Amir H. Alavi

University of Pittsburgh | Pitt · Civil and Environmental Engineering & Bioenginerring

Despite significant advancements in wireless smart implants over the last two decades, current implantable devices still operate passively and require additional electronic modules for wireless transmission of the stored biological data. To address these challenges, we propose an innovative wireless force sensing paradigm for implantable systems th...

Triboelectric nanogenerators offer an environmentally friendly approach to harvesting energy from mechanical excitations. This capability has made them widely sought‐after as an efficient, renewable, and sustainable energy source, with the potential to decrease reliance on traditional fossil fuels. However, developing triboelectric nanogenerators w...

Mechanical metamaterials enable the creation of structural materials with unprecedented mechanical properties. However, thus far, research on mechanical metamaterials has focused on passive mechanical metamaterials and the tunability of their mechanical properties. Deep integration of multifunctionality, sensing, electrical actuation, information p...

There is an unceasing quest to create novel forms of intelligent active matter that exhibits sensing, energy harvesting, actuating, computing, and communication functionalities. Realizing such capabilities can provide new road maps to autonomous and electronic materials with numerous applications in robotics, human–machine interfacing, micro/nano-e...

Creating multifunctional concrete materials with advanced functionalities and mechanical tunability is a critical step toward reimagining the traditional civil infrastructure systems. Here, the concept of nanogenerator‐integrated mechanical metamaterial concrete is presented to design lightweight and mechanically tunable concrete systems with energ...

Harnessing the power of natural evolution for automated exploration of novel forms of metastructures is likely to be the next technological revolution of the material science. Herein, the principles of evolution into the metamaterial design and discovery process to directly evolve thousands of metastructures with hitherto‐unknown structures and new...

Delamination is one of the most critical defects assessed during bridge deck inspections. Recently, infrared (IR) thermography has gained more attention for delamination detection since it provides fast and effective inspections with reasonable accuracy. However, point-by-point inspections with handheld IR cameras and manual data interpretation are...

Information storage is an important functionality to produce a sense-decide-respond loop in active mechanical metamaterial systems. Here, we propose a new class of mechanical metamaterials with self-powered digital information storage capability. In the so-called mechanically-responsive data storage metamaterials, data is incorporated into a set of...

Online education has been facing difficulty in predicting the academic performance of students due to the lack of usage of learning process, summative data and a precise prediction of quantitative relations between variables and achievements. To address these two obstacles, this study develops an artificial intelligence-enabled prediction model for...

There is an unmet need for developing a new class of smart medical implants with novel properties and advanced functionalities. Here, the concept of “self‐aware implants” is proposed to enable the creation of a new generation of multifunctional metamaterial implantable devices capable of responding to their environment, empowering themselves, and s...

Natural evolution has been a major source of inspiration for scientists for decades. Here, we aim to harness the power of natural evolution for automated design and discovery of novel forms of metastructures. In the proposed process, evolution takes place by randomly creating an initial population of metamaterial entities that will pass on their ge...

We demonstrate our striking vision towards developing a new generation of multifunctional concrete materials with unprecedented mechanical properties. The proposed “metamaterial concrete” is based on the integration of snapping metamaterial and concrete design concepts. We show how integrating the concrete mixture with auxetic polymer structures wi...

Structural health monitoring (SHM) of reinforced concrete structures is a rapidly developing field with significant advancements over the last decade. Most of the existing SHM systems rely on a large number of point sensors attached to or embedded inside the structures. The sensor network should be densely distributed on the structure or around the...

Developing lightweight multifunctional structures with sensing, energy harvesting and mechanical tunability capabilities has been the holy grail for scientists. This study presents our vision toward the next stage of the technological revolution in multifunctional structures science where a so-called Engineered Self-aware Structure (ES2) can sense,...

Triboelectric nanogenerators have received significant research attention in recent years. Structural design plays a critical role in improving the energy harvesting performance of triboelectric nanogenerators. Here, we develop the magnetic capsulate triboelectric nanogenerators (MC-TENG) for energy harvesting under undesirable mechanical excitatio...

Recent advances in multifunctional material technologies have paved the way for the creation of innovative multifunctional structures. Exploring multifunctional structures with advanced functionalities is a major step toward a new era of autonomous structural systems for future smart cities. Autonomous structures can respond to their environment, s...

This chapter presents state-of-the-art knowledge and cutting-edge innovations in the area of smart cities, advanced structural sensing and monitoring systems along with complementary technological paradigms. Various aspects of smart infrastructure and integrated components of structural health monitoring (SHM) and nondestructive testing and evaluat...

Artificial intelligence (AI) and Machine learning (ML) train machines to achieve a high level of cognition and perform human-like analysis. Both AI and ML seemingly fit into our daily lives as well as complex and interdisciplinary fields. With the rise of commercial, open-source, and user-catered AI/ML tools, a key question often arises whenever AI...

There is a critical shortage in research needed to explore a new class of multifunctional structural components that respond to their environment, empower themselves and self-monitor their condition. Here, the novel concept of triboelectric nanogenerator-enabled structural elements (TENG-SEs) is proposed to build the foundation for the next generat...

Objective: This study investigates the feasibility of using a new self-powered sensing and data logging system for postoperative monitoring of spinal fusion progress. The proposed diagnostic technology directly couples a piezoelectric transducer signal into a Fowler-Nordheim (FN) quantum tunneling-based synchronized dynamical system to record the...

Sand and dust storms (SDS) have long been considered as a type of disastrous weather. Minimizing the severe influence of SDS on the environment and human life has been the focus of many studies. In this paper, we report the principles and characteristics of SDS while mainly focusing on the monitoring technologies of the natural disaster. Space- and...

Purpose Engineering design and operational decisions depend largely on deep understanding of applications that requires assumptions for simplification of the problems in order to find proper solutions. Cutting-edge machine learning algorithms can be used as one of the emerging tools to simplify this process. In this paper, we propose a novel scalab...

Induced heating-healing of asphalt concrete is rapidly emerging as an innovative repairing technique in pavement engineering. In this method, asphalt concrete is heated using the external electromagnetic field. As a result, the viscosity of the asphalt binder decreases, leading to the movement of the asphalt binder through the cracks and healing th...

In this paper, a new structural damage detection approach is proposed, based on monitoring of the magnetic field intensity variations using smartphones. Experimental and numerical studies are conducted on steel plates to verify the efficiency of the proposed approach. Various damage scenarios are introduced to the steel plates. Then, a smartphone m...

Rapid development in structural health monitoring systems has led to the invention of various sensing technologies. Nonetheless, difficulties in deploying and maintaining traditional wired sensors and managing vast amount of data collated from a dense array of wired sensors were fundamental drawbacks of using such systems. Wireless sensor networks...

Modern and heterogeneous asphalt mixtures are usually produced using various kinds of modifiers such as rubber, polymer, and fiber. These materials are incorporated to improve sustainability and reduce the extent and severity of distresses such as rutting and low-temperature cracking. Currently, there is a lack of a robust real-time method for the...

Discovering novel multifunctional metamaterials with energy harvesting and sensing functionalities is likely to be the next technological evolution of the metamaterial science. Here, we introduce a novel concept called self-aware composite mechanical metamaterial (SCMM) that can transform mechanical metamaterials into nanogenerators and active sens...

We present a two-stage method for detection and quantification of surface defects in concrete bridge decks using a hybrid deep learning and image processing technique. In the first stage, a multi classifier based on an integrated convolutional neural network and long short-term memory architecture is developed to detect cracking and spalling region...

Over the past two decades, machine learning has been gaining significant attention for solving complex engineering problems. Genetic programing (GP) is an advanced framework that can be used for a variety of machine learning tasks. GP searches a program space instead of a data space without a need to pre-defined models. This method generates transp...

The Hamburg wheel tracking test (HWTT) is a widely used testing procedure designed to accelerate and simulate the rutting phenomena in the laboratory. Rut depth, as one of the outputs of the HWTT, is dependent on a number of parameters related to mix design and testing conditions. This study introduces a new model for predicting the rutting depth o...

This paper presents a deep learning approach for automated detection and quantification of cracks and spalls in concrete bridge decks. The proposed concrete defect detection approach is based on the integration of convolutional neural network with a long short-term memory architecture. Thousands of manually labeled images collected from the concret...

This study presents a new geometry-based crack detection approach for plate structures based on the integration of a dynamic extended finite element method (XFEM) and a physics-based optimization algorithm called enhanced vibrating particles system (EVPS). The so-called XFEM-EVPS method is applied to solve a forward problem. The problem is unravele...

Purpose Unbonded concrete overlays (UBOLs) are commonly used in pavement rehabilitation. The current models included in the Mechanistic-Empirical Pavement Design Guide cannot properly predict the structural response of UBOLs. In this paper, a multigene genetic programming (MGGP) approach is proposed to derive new prediction models for the UBOLs res...

This book is a compendium of the proceedings of the International Conference on Big-Data and Cloud Computing. The papers discuss the recent advances in the areas of big data analytics, data analytics in cloud, smart cities and grid, etc. This volume primarily focuses on the application of knowledge which promotes ideas for solving problems of the s...

Designing architected metastructures with desirable characteristics is typically associated with complex fabrication and testing procedures. The challenges ahead for nanoscale fabrication of these engineered structures lead to severe obstacles to investigate their complex design patterns and corresponding mechanical properties. Here, we introduce a...

Swarm intelligence (SI) is the collective behavior of decentralized, self-organized natural or artificial systems. Monarch butterfly optimization (MBO) algorithm is a class of swarm intelligence metaheuristic algorithm inspired by the migration behavior of monarch butterflies. Through the migration operation and butterfly adjusting operation, indiv...

This paper presents a vision-based crack detection approach for concrete bridge decks using an integrated one-dimensional convolutional neural network (1D-CNN) and long short-term memory (LSTM) method in the image frequency domain. The so-called 1D-CNN-LSTM algorithm is trained using thousands of images of cracked and non-cracked concrete bridge de...

Rutting continues to be one of the principal distresses in asphalt pavements worldwide. This type of distress is caused by permanent deformation and shear failure of the asphalt mix under the repetition of heavy loads. The Hamburg wheel tracking test (HWTT) is a widely used testing procedure designed to accelerate, and to simulate the rutting pheno...

Mechanical metamaterials have opened an exciting venue for control and manipulation of architected structures in recent years. Research in the area of mechanical metamaterials has covered many of their fabrication, mechanism characterisation and application aspects. More recently, however, a paradigm shift has emerged to an exciting research direct...

This paper presents an enhanced adaptive global‐best harmony search (EAGHS) to solve global continuous optimization problems. The global‐best HS (GHS) is one of the strongest versions of the classical HS algorithm that hybridizes the concepts of swarm intelligence and conventional HS. However, randomized selection of harmony in the permissible inte...

Elephant herding optimization (EHO) is a nature-inspired metaheuristic optimization algorithm based on the herding behavior of elephants. EHO uses a clan operator to update the distance of the elephants in each clan with respect to the position of a matriarch elephant. The superiority of the EHO method to several state-of-the-art metaheuristic algo...

This study presents a new model for the prediction of rutting depth of asphalt mixtures using a machine learning technique called gene expression programming (GEP). A database containing a comprehensive collection of Hamburg test results is used to develop a GEP-based prediction model. The database includes 96 tests results for various asphalt mixt...

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Recently, there has been a growing interest in deploying smart materials as sensing components of structural health monitoring systems. In this arena, piezoelectric materials offer great promise for researchers to rapidly expand their many potential applications. The main goal of this study is to review the state-of-the-art piezoelectric-based sens...

Machine learning (ML) is the field of training machines to achieve high level of cognition and perform human-like analysis. Since ML is a data-driven approach, it seemingly fits into our daily lives and operations as well as complex and interdisciplinary fields. With the rise of commercial, open-source and user-catered ML tools, a key question ofte...

Recently, significant attention has been devoted to vaccine-derived poliovirus (VDPV) surveillance due to its severe consequences. Prediction of the outbreak incidence of VDPF requires an accurate analysis of the alarming data. The overarching aim to this study is to develop a novel hybrid machine learning approach to identify the key parameters th...

Internet of Things (IoT) is changing the world by connecting billions of physical and virtual objects with distinctive identities to the Internet. This fusion results in generating huge volumes of data that might not be manageable using today's storage and data analytics technologies. Although cloud computing offers services to tackle this issue at...

The elephant herding optimization (EHO) is a recent swarm intelligence algorithm. This algorithm simulates the clan updating and separation behavior of elephants. The EHO method has been successfully deployed in various fields. However, a more reliable implementation of the standard EHO algorithm still requires improving the control and selection o...

The massive amount of data generated by structural health monitoring (SHM) systems usually affects the system’s capacity for data transmission and analysis. This paper proposes a novel concept based on the probability theory for data reduction in SHM systems. The beauty salient feature of the proposed method is that it alleviates the burden of coll...