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.

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.

Multifunctional structural materials are very promising in the field of engineering. Particularly, their strain sensing ability draws much attention for structural health monitoring applications. Generally, strain sensing materials are produced by adding a certain amount of conductive fillers, around the so-called “percolation threshold”, to the cement or composite matrix. Recently, graphite has been found to be a suitable filler for strain sensing. However, graphite requires high amounts of doping to reach percolation threshold. In order to decrease the amount of inclusions, this paper proposes cementitious materials doped with new hybrid carbon inclusions, i.e., graphite and carbon microfibers. Carbon microfibers having higher aspect ratio than graphite accelerate the percolation threshold of the graphite particles without incurring into dispersion issues. The resistivity and strain sensitivity of different fibers’ compositions are investigated. The electromechanical tests reveal that, when combined, carbon microfibers and graphite hybrid fillers reach to percolation faster and exhibit higher gauge factors and enhanced linearity.

The need for ageing infrastructure monitoring has recently emerged as an urgent priority in many countries. Smart road infrastructure is a technology that could be used to address this issue by enabling on-time decisions such as condition-based maintenance. For example, automatic traffic monitoring could be beneficial by improving fatigue analysis, and maintenance priority planning and management of overloaded vehicles. This can be done through image-based traffic monitoring, weight measurements by static scales, and weigh-in-motion (WIM) stations. WIM can be used to identify and classify the type, the number, and the weight of vehicles passing over a given road segment without interrupting the traffic flow. The general drawbacks of existing WIM technologies are their high costs, low durability, and complex deployment. This paper proposes a new asphalt-like composite enabling self-sensing road pavements that can serve as a low-cost and durable WIM sensor. The proposed novel material consists of a commercial binder called EVIzero, doped with natural aggregates and carbon microfibers. These microfibers provide electrical conductivity and piezoresistive properties through electrical percolation. Here, both the material preparation for road applications and its electromechanical characterization are examined. Various cylindrical samples fabricated using different percentages of carbon microfibers were produced and investigated in order to evaluate their signal quality and strain sensing capabilities. It is found that the material mix fabricated with 1% carbon microfibers with respect to the binder weight has the best sensing performance due to electrical percolation. A mid-size slab sample is produced using this optimal mix in order to achieve a preliminary demonstration of material’s feasibility for WIM sensing. The results show that a linear relationship between the electrical response of the slab sample and the induced strain is established with an $$R^2$$ R 2 of 93%.

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.