This study was conducted to evaluate the effect of the carbon-based nanomaterial type on the electrical properties of cement paste. Three different nanomaterials, multi-walled carbon nanotubes (MWCNTs), graphite nanofibers (GNFs), and graphene (G), were incorporated into the cement paste at a volume fraction of 1%. The self-sensing capacity of the cement composites was also investigated by comparing the compressive stress/strain behaviors by evaluating the fractional change of resistivity (FCR). The electrical resistivity of the plain cement paste was slightly reduced by adding 1 vol % GNFs and G, whereas a significant decrease of the resistivity was achieved by adding 1 vol % MWCNTs. At an identical volume fraction of 1%, the composites with MWCNTs provided the best self-sensing capacity with insignificant noise, followed by the composites containing GNFs and G. Therefore, the addition of MWCNTs was considered to be the most effective to improve the self-sensing capacity of the cement paste. Finally, the composites with 1 vol % MWCNTs exhibited a gauge factor of 113.2, which is much higher than commercially available strain gauges.

Cement-based sensors with hybrid conductive fillers using both carbon fibers (CFs) and multi-walled carbon nanotubes (MWCNTs) were experimentally investigated in this study. The self-sensing capacities of cement-based composites with only CFs or MWCNTs were found based on preliminary tests. The results showed that the percolation thresholds of CFs and MWCNTs were 0.5–1.0 vol.% and 1.0 vol.%, respectively. Based on these results, the feasibility of self-sensing composites with four different amounts of CFs and MWCNTs was considered under cyclic compression loads. When the amount of incorporated CFs increased and the amount of incorporated MWCNTs decreased, the self-sensing capacity of the composites was reduced. It was concluded that cement-based composites containing both 0.1 vol.% CFs and 0.5 vol.% MWCNTs could be an alternative to cement-based composites with 1.0 vol.% MWCNTs in order to achieve equivalent self-sensing performance at half the price. The gauge factor (GF) for that composite was 160.3 with an R-square of 0.9274 in loading stages I and II, which was similar to the GF of 166.6 for the composite with 1.0 vol.% MWCNTs.

This study investigates the effect of nanomaterials on the piezoresistive sensing capacity of cement-based composites. Three different nanomaterials—multi-walled carbon nanotubes, graphite nanofibers, and graphene oxide—were considered along with a plain mortar, and a cyclic compressive test was performed. Based on a preliminary test, the optimum flowability was determined to be 150 mm in terms of fiber dispersion. The electrical resistivity of the composites substantially decreased by incorporating 1 wt% multi-walled carbon nanotubes, but only slightly decreased by including 1 wt% graphite nanofibers and graphene oxide. This indicates that the use of multi-walled carbon nanotubes is most effective in improving the conductivity of the composites compared to the use of graphite nanofibers and graphene oxide. The fractional change in resistivity of the composites with nanomaterials exhibited similar behavior to that of the cyclic compressive load, but partial reversibility in fractional change in resistivity was obtained beyond 60% of the peak load. A linear relationship between the fractional change in resistivity and cyclic compression strain (up to 1500 με) was observed in the composites with multi-walled carbon nanotubes, and the gauge factor was found to be 166.6. It is concluded that cement-based composites with 1 wt% multi-walled carbon nanotubes can be used as piezoresistive sensors for monitoring the stress/strain generated in concrete structures.

This study investigates the effects of hooked-end fiber geometry and volume fraction on the flexural behavior of concrete pedestrian decks. To achieve this, three different fiber geometries, i.e., three-dimensional (3D), four-dimensional (4D), and five-dimensional (5D), and volume fractions of 0.37%, 0.6%, and 1.0% were considered. Test results indicate that a higher number of hook ends can more effectively enhance the flexural strength and flexural strength margin at all volume fractions than a lower number, so that the order of effectiveness of hooked-end fibers on the flexural strength parameters was as follows: 5D > 4D > 3D. To satisfy the ductility index of 0.39, the amounts of 3D, 4D, and 5D hooked steel fibers should be in the range of 0.98%‒1.10%. Moreover, at a fiber volume fraction of 1.0%, only multiple cracking behaviors were observed, and the numerical results indicated that the volume fraction should be equal to 1.0% to guarantee a deflection-hardening response of pedestrian decks, regardless of the hooked-end fiber geometry. Consequently, a 1.0% by volume of hooked-end steel fiber is recommended to replace the minimum longitudinal steel rebars and guarantee a ductile flexural behavior with multiple cracks for pedestrian decks made of high-strength concrete.

This study investigated the effects of carbon fiber (CF) length, electrode spacing, and probe configuration on the electrical conductivity of cement composites. Accordingly, 57 different types of samples were prepared, considering three different CF lengths, five different CF contents, three different electrode spacings, and two different probe configurations. This research found that the influence of CF length on the electrical resistivity of cement composite depends electrode spacing. For the cement composite with wide electrode spacing of 40 mm, its resistivity decreased as increasing CF length as in the previous study. However, when the electrode spacing is 10 mm, which is narrow (10 mm), the resistivity of the cement composite rather increased with increasing CF length. The results implied that when an electrode is designed for the cement composite incorporating CF, the CF length should be short compared to the electrode spacing. The percolation threshold of CF measured by the two-probe configuration was 2% or more. This is higher than that measured by the four-probe configuration (1%). At a lower CF content than 2%, the two-probe configuration gave higher resistivity of the cement composite than the four-probe configuration. However, the difference coming from the different probe configurations was marginal as increasing the CF content.

A deterioration model plays an important role to predict the valid total maintenance cost for sustainable maintenance of bridges. In the current state-of-the-art, the deterioration model has regression parameters as a probabilistic process by an initially determined mean and standard deviation, called an existing model. However, the existing model has difficulty to predict maintenance costs accurately, because it cannot reflect an information based on structural damage at an operational stage. In this research, updating the probabilistic deterioration model is presented for the prediction of pre-stressed concrete I-type (PSCI) girder bridges using a particle filtering technique which is an advanced Bayesian updating method based on big data analysis. The method enables predicting maintenance cost fitted in the current structural status, which includes the recent information by inspection with bridge-monitoring. The method is adapted in the Mokdo Bridge which is currently being used for evaluating the efficiency of maintenance cost by effects on updated probabilistic values with two different scenarios. As the result, it is shown that the proposed method is effective in predicting maintenance costs.