ISMART Lab

Intelligent Structures and Architected Materials.

Mechanical Metamaterials Nanogenerator Composites Engineering Informatics

We are the "Intelligent Structures and Architected Materials Research and Testing (ISMART)" Laboratory at the University of Pittsburgh led by Dr. Amir H. Alavi. We focus on fundamental research that enables new methods for designing, fabricating, and modeling novel engineered materials and structures with advanced functionalities. We are exploring how these principles and concepts can support a wide range of structural systems, from large civil infrastructure to small medical implants and devices.

Thrust 1

Mechanics and Electronics of Multifunctional Materials

Our primary focus lies in the exploration, design, additive manufacturing, and computational modeling of novel engineered material systems (e.g. mechanical metamaterials, nanogenerator composites) with advanced functionalities. Our goal is to gain a comprehensive understanding of the mechanics and electronics of these material systems across multiple scales. We believe that integrating advanced functionalities into the fabric of engineered material systems is the key to equipping them with “a level of” cognition and primitive intelligence! Cognition and intelligence are intertwined but distinct concepts. Cognitive abilities play a pivotal role in crafting truly intelligent material systems and structures. Cognition is a process through which the material system can acquire, organize, process, and communicate knowledge. Intelligence integrates cognitive abilities for learning from experiences, comprehending complex situations, adapting, and responding purposefully. Our vision is to build the foundations for intelligent material systems and structures that can deploy their cognitive abilities for sensing, self-powering, information processing, and communicating to interact with the surrounding environments, optimize their response, and create a sense–decide–respond loop.

Relevant Funding Support: NIH 1R21EB034457-01, NIH R21AR085218, NSF CMMI-2235494, Gates Foundation #AWD00011007

Thrust 2

Self-powered Sensing and Monitoring Systems

Our research group is primarily focused on designing and fabricating innovative solutions for structural health monitoring (SHM) and condition assessment of structural systems. We create novel, automated sensing and monitoring technologies that are enhanced by engineering informatics for better decision making. In this arena, we are pioneering the “Self-powered Monitoring Concept”, where the operating power of the smart wireless sensors is directly harvested from the signal being monitored. Under this concept, we can continuously monitor the condition of engineering systems, drastically reduce the form factor of the embedded sensing and wireless modules, and store the sensed data for offline analysis. We are exploring the applications of the created principles and concepts for various structural systems, ranging from large civil infrastructure systems to tiny medical implants and devices. We have also invented the striking concept of “Triboelectric Nanogenerator-enabled Structural Elements (TENG-SEs)” for distributed SHM of structural systems. This is the first-of-its-kind systematic study in the literature on integrating the TENG systems into the fabric of large-scale structural elements.

Relevant Funding Support: NIH R21AR075242-01, PA Turnpike #718405, IRISE/PennDOT #23-P22-01-01

Thrust 3

Data-Driven Modeling of Engineering Systems

One of the missions of the Pitt’s ISMART group is to explore the capabilities of engineering systems informatics in various engineering domains. Our broad research portfolio in this arena involves designing computational models for data-driven discovery and design of materials, developing advanced data interpretation/fusion frameworks for processing and analyzing the SHM data, and creating novel data mining algorithms for characterizing engineering systems. To achieve these goals, we are developing and deploying advanced machine learning and optimization methods. Our recent focus is on generative AI (Gen-AI) frameworks for material design and discovery. For example, we have developed an evolutionary Gen-AI algorithm that allows us to create and refine large numbers of unit cell architectures based on one or multiple selected objectives. This includes stiffness, compliance, energy absorption, or other performance targets. The Gen-AI methods give us access to design regions that cannot be explored manually and support rapid optimization of metamaterials for both medical implants and structural systems.

Relevant Funding Support: IRISE/PennDOT #23-P22-01-01, PA Turnpike #718318