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36 articles for “Prediction of Elasticity”
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Predicting Elastic and Plastic Stress Concentration Factor using Notch Strength Analysis
Abstract: Structural sizing is getting more sophisticated as weight and cost of any structure is important. Weight optimization is a key player in enhancing business revenues and reducing fuel consumption for a greener planet. Thus, millennial designers attempt to develop weight optimized structures, while meeting structural stiffness, strength and integrity, and safety requirements as specified by international certification agencies. This can be achieved by innovative analyses approaches, which predict realistic factor …
Published in Journal of Aerospace Engineering & Technology · Vol. 9, Issue 1, 2019 · pp. 1–14 Read article
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Deep Learning-Based Prediction of Elasticity and Mechanical Strength for Structure–Property Characterization of Functional Polymer Materials
Abstract: The prediction of the elasticity and mechanical strength of functional polymer materials is one of the key requirements for characterizing the structure–property relationship of such materials, which can greatly speed up polymer design, reduce the need for experimental characterization and enable the development of materials with mechanical properties appropriate to their functions. The existing works are based on the traditional machine learning model or deep learning model, which cannot be …
Published in Journal of Polymer & Composites · Vol. 14, Issue 5, 2026 Read article
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Utilization of Statistical Learning Algorithms for Prediction of Elastic Modulus of Jointed Rock Mass
Abstract: This study uses two statistical learning algorithms for the prediction of elastic modulus (Ej) of jointed rockmass. The first algorithm uses support vector machine (SVM) that is firmly based on the theory of statisticallearning and uses regression technique by introducing -insensitive loss functionhas been adopted. Thesecond algorithm uses relevance vector machine (RVM). It is based on a Bayesian formulation of a linearmodel with an appropriate prior that results in a …
Published in Recent Trends in Civil Engineering & Technology · Vol. 1, Issue 1-3, 2011 · pp. 81–94 Read article
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Predictive Modeling of Polymer Elasticity and Mechanical Strength Using Deep Learning for Functional Polymer Material Characterization and Performance Assessment
Abstract: Knowing the polymers elasticity and mechanical strength is essential in the characterization of functional polymer materials, for rapid characterization and to avoid extensive experiments. Traditional polymer characterization methods are often time consuming and resource intensive and the mechanical response of polymers can be nonlinear, thus complicating the prediction of the properties. These limitations can be overcome by introducing a Variational Temporal Fusion Transformer (VaTFT) for the deep learning prediction of …
Published in Journal of Polymer & Composites · Vol. 14, Issue 5, 2026 Read article
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Integrative Machine Learning Approaches for Predicting the Rheological Behaviour of Soft Magnetorheological Elastomers
Abstract: Magnetorheological Elastomers (MREs) are advanced composite materials known for their ability to alter mechanical properties under external magnetic fields, making them highly valuable in adaptive damping systems, vibration control, and smart devices. The accurate prediction of rheological behavior in soft MREs remains a significant challenge due to the complex interplay between material composition and magnetic fields. To address this challenge, this study employs a multi-pronged approach that integrates traditional material …
Published in Journal of Polymer & Composites · Vol. 13, Issue 1, 2025 · pp. 1083–1096 Read article
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Modelling of Large Elasto-plastic Deformations by EFGM
Abstract: Current work reports modelling and simulation of geometric and material nonlinearities arising due large elasto-plastic displacements in structural specimens by invoking enriched element free Galerkin method (EFGM). The displacement approximations are constructed by using moving least square approach. Standard displacement based approximations are modified by incorporating suitable enrichment functions depending on the nature of interfaces present in the components. Large deformations give rise to geometric nonlinearities which have been modelled …
Published in Journal of Polymer & Composites · Vol. 12, Issue 2, 2024 · pp. 130–143 Read article
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Artificial Intelligence and Constitutive Modeling Equations for Predictive Design of High-Performance Polymer Composites
Abstract: Growing polymer composite applications demand accurate mechanical prediction, yet complex interactions and conventional constitutive models limit predictive capability and require extensive calibration. To report these challenges, this research recommends a combined Artificial Intelligence (AI) and constitutive modeling approach based on an Enhanced Tasmanian Devil Optimizer-tuned Residual Neural Network with Multilayer Perceptron (ETDO-ResNet-MLP) for the predictive design of high-performance polymer composites. The study uses a publicly available Polymer Composite Property Dataset …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 Read article
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Prediction of Mechanical Properties for Advanced Engineering Applications utilizing Polymer Composite Materials by Machine Learning
Abstract: Polymer composites show great promise as engineering materials because of their mechanical performance, resistance to corrosion, lightweight nature, and adaptability in design. Aerospace, automotive, biomedical, maritime, and civil engineers all rely on mechanical property prediction to cut down on trial expenses, expedite product development, and optimize material selection. Speedy design optimization is not possible using traditional numerical and experimental methods due to the high costs associated with material characterisation, computational …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 1258–1284 Read article
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Experimental Analysis and Predictive Modeling of Mechanical Behavior in Epoxy Composites Reinforced with Waste Tyre Rubber Particles
Abstract: The disposal of end-of-life tyres poses a significant environmental and resource challenge owing to their large volumes and non-biodegradable nature. In this work, we explore the incorporation of waste tyre rubber particles (WTRP) into an epoxy resin matrix to develop sustainable polymer composites and examine their mechanical behavior both experimentally and through predictive modelling. Composites with differing epoxy: WTRP ratios (80:20, 75:25, 70:30 wt.%) and varying rubber particle mesh sizes …
Published in Journal of Polymer & Composites · Vol. 14, Issue 1, 2026 · pp. 1754–1765 Read article
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Phase – Field Modeling of Brittle and Ductile Fracture Under Complex Loading Conditions
Abstract: Phase-field modeling has emerged as a powerful computational framework for predicting fracture behavior in engineering materials, offering a unified description of crack initiation, propagation, branching, and coalescence without the need for explicit crack tracking. This study presents an in-depth examination of phase-field modeling applied to both brittle and ductile fracture under complex loading conditions, including multiaxial stress states, cyclic loading, thermal gradients, and dynamic impact. The phase-field approach regularizes the …
Published in International Journal of Fracture Mechanics and Damage Science · Vol. 3, Issue 2, 2025 · pp. 13–18 Read article
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Genomic Selection for Grain Yield in Wheat Using Machine Learning on DArT Molecular Markers: A Comparative Evaluation Across Multi-Environment Trials
Abstract: Genomic selection (GS) predicts complex quantitative traits directly from genome-wide molecular markers, bypassing the need for extensive phenotypic trials and accelerating plant breeding cycles. We conducted a comparative evaluation of seven regression approaches — ridge regression (the machine-learning equivalent of RR-BLUP), Lasso, Elastic Net, Partial Least Squares, linear Support Vector Regression, Random Forest, and Gradient Boosting — for predicting grain yield from 1,279 Diversity Array Technology (DArT) molecular markers genotyped …
Published in Research and Reviews : Journal of Computational Biology · Vol. 15, Issue 2, 2026 Read article
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Development of an Intelligent and Statistical Model for Prediction of Rock Mass Deformation Modulus
Abstract: The rock mass deformation modulus (Em) takes into account the plastic and the elastic deformation of the rock mass. It has been widely used for designing structures such as dams, tunnels, caverns, and mines, etc. Since the tests available for ascertaining Em are expensive and time consuming, several equations were suggested in the past. However, it has been found that the existing models are limited to specific type of rock …
Published in Journal of Geotechnical Engineering · Vol. 6, Issue 2, 2019 · pp. 39–50 Read article
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AI-Driven Prediction of Mechanical and Thermal Properties in Polymer-Based Functionally Graded Composites
Abstract: The proposed architecture of the current paper is an artificial intelligence (AI)-driven model of forecasting mechanical and thermal aspects of polymer-based functionally-graded composites (FGCs). Traditional micromechanical and finite element models, which are practical in homogeneous composites, might not be able to account in nonlinear interaction that is caused by compositional gradient. To overcome the challenge, machine learning (ML) models like artificial neural network (ANN), support vectors regression (SVR), and gradient-boosted …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 70–89 Read article
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Analysis and A.I. Modelling of Parameters of Symmetrical Multistory Building using Non-linear Static Analysis
Abstract: Non-linear static analysis (Push over Analysis) is the emerging field in seismic design of structures.The major drawback of elastic analysis is that the response beyond the linear state is neither linearnor simple extrapolation. Although elastic analysis gives a good indication of the elastic capacity ofstructures, but it cannot account for redistribution of forces during progressive yielding of a structurethat follows and to predict its failure mechanism or detect possible location …
Published in Journal of Construction Engineering, Technology & Management · Vol. 11, Issue 1, 2021 · pp. 1–15 Read article
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Advances in Polymer-Based Orthodontic Materials for Enhanced Treatment Efficiency
Abstract: Polymers, particularly shape-memory polymers, fiber-reinforced composites, and elastomeric materials, provide enhanced mechanical properties such as flexibility, durability, and controlled force application, leading to improved treatment outcomes. These materials offer superior biocompatibility, reducing irritation and discomfort for patients while ensuring efficient and predictable tooth movement. It is important to note one of the key advancements in orthodontics is the use of shape-memory polymers, which can respond to temperature or external stimuli, …
Published in Journal of Polymer & Composites · Vol. 13, Issue 6, 2025 · pp. 1016–1024 Read article
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ML-Enhanced Smart Sensing Framework for IoT- Based Structural Health Monitoring Using Conductive Polymer Composites
Abstract: The growing demand for intelligent structural health monitoring (SHM) in dynamic infrastructures necessitates flexible sensing systems that are not only mechanically robust but also capable of real-time interpretation. Conventional SHM frameworks often rely on brittle sensor configurations and cloud-dependent processing pipelines, which suffer from latency, limited durability, and poor adaptability under variable loading conditions. Despite recent advances in composite materials and machine learning, current approaches lack a unified framework that …
Published in Journal of Polymer & Composites · Vol. 13, Issue 6, 2025 · pp. 348–369 Read article
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Prediction of Scale Dependent Material Properties of Single Walled Carbon Nanotubes
Abstract: The present work explores the potential application of nonlocal continuum mechanics related to nanotechnology applications. Nonlocal continuum models represent attempts to extend the continuum approach to smaller length scales while retaining most of its many advantages. This paper presents the prediction of nonlocal scaling parameter for single walled carbon nanotubes. The scale effects are introduced into the continuum models using non-local elasticity constitute model. A relationship among the carbon nanotube …
Published in Journal of Nanoscience, NanoEngineering & Applications · Vol. 2, Issue 1, 2012 · pp. 13–22 Read article
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Rheology and Flow Properties of Fracturing Gels in Straight and Coiled Tubing
Abstract: This study involves experimental investigation of rheological and hydraulic characteristics of aqueous based polymeric and surfactant fluids in straight and coiled tubing. The fluids matrix includes guar, HPG, PHPA, welan, xanthan, and surfactant. Bohlin rheometer was used to evaluate rheological and viscoelastic characteristics. For hydraulic characteristics, small- and large-scale flow loops were used. It is observed that all fluids exhibit comparable non-Newtonian behavior and improved viscous and elastic properties. Among …
Published in Journal of Petroleum Engineering & Technology · Vol. 7, Issue 1, 2017 · pp. 27–43 Read article
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Computational Modeling of Fluid Flow through A Fractured Media under Overburden Pressures
Abstract: Computational analyses were conducted to investigate fluid flow characteristics of a real fractured media. Finite element (FE) and computational fluid dynamics (CFD) analyses were implemented to carry out these computational analyses. The fractured media was exposed to external pressurization to predict permeability and asperity deformation. Furthermore, a series of CFD-FE analyses were also conducted to determine the reduction in permeability of the fractured media under the external pressure. An elastic-plastic …
Published in Journal of Petroleum Engineering & Technology · Vol. 5, Issue 1, 2015 · pp. 25–43 Read article
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Data-Driven Material Design and Performance Improvement: Constructing Sustainable Polymer Nanocomposites Using Deep Learning
Abstract: In the formation of sustainable polymer nanocomposites, the effective material techniques are required to balance the mechanical qualities, environmental compatibility and processing efficiency. The optimization of polymer matrix, nanofiller loading, processing conditions and material properties is typically time consuming, resource intensive and highly dependent on trial-error methodology using standard experimental techniques. The present work provides a data-driven approach that combines deep learning with sustainable polymer nanocomposite design for predicting and …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 Read article