Auxetic Cementitious Composites for Energy Harvesting

Abstract

Auxetic cementitious composites (ACCCs), composed of fiber-reinforced cementitious materials, exhibit notable deformability and structural resilience due to their auxetic characteristics. These properties make them excellent candidates for efficient energy harvesting, especially when integrated with surface-mounted piezoelectric elements. Moreover, ACCCs demonstrate reversible deformation under cyclic loading and maintain their quasi-elastic behavior over extended use, which underscores their promise for long-term energy harvesting applications. Despite growing interest in energy harvesting, most existing research has concentrated on auxetic materials derived from metals and polymers, while auxetic cementitious composites remain largely underexplored. Current investigations into ACCCs have predominantly focused on their mechanical properties, with limited emphasis on their potential for multifunctional applications such as energy harvesting. Given the abundance and low cost of cementitious materials, ACCCs offer a promising and sustainable avenue for the development of cost-effective energy harvesting systems. In this thesis, the feasibility of utilizing ACCCs for energy harvesting was systematically investigated.

First, a novel piezoelectric energy harvester (PEH) integrating ACCCs with surface-mounted polyvinylidene fluoride (PVDF) films is proposed to convert strain energy into usable electrical power. In this configuration, the ACCC–PVDF system is initially compressed to a prescribed displacement and subsequently subjected to cyclic loading, thereby enabling continuous voltage generation for energy harvesting. The influence of loading amplitude and frequency on the output voltage response of the harvester is further investigated.

Then, this study employs numerical modeling to quantitatively analyze the piezoelectric mechanisms governing the ACCC-based energy harvester. A general theoretical framework was first developed to evaluate the energy harvesting performance of fiber-reinforced cementitious materials with surface-mounted PVDF. Building on this computational framework, a specialized computational model was formulated to capture both the mechanical deformation and electrical response of the ACCC harvester. The mechanical behavior of ACCCs was represented using the concrete damage plasticity (CDP) model during the preloading stage, which was subsequently transitioned to an elastic model for cyclic loading. Analytical expressions describing the piezoelectric effect were then derived from the simulated mechanical responses to predict the PVDF output voltage. Modelling results showed good agreement with experimental data.

The final stage of this work focuses on optimizing the ACCC-based energy harvester. At the material level, a strain-hardening cementitious composite (SHCC) mixture with enhanced softening behavior was developed to enhance fiber-bridging performance at ACCC joints, thereby enabling stable auxetic behavior across diverse geometries. This advancement permits systematic evaluation of geometric configurations to identify designs with superior energy harvesting performance. Nevertheless, the inherent brittleness of cementitious materials remains a critical challenge, as geometric modifications can induce splitting and compromise auxetic behavior. To overcome the limitations of labor-intensive experiments, the previously developed energy harvesting models were employed to generate simulation datasets; however, large-scale simulations proved computationally demanding. To address this issue, a machine learning–assisted framework integrating the energy harvesting model with Bayesian Optimization (BO) was implemented, enabling efficient identification of high-performance ACCC geometries for energy harvesting while using constraints to prevent splitting failure and non-auxetic behavior of ACCCs under compression. The optimal configuration derived from this framework was fabricated using additive manufacturing–assisted casting and experimentally validated, confirming its enhanced energy harvesting capability. Furthermore, the machine learning–driven design was extended by assembling the optimized single-cell ACCC into a 2×2 multi-cell configuration, demonstrating the scalability and practicality of the approach for real-world energy harvesting applications.

This PhD study proposed a strain-based ACCC energy harvester that leverages the recoverable deformation within the auxetic behavior range of ACCC structures. Combining experimental evaluation, numerical modeling, and machine learning, the research enhances the recoverable deformation capacity of ACCCs, thereby increasing the energy output of the harvester. The recoverable deformation of ACCCs is identified as the governing mechanism, as the harvested energy is determined by the mechanical energy generated during deformation and its subsequent conversion. Therefore, these findings also provide broader insights for future studies on other energy harvesting strategies employing auxetic cementitious composites as substrates.