Modelling the bending behaviour of plain-woven fabric using flat shell element and strain smoothing technique

This paper describes a new approach to improve on modelling the bending behaviour of plain-woven fabric. The four-node flat shell element is developed by incorporating a strain smoothing technique, six degrees of freedom at each node. The material laws for in-plane and out-of-plane behaviors are expressed in terms of orthotropic elastic material. The physical and mechanical parameters of fabric samples are measured using Kawabata Evaluating System for Fabric (KES-F). An improved numerical model with a strain smoothing operation for modelling the bending behaviour of plain-woven fabric is then carried out. The bending behavior of a rectangular plain-woven fabric sheet with clamped edges is simulated.Fundação para a Ciência e a Tecnologia (FCT

A node-based strain smoothing technique for free vibration analysis of textile-like sheet materials

This paper presents an implementation of the node-based smoothed finite element method and Reissner-Mindlin plate theory for a four node isoparametric shell element to improve the numerical precision and computational efficiency subjected to free vibration analysis of textile-like sheet materials. A one smoothing cell integration scheme in the strain smoothing technique is implemented to contrast the shear locking phenomenon that may exists in the analysis for moderatelythick and thick shell models. Various numerical results of free vibration analysis for a multi-layer nonwoven fabric sample are compared with other existing analytical solutions and numerical solutions in literatures to demonstrate the effectiveness of the present method. An advantage of the present formulation is that it can improve the numerical precision without decreasing the computational efficiency.The first and fourth author acknowledge FCT for the conceded financial support through Project UID/CTM/00264/2019 of 2C2T – Centro de Ciência e Tecnologia Têxtil, hold by National Founds of FCT/MCTES. The second and third author acknowledge support by FCT/MCTES through national funds and when applicable co-funded EU funds under the project SFRH/BD/136554/2018

Eigenvalue analysis for plain-woven fabric structure using shell element and one smoothing cell in the smoothed finite element method

An efficient four-node quadrilateral (Q4) shell element based on the first-order shear deformation theory of plate (FSDT) and the strain smoothing technique in finite elements (referred as SFEM) was proposed for eigenvalue analysis of plain-woven fabric structure. A one smoothing domain (or cell) integration scheme in SFEM was proposed to evaluate the nodal train fields of Q4 shell elements. The numerical result of eigenvalue analysis, which was in the case of free vibration analysis, approximated to that one implemented in the finite element method (FEM) but gave a higher efficiency in computation in terms of central processing unit (CPU) time and numerical implementation.The authors wish to express their acknowledgment to FCT funding from FCT – Foundation for Science and Technology within the scope of the project “PEST UID/CTM/00264; POCI-01-0145-FEDER 007136

Buckling analysis of plain-woven fabric structure using shell element and a one cell-based integration scheme in smoothed finite element method

A one smoothing cell integration scheme in the strain smoothing technique in finite elements (referred as SFEM) was proposed to evaluate the nodal train fields of a four-node quadrilateral (Q4) shell element, which is based on the first-order shear deformation theory of plate (FSDT). A mixed interpolation of tensorial components (MITC) approaches for Q4 transverse shear strains also applied to eliminate a shear locking phenomenon that may occur when the thin plate/shell elements are geometrically distorted in curved geometries of fabric sheet. The numerical eigenvalues of buckling analysis of a plain-woven fabric sample, of which physical and mechanical parameters extracted from Kawabata evaluation system for fabrics (KES-FB), obtained a higher efficiency in numerical computation and approximated to Q4 shell element implemented in the finite element method (FEM).The authors wish to express their acknowledgment to FCT funding from FCT – Foundation for Science and Technology within the scope of the project “PEST UID/CTM/00264; POCI-01-0145-FEDER 007136”

A Novel Self-organizing Fuzzy Cerebellar Model Articulation Controller Based Overlapping Gaussian Membership Function for Controlling Robotic System

This paper introduces an effective intelligent controller for robotic systems with uncertainties. The proposed method is a novel self-organizing fuzzy cerebellar model articulation controller (NSOFC) which is a combination of a cerebellar model articulation controller (CMAC) and sliding mode control (SMC). We also present a new Gaussian membership function (GMF) that is designed by the combination of the prior and current GMF for each layer of CMAC. In addition, the relevant data of the prior GMF is used to check tracking errors more accurately. The inputs of the proposed controller can be mixed simultaneously between the prior and current states according to the corresponding errors. Moreover, the controller uses a self-organizing approach which can increase or decrease the number of layers, therefore the structures of NSOFC can be adjusted automatically. The proposed method consists of a NSOFC controller and a compensation controller. The NSOFC controller is used to estimate the ideal controller, and the compensation controller is used to eliminate the approximated error. The online parameters tuning law of NSOFC is designed based on Lyapunov’s theory to ensure stability of the system. Finally, the experimental results of a 2 DOF robot arm are used to demonstrate the efficiency of the proposed controller

Load Shedding in Microgrid System with Combination of AHP Algorithm and Hybrid ANN-ACO Algorithm

This paper proposes a new load shedding method based on the application of intelligent algorithms, the process of calculating and load shedding is carried out in two stages. Stage-1 uses a backpropagation neural network to classify faults in the system, thereby determining whether or not to shed the load in that particular case. Stage-2 uses an artificial neural network combined with an ant colony algorithm (ANN-ACO) to determine a load shedding strategy. The AHP algorithm is applied to propose load shedding strategies based on ranking the importance of loads in the system. The proposed method in the article helps to solve the integrated problem of load shedding, classifying the fault to determine whether or not to shedding the load and proposing a correct strategy for shedding the load. The IEEE 25-bus 8-generator power system is used to simulate and test the effectiveness of the proposed method, the results show that the frequency of recovery is good in the allowable range

Identification of apple fruit-skin constitutive laws by full-field methods using uniaxial tensile loading

The protective and preservative role of apple skin in maintaining the integrity of the fruit is well-known, with its mechanical behaviour playing a pivotal role in determining fruit storage capacity. This study employs a combination of experimental and numerical methodologies, specifically utilising the digital image correlation (DIC) technique. A specially devised inverse strategy is applied to evaluate the mechanical behaviour of apple skin under uniaxial tensile loading. Three apple cultivars were tested in this work: Malus domestica Starking Delicious, Malus pumila Rennet, and Malus domestica Golden Delicious. Stress–strain curves were reconstructed, revealing distinct variations in the mechanical responses among these cultivars. Yeoh’s hyperelastic model was fitted to the experimental data to identify the coefficients capable of reproducing the non-linear deformation. The results suggest that apple skin varies significantly in composition and structure among the tested cultivars, as evidenced by differences in elastic properties and non-linear behaviour. These differences can significantly affect how fruit is handled, stored, and transported. Thus, the insights resulting from this research enable the development of mathematical models based on the mechanical behaviour of apple tissue, constituting important data for improvements in the economics of the agri-food industry.The authors acknowledge the Portuguese Foundation for Science and Technology (FCT—MCTES) for the conceded financial support through the reference grant EXPL/EMEAPL/0587/2021. The second author also acknowledges FCT for his grant ref. BI/UTAD/22/2022. The third author also acknowledges FCT for its financial support via the projects UIDB/00667/2020 and UIDP/00667/2020 (UNIDEMI)

Developing a comprehensive quality control framework for roadway bridge management: a case study approach using key performance indicators

Transportation infrastructures, especially roadway bridges, play a pivotal role in socioeconomic development. Recently, bridge engineers are increasingly facing the challenge in terms of shifting their strategy from building new facilities to maintaining the existing aging infrastructures, to preserve their service performance during the operational stage. In fact, the infrastructure administrators lack a quality control (QC) strategy for the existing roadway bridges, which leads to the decision-making application and tool being still minor. To overcome those challenging issues, this paper proposes a quality control framework for roadway bridge management using key performance indicators (KPIs). The case study methodology is suggested to be used and then conducted for several bridges, mostly in European countries. In which the performance indicators (PIs) and goals (PGs) are defined, after assessing the bridges and vulnerable zones, the derivation KPIs from those PIs are introduced and developed considering time functions and different maintenance scenarios. Eventually, a two-stage quality control framework will be proposed in which the static stage includes preparatory works, inspection responsibilities, and a quick assessment of KPIs; while the dynamic stage helps the decision maker in estimating the time remaining of the bridge service life, managing the evolution of KPIs as well as planning the best possible maintenance strategy. The selected two case studies are present and curated, which show the excellent potential to develop a long-term strategy for roadway bridge management on a lifecycle level.This research was funded by FCT/MCTES through national funds (PIDDAC) from the R&D Unit Institute for Sustainability and Innovation in Structural Engineering (ISISE), under the reference UIDB/04029/2020, and from the Associate Laboratory Advanced Production and Intelligent Systems ARISE, under the reference LA/P/0112/2020, as well as financial support of the project re-search “B2022-GHA-03” from the Ministry of Education and Training. And The APC was funded by ANI (“Agência Nacional de Inovação”) through the financial support given to the R&D Project “GOA Bridge Management System—Bridge Intelligence”, with reference PO-CI-01-0247- FEDER-069642, which was cofinanced by the European Regional Development Fund (FEDER) through the Operational Competitiveness and Internationalisation Program (POCI).Minh Q. Tran acknowledges the support by the doctoral grant reference PRT/BD/154268/2022, financed by Portuguese Foundation for Science and Technology (FCT), under the MIT Portugal Program (2022 MPP2030-FCT)

Structural assessment based on vibration measurement test combined with an artificial neural network for the steel truss bridge

Damage assessment is one of the most crucial issues for bridge engineers during the operational and maintenance phase, especially for existing steel bridges. Among several methodologies, the vibration measurement test is a typical approach, in which the natural frequency variation of the structure is monitored to detect the existence of damage. However, locating and quantifying the damage is still a big challenge for this method, due to the required human resources and logistics involved. In this regard, an artificial intelligence (AI)-based approach seems to be a potential way of overcoming such obstacles. This study deployed a comprehensive campaign to determine all the dynamic parameters of a predamaged steel truss bridge structure. Based on the results for mode shape, natural frequency, and damping ratio, a finite element model (FEM) was created and updated. The artificial intelligence network’s input data from the damage cases were then analysed and evaluated. The trained artificial neural network model was curated and evaluated to confirm the approach’s feasibility. During the actual operational stage of the steel truss bridge, this damage assessment system showed good performance, in terms of monitoring the structural behaviour of the bridge under some unexpected accidents.This research was funded by FCT/MCTES through national funds (PIDDAC) from the R&D Unit Institute for Sustainability and Innovation in Structural Engineering (ISISE), under the reference UIDB/04029/2020, and from the Associate Laboratory Advanced Production and Intelligent Systems ARISE, under the reference LA/P/0112/2020, as well as financial support of the project research “B2022-GHA-03” from the Ministry of Education and Training. And The APC was funded by ANI (“Agência Nacional de Inovação”) through the financial support given to the R&D Project “GOA Bridge Management System—Bridge Intelligence”, with reference POCI-01-0247-FEDER069642, which was cofinanced by the European Regional Development Fund (FEDER) through the Operational Competitiveness and Internationalisation Program (POCI)