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Article
Publication date: 10 April 2017

U.G. Eziefula, D.O. Onwuka and O.M. Ibearugbulem

The purpose of this paper is to analyze the inelastic buckling of a rectangular thin flat isotropic plate subjected to uniform uniaxial in-plane compression using a work…

Abstract

Purpose

The purpose of this paper is to analyze the inelastic buckling of a rectangular thin flat isotropic plate subjected to uniform uniaxial in-plane compression using a work principle, a deformation plasticity theory and Taylor–Maclaurin series formulation.

Design/methodology/approach

The non-loaded longitudinal edges of the rectangular plate are clamped, whereas the loaded edges are simply supported (CSCS). Total work error function is applied to Stowell’s plasticity theory in the derivation of the inelastic buckling equation. Mathematical formulation of the Taylor–Maclaurin series deflection function satisfied the boundary conditions of the CSCS rectangular plate. The critical inelastic load of the plate is then derived by applying variational principles.

Findings

Values of the plate buckling coefficient are calculated using various values of moduli ratio for aspect ratios ranging from 0.1 to 1.0, in intervals of 0.1. The accuracy of the proposed technique is validated by comparing the results obtained in the present study with solutions from a previous investigation. The percentage differences in the values of the buckling coefficient ranged from −0.122 to −4.685 per cent.

Originality/value

The results indicate that the work principle approach can be used as an alternative approximate method for analyzing inelastic buckling of rectangular thin flat isotropic plates under uniform in-plane compressive loads.

Details

World Journal of Engineering, vol. 14 no. 2
Type: Research Article
ISSN: 1708-5284

Keywords

Article
Publication date: 20 October 2021

Suhas Vijay Patil, K. Balakrishna Rao and Gopinatha Nayak

Recycling construction waste is a promising way towards sustainable development in construction. Recycled aggregate (RA) is obtained from demolished concrete structures…

Abstract

Purpose

Recycling construction waste is a promising way towards sustainable development in construction. Recycled aggregate (RA) is obtained from demolished concrete structures, laboratory crushed concrete, concrete waste at a ready mix concrete plant and the concrete made from RA is known as RA concrete. The purpose of this study is to apply multiple linear regressions (MLRs) and artificial neural network (ANN) to predict the mechanical properties, such as compressive strength (CS), flexural strength (FS) and split tensile strength (STS) of concrete at the age of 28 days curing made completely from the recycled coarse aggregate (RCA).

Design/methodology/approach

MLR and ANN are used to develop a prediction model. The model was developed in the training phase by using data from a previously published research study and a developed model was further tested by obtaining data from laboratory experiments.

Findings

ANN shows more accuracy than MLR with an R2-value of more than 0.8 in the training phase and 0.9 in a testing phase. The high R2-value indicates strong relation between the actual and predicted values of mechanical properties of RCA concrete. These models will help construction professionals to save their time and cost in predicting the mechanical properties of RCA concrete at 28 days of curing.

Originality/value

ANN with rectified linear unit transfer function and backpropagation algorithm for training is used to develop a prediction model. The outcome of this study is the prediction model for CS, FS and STS of concrete at 28 days of curing.

Details

Journal of Engineering, Design and Technology , vol. 21 no. 6
Type: Research Article
ISSN: 1726-0531

Keywords

Article
Publication date: 7 February 2022

Muralidhar Vaman Kamath, Shrilaxmi Prashanth, Mithesh Kumar and Adithya Tantri

The compressive strength of concrete depends on many interdependent parameters; its exact prediction is not that simple because of complex processes involved in strength…

Abstract

Purpose

The compressive strength of concrete depends on many interdependent parameters; its exact prediction is not that simple because of complex processes involved in strength development. This study aims to predict the compressive strength of normal concrete and high-performance concrete using four datasets.

Design/methodology/approach

In this paper, five established individual Machine Learning (ML) regression models have been compared: Decision Regression Tree, Random Forest Regression, Lasso Regression, Ridge Regression and Multiple-Linear regression. Four datasets were studied, two of which are previous research datasets, and two datasets are from the sophisticated lab using five established individual ML regression models.

Findings

The five statistical indicators like coefficient of determination (R2), mean absolute error, root mean squared error, Nash–Sutcliffe efficiency and mean absolute percentage error have been used to compare the performance of the models. The models are further compared using statistical indicators with previous studies. Lastly, to understand the variable effect of the predictor, the sensitivity and parametric analysis were carried out to find the performance of the variable.

Originality/value

The findings of this paper will allow readers to understand the factors involved in identifying the machine learning models and concrete datasets. In so doing, we hope that this research advances the toolset needed to predict compressive strength.

Details

Journal of Engineering, Design and Technology , vol. 22 no. 2
Type: Research Article
ISSN: 1726-0531

Keywords

Article
Publication date: 10 January 2023

Sumran Ali, Jawaria Ashraf, Muhammad Ghufran, Peng Xiaobao and Liu Zhiying

This study has aimed to analyse the role of innovation-sharing collaboration in the large-scale manufacturing of Covid-19 vaccination across the globe and its impact on the…

Abstract

Purpose

This study has aimed to analyse the role of innovation-sharing collaboration in the large-scale manufacturing of Covid-19 vaccination across the globe and its impact on the mortality rate of the countries where the pharmaceutical manufacturers received such innovation.

Design/methodology/approach

The authors have relied upon the difference-in-difference (DID) approach by utilizing the data available on public platforms such as World Health Organization (WHO) databank, organization for economic co-operation and development (OECD) data bank, istat, Indian bureau of statistics and European centre for disease prevention and control (ecdc) from 2020 to 2021 to establish the empirical inference of the analysis.

Findings

This study’s results present that after the invention and commercialization of the vaccine, the Covid-19 impact was still intact and people were dying continuously. However, it was impossible to fulfil the demand of the 7 billion population in a short time. In the light of these facts, the WHO encouraged sharing vaccine innovation with other countries to enhance production capacity. The authors found that after vaccine innovation sharing, Covid-19’s devastation slowed: the fatality rate was marginally reduced, and economic conditions started their recovery journey.

Originality/value

This study’s findings present that the Covid-19 vaccine played a pivotal role in tackling the Covid-19’s devastating impact on the entire world. It emphasizes the role of innovation-sharing collaborations in curtailing hazardous consequences, including the mortality rate during a crisis, and such collaborations’ impact on the countries where institutions involved in them reside.

Details

European Journal of Innovation Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1460-1060

Keywords

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