Connecting concrete technology and machine learning: proposal for application of ANNs and CNT/concrete composites in structural health monitoring

Effect of surfactant concentration on the evaporation-driven deposition of carbon nanotubes: from coffee-ring effect to strain sensing

Carbon nanotubes (CNTs) as electrically conductive materials are of great importance in the fabrication of flexible electronic devices and wearable sensors. In this regard, the evaporation-driven self-assembly of CNTs has attracted increasing attention. CNT-based applications are mostly concerned with the alignment of CNTs and the density of CNT films. In the present work, we focus on the latter by trying to achieve an optimal evaporation-driven deposition with the densest CNT ring. Although surfactants are used for effective dispersion and colloidal stabilization of CNTs in the aqueous phase, their excessive usage induces Marangoni eddies in the evaporating sessile droplets, leading to poor ring depositions. Thus, there is an optimum surfactant concentration that contributes to CNTs deagglomeration and results in the densest ring-like deposition with relatively high thickness. We report that this optimum concentration for sodium dodecyl sulfate (SDS) as a surfactant can be approximately considered as much as the concentration of multi-walled carbon nanotubes (MWCNTs) as the colloidal nanoparticles. Optimal depositions show the lowest electrical resistances for each CNT concentration, making them suitable for electronic applications. We also propose the multiple depositions method in which a new droplet is printed after the complete evaporation of the previous droplet. This method can lead to denser rings with a higher conductivity using lower concentrations of CNTs. Lastly, we fabricate strain sensors based on the optimal evaporation-driven deposition of CNTs which show higher gauge factors than the commercial strain gauges, corroborating the applicability of our method.

A Neural Network Model of Smart Aging Combining Family Structure Change Factors

In this paper, we analyze the changes in family structure and explore the changes in detail, based on which we construct a neural network model of smart aging. Based on the gender perspective, the individual growth model in the multilayer linear model is used to examine the effects of family structure changes on the elderly in terms of economic exchange, daily care, and emotional support. The results show that there is no significant gender difference in the family structure changes on the elderly in terms of economic exchange and daily care, but there is a significant gender difference in terms of emotional support. To solve the problem of data imbalance in the daily activity categories of the elderly, this paper resamples the data and uses different neural network models for activity recognition of the sensor data generated from the daily activities of the elderly. In this paper, the daily behavior patterns of the elderly over a while are studied by correlating three conditions of time distance, optimal path, and sensor distance to discover the daily behavior patterns of the elderly, while the abnormal behavior patterns can be well separated by EM clustering algorithm. The daily behavior of the elderly is a coarse-grained representation of their daily activities. It is not limited to a specific activity and does not require the sensor ID, trigger time, and location triggered by the activity to be consistent, but in long-term daily activity data, it abstracts the general behavior rules of the elderly activities. Through the research of this paper, the existing system is improved, and the multifaceted needs of the elderly are fully considered, from housing needs to spiritual needs, to face the current elderly care problems with a positive attitude, create a good social elderly care environment for the elderly, and realize the real elderly care.

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.

Mechanical properties of fiber-reinforced asphalt concrete: Finite element simulation and experimental study

Polymer fiber reinforcement can elevate the pavement performance indicators of asphalt concrete, including low temperature crack resistance, high temperature stability, and water stability. Fiber-reinforced asphalt concrete (FRAC) is a suitable solution to improve the road performance of asphalt concrete. However, the mechanism of the fiber parameters in asphalt concrete has still not been introduced. Herein we studied the influence of fiber geometry characteristics on improving the mechanical properties of asphalt concrete through numerical simulation. The finite element method (FEM) was used to establish an elastoplastic mesoscopic model for indirect tensile test of the asphalt mixtures via ABAQUS. The best fiber length, volume ratio, diameter, and modulus were confirmed based on the simulated results. Furthermore, the indirect tensile tests were undertaken to prove the influence of polyacrylonitrile (PAN) and polyvinyl alcohol (PVA) fiber parameters on the FRAC’s splitting stability. This study fills the gap between theoretical simulations and laboratory experiments, and it is a promising solution to improve engineered FRAC road performance.