical tests performed on samples of different amounts of graphite for the identification of the optimal mix in terms of signal sensitivity. An optimum inclusion level of 20% by weight of cement is obtained and selected for the fabrication of a plate of 30 × 15 × 5 cm³. Results from load identification tests conducted on the plate show that the proposed technology is capable of WIM.

Effects of Water Content and Temperature on Bulk Resistivity of Hybrid Cement/Carbon Nanofiber Composites

Cement nanocomposites with carbon nanofibers (CNFs) are electrically conductive and sensitive to mechanical loads. These features make them useful for sensing applications. The conductive and load sensing properties are well known to be dependent on carbon nanofiber content; however, much less is known about how the conductivity of hybrid cement–CNF depend on other parameters (e.g., water to cement ratio (w/c), water saturation of pore spaces and temperatures above ambient temperature). In this paper we fill-in these knowledge gaps by: (1) determining a relationship between the cement–CNF bulk resistivity and w/c ratio; (2) determining the effect of water present in the pores on bulk resistivity; (3) describing the resistivity changes upon temperature changes up to 180 °C. Our results show that the increase in the water to cement ratio results in increased bulk resistivity. The decrease in nanocomposite resistivity upon a stepwise temperature increase up to 180 °C was found to be related to free water release from cement pores and the dry materials were relatively insensitive to temperature changes. The re-saturation of pores with water was not reversible with respect to electrical resistivity. The results also suggest that the change in the type of electrical connection can lead to two orders of magnitude different bulk resistivity results for the same material. It is expected that the findings from this paper will contribute to application of cement–CNF-based sensors at temperatures higher than ambient temperature.

A Weigh-in-Motion Characterization Algorithm for Smart Pavements Based on Conductive Cementitious Materials

Smart materials are promising technologies for reducing the instrumentation cost required to continuously monitor road infrastructures, by transforming roadways into multifunctional elements capable of self-sensing. This study investigates a novel algorithm empowering smart pavements with weigh-in-motion (WIM) characterization capabilities. The application domain of interest is a cementitious-based smart pavement installed on a bridge over separate sections. Each section transduces axial strain provoked by the passage of a vehicle into a measurable change in electrical resistance arising from the piezoresistive effect of the smart material. The WIM characterization algorithm is as follows. First, basis signals from axles are generated from a finite element model of the structure equipped with the smart pavement and subjected to given vehicle loads. Second, the measured signal is matched by finding the number and weights of appropriate basis signals that would minimize the error between the numerical and measured signals, yielding information on the vehicle’s number of axles and weight per axle, therefore enabling vehicle classification capabilities. Third, the temporal correlation of the measured signals are compared across smart pavement sections to determine the vehicle weight. The proposed algorithm is validated numerically using three types of trucks defined by the Eurocodes. Results demonstrate the capability of the algorithm at conducting WIM characterization, even when two different trucks are driving in different directions across the same pavement sections. Then, a noise study is conducted, and the results conclude that a given smart pavement section operating with less than 5% noise on measurements could yield good WIM characterization results.

Influence of Uniaxial Stress on the Shear-Wave Spectrum Propagating in Steel Members

Structural health monitoring technologies have provided extensive methods to sense the stress of steel structures. However, monitored stress is a relative value rather than an absolute value in the structure’s current state. Among all the stress measurement methods, ultrasonic methods have shown great promise. The shear-wave amplitude spectrum and phase spectrum contain stress information along the propagation path. In this study, the influence of uniaxial stress on the amplitude and phase spectra of a shear wave propagating in steel members was investigated. Furthermore, the shear-wave amplitude spectrum and phase spectrum were compared in terms of characteristic frequency (CF) collection, parametric calibration, and absolute stress measurement principles. Specifically, the theoretical expressions of the shear-wave amplitude and phase spectra were derived. Three steel members were used to investigate the effect of the uniaxial stress on the shear-wave amplitude and phase spectra. CFs were extracted and used to calibrate the parameters in the stress measurement formula. A linear relationship was established between the inverse of the CF and its corresponding stress value. The test results show that both the shear-wave amplitude and phase spectra can be used to evaluate uniaxial stress in structural steel members.

Intrinsic Sensing Properties of Chrysotile Fiber Reinforced Piezoelectric Cement-Based Composites

Lead-zirconate-titanate (PZT) nanoscale powder was first synthesized by the sol-gel method, then PZT and 0–3 type PZT/chrysotile fiber (CSF)/cement composite (PZTCC) wafers were fabricated after grind-mixing PZT powder with strontium carbonate and/or cement, ductile CSF in tandem with press-sintered process, respectively. The crystal structure (XRD), microstructure (SEM), piezoelectric properties after surface silver penetration, and polarization of the PZT and PZTCC wafer were investigated. Furthermore, self-sensing responses under either impulse or cyclic loading and micro-hardness toughness of PZTCC were also investigated. Results show that the incorporation of CSF and cement admixture weakens the perovskite crystalline peak of PZTCC; reduces the corresponding piezoelectric coefficient from 119.2 pC/N to 32.5 pC/N; but effectively bridges the gap on the toughness between PZTCC and concrete since the corresponding microhardness with 202.7 MPa of PZTCC is close to that of concrete. A good linear and fast electrical response against either impulse or cyclic loading of the PZTCC is achieved with their respective sensitivity, linearity, and repeatability to 1.505 mV/N, 2.42%, and 2.11%. The sensing responses and toughness of PZTCC is encouraging as an intrinsic piezoelectric sensor for real-time health monitoring of ductile concrete structures.