Adnan Rasul, Saravanan Karuppanan, Veeradasan Perumal, Mark Ovinis, Mohsin Iqbal and Khurshid Alam
Stress concentration factors (SCFs) are commonly used to assess the fatigue life of tubular T-joints in offshore structures. SCFs are usually estimated from parametric equations…
Abstract
Purpose
Stress concentration factors (SCFs) are commonly used to assess the fatigue life of tubular T-joints in offshore structures. SCFs are usually estimated from parametric equations derived from experimental data and finite element analysis (FEA). However, these equations provide the SCF at the crown and saddle points of tubular T-joints only, while peak SCF might occur anywhere along the brace. Using the SCF at the crown and saddle can lead to inaccurate hotspot stress and fatigue life estimates. There are no equations available for calculating the SCF along the T-joint's brace axis under in-plane and out-of-plane bending moments.
Design/methodology/approach
In this work, parametric equations for estimating SCFs are developed based on the training weights and biases of an artificial neural network (ANN), as ANNs are capable of representing complex correlations. 1,250 finite element simulations for tubular T-joints with varying dimensions subjected to in-plane bending moments and out-of-plane bending moments were conducted to obtain the corresponding SCFs for training the ANN.
Findings
The ANN was subsequently used to obtain equations to calculate the SCFs based on dimensionless parameters (α, β, γ and τ). The equations can predict the SCF around the T-joint's brace axis with an error of less than 8% and a root mean square error (RMSE) of less than 0.05.
Originality/value
Accurate SCF estimation for determining the fatigue life of offshore structures reduces the risks associated with fatigue failure while ensuring their durability and dependability. The current study provides a systematic approach for calculating the stress distribution at the weld toe and SCF in T-joints using FEA and ANN, as ANNs are better at approximating complex phenomena than typical data fitting techniques. Having a database of parametric equations enables fast estimation of SCFs, as opposed to costly testing and time-consuming FEA.
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R. Abghari, S. Shaikhzadeh Najar, M. Haghpanahi and M. Latifi
To investigate the relation of in‐plane fabric tensile properties with woven fabrics bagging behavior, a new test method was developed and a real time data acquisition and strain…
Abstract
To investigate the relation of in‐plane fabric tensile properties with woven fabrics bagging behavior, a new test method was developed and a real time data acquisition and strain gauge technique were used. The bagging procedure was carried out while the woven fabric tensile deformations along warp and weft directions were measured. The fabric bagging behavior was characterized by bagging resistance, bagging fatigue, residual bagging height and residual bagging hysteresis. The experimental results show that the bagging load, work, hysteresis, residual hysteresis and fatigue are highly linearly correlated with corresponding parameters in warp and weft directions. An empirical relationship obtained between residual bagging height and bagging fatigue and resistance (R2=0.83) suggests that the proposed new test method is able to evaluate bagging behavior of fabrics.
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In the present paper, the new concept of “memory dependent derivative” in the Pennes’ bioheat transfer and heat-induced mechanical response in human living tissue with variable…
Abstract
Purpose
In the present paper, the new concept of “memory dependent derivative” in the Pennes’ bioheat transfer and heat-induced mechanical response in human living tissue with variable thermal conductivity and rheological properties of the volume is considered.
Design/methodology/approach
A problem of cancerous layered with arbitrary thickness is considered and solved analytically by Kirchhoff and Laplace transformation. The analytical expressions for temperature, displacement and stress are obtained in the Laplace transform domain. The inversion technique for Laplace transforms is carried out using a numerical technique based on Fourier series expansions.
Findings
Comparisons are made with the results anticipated through the coupled and generalized theories. The influence of variable thermal, volume materials properties and time-delay parameters for all the regarded fields for different forms of kernel functions is examined.
Originality/value
The results indicate that the thermal conductivity and volume relaxation parameters and MDD parameter play a major role in all considered distributions. This dissertation is an attempt to provide a theoretical thermo-viscoelastic structure to help researchers understand the complex thermo-mechanical processes present in thermal therapies.
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Senthilnathan T., Sujay Aadithya B. and Balachandar K.
This study aims to predict the mechanical properties such as equivalent tensile strength and micro-hardness of friction-stir-welded dissimilar aluminium alloy plates AA 6063-O and…
Abstract
Purpose
This study aims to predict the mechanical properties such as equivalent tensile strength and micro-hardness of friction-stir-welded dissimilar aluminium alloy plates AA 6063-O and AA 2014-T6, using artificial neural network (ANN).
Design/methodology/approach
The ANN model used for the experiment was developed through back propagation algorithm. The input parameter of the model consisted of tool rotational speed and weld-traverse speed whereas the output of the model consisted of mechanical properties (tensile strength and hardness) of the joint formed by friction-stir welding (FSW) process. The ANN was trained for 60% of the experimental data. In addition, the impact of the process parameters (tool rotational speed and weld-traverse speed) on the mechanical properties of the joint was determined by Taguchi Grey relational analysis.
Findings
Subsequently, testing and validation of the ANN were done using experimental data, which were not used for training the network. From the experiment, it was inferred that the outcomes of the ANN are in good agreement with the experimental data. The result of the analyses showed that the tool rotational speed has more impact than the weld-traverse speed.
Originality/value
The developed neural network can be used to predict the mechanical properties of the weld. Results indicate that the network prediction is similar to the experiment results. Overall regression value computed for training, validation and testing is greater than 0.9900 for both tensile strength and microhardness. In addition, the percentage error between experimental and predicted values was found to be minimal for the mechanical properties of the weldments. Therefore, it can be concluded that ANN is a potential tool for predicting the mechanical properties of the weld formed by FSW process. Similarly, the results of Taguchi Grey relational analysis can be used to optimize the process parameters of the weld process and it can be applied extensively to ascertain the most prominent factor. The results of which indicates that rotational speed of 1,270 rpm and traverse speed of 30 mm/min are to be the optimized process parameters. The result also shows that tool rotational speed has more impact on the mechanical properties of the weld than that of traverse speed.
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S. Shaikhzadeh Najar, E. Hezavehi, Sh. Hoseini Hashemi and A. Rashidi
The purpose of this paper is to describe a unique approach to investigate the wrinkle force of textile structures in a cylindrical model.
Abstract
Purpose
The purpose of this paper is to describe a unique approach to investigate the wrinkle force of textile structures in a cylindrical model.
Design/methodology/approach
In this research, an apparatus was designed and constructed in order to investigate the torsional and wrinkle behavior of textile structures in a cylindrical model under a different rotational level using data acquisition and micro‐controller systems.
Findings
In the light of research results, the fiber and fabric type, fabric physical and mechanical properties and imposed rotational level significantly contributed to wrinkle characteristics of worsted fabrics. It was noticed that with increase of rotational level, the wrinkle force, and energy increased along weft and warp directions. Wrinkle characteristics along warp direction exhibited greater values than in weft direction.
Originality/value
The study is aimed at determining wrinkle behavior of worsted fabrics under the combined influences of compression and torsional strains.
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Emadaldin Hezavehi, Saeed Shaikhzadeh Najar, P. Zolgharnein and Hamed Yahya
The purpose of this paper is to analyze the stress‐relaxation behavior of different woven fabrics under constant torsional strain in a wrinkled state. For this purpose, a new…
Abstract
Purpose
The purpose of this paper is to analyze the stress‐relaxation behavior of different woven fabrics under constant torsional strain in a wrinkled state. For this purpose, a new method for determination of stress‐relaxation behavior of the fabric was used while keeping the torsional strain constant.
Design/methodology/approach
In this study, the behavior of stress relaxation of fabric is examined with modification of wrinkle force tester sophisticated electro‐mechanical method and fabricating a device which uses a computer and micro controller, with constant torsional strain by a rotational level of 9.1 turn/m in 280°, and in 300 s.
Findings
The results depict that stress‐relaxation percentage in fabric in weft alignment is more than warp alignment and the fabrics which tolerate more torsional force, possess less stress‐relaxation percentages. In this way, with increasing polyester percentage in fabric the scale of stress‐relaxation percentage decreases. Also, adoption of data derived from experiments with Maxwell model shows that the interlaced model is a suitable model for explaining the stress relaxation decline in fabric. Correlation coefficient of fabrics in weft alignment with Maxwell model is more than warp alignment.
Practical implications
This study has practical implications in the clothing as well as in technical textiles areas.
Originality/value
Knowing visco‐elastic properties is very important. However, there is no information available to study the stress relaxation of woven fabrics under the combined influences of compression and constant torsional strains.
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Abdul Waheed Siyal, Hongzhuan Chen, Gang Chen, Muhammad Mujahid Memon and Zainab Binte
Mobile taxi booking apps (MTB) have revolutionalized the transportation industry. As taxis can be hired via smartphones, irrespective of any time or place, the business platform…
Abstract
Purpose
Mobile taxi booking apps (MTB) have revolutionalized the transportation industry. As taxis can be hired via smartphones, irrespective of any time or place, the business platform for taxi service has completely changed. Now customers are saved from the hassle of going to the designated taxi stands or waiting along the roadside. But, the long-term sustainability of this service depends on its continued use. Therefore, this study aims to explore factors that hedonically incline people toward continuance of MTB. To achieve the purpose, the unified theory of acceptance and use of technology (UTAUT) was extended with mediation effects of hedonic motivation.
Design/methodology/approach
The data were collected from existing users of MTB and analyzed through structural equation modeling and revalidated via artificial neural networks.
Findings
The statistical results show that the main factors of UTAUT substantially create hedonic motivation to use the apps and significantly mediate their effects on behavioral intention to continue using MTB. However, mediation between social influence and continuity intent was not statistically supported. The findings represent important contributions to the extended UTAUT.
Practical implications
This study adds value to the theoretical horizon and also presents M-taxi companies with useful and pertinent plans for efficient designing and effective implementation of MTB. Moreover, limitations and suggestions for future researchers are also discussed.
Originality/value
This study extends UTAUT with the mediating role of hedonic motivation to predict continued use of MTB, which further initiates the applicability of UTAUT in a new setting and a new perspective (post adoption). This, in turn, significantly expands theory by using hedonic motivation as an important attribute that could mediate impact of all main antecedents to shape customers loyalty toward system use.
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Khair Ul Faisal Wani and Nallasivam K.
The purpose of this study is to numerically model the rigid pavement resting on two-parameter soil and to examine its modal parameters.
Abstract
Purpose
The purpose of this study is to numerically model the rigid pavement resting on two-parameter soil and to examine its modal parameters.
Design/methodology/approach
This study is carried out using a one-dimensional beam element with three rotational and three translational degrees of freedom based on the finite element method. MATLAB programming is used to perform the free vibration analysis of the rigid pavement.
Findings
Cyclic frequency and their corresponding mode shapes were determined. It has been investigated how cyclic frequency changes as a result of variations in the thickness, span length of pavement, shear modulus, modulus of subgrade, different boundary conditions and element discretization. Thickness of the pavement and span length has greater effect on the cyclic frequency. Maximum increase of 29.7% is found on increasing the thickness, whereas the cyclic frequency decreases by 63.49% on increasing span length of pavement.
Research limitations/implications
The pavement's free vibration is the sole subject of the current investigation. This study limits for the preliminary design phase of rigid pavements, where a complete three-dimensional finite element analysis is unnecessary. The current approach can be extended to future research using a different method, such as finite element grilling technique, mesh-free technique on reinforced concrete pavements or jointed concrete pavements.
Originality/value
The finite element approach adopted in this paper involves six degrees of freedom for each node. Furthermore, to the best of the authors’ knowledge, no prior study has done seven separate parametric investigations on the modal analysis of rigid pavement resting on two-parameter soil.
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Jeevananthan Manickavasagam and Visalakshmi S.
The algorithmic trading has advanced exponentially and necessitates the evaluation of intraday stock market forecasting on the grounds that any stock market series are foreseen to…
Abstract
Purpose
The algorithmic trading has advanced exponentially and necessitates the evaluation of intraday stock market forecasting on the grounds that any stock market series are foreseen to follow the random walk hypothesis. The purpose of this paper is to forecast the intraday values of stock indices using data mining techniques and compare the techniques’ performance in different markets to accomplish the best results.
Design/methodology/approach
This study investigates the intraday values (every 60th-minute closing value) of four different markets (namely, UK, Australia, India and China) spanning from April 1, 2017 to March 31, 2018. The forecasting performance of multivariate adaptive regression spline (MARSplines), support vector regression (SVR), backpropagation neural network (BPNN) and autoregression (1) are compared using statistical measures. Robustness evaluation is done to check the performance of the models on the relative ratios of the data.
Findings
MARSplines produces better results than the compared models in forecasting every 60th minute of selected stocks and stock indices. Next to MARSplines, SVR outperforms neural network and autoregression (1) models. The MARSplines proved to be more robust than the other models.
Practical implications
Forecasting provides a substantial benchmark for companies, which entails long-run operations. Significant profit can be earned by successfully predicting the stock’s future price. The traders have to outperform the market using techniques. Policy makers need to estimate the future prices/trends in the stock market to identify the link between the financial instruments and monetary policy which gives higher insights about the mechanism of existing policy and to know the role of financial assets in many channels. Thus, this study expects that the proposed model can create significant profits for traders by more precisely forecasting the stock market.
Originality/value
This study contributes to the high-frequency forecasting literature using MARSplines, SVR and BPNN. Finding the most effective way of forecasting the stock market is imperative for traders and portfolio managers for investment decisions. This study reveals the changing levels of trends in investing and expectation of significant gains in a short time through intraday trading.
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Fuad Ali Mohammed Al-Yarimi, Nabil Mohammed Ali Munassar and Fahd N. Al-Wesabi
Digital computing and machine learning-driven predictive analysis in the diagnosis of non-communicable diseases are gaining significance. Globally many research studies are…
Abstract
Purpose
Digital computing and machine learning-driven predictive analysis in the diagnosis of non-communicable diseases are gaining significance. Globally many research studies are focusing on developing comprehensive models for such detection. Categorically in the proposed diagnosis for arrhythmia, which is a critical diagnosis to prevent cardiac-related deaths, any constructive models can be a value proposition. In this study, the focus is on developing a holistic system that predicts the scope of arrhythmia from the given electrocardiogram report. The proposed method is using the sequential patterns of the electrocardiogram elements as features.
Design/methodology/approach
Considering the decision accuracy of the contemporary classification methods, which is not adequate to use in clinical practices, this manuscript coined a new dimension of features to perform supervised learning and classification using the AdaBoost classifier. The proposed method has titled “Electrocardiogram stream level correlated patterns as features (ESCPFs),” which takes electrocardiograms (ECGs) signal streams as input records to perform supervised learning-based classification to detect the arrhythmia scope in given ECG record.
Findings
From the results and comparative reports generated for the study, it is evident that the model is performing with higher accuracy compared to some of the earlier models. However, focusing on the emerging solutions and technologies, if the accuracy factors for the model can be improved, it can lead to compelling predictions and accurate outcome from the process.
Originality/value
The authors represent complete automatic and rapid arrhythmia as classifier, which could be applied online and examine long ECG records sequence efficiently. By releasing the needs for extraction of features, the authors project an application based on raw signals, one result to heart rates date, whose objective is to lessen computation time when attaining minimum classification error outcomes.