Durability of Cement Composites Reinforced with Sisal Fiber

This dissertation focuses mainly on investigating the aging mechanisms and degradation kinetics of sisal fiber, as well as the approaches to mitigate its degradation in the matrix of cement composites. In contrast to previous works reported in the literature, a novel approach is proposed in this study to directly determine the fiber's degradation rate by separately studying the composition changes, mechanical and physical properties of the embedded sisal fibers. Cement hydration is presented to be a crucial factor in understanding fiber degradation behavior. The degradation mechanisms of natural fiber consist of mineralization of cell walls, alkali hydrolysis of lignin and hemicellulose, as well as the cellulose decomposition which includes stripping of cellulose microfibrils and alkaline hydrolysis of amorphous regions in cellulose chains. Two mineralization mechanisms, CH-mineralization and self-mineralization, are proposed. The degradation kinetics of sisal fiber in the cement matrix are also analyzed and a model to predict the degradation rate of cellulose for natural fiber embedded in cement is outlined. The results indicate that the time needed to completely degrade the cellulose in the matrix with cement replacement by 30wt.% metakaolin is 13 times longer than that in pure cement. A novel and scientific method is presented to determine accelerated aging conditions, and to evaluating sisal fiber's degradation rate and durability of natural fiber-reinforced cement composites. Among the static aggressive environments, the most effective approach for accelerating the degradation of natural fiber in cement composites is to soak the samples or change the humidity at 70 ºC and higher temperature. However, the dynamic wetting and drying cycling treatment has a more accelerating effect on the alkali hydrolysis of fiber's amorphous components evidenced by the highest crystallinity indices, minimum content of holocellulose, and lowest tensile strength. Based on the understanding of degradation mechanisms, two approaches are proposed to mitigate the degradation of sisal fiber in the cement matrix. In order to relieve the aggressive environment of hydrated cement, cement substitution by a combination of metakaolin and nanoclay, and a combination of rice husk ash and limestone are studied. Both metakaolin and nanoclay significantly optimize the cement hydration, while the combination of these two supplementary cementitious materials validates their complementary and synergistic effect at different stages of aging. The presented approaches effectively reduce the calcium hydroxide content and the alkalinity of the pore solution, thereby mitigating the fiber degradation and improving both the initial mechanical properties and durability of the fiber-cement composites. The role of rice husk ash in cement modification is mainly as the active cementitious supplementary material. In order to improve the degradation resistance of sisal fiber itself, two novel, simple, and economical pretreatments of the fibers (thermal and sodium carbonate treatment) are investigated. Both thermal treatment and Na2CO3 treatment effectively improve the durability of sisal fiber-reinforced concrete. The thermal treatment achieves improvement of cellulose's crystallization, which ensures the initial strength and improved durability of sisal fiber. A layer consisting of calcium carbonate sediments, which protects the internals of a fiber from the strong alkali pore solution, is formed and filled in pits and cavities on the Na2CO3 treated sisal fiber's surface

Ultra-High-Performance Concrete Reinforced with Multi-Scale Hybrid Fibers and Its Durability-Related Properties

Due to its excellent mechanical properties, dense microstructure, low permeability, ease of placement and volume stability, ultra-high performance concrete (UHPC) is considered the next-generation structural concrete and is increasingly used in transportation infrastructure. While previous research efforts generated valuable results, to achieve the desired performance, UHPC needs to be well formulated with precise and optimized quantities of cementitious materials, fillers, fine aggregate, water, chemical admixtures, and fibers. In addition, the mixture design of UHPC and its correlation with the performance evolution under different curing conditions remain unclear, and there exist critical significant gaps in understanding the efficiency of fibers and mixture design on the properties of UHPC, especially the mechanical and durability-related performance. Massachusetts Department of Transportation (MassDOT) is exploring multiple infrastructure applications that can incorporate UHPC, including joints, overlays, repairs, rehabilitation, and bridge beam fabrication. This project aims to develop UHPC mix design formulations that can be implemented at ready-mix batching plants or precast/prestressed concrete fabrication facilities by identifying and maximizing the roles of fibers and additives in enhancing mechanical and durability-related properties. Four fiber-reinforced mixes and seven UHPC mixes with different fibers were investigated and a UHPC mix for large-scale batching and field applications was recommended

Bayesian estimation of human impedance and motion intention for human-robot collaboration

This article proposes a Bayesian method to acquire the estimation of human impedance and motion intention in a human-robot collaborative task. Combining with the prior knowledge of human stiffness, estimated stiffness obeying Gaussian distribution is obtained by Bayesian estimation, and human motion intention can be also estimated. An adaptive impedance control strategy is employed to track a target impedance model and neural networks are used to compensate for uncertainties in robotic dynamics. Comparative simulation results are carried out to verify the effectiveness of estimation method and emphasize the advantages of the proposed control strategy. The experiment, performed on Baxter robot platform, illustrates a good system performance

Gait Recognition as a Service for Unobtrusive User Identification in Smart Spaces

Recently, Internet of Things (IoT) has raised as an important research area that combines the environmental sensing and machine learning capabilities to flourish the concept of smart spaces, in which intelligent and customized services can be provided to users in a smart manner. In smart spaces, one fundamental service that needs to be provided is accurate and unobtrusive user identification. In this work, to address this challenge, we propose a Gait Recognition as a Service (GRaaS) model, which is an instantiation of the traditional Sensing as a Service (S2aaS) model, and is specially deigned for user identification using gait in smart spaces. To illustrate the idea, a Radio Frequency Identification (RFID)-based gait recognition service is designed and implemented following the GRaaS concept. Novel tag selection algorithms and attention-based Long Short-term Memory (At-LSTM) models are designed to realize the device layer and edge layer, achieving a robust recognition with 96.3% accuracy. Extensive evaluations are provided, which show that the proposed service has accurate and robust performance and has great potential to support future smart space applications