Metamaterials and smart structures: Leveraging AI for design, optimization and adaptive engineering solutions

 Corresponding author: Abass Olalekan Ogunniran. Email: Copyright © 2025 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0. Metamaterials and smart structures: Leveraging AI for design, optimization and adaptive engineering solutions Abass Olalekan Ogunniran 1, *, Oluwafemi Odunayo Olusesan 2, Joseph Tosin Salako 3, Anietie Ime Edet 4, Elizabeth Olawumi Oyelami 5, Oluwatoyin Olawale Akadiri 6 and Azeez Arisekola Jimoh 7 1 Department of Metallurgical and Materials Engineering, Federal University of Technology Akure (FUTA). 2 Department of Materials and Metallurgical Engineering, University of Ilorin, Ilorin, Nigeria. 3 Department of Civil and Environmental Engineering, Federal University of Technology Akure, Ondo State. 4 Department of Civil Engineering, Akwa Ibom State University. 5 Department of Electronic and Electrical Engineering, Ladoke Akintola University of Technology Ogbomoso Oyo State Nigeria. 6 Department of Information Sciences, Bay Atlantic University, United States. 7 Department of Civil Engineering, Federal University of Technology Akure (FUTA). Global Journal of Engineering and Technology Advances, 2025, 24(03), 115–139 Publication history: Received on 25 July 2025; revised on 30 August 2025; accepted on 03 September 2025 Article DOI: https://doi.org/10.30574/gjeta.2025.24.3.0260 Abstract In an era where engineering demands increasingly adaptive and resilient systems, this review delves into the profound synergy between metamaterials artificially engineered composites exhibiting extraordinary properties like negative refraction, tunable stiffness, and wave cloaking and smart structures, which embed sensing, actuation, and control mechanisms to dynamically respond to environmental stimuli, all amplified by the revolutionary power of artificial intelligence (AI). Exploring the fundamentals of metamaterials across electromagnetic, acoustic, mechanical, and thermal domains, alongside the principles of smart structures that enable self-monitoring and reconfiguration, the paper illuminates how AI techniques such as machine learning, deep learning, generative algorithms, and reinforcement learning transform design processes through inverse engineering, data-driven discovery, and multi-objective optimization, drastically reducing computational burdens and accelerating the creation of bespoke architectures for applications in aerospace morphing wings, seismic-resistant infrastructure, biomedical implants, and energyharvesting devices. By integrating AI with structural health monitoring, adaptive control systems, and Internet of Things frameworks, smart structures evolve into intelligent entities capable of real-time diagnostics, predictive maintenance, and autonomous adaptation, as evidenced in case studies of vibration-damping skyscrapers and self-healing materials. Yet, acknowledging persistent hurdles like scalability constraints, manufacturing precision, and interdisciplinary silos, the discussion ventures into emerging trends including multi-scale modeling, sustainable hybrid designs, and explainable AI, while pinpointing research gaps in data standardization and model interpretability that beckon innovative pursuits. Ultimately, this synthesis not only underscores AI's pivotal role in unlocking metamaterials' and smart structures' untapped potential for transformative engineering solutions but also calls for collaborative advancements to forge a future of sustainable, resilient technologies that redefine human ingenuity in tackling global challenges. Keywords: Metamaterials; Smart Structures; Artificial Intelligence; Machine Learning; Inverse Design; Structural Health Monitoring; Adaptive Control; IoT Integration Global Journal of Engineering and Technology Advances, 2025, 24(03), 115–139 116 1. Introduction Metamaterials and smart structures represent transformative advancements in materials science and engineering, enabling unprecedented control over physical phenomena such as electromagnetic waves, acoustics, and mechanical responses through engineered architectures rather than inherent material properties. These innovations have paved the way for adaptive engineering solutions that respond dynamically to environmental stimuli, with artificial intelligence (AI) emerging as a pivotal tool for their design, optimization, and implementation. By integrating AI algorithms, researchers can accelerate the discovery of novel configurations, predict performance under complex conditions, and facilitate real-time adaptability in applications ranging from aerospace to biomedical devices. This review explores the synergy between metamaterials, smart structures, and AI, highlighting how machine learning and optimization techniques are revolutionizing adaptive engineering. 1.1. Overview of Metamaterials and Smart Structures Metamaterials are artificially engineered composites designed to exhibit properties not found in naturally occurring materials, such as negative refractive indices or tunable wave manipulation, achieved through periodic subwavelength structures. According to Smith et al. [1], these materials derive their unique behaviors from geometric arrangements rather than chemical composition, allowing for phenomena like electromagnetic cloaking and superlensing that defy conventional physics. This foundational concept has evolved from early theoretical proposals to practical implementations, with initial focus on electromagnetic metamaterials expanding to include acoustic, mechanical, and thermal variants. For instance, early experiments demonstrated backward-wave propagation, challenging traditional wave theories and opening avenues for advanced optics and sensing. The classification of metamaterials often includes categories based on their operational domains, such as photonic for light manipulation or phononic for sound control, each tailored to specific engineering challenges [2]. Over the past two decades, advancements in fabrication techniques like 3D printing have enabled the realization of complex geometries, making metamaterials more accessible for realworld applications. However, the design process remains intricate, requiring precise control over unit cell configurations to achieve desired macroscopic properties, which underscores the need for sophisticated computational tools [1]. The unique properties of metamaterials stem from their ability to manipulate waves at scales smaller than the wavelength, leading to exotic effects like negative permittivity, permeability, or density. Findings from Zhang et al. [3] indicate that chiral metamaterials can induce negative refractive indices through asymmetric structures, enabling applications in polarization control and imaging beyond diffraction limits. Similarly, mechanical metamaterials exhibit programmable stiffness and energy absorption via auxetic designs, where negative Poisson's ratios allow expansion under tension, contrasting with conventional materials. These mechanisms are governed by resonance effects, bandgap formations, and local resonances that trap or redirect energy. In acoustic contexts, as explored by Cummer et al. [4], metamaterials can create sound barriers or lenses by controlling phonon propagation, with implications for noise reduction in urban environments. Thermal metamaterials, on the other hand, manipulate heat flow through cloaking or concentrating effects, achieved via layered or porous architectures. The versatility of these properties arises from scalable design principles, where unit cell symmetry and material constituents can be tuned for multifunctionality, such as combining electromagnetic shielding with mechanical robustness. Despite these advantages, challenges persist in broadband performance and loss minimization, driving ongoing research into hybrid metamaterials that integrate multiple functionalities. The significance of metamaterials and smart structures in engineering applications lies in their potential to address critical challenges in efficiency, sustainability, and adaptability across diverse sectors. In aerospace, metamaterials enable lightweight components with superior vibration damping and radar absorption, enhancing stealth and fuel efficiency. A comprehensive review by Bertoldi et al. [2] highlights how flexible mechanical metamaterials facilitate deployable structures, such as morphing wings that adapt to flight conditions, reducing structural failures and maintenance costs. In biomedical engineering, these materials support implantable devices with tunable stiffness for tissue matching, improving biocompatibility and patient outcomes. Civil engineering benefits from seismic-resistant designs incorporating metamaterials for wave attenuation, protecting infrastructure from earthquakes. Energy harvesting applications leverage piezoelectric-integrated metamaterials to convert ambient vibrations into electricity, promoting renewable sources. Overall, the integration of metamaterials with smart structures amplifies their impact, creating systems that sense, actuate, and self-heal, thereby revolutionizing adaptive engineering. Recent developments have further elevated the role of metamaterials and smart structures by incorporating advanced manufacturing and multifunctional designs. For example, 3D metamaterials, as discussed by Kadic et al. [5], extend twodimensional concepts into volumetric architectures, enabling isotropic properties for omnidirectional wave control in Global Journal of Engineering and Technology Advances, 2025, 24(03), 115–139 117 optics and acoustics. This progression has been fueled by computational simulations that predict emergent behaviors, bridging theoretical models with experimental validation. In smart structures, the emphasis on integration with sensors and actuators creates closed-loop systems capable of real-time response, such as buildings that adjust damping during winds. The convergence of these fields promises sustainable solutions, like energy-efficient buildings with thermal metamaterials that regulate indoor climates without excessive HVAC reliance [5-7]. As engineering demands grow for resilient and intelligent systems, metamaterials and smart structures stand at the forefront, offering scalable innovations that align with global sustainability goals. 1.2. Role of AI in Engineering Design Artificial intelligence has revolutionized engineering design by automating complex processes, enhancing predictive accuracy, and enabling data-driven decision-making in domains traditionally reliant on human intuition and iterative experimentation. In material and structural engineering, AI techniques such as machine learning facilitate the exploration of vast design spaces, identifying optimal configurations that outperform conventional methods. According to Wang et al. [8], deep generative models can learn mechanistic behaviors of metamaterial systems, allowing for the synthesis of novel structures with tailored properties like stiffness or conductivity. This shift from rule-based to learning-based approaches reduces design cycle times, particularly in high-dimensional problems where traditional simulations are computationally prohibitive. AI's role extends to inverse design, where desired functionalities dictate geometry, inverting the forward simulation paradigm. Supervised learning algorithms train on datasets of simulated or experimental results, predicting outcomes for unseen inputs with high fidelity [6,8]. Moreover, reinforcement learning enables adaptive optimization, refining designs through trial-and-error in virtual environments. The integration of AI thus democratizes advanced engineering, empowering designers to tackle multidisciplinary challenges with greater efficiency. In optimization contexts, AI excels at handling nonlinear, multi-objective problems inherent to metamaterial and smart structure design, such as balancing weight, strength, and adaptability. Genetic algorithms and neural networks, as applied in structural health monitoring, optimize sensor placements for maximal damage detection coverage. A study by Song et al. [10] demonstrates how AI streamlines the design of innovative metamaterials, from photonic to mechanical types, by predicting bandgaps and resonance frequencies with minimal human intervention. These tools incorporate uncertainty quantification, accounting for manufacturing variabilities and environmental factors, leading to robust designs. Big data analytics further enhances AI's utility, processing vast simulation outputs to uncover patterns inaccessible through manual analysis. For smart structures, AI-driven control systems predict and mitigate vibrations in real-time, using sensor fusion to adapt actuation strategies. The synergy between AI and finite element methods accelerates topology optimization, generating lightweight yet durable components for aerospace and automotive applications. Challenges like data scarcity are addressed through transfer learning, leveraging pre-trained models from related domains to bootstrap new designs. The transformative impact of AI in engineering design is evident in its facilitation of interdisciplinary integration, merging materials science with computational intelligence for holistic solutions. In adaptive engineering, AI enables predictive maintenance in smart structures by analyzing sensor data for anomaly detection, prolonging service life. Insights from Qin et al. [11] on intelligent design systems for shear wall structures illustrate how large language models and generative AI automate layout optimization, considering seismic loads and material constraints. This approach not only enhances sustainability by minimizing resource use but also fosters innovation in eco-friendly materials. AI's role in human-AI collaboration is crucial, where designers oversee creative aspects while algorithms handle repetitive computations. Ethical considerations, such as bias in training data, are increasingly addressed to ensure equitable outcomes. Overall, AI empowers engineers to push boundaries, from conceptual ideation to prototyping, fostering a new era of intelligent design. Emerging trends in AI for engineering underscore its potential for real-time adaptability and scalability, particularly in dynamic environments. Hybrid AI frameworks combine physics-informed neural networks with traditional simulations, ensuring compliance with physical laws while accelerating convergence. For instance, in metamaterial optimization, AI can iteratively refine designs based on feedback loops from fabrication outcomes, closing the gap between simulation and reality. In smart infrastructure, AI optimizes energy consumption in buildings through predictive algorithms, integrating IoT for seamless operation [7]. Future directions include explainable AI, providing insights into decision processes to build trust among practitioners. As computational power advances, AI's role will expand, enabling autonomous design pipelines that evolve with user needs. Global Journal of Engineering and Technology Advances, 2025, 24(03), 115–139 118 1.3. Objectives and Scope of the Review The primary objective of this review is to synthesize the current state of AI integration in the design, optimization, and adaptive applications of metamaterials and smart structures, providing a comprehensive framework for researchers and engineers. By examining AI-driven methodologies, the paper aims to elucidate how machine learning accelerates inverse design processes, enabling the creation of metamaterials with bespoke properties for specific engineering challenges. In a novel exploration by Tian et al. [6], multi-agent frameworks incorporating large language models demonstrate accelerated metamaterial design, highlighting the potential for generative AI in collaborative workflows. The scope encompasses foundational concepts, practical case studies, and future trends, focusing on interdisciplinary synergies while excluding unrelated fields like pure software engineering. This targeted approach ensures depth in AI's application to adaptive systems, addressing gaps in scalability and real-world deployment. Secondary objectives include identifying key challenges, such as computational demands and data quality, and proposing mitigation strategies through hybrid AI techniques. The review's scope is delimited to peer-reviewed literature from the last two decades, emphasizing verifiable advancements in AI-enhanced engineering solutions. It covers subsections on fundamentals, AI techniques, applications, and challenges, structured to guide readers from basics to advanced topics. Findings from Chopra [9] on smart structures underscore the evolution from passive to active systems, setting the stage for AI's role in modern integrations. By including diverse applications like vibration control and structural health monitoring, the paper illustrates AI's versatility. Exclusions involve non-AI methods or outdated technologies, maintaining relevance to contemporary practices. The objectives extend to recommending research directions, such as AI-IoT fusions for realtime adaptability, fostering innovation in sustainable engineering. This review contributes by offering a consolidated perspective on AI's transformative potential, bridging theoretical insights with practical implementations. It aims to equip stakeholders with actionable knowledge for leveraging AI in metamaterial and smart structure development. A classic study by Song [10] on piezoceramic-based vibration control in civil structures provides historical context, evolving into AI-optimized frameworks today. The scope includes critical analysis of limitations, promoting balanced views on AI's efficacy. Ultimately, the paper seeks to inspire interdisciplinary collaboration, advancing adaptive engineering for societal benefits. 2. Fundamentals of Metamaterials 2.1. Definition and Classification Metamaterials are artificially engineered structures designed to exhibit electromagnetic, acoustic, or mechanical properties unattainable in natural materials, achieved through precise geometric arrangements at subwavelength scales. Veselago et al. [12] first theorized materials with simultaneous negative permittivity and permeability, laying the groundwork for modern metamaterials that manipulate wave propagation in unconventional ways. These materials are defined by their ability to control physical phenomena through structure rather than composition, enabling applications like invisibility cloaking and superlensing. Metamaterials are classified based on their operational domains, including electromagnetic (manipulating light), acoustic (controlling sound), mechanical (altering deformation), and thermal (managing heat flow). This classification reflects their versatility, as each type targets specific wave or energy interactions, driven by periodic or non-periodic unit cells. Advances in nanofabrication and additive manufacturing have expanded the feasibility of these designs, allowing complex architectures that were previously theoretical. The development of electromagnetic metamaterials marked the initial breakthrough, focusing on manipulating light through negative refractive indices. Shelby et al. [13] demonstrated the first experimental realization of negative refraction using split-ring resonators, validating theoretical predictions and sparking widespread research. These materials typically consist of metallic or dielectric elements arranged in lattices, enabling phenomena like perfect lensing or electromagnetic wave bending. Acoustic metamaterials, conversely, control sound waves through resonant structures like Helmholtz resonators or membranes, as explored by Liu et al. [14], who showed how locally resonant sonic materials create bandgaps to block specific frequencies. Mechanical metamaterials exploit structural instabilities or auxetic behaviors to achieve programmable stiffness or energy absorption, while thermal metamaterials redirect heat flux via anisotropic conductivity. Each category relies on tailored unit cell designs, often requiring computational tools to optimize performance across scales. Classification also extends to the dimensionality and functionality of metamaterials, with 1D, 2D, and 3D architectures offering distinct advantages. Three-dimensional metamaterials, as discussed by Tserkezis et al. [15], provide isotropic properties for omnidirectional wave control, critical for applications like radar absorption. Hybrid metamaterials Global Journal of Engineering and Technology Advances, 2025, 24(03), 115–139 119 combine multiple functionalities, such as electromagnetic and acoustic wave manipulation, to create multifunctional devices. The ability to engineer these properties stems from advances in materials science, where composites integrate metals, polymers, or ceramics to achieve desired responses. However, challenges in scalability and fabrication precision persist, necessitating innovative design approaches like AI-driven optimization to bridge theoretical models with practical outcomes. The significance of this classification lies in its guidance for application-specific designs, enabling engineers to select appropriate metamaterial types based on target functionalities. For instance, electromagnetic metamaterials dominate in photonics, while mechanical variants excel in aerospace for lightweight, resilient structures. Recent papers [16,17] highlight the growing trend of multifunctional metamaterials, integrating sensing and actuation for adaptive systems. This diversity underscores the need for interdisciplinary approaches, combining physics, engineering, and computational methods to unlock the full potential of metamaterials in solving complex engineering challenges. To provide a structured overview of metamaterial classifications, Table 1 summarizes key domains, their structural characteristics, operational principles, examples, and associated references, highlighting the diversity that enables tailored engineering solutions. Global Journal of Engineering and Technology Advances, 2025, 24(03), 115–139 120 Table 1 Classification of Metamaterials by Domain Domain Structural Characteristics Operational Principles Key Examples Applications Challenges References Electromagnetic Periodic metallic/dielectric lattices (e.g., split-ring resonators) Negative permittivity/permeability for wave reversal Negative refractive index materials, chiral structures Superlensing, cloaking, radar absorption Narrowband performance, losses [1], [3], [12], [13], [17], [21], [23], [24] Acoustic Resonant structures (e.g., Helmholtz resonators, membranes) Local resonances creating bandgaps Phononic crystals, sonic barriers Noise reduction, sound lenses Scalability for low frequencies [4], [14], [19], [27], [28] Mechanical Auxetic lattices or instability-based designs Programmable stiffness via buckling/folding Negative Poisson's ratio foams, morphing structures Energy absorption, deployable wings Manufacturing precision for 3D designs [2], [16], [18], [25] Thermal Layered/porous architectures Anisotropic conductivity for heat redirection Thermal cloaks, concentrators Heat management in buildings, electronics Broadband thermal control [20], [30] Photonic Subwavelength periodic structures Light manipulation beyond diffraction limits Photonic crystals, plasmonic devices Improved photovoltaics, optical cloaking Integration with electronics [5], [15], [22], [26] Hybrid (multifunctional) Combined elements (e.g., electromagnetic-acoustic) Integrated wave control across domains Chiral-mechanical hybrids Multifunctional devices (e.g., shielding + damping) Design complexity, material compatibility [2], [5], [16], [23] 1D Metamaterials Linear arrays or layered stacks Unidirectional wave control Wire arrays for negative index Fiber optics, waveguides Limited isotropy [5], [15] 2D Metamaterials Planar surfaces or metasurfaces Surface wave manipulation Graphene-based metasurfaces Flat lenses, holography Fabrication scalability [5], [22], [26] 3D Metamaterials Volumetric lattices Isotropic properties for omnidirectional control Cubic unit cells with resonators Omnidirectional cloaking, structural components High computational demands [5], [15], [22], [25] Chiral Metamaterials Asymmetric helical structures Polarization control via asymmetry Spiral resonators Circular polarization filters Chirality tuning [3], [15] Reconfigurable Metamaterials Stimuli-responsive elements (e.g., liquid crystals) Dynamic tuning of properties Phase-change material hybrids Adaptive antennas, optics Response speed, durability [21], [22] Global Journal of Engineering and Technology Advances, 2025, 24(03), 115–139 121 Complementing the structured overview in Table 1, Figure 1 presents a comprehensive classification diagram of metamaterials, illustrating their categorization by domain and functionality to aid in understanding application-specific designs. Reproduced with permission from ref [16] Figure 1 Classification diagram of metamaterials and metachips. 2.2. Unique Properties and Mechanisms The hallmark of metamaterials is their ability to exhibit exotic properties, such as negative refractive index, tunable stiffness, or energy cloaking, driven by carefully engineered microstructures. Smith et al. [16,17] demonstrated that negative refractive indices arise from simultaneous negative permittivity and permeability, enabling wave reversal that defies conventional optics. This property facilitates applications like superlensing, where sub-diffraction imaging becomes possible, as shown in early experiments with planar metamaterials. The underlying mechanism involves resonant interactions within unit cells, such as split-ring resonators or chiral structures, which induce effective medium responses distinct from constituent materials. These resonances create frequency-dependent behaviors, allowing precise control over wave propagation, whether electromagnetic, acoustic, or mechanical. Mechanical metamaterials leverage structural instabilities or auxetic properties to achieve counterintuitive behaviors, such as expansion under tension. Lakes et al. [18] introduced the concept of negative Poisson’s ratio materials, where lattice designs enable stretching in multiple directions, enhancing energy absorption in impact-resistant structures. This is achieved through mechanisms like buckling or folding in unit cells, which can be programmed to respond to external stimuli. Acoustic metamaterials, as studied by Yang et al. [19], rely on local resonances to create bandgaps, blocking sound within specific frequency ranges for noise mitigation. Thermal metamaterials, conversely, manipulate heat flow through transformation optics principles, redirecting thermal energy to achieve cloaking or concentration, as Global Journal of Engineering and Technology Advances, 2025, 24(03), 115–139 122 demonstrated by Narayana et al. [20]. These diverse mechanisms highlight the role of structural periodicity and material selection in tailoring macroscopic properties. The tunability of metamaterials is a critical feature, allowing dynamic adaptation to changing conditions. For example, electromagnetic metamaterials can incorporate tunable elements like liquid crystals or phase-change materials to adjust their response in real-time, as explored by Shalaev et al. [21]. This adaptability is vital for applications like reconfigurable antennas or adaptive optics, where environmental variations demand rapid adjustments. Similarly, mechanical metamaterials can be designed with reconfigurable unit cells, enabling stiffness modulation for applications like soft robotics. The work of Wegener et al. [22] on reconfigurable 3D metamaterials illustrates how stimuliresponsive designs enhance functionality, bridging static structures with dynamic systems. However, achieving broadband performance and minimizing losses remain challenges, as resonances are often narrowband and materialdependent. These unique properties are enabled by a synergy of theoretical modeling, experimental validation, and advanced fabrication. Computational tools simulate wave interactions at subwavelength scales, predicting emergent behaviors that guide design. Experimental techniques, such as electron-beam lithography for electromagnetic metamaterials or 3D printing for mechanical ones, translate these models into reality. A comprehensive study by Engheta et al. [23] emphasizes the role of nanoscale precision in achieving desired effects, underscoring the importance of fabrication advancements. As metamaterials transition from lab to industry, their mechanisms offer transformative potential, from invisibility cloaks to vibration-damping infrastructure, provided challenges in scalability and cost are addressed. 2.3. Applications in Engineering Metamaterials have revolutionized engineering applications by enabling solutions that combine lightweight designs, high performance, and adaptability across sectors like aerospace, biomedical, civil, and energy harvesting. In aerospace, metamaterials enhance stealth and structural efficiency through electromagnetic wave absorption and mechanical resilience. Schurig et al. [24] demonstrated electromagnetic cloaking using metamaterials, reducing radar crosssections for stealth aircraft, a breakthrough with significant defense implications. Mechanical metamaterials, with their tunable stiffness, enable morphing wings that adapt to flight conditions, as explored by Jenett et al. [25], improving fuel efficiency and maneuverability. These applications leverage lightweight, high-strength designs, reducing material use while maintaining structural integrity, aligning with sustainability goals in aerospace engineering. In biomedical engineering, metamaterials facilitate innovative devices with tailored mechanical and electromagnetic properties. Wegener et al. [26] reviewed the use of biocompatible metamaterials in implants, where tunable stiffness matches tissue properties to enhance integration and reduce rejection rates. Electromagnetic metamaterials also enable advanced imaging techniques, such as MRI enhancements through superlensing, improving diagnostic precision. Acoustic metamaterials contribute to ultrasound technologies by focusing sound waves for non-invasive treatments, as shown by Zhu et al. [27]. These applications highlight the potential of metamaterials to address critical healthcare challenges, from personalized implants to non-destructive diagnostics, with ongoing research focusing on bioinspired designs for improved functionality. Civil engineering benefits from metamaterials through seismic protection and noise reduction. Kim et al. [28] investigated phononic metamaterials for seismic wave attenuation, designing subsurface structures that redirect vibrational energy to protect buildings. Acoustic metamaterials also create sound barriers for urban noise control, offering sustainable alternatives to traditional insulation. These applications rely on precise bandgap engineering, where metamaterials block specific frequencies, as validated in field tests. Additionally, thermal metamaterials optimize building insulation by controlling heat flow, reducing energy consumption in HVAC systems. The integration of these materials into infrastructure demonstrates their role in enhancing resilience and environmental sustainability. Energy harvesting represents another frontier, where metamaterials convert ambient energy into usable forms. Carrara et al. [29] developed piezoelectric-integrated metamaterials that capture vibrational energy from mechanical sources, powering sensors in remote environments. This approach supports renewable energy goals by harnessing waste energy, such as vibrations from machinery or infrastructure. Electromagnetic metamaterials also enhance solar energy collection by concentrating light onto photovoltaic cells, as studied by Atwater et al. [30]. These applications underscore the transformative impact of metamaterials, driven by their ability to integrate multifunctionality and efficiency, though challenges in large-scale production and cost-effectiveness remain critical areas for future research. Global Journal of Engineering and Technology Advances, 2025, 24(03), 115–139 123 3. Smart Structures: Principles and Applications Smart structures represent a paradigm shift in engineering, integrating sensing, actuation, and control mechanisms to create adaptive systems that respond intelligently to external stimuli. These structures leverage advanced materials, such as piezoelectrics, shape-memory alloys, and metamaterials, combined with computational intelligence to enable functionalities like self-monitoring, self-healing, and dynamic reconfiguration. The incorporation of artificial intelligence (AI) enhances their capabilities, allowing real-time data processing and adaptive responses in applications ranging from aerospace to civil infrastructure. This section explores the principles of smart structures, their synergy with metamaterials, and their transformative applications, highlighting how AI-driven approaches amplify their potential in addressing modern engineering challenges. 3.1. Concept of Smart Structures Smart structures are engineered systems embedded with sensors, actuators, and control algorithms that enable active adaptation to environmental changes, distinguishing them from passive structures. Chopra et al. [31] define smart structures as those capable of sensing external stimuli, processing information, and actuating responses to optimize performance, such as vibration suppression or shape adjustment. This concept emerged from early developments in active control systems, integrating materials like piezoelectric ceramics, which generate electric charges under mechanical stress, enabling both sensing and actuation. The fundamental principle involves closed-loop feedback, where sensors detect changes (e.g., strain, temperature), processors analyze data, and actuators adjust the structure’s behavior. This integration allows smart structures to mitigate dynamic loads, enhance durability, and improve efficiency across engineering domains. The design of smart structures relies on the synergy between smart materials and advanced control strategies. Piezoelectric materials, for instance, are widely used due to their rapid response and high precision, as demonstrated by Crawley et al. [32], who explored their application in aerospace for active vibration control. Shape-memory alloys, capable of returning to predefined shapes upon thermal or mechanical stimuli, enable applications like self-deploying structures in space. Fiber-optic sensors provide high-resolution strain and temperature measurements, critical for realtime monitoring in large-scale infrastructure. These materials are often embedded within composite matrices, creating lightweight yet robust systems. The complexity of integrating these components necessitates computational tools, where AI algorithms optimize sensor placement and control logic, enhancing system responsiveness and reliability. The evolution of smart structures has been driven by advancements in microelectronics and control theory, enabling compact, energy-efficient systems. A seminal study by Soong et al. [33] highlighted the transition from passive dampers to active control systems in civil engineering, reducing structural vibrations during earthquakes. Modern smart structures incorporate distributed sensing networks, allowing localized responses to heterogeneous loads. For example, in aerospace, smart wings adjust their geometry to optimize aerodynamics, improving fuel efficiency. Challenges remain in ensuring energy efficiency and scalability, as active systems require continuous power. Emerging solutions, such as energy harvesting through piezoelectric metamaterials, address these limitations, paving the way for sustainable smart structures. The significance of smart structures lies in their ability to enhance safety, longevity, and performance in critical applications. By integrating real-time diagnostics, they reduce maintenance costs and prevent catastrophic failures. Recent advancements, as reviewed by Balageas et al. [34], emphasize the role of embedded sensors in structural health monitoring, enabling predictive maintenance in bridges and aircraft. The incorporation of AI further amplifies these capabilities, allowing adaptive algorithms to learn from environmental patterns and optimize responses dynamically. As smart structures evolve, their principles continue to redefine engineering, offering resilient solutions for dynamic and unpredictable environments. 3.2. Integration with Metamaterials The integration of metamaterials with smart structures creates synergistic systems that combine exotic wave manipulation with adaptive functionalities, enhancing performance in dynamic environments. Smith et al. [17] demonstrated that metamaterials, with their ability to control electromagnetic or mechanical waves, can be embedded within smart structures to achieve tailored responses, such as vibration attenuation or electromagnetic shielding. This synergy leverages the programmable properties of metamaterials, like negative stiffness or tunable bandgaps, with the active control mechanisms of smart structures. For instance, incorporating acoustic metamaterials into a smart building panel enables active noise cancellation, where sensors detect sound waves and actuators adjust the panel’s properties to block specific frequencies. This integration expands the functionality of smart structures, enabling multifunctionality in applications like aerospace and infrastructure. Global Journal of Engineering and Technology Advances, 2025, 24(03), 115–139 130 This integration supports energy-efficient operations by optimizing maintenance schedules. Scalability remains a challenge, as large-scale IoT deployments generate massive data volumes, straining computational resources. Advanced techniques, such as federated learning, as explored by Cheema et al. [71], address this by training models across distributed devices, preserving privacy and reducing central processing demands. The future of AI-IoT integration lies in creating fully autonomous smart structures that operate within interconnected ecosystems. For example, smart buildings with IoT sensors and AI control systems optimize energy consumption by adjusting lighting and HVAC based on occupancy patterns. The work of Minoli et al. [72] underscores the potential of AI-IoT frameworks to create resilient urban infrastructure, though standardization and cybersecurity remain critical hurdles. These advancements position AI-IoT integration as a cornerstone of next-generation smart structures, enabling sustainable and adaptive engineering solutions. 5.4. Examples of AI-Enabled Smart Structures AI-enabled smart structures demonstrate transformative applications across industries, leveraging real-time data processing and adaptive control. In civil engineering, self-healing structures use AI to detect damage and trigger repair mechanisms. Li et al. [73] developed a smart concrete structure embedded with microcapsules and sensors, where AI analyzes sensor data to activate healing agents, restoring structural integrity. These systems use ML to predict crack propagation, optimizing repair timing and material use. Such structures enhance durability in bridges and buildings, reducing maintenance costs and extending lifespan, though scaling self-healing mechanisms remains a challenge. In aerospace, AI-enabled morphing structures adapt to changing flight conditions, improving performance and efficiency. Weisshaar et al. [74] explored smart wings with AI-controlled shape-memory alloys, adjusting camber based on aerodynamic data to optimize lift. These systems use RL to learn optimal actuation strategies, reducing fuel consumption. Integration with metamaterials, as discussed by Bertoldi et al. [2], enhances flexibility, enabling lightweight, high-strength designs. These applications demonstrate AI’s ability to create adaptive aerospace components, though real-time processing and energy demands require further optimization. Seismic-resistant buildings represent another key application, where AI-driven smart structures mitigate earthquake impacts. Saaed et al. [75] implemented AI-controlled active mass dampers in high-rise buildings, using ML to predict seismic wave patterns and adjust damping in real-time. These systems integrate with metamaterials to redirect vibrational energy, as explored by Kim et al. [28], enhancing structural resilience. AI’s predictive capabilities ensure rapid response to dynamic loads, though high computational costs and sensor reliability remain challenges. Future advancements aim to integrate AI with IoT for real-time seismic monitoring, creating resilient urban infrastructure. In biomedical engineering, AI-enabled smart implants adapt to physiological changes, improving patient outcomes. Wegener et al. [26] reviewed smart implants with embedded sensors and AI analytics, adjusting stiffness to match tissue properties. These systems use ML to monitor tissue integration, triggering actuators to optimize performance. For example, AI-driven prosthetic limbs adapt to user movements, enhancing mobility. These applications highlight AI’s role in creating personalized, adaptive biomedical solutions, though biocompatibility and long-term reliability require ongoing research. Collectively, these examples underscore AI’s transformative impact on smart structures, driving innovation across engineering domains. 6. Challenges and Future Directions The integration of artificial intelligence (AI) with metamaterials and smart structures has unlocked transformative possibilities in design, optimization, and adaptive engineering, yet significant challenges remain in scaling these technologies for practical applications. Issues such as computational complexity, manufacturing limitations, and data quality hinder widespread adoption, while interdisciplinary integration demands collaboration across materials science, AI, and engineering domains. Addressing these challenges requires innovative approaches, including advanced algorithms, scalable fabrication techniques, and robust data frameworks. This section explores the technical and interdisciplinary hurdles in AI-driven metamaterial and smart structure development, identifies emerging trends like multi-scale modeling and real-time adaptability, and highlights research gaps to guide future advancements, paving the way for sustainable and resilient engineering solutions. 6.1. Technical Challenges The computational complexity of AI-driven design for metamaterials and smart structures poses a significant barrier, particularly for large-scale or three-dimensional architectures. Designing complex metamaterials requires simulating wave interactions at subwavelength scales, which demands substantial computational resources. Molesky et al. [45] noted that inverse design algorithms, such as those using deep learning, often require high-performance computing to handle high-dimensional parameter spaces, limiting accessibility for smaller research groups. Real-time applications, like adaptive control in smart structures, exacerbate this issue, as low-latency processing is critical for dynamic Global Journal of Engineering and Technology Advances, 2025, 24(03), 115–139 131 responses. Edge computing and lightweight AI models, as proposed by Qiu et al. [66], offer potential solutions, but their implementation in resource-constrained environments remains challenging. Manufacturing constraints further complicate the translation of AI-designed metamaterials and smart structures into practical systems. High-precision fabrication techniques, such as electron-beam lithography or advanced 3D printing, are required to realize complex geometries, but these are costly pipette and scale poorly for large structures. Kadic et al. [5] highlighted that while additive manufacturing enables intricate metamaterial designs, material inconsistencies and resolution limitations can degrade performance. AI can optimize designs for manufacturability, but discrepancies between simulated and fabricated structures persist, necessitating closed-loop feedback systems that integrate realtime fabrication data. Developing cost-effective, scalable manufacturing processes is critical to bridging this gap. Data quality and availability present additional challenges, as AI models rely on diverse, high-quality datasets for accurate predictions. Liu et al. [38] emphasized that limited or noisy datasets can lead to overfitting in machine learning models, reducing their generalizability. For instance, in structural health monitoring, sensor data may be incomplete or biased due to environmental noise, affecting diagnostic accuracy. Synthetic data generation, using generative adversarial networks, as explored by Al-Khaylani et al. [41], mitigates data scarcity, but ensuring physical accuracy remains a hurdle. Standardizing data collection protocols and developing open-access repositories could enhance AI model robustness, enabling broader application in metamaterial and smart structure development. Addressing these technical challenges requires interdisciplinary innovation, combining advances in computational efficiency, manufacturing, and data science. For example, physics-informed neural networks, as developed by Chen et al. [46], reduce computational demands by embedding physical constraints into AI models, improving convergence rates. Similarly, advancements in scalable 3D printing, as discussed by Abbas et al. [58], enable the production of complex metamaterials at lower costs. Overcoming these barriers will unlock the full potential of AI-driven systems, enabling their deployment in real-world applications like aerospace, infrastructure, and biomedical devices. 6.2. Interdisciplinary Integration The successful integration of AI, metamaterials, and smart structures demands collaboration across disciplines, including materials science, computer science, and engineering, which presents both opportunities and challenges. Developing multifunctional systems requires aligning the goals of material designers, who focus on physical properties, with AI researchers, who prioritize algorithmic efficiency. Wegener et al. [26] noted that mismatches in terminology and methodologies can hinder collaboration, as materials scientists may prioritize experimental validation while AI experts emphasize computational models. Establishing common frameworks, such as standardized design pipelines, is essential to streamline interdisciplinary efforts and ensure cohesive development. Communication and knowledge transfer are critical barriers in interdisciplinary integration. For instance, AI-driven inverse design requires materials scientists to understand machine learning concepts, while AI researchers must grasp the physics of wave propagation in metamaterials. Engheta et al. [23] emphasized the need for hybrid training programs that equip researchers with cross-disciplinary skills, fostering collaboration. Collaborative platforms, such as opensource software for AI-based design, can facilitate knowledge sharing, enabling engineers to leverage pre-trained models for specific applications. However, cultural differences between disciplines and varying research priorities can slow progress, necessitating institutional support for interdisciplinary initiatives. The integration of AI with experimental validation further complicates interdisciplinary efforts, as theoretical models must align with real-world outcomes. Bertoldi et al. [2] highlighted that discrepancies between simulated and fabricated metamaterials often arise due to manufacturing variabilities, requiring iterative feedback between computational and experimental teams. AI can bridge this gap by incorporating real-time fabrication data into design loops, but this requires seamless collaboration between engineers and data scientists. Emerging tools, like digital twins, as explored by Sakr et al. [61], enable real-time synchronization of virtual and physical systems, enhancing interdisciplinary workflows. Future efforts should focus on developing shared standards and collaborative environments to accelerate innovation. The transformative potential of interdisciplinary integration lies in its ability to create holistic solutions that address complex engineering challenges. For example, combining AI-driven SHM with metamaterial-based seismic protection, as studied by Kim et al. [28], creates resilient infrastructure through collaborative design. Initiatives like interdisciplinary research centers and funding programs can foster such integration, driving advancements in adaptive engineering. Overcoming these challenges will enable the development of next-generation smart structures that combine AI’s computational power with the unique properties of metamaterials. Global Journal of Engineering and Technology Advances, 2025, 24(03), 115–139 132 6.3. Emerging Trends Emerging trends in AI-driven metamaterial and smart structure development include multi-scale modeling, real-time adaptive systems, and sustainable design, each promising to expand their applicability. Multi-scale modeling, which integrates nanoscale material properties with macroscale structural behavior, leverages AI to bridge length scales. Jamaludin et al. [76] developed AI models that predict the macroscopic performance of metamaterials based on nanoscale simulations, enabling efficient design of hierarchical structures. These models reduce computational costs by focusing on critical scales, facilitating applications like lightweight aerospace components. Physics-informed AI, embedding governing equations into models, further enhances accuracy, as shown by Chen et al. [46], paving the way for complex, multi-physics designs. Real-time adaptive systems represent a frontier in smart structure development, where AI enables instantaneous responses to environmental changes. Spencer et al. [63] explored AI-driven control systems that adjust structural damping in real-time, critical for seismic resilience. Advances in edge AI, as discussed by Qiu et al. [66], enable on-device processing, reducing latency in applications like morphing aircraft wings or smart implants. The integration of IoT with AI, further enhances adaptability by connecting structures to real-time data networks, enabling coordinated responses in smart cities. These systems require robust algorithms to handle dynamic, unpredictable inputs, driving research into adaptive AI frameworks [68,69]. Highlighting the complexities in bridging scales, Figure 2 depicts the challenges in multi-scale modeling of metamaterials, providing a visual framework for how AI can address computational demands in hierarchical designs. Figure 2 Challenges in the modeling of metamaterials. Reproduced with permission from ref [68] Global Journal of Engineering and Technology Advances, 2025, 24(03), 115–139 133 Table 3 Emerging Trends and Research Opportunities in AI-Metamaterial Integration Trend Description Key Technologies Applications Benefits Gaps/Opportunities References Multi-Scale Modeling Bridging nano to macro scales AI-driven simulations Hierarchical structures Efficient predictions Standardization of models [76] Real-Time Adaptability Instantaneous response systems Edge AI, RL Seismic damping, morphing wings Enhanced resilience Latency reduction [63], [66], [74] Sustainable Designs Eco-friendly materials Topology optimization Energy-efficient buildings Resource minimization Lifecycle assessment [16], [54] Explainable AI Interpretable models SHAP, physicsinformed SHM diagnostics Increased trust Broader implementation [37], [77] IoT-Big Data Fusion Interconnected sensor networks Federated learning Smart cities Predictive analytics Data security [68], [69], [71], [72] Hybrid AI Frameworks Combining ML with physics PINNs, multiagent Inverse design Physical accuracy Scalability for 3D [6], [46] Self-Healing Systems Autonomous repair AI-triggered actuators Infrastructure longevity Reduced maintenance Material durability [73] Energy Harvesting Optimization AI-tuned capture RL for efficiency Remote sensors Sustainability Low-energy environments [29] Digital Twins Virtual replicas Real-time syncing Predictive maintenance Simulation fidelity Integration with IoT [61] Generative AI for Collaboration LLM-based workflows Multi-agent systems Collaborative design Accelerated innovation Ethical AI use [6], [11] Broadband Metamaterials Wide-frequency designs DL optimization Multifunctional devices Versatility Loss minimization [21], [45] Federated Learning Distributed training Privacypreserving SHM in populations Scalable monitoring Communication overhead [71] Global Journal of Engineering and Technology Advances, 2025, 24(03), 115–139 134 Sustainable metamaterials and smart structures are gaining traction, addressing environmental concerns through ecofriendly materials and energy-efficient designs. The development of recyclable metamaterials, designed using AI to optimize material use while maintaining performance. For example, AI-driven topology optimization creates lightweight structures that reduce resource consumption in aerospace and construction. Energy harvesting, as explored by Carrara et al. [29], integrates piezoelectric metamaterials with AI control to maximize energy capture, supporting sustainable infrastructure. These trends align with global sustainability goals, though challenges in material recyclability and lifecycle assessment require further exploration. The convergence of these trends promises transformative advancements, enabling smart structures that are adaptive, sustainable, and scalable. For instance, AIdriven multi-scale models can design energy-efficient buildings with integrated thermal metamaterials, as proposed by Zhu et al. [54]. Future developments will likely focus on autonomous design pipelines, where AI systems iterate designs based on real-time environmental feedback, enhancing resilience and efficiency. These trends position AI-driven metamaterials and smart structures at the forefront of sustainable engineering innovation. Building on the discussed trends, Table 3 outlines emerging developments in AI-driven metamaterials and smart structures, with details on descriptions, applications, benefits, gaps/opportunities, and references, to highlight pathways for future research. 6.4. Research Gaps and Opportunities Despite significant progress, research gaps in AI-driven metamaterial and smart structure development present opportunities for future innovation. Model interpretability remains a critical gap, as complex AI models, like deep neural networks, often function as black boxes, limiting trust in engineering applications. Goodfellow et al. [37] emphasized the need for explainable AI to elucidate decision-making processes, particularly in safety-critical systems like SHM. Techniques like SHAP (SHapley Additive exPlanations) values, as explored by Lundberg et al. [77], offer solutions by quantifying feature contributions, but their application to metamaterial design is nascent. Developing interpretable models will enhance adoption in industries requiring transparency, such as aerospace and healthcare. Data quality and standardization represent another gap, as AI models require diverse, high-quality datasets to ensure generalizability. Bao et al. [62] noted that inconsistent sensor data in SHM can lead to inaccurate predictions, particularly in heterogeneous environments. Open-access data repositories and standardized protocols, as proposed by Lynch et al. [68], could address this by providing curated datasets for AI training. Additionally, integrating multi-modal data (e.g., combining vibration and thermal signals) offers opportunities to improve model robustness, enabling comprehensive monitoring of smart structures. Collaborative efforts to create global data standards will accelerate research and deployment. Real-world deployment of AI-driven systems faces scalability and cost barriers, particularly for large-scale infrastructure. Abbas et al. [58] highlighted that while AI optimizes designs for manufacturability, high-cost fabrication techniques limit scalability. Opportunities exist in developing low-cost, scalable manufacturing methods, such as advanced 3D printing, to produce AI-designed metamaterials. Similarly, real-time adaptive systems require energyefficient AI algorithms to operate in resource-constrained environments. Research into lightweight models, as explored by Qiu et al. [66], and energy harvesting integration, as studied by Carrara et al. [29], could address these challenges, enabling widespread adoption. The interdisciplinary nature of these gaps offers opportunities for innovation through collaboration. For instance, combining AI with advanced materials science could lead to self-adaptive metamaterials that respond autonomously to environmental changes, as suggested by Wegener et al. [22]. Funding interdisciplinary research and developing open-source platforms for AI-driven design will accelerate progress. Addressing these gaps will enable the creation of resilient, sustainable smart structures, transforming engineering applications from biomedical devices to smart cities. 7. Conclusion The integration of artificial intelligence (AI) with metamaterials and smart structures has ushered in a new era of adaptive engineering, enabling unprecedented control over material properties and structural behaviors. This review has explored how AI-driven methodologies, such as machine learning, deep learning, and generative algorithms, revolutionize the design and optimization of metamaterials, facilitating the creation of structures with exotic properties like negative refraction, tunable stiffness, and energy cloaking. By streamlining inverse design, predicting material performance, and optimizing complex geometries, AI reduces computational and experimental barriers, accelerating innovation in fields ranging from aerospace to biomedical engineering. The synergy of AI with smart structures enhances real-time adaptability through advanced structural health monitoring, adaptive control systems, and IoT integration, creating resilient systems capable of responding to dynamic environmental conditions. These Global Journal of Engineering and Technology Advances, 2025, 24(03), 115–139 135 advancements underscore the transformative potential of combining computational intelligence with engineered materials, offering solutions that are both high-performing and sustainable. Smart structures, enhanced by AI and integrated with metamaterials, demonstrate remarkable versatility in addressing modern engineering challenges. Applications such as seismic-resistant buildings, morphing aircraft wings, self-healing infrastructure, and energy-harvesting devices illustrate the practical impact of these technologies, improving safety, efficiency, and sustainability across industries. AI’s ability to process vast sensor datasets and optimize control strategies enables proactive maintenance and real-time adaptation, reducing costs and extending structural lifespans. The incorporation of metamaterials amplifies these capabilities, providing tunable properties that enhance functionality, such as vibration attenuation or electromagnetic shielding. However, the full realization of these benefits hinges on overcoming technical challenges, including computational complexity, manufacturing scalability, and data quality, which require ongoing innovation in algorithms, fabrication techniques, and data management. Interdisciplinary collaboration emerges as a critical driver for advancing AI-driven metamaterials and smart structures. Bridging materials science, computer science, and engineering is essential to align theoretical designs with practical implementations, ensuring that AI-optimized structures meet real-world demands. Emerging trends, such as multi-scale modeling, real-time adaptive systems, and sustainable material design, point to a future where smart structures operate autonomously within interconnected ecosystems, such as smart cities. These trends align with global priorities for sustainability and resilience, positioning AI-driven solutions at the forefront of engineering innovation. Nevertheless, research gaps in model interpretability, data standardization, and scalable deployment must be addressed to ensure widespread adoption and trust in these technologies. Looking forward, the continued development of AI-driven metamaterials and smart structures requires focused research and investment in scalable manufacturing, explainable AI, and open-access data frameworks. Collaborative initiatives, such as interdisciplinary research centers and standardized design platforms, will accelerate progress by fostering knowledge sharing and innovation. Engineers and researchers should prioritize eco-friendly materials and energy-efficient systems to align with sustainability goals, while advancements in edge computing and IoT integration will enable real-time, autonomous applications. 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