Lin Cheng, Pu Zhang, Emre Biyikli, Jiaxi Bai, Joshua Robbins and Albert To
The purpose of the paper is to propose a homogenization-based topology optimization method to optimize the design of variable-density cellular structure, in order to achieve…
Abstract
Purpose
The purpose of the paper is to propose a homogenization-based topology optimization method to optimize the design of variable-density cellular structure, in order to achieve lightweight design and overcome some of the manufacturability issues in additive manufacturing.
Design/methodology/approach
First, homogenization is performed to capture the effective mechanical properties of cellular structures through the scaling law as a function their relative density. Second, the scaling law is used directly in the topology optimization algorithm to compute the optimal density distribution for the part being optimized. Third, a new technique is presented to reconstruct the computer-aided design (CAD) model of the optimal variable-density cellular structure. The proposed method is validated by comparing the results obtained through homogenized model, full-scale simulation and experimentally testing the optimized parts after being additive manufactured.
Findings
The test examples demonstrate that the homogenization-based method is efficient, accurate and is able to produce manufacturable designs.
Originality/value
The optimized designs in our examples also show significant increase in stiffness and strength when compared to the original designs with identical overall weight.
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Liping Wang, Pu Zhang, Pei Zhang, Rongbo Li, Yanke Zhang and Yueqiu Wu
Public–private partnership (PPP) projects are increasingly significant in many countries. The purpose of this paper is to assess the impact of critical success factors (CSFs) on…
Abstract
Purpose
Public–private partnership (PPP) projects are increasingly significant in many countries. The purpose of this paper is to assess the impact of critical success factors (CSFs) on PPP projects and comprehensively consider the interrelations and interaction among dimensions and factors to achieve a better understanding of PPP project management.
Design/methodology/approach
An evaluation index system for PPP projects such as the presented case study is proposed based on a literature review and a survey. Then, interpretative structural modeling is used to transform the CSFs dimension into a multi-level hierarchical model to reflect the driven-dependency relation of each dimension; the fuzzy analytic network process model optimized by moment estimation theory is used to investigate the impact of CSFs by considering their internal impact.
Findings
Regarding the project used as the case study, the driving force and dependence for driving layer and dependent layer are determined. Moreover, in driving layer, efficient and well-structured payment mechanism is the most important CSF if considering the internship and interaction among CSFs, and efficient and well-structured payment mechanism and good governance provide most positive interaction; in dependent layer, population of beneficiaries is the most important CSF if considering the internship and interaction among CSFs, and public client’s satisfaction provides most positive interaction.
Originality/value
This paper developed an evaluation model to explore the interrelationships of dimensions and factors and then determine the impact of CSFs. The model propose in this paper relaxes the independence assumptions of traditional methods and is more in line with reality; besides, weighting method is optimized to obtain more objective and reasonable evaluation results. Through an empirical study, the validity of the model has been verified; therefore, the study can help project stakeholders better understand the CSFs and further improve project performance.
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Aneel Manan, Zhang Pu, Jawad Ahmad and Muhammad Umar
Rapid industrialization and construction generate substantial concrete waste, leading to significant environmental issues. Nearly 10 billion metric tonnes of concrete waste are…
Abstract
Purpose
Rapid industrialization and construction generate substantial concrete waste, leading to significant environmental issues. Nearly 10 billion metric tonnes of concrete waste are produced globally per year. In addition, concrete also accelerates the consumption of natural resources, leading to the depletion of these natural resources. Therefore, this study uses artificial intelligence (AI) to examine the utilization of recycled concrete aggregate (RCA) in concrete.
Design/methodology/approach
An extensive database of 583 data points are collected from the literature for predictive modeling. Four machine learning algorithms, namely artificial neural network (ANN), random forest (RF), ridge regression (RR) and least adjacent shrinkage and selection operator (LASSO) regression (LR), in predicting simultaneously concrete compressive and tensile strength were evaluated. The dataset contains 10 independent variables and two dependent variables. Statistical parameters, including coefficient of determination (R2), mean square error (MSE), mean absolute error (MAE) and root mean square error (RMSE), were employed to assess the accuracy of the algorithms. In addition, K-fold cross-validation was employed to validate the obtained results, and SHapley Additive exPlanations (SHAP) analysis was applied to identify the most sensitive parameters out of the 10 input parameters.
Findings
The results indicate that the RF prediction model performance is better and more satisfactory than other algorithms. Furthermore, the ANN algorithm ranks as the second most accurate algorithm. However, RR and LR exhibit poor findings with low accuracy. K-fold cross-validation was successfully applied to validate the obtained results and SHAP analysis indicates that cement content and recycled aggregate percentages are the effective input parameter. Therefore, special attention should be given to sensitive parameters to enhance the concrete performance.
Originality/value
This study uniquely applies AI to optimize the use of RCA in concrete production. By evaluating four machine learning algorithms, ANN, RF, RR and LR on a comprehensive dataset, this study identities the most effective predictive models for concrete compressive and tensile strength. The use of SHAP analysis to determine key input parameters and K-fold cross-validation for result validation adds to the study robustness. The findings highlight the superior performance of the RF model and provide actionable insights into enhancing concrete performance with RCA, contributing to sustainable construction practice.
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Aneel Manan, Pu Zhang, Shoaib Ahmad and Jawad Ahmad
The purpose of this study is to assess the incorporation of fiber reinforced polymer (FRP) bars in concrete as a reinforcement enhances the corrosion resistance in a concrete…
Abstract
Purpose
The purpose of this study is to assess the incorporation of fiber reinforced polymer (FRP) bars in concrete as a reinforcement enhances the corrosion resistance in a concrete structure. However, FRP bars are not practically used due to a lack of standard codes. Various codes, including ACI-440-17 and CSA S806-12, have been established to provide guidelines for the incorporation of FRP bars in concrete as reinforcement. The application of these codes may result in over-reinforcement. Therefore, this research presents the use of a machine learning approach to predict the accurate flexural strength of the FRP beams with the use of 408 experimental results.
Design/methodology/approach
In this research, the input parameters are the width of the beam, effective depth of the beam, concrete compressive strength, FRP bar elastic modulus and FRP bar tensile strength. Three machine learning algorithms, namely, gene expression programming, multi-expression programming and artificial neural networks, are developed. The accuracy of the developed models was judged by R2, root means squared and mean absolute error. Finally, the study conducts prismatic analysis by considering different parameters. including depth and percentage of bottom reinforcement.
Findings
The artificial neural networks model result is the most accurate prediction (99%), with the lowest root mean squared error (2.66) and lowest mean absolute error (1.38). In addition, the result of SHapley Additive exPlanation analysis depicts that the effective depth and percentage of bottom reinforcement are the most influential parameters of FRP bars reinforced concrete beam. Therefore, the findings recommend that special attention should be given to the effective depth and percentage of bottom reinforcement.
Originality/value
Previous studies revealed that the flexural strength of concrete beams reinforced with FRP bars is significantly influenced by factors such as beam width, effective depth, concrete compressive strength, FRP bars’ elastic modulus and FRP bar tensile strength. Therefore, a substantial database comprising 408 experimental results considered for these parameters was compiled, and a simple and reliable model was proposed. The model developed in this research was compared with traditional codes, and it can be noted that the model developed in this study is much more accurate than the traditional codes.
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Yuhai Shen, Yanshuang Wang, Jianghai Lin, Pu Zhang, Xudong Gao and Zijun Wang
This paper aims to determine a suitable anti-wear and friction-reducing compounding additive for lithium greases (LG) by investigating the effects of three single additives…
Abstract
Purpose
This paper aims to determine a suitable anti-wear and friction-reducing compounding additive for lithium greases (LG) by investigating the effects of three single additives potassium borate (PB), zinc dialkyl dithiophosphate and molybdenum dialkyl dithiophosphate (MoDDP) and two compound additives on the friction, wear and extreme pressure properties of LG.
Design/methodology/approach
The effects of the above five additives on the friction, wear and extreme pressure properties of LG were investigated using an SRV-5 friction tester. An X-ray photoelectron spectrometer was used to analyze the various elements presented on the wear surface as well as the types of compounds.
Findings
The compound additive suitable for grease consists of PB and MoDDP, which have excellent friction reduction, anti-wear and extreme pressure properties. And a boundary protection film consisting of oxide and MoS2 is formed on the friction surface, thus improving the friction reduction and anti-wear performance of the grease.
Originality/value
This study can improve the anti-wear and friction-reduction performance of greases, which is of great importance in the field of industrial lubrication. The results of this paper are expected to be useful to researchers and academics of grease.
Peer review
The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-11-2022-0350/
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The purpose of this paper is to empirically determine the key factors influencing the US consumers’ intentions to use apparel m-commerce.
Abstract
Purpose
The purpose of this paper is to empirically determine the key factors influencing the US consumers’ intentions to use apparel m-commerce.
Design/methodology/approach
An enhanced consumer’s apparel m-commerce adoption model was developed through integrating the existing e-commerce/m-commerce theories (i.e. theory of reasoned action, Technology acceptance model and diffusion of innovation theory). The investigated factors included nine independent variables – perceived usefulness (PU), perceived ease-of-use, subjective norm (SN), personal innovativeness traits, security and privacy concerns, compatibility, observability, trust and past non-store shopping experience (PE), and five control variables – age, gender, education level, income level and ethnicity. The dependent factor is consumer’s intention to use (IU) apparel m-commerce. The primary data were gathered by an online survey of US consumers via Amazon Mechanical Turk. In total, 317 eligible responses were received. The applied statistical techniques were factor analysis and multiple regression analysis.
Findings
The results show that the US consumer’s IU apparel m-commerce is significantly affected by PU, SN, compatibility and PE and education level. Overall, 67.3 percent of variation in the US consumer’s IU apparel m-commerce is explained by the developed model, which suggests a high explanatory power.
Practical implications
Companies should provide those functions and features on their mobile websites that enable consumers to easily find the products wanted and complete transactions efficiently. Companies should particularly target the consumers with innovativeness traits and/or those having prior non-store shopping experience. Enhancing the trust in m-commerce among the US consumers with higher education level could help companies attract more potential users. Elderly, female or lower income consumers could be the next business opportunities for apparel e-tailers.
Originality/value
As one of the first efforts made to understand the emerging apparel m-commerce phenomenon, this study empirically determined the key factors influencing the US consumer’s IU apparel m-commerce.
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Mohit Jamwal, Honey Kanojia and Neeraj Dhiman
Wearable medical devices (WMDs) are improving people’s health and well-being in a noble way, as these aid in effective personal health monitoring, remote surveillance and overall…
Abstract
Purpose
Wearable medical devices (WMDs) are improving people’s health and well-being in a noble way, as these aid in effective personal health monitoring, remote surveillance and overall illness management. Despite its wider applicability and usage, it is prevalent that users discontinue its usage, which presents an obstacle in the proliferation of such vital innovations among the masses. Therefore, relying on the expectation-confirmation model (ECM), this study aims to delve deeper to explain the factors that motivate users to continually use WMDs by incorporating novel variables, namely, health belief, health information accuracy and privacy protection.
Design/methodology/approach
The study proposes and tests an extended ECM perspective to predict the continuance intention (CI) of WMDs among users. By using structural equation modelling using SmartPLS, the authors tested the model on Indian people (n = 451) who had an erstwhile experience of using WMDs.
Findings
The study results show that confirmation of users’ expectations positively impacts their usefulness and satisfaction towards WMDs. Moreover, satisfaction towards WMDs is the strongest predictor of users’ CI, followed by perceived usefulness. Interestingly, personal factor such as health beliefs reveals a greater influence on perceived usefulness than technological factors like health information accuracy and privacy protection.
Research limitations/implications
The study findings demonstrate the significance of using the expectation-confirmation perspective in technology-based studies in general and WMDs, in particular. This study aids by offering an integrated model of WMD’s continued usage intention for the users, in addition to practical implications for marketers and policymakers.
Originality/value
A paucity of research exists when understanding the predictors of CI for WMDs. This study fills this gap and adds to behavioural literature by offering a noble viewpoint involving an extended ECM perspective.
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Vikas Kumar, Arun Kumar Kaushik and Gubir Singh
The present study aims to develop and offer a model to evaluate the customers' attitude and intention to adopt solar net metering systems (commonly called solar NMS) in a…
Abstract
Purpose
The present study aims to develop and offer a model to evaluate the customers' attitude and intention to adopt solar net metering systems (commonly called solar NMS) in a developing economy. Therefore, the research examines different factors affecting the Indian households' attitudes and intention to adopt solar NMS.
Design/methodology/approach
The data were collected from 247 solar NMS users from India. The structural equation modeling (SEM) technique was applied using SmartPLS 3.3.2 software to analyze the impact of various factors on their adoption intention. The conceptual model comprises environmental concern, perceived ease of use (PEOU), subjective norms, perceived usefulness (PU), attitude and behavioral intention to adopt solar NMS.
Findings
Subjective norms and environmental concerns significantly influence the PU and PEOU of solar NMS. Also, PU and PEOU significantly influence their attitude and intentions toward adopting solar NMS. Thus, the perceived social pressure and environmental concern affect their perception of solar NMS's usefulness and ease of use, leading to favorable attitudes and adoption intentions. Additionally, the solar NMS benefits the customers, society and the environment by enhancing environmental quality, compatibility with the modern lifestyle, and reducing dependency on the power grid and electricity bills. These benefits shape the customers' overall perception and increase the adoption of solar technologies.
Originality/value
The present research helps bridge the gaps in the existing literature by identifying (1) factors affecting customers' intention toward solar technologies in developing nations and (2) describing the significant prediction of environmental concern and subjective norms to increase solar technologies adoption.
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Zhihao Luo, Yongbo Guo, Yourui Cao, Zheyingzi Zhu, Wan Ma, Songquan Wang and Dekun Zhang
This study aims to study the influence of friction influencing factors between the wire rope and the liner on the safe use of the wire rope, which can provide guidance for the…
Abstract
Purpose
This study aims to study the influence of friction influencing factors between the wire rope and the liner on the safe use of the wire rope, which can provide guidance for the reliability design of the lifting system with strong dynamic response such as high speed, heavy load, etc., and improve the friction-driven stability of the system.
Design/methodology/approach
In this paper, the friction mechanism of wire rope and liner under the condition of excitation is investigated by means of wire rope-liner friction-vibration experimental platform and dynamic viscoelastic test of liner.
Findings
The results show that: With increasing excitation frequency, the friction between the three liner materials (G30, K25, PU) and the wire rope decreased, and the wear of the surface shape of the liners was greater. The dynamic thermomechanical analysis (DMA) test results showed that the viscoelasticity of the three liner materials increased when the frequency was increased.
Research limitations/implications
Wire ropes are widely used in deep shaft hoisting and building elevators. Its operational reliability depends on whether there is sufficient friction between the wire rope and the friction liner, and whether the friction liner has good wear resistance. The study of the friction between the wire rope and the liner influencing factors is of great significance for the safe service of the wire rope.
Practical implications
The related results can provide guidance for the reliability design of lifting systems with strong dynamic response, such as high speed and heavy load, to improve the friction drive stability of the system.
Originality/value
With the increase of mining depth, to improve the transportation efficiency of the hoist used in deep and ultra-deep mines, as well as to ensure the safety and reliability of its operation, it is crucial that the large friction hoisting equipment has sufficient friction between the wire rope and the friction lining, as well as whether the friction lining has a good abrasion resistance.
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Zhenyu Tang, Xiaoyan Tang, Shi Pu, Yimeng Zhang, Hang Zhang, Yuming Zhang and Song Bo
To use the 4H-SiC material in integrated circuits for high temperature application, an accurate and simple circuit model of n-channel planar 4H-SiC MOSFET is required.
Abstract
Purpose
To use the 4H-SiC material in integrated circuits for high temperature application, an accurate and simple circuit model of n-channel planar 4H-SiC MOSFET is required.
Design/methodology/approach
In this paper, a SPICE model of n-channel planar 4H-SiC MOSFET was built based on the device simulation results and measurement results. Firstly, a device model was simulated with Sentaurus TCAD, with measured parameters from fabricated planar 4H-SiC MOSFET previously. Then the device simulation results were analyzed and parameters for SPICE models were extracted. With these parameters, an accurate SPICE model was built and simulated.
Findings
The SPICE model exhibits the same performance as the measured results with different environment temperatures. The simulation results indicate that the maximum fitting error is 0.22 mA (7.33% approximately) at 200 °C. A common-source amplifier with this model is also simulated and the simulated gain is stable at different environment temperatures.
Originality/value
This paper provides a reliable modeling method for n-Channel Planar 4H-SiC MOSFET and reference value for the design of 4H-SiC high temperature integrated circuit.