Gideon Lyngdoh

George Fox University ·  Department of Engineering

This paper investigates the electromechanical performance of textile fabric with conductive yarn elements for data transmission capabilities. Electromechanical experiments were conducted to evaluate the electrical response of copper yarn elements stitched axially to the textile fabric, while assessing the mechanical response of the system during te...

This paper implements reactive forcefield molecular dynamics (MD) simulation to evaluate the mechanical performance of the fusion bond line formed between acrylonitrile butadiene styrene (ABS), and thermoplastic polyurethane (TPU). The simulated interfacial adhesion responses, as obtained from MD simulation, are further implemented as the interfaci...

This paper implements molecular dynamics (MD) simulation using reactive force field (ReaxFF) to evaluate the atomistic origin of the interfacial behavior in the overmolded hybrid unidirectional continuous carbon fiber low-melt PAEK (CFR-LMPAEK)-short carbon fiber reinforced PEEK (SFR-PEEK) polymer composites. From the MD simulation, it was observed...

Studying the fatigue of 3D-printed (3DP) structures usually requires time-consuming and ad hoc material characterizations. This paper experimentally demonstrates a non-intrusive structural health monitoring (SHM) framework capable of monitoring and modeling fatigue damage of 3DP structures. The experiments emulate realistic working conditions of ma...

The paper presents indentation studies on wollastonite fiber incorporated cementitious systems. The acicular nature of the fibers is poised to delay the coalescence of micro-cracks in such systems thus leading to tougher building materials. Towards that end, load-penetration depth results from the indentation studies are employed to ascertain elast...

Machine learning (ML)-based prediction of non-linear composition-strength relationship in concretes requires a large, complete, and consistent dataset. However, the availability of such datasets is limited as the datasets often suffer from incompleteness because of missing data corresponding to different input features, which makes the development...

With the advent of 3D printing, auxetic cellular cementitious composites (ACCCs) have recently garnered significant attention owing to their unique mechanical performance. To enable seamless performance prediction of the ACCCs, interpretable machine learning (ML)-based approaches can provide efficient means. However, the prediction of Poisson’s rat...

Prediction of underwater explosion response of coated composite cylinders using machine learning (ML) requires a large, consistent, accurate, and representative dataset. However, such reliable large experimental dataset is not readily available. Besides, the ML algorithms need to abide by the fundamental laws of physics to avoid non-physical predic...

This paper investigates the dynamic compressive behavior of wollastonite fiber-reinforced cementitious mortars using multiscale numerical simulations. The rate dependent behavior of the multiphase heterogeneous systems is captured in a multiscale framework that implements continuum damage towards effective property prediction. The influence of woll...

Prediction of in-situ strain sensing efficiency of self-sensing cementitious composites using machine learning (ML) requires a large, representative, consistent, and accurate dataset. However, such large experimental dataset is not readily available. Moreover, the success of the ML approach depends on its ability to abide by the fundamental laws of...

Prediction of material behavior using machine learning (ML) requires consistent, accurate, and, representative large data for training. However, such consistent and reliable experimental datasets are not always available for materials. To address this challenge, we synergistically integrate ML with high-throughput reactive molecular dynamics (MD) s...

This paper presents the fracture toughness of sodium aluminosilicate hydrate (N-AS -H) gel formed through alkaline activation of fly ash. While the fracture toughness of N-AS -H is obtained experimentally from nanoindentation experiment implementing the principle of conservation of energy, the numerical investigation is performed via reactive force...

This paper presents the dynamics of confined water and its interplay with alkali cations in disordered sodium aluminosilicate hydrate (N-AS -H) gel using reactive force field molecular dynamics. N-AS -H gel is the primary binding phase in geopolymers formed via alkaline activation of fly ash. Despite attractive mechanical properties, geopolymers su...

This paper evaluates the fracture toughness of sodium aluminosilicate hydrate (N-A-S-H) gel formed through alkaline activation of fly ash via molecular dynamics (MD) simulations. The short- and medium-range order of the constructed N-A-S-H structures shows good correlation with the experimental observations, signifying the viability of the N-A-S-H...

The existing literature on Bayesian updating of structural models have assigned equal variances (homoscedasticity) in the measured observables across all modes by assuming a Gaussian error distribution. This paper relaxes the assumption by allowing the error distribution to be conditionally heteroscedastic, but marginally follows the Student's t-d...

Geopolymers, synthesized through alkaline activation of aluminosilicates, have emerged as a sustainable alternative for traditional ordinary portland cement. In spite of the satisfactory mechanical performance and sustainability-related benefits, the large scale acceptance of geopolymers in the construction industry is still limited due to poor und...

Use of phase change materials (PCMs) to tailor the thermal performance of concretes by efficient energy storage and transmission has gained traction in recent years. This study incorporates microencapsulated PCMs as sand-replacement in concrete bridge decks and performs numerical simulation involving multiple interactive length scales to elucidate...