Search results

This paper aims to propose an effective dynamic calibration and compensation method to solve the problem that the statically calibrated force sensor would produce large dynamic errors when measuring dynamic signals.

The dynamic characteristics of the force sensor are analyzed by modal analysis and negative step dynamic force calibration test, and the dynamic mathematical model of the force sensor is identified based on a generalized least squares method with a special whitening filter. Then, a compensation unit is constructed to compensate the dynamic characteristics of the force measurement system, and the compensation effect is verified based on the step and knock excitation signals.

The dynamic characteristics of the force sensor obtained by modal analysis and dynamic calibration test are consistent, and the time and frequency domain characteristics of the identified dynamic mathematical model agree well with the actual measurement results. After dynamic compensation, the dynamic characteristics of the force sensor in the frequency domain are obviously improved, and the effective operating frequency band is widened from 500 Hz to 1,560 Hz. In addition, in the time domain, the rise time of the step response signal is reduced from 0.29 ms to 0.17 ms, and the overshoot decreases from 26.6% to 9.8%.

An effective dynamic calibration and compensation method is proposed in this paper, which can be used to improve the dynamic performance of the strain-gauge-type force sensor and reduce the dynamic measurement error of the force measurement system.

Evaluate the performance of progressive collapse of full-scale three-dimensional structure (3D) beam-slab substructures with and without the presence of reinforced concrete (RC) balconies using two concrete mixes [normal concrete (NC) and rubberized concrete (RuC)].

This study examines two concrete mixes to evaluate the progressive collapse performance of full-scale 3D beam-slab substructures with and without the presence of RC balconies using the finite element (FE) method.

The results showed that the vertical loads that affect the structures of the specimens after including the balconies in the modeling increased by an average of 29.3% compared with those of the specimens without balconies. The specimens with balconies exhibited higher resistance to progressive collapse in comparison with the specimens without balconies. Moreover, the RuC specimens performed very efficiently during the catenary stage, which significantly enhanced robustness to substantial deformation to delay or mitigate the progressive collapse risk.

All the experimental and numerical studies of the RC beam-slab substructures under progressive collapse scenarios are limited and do not consider the balcony’s presence in the building. Although balconies represent a common feature of multistory residential buildings, their presence in the building has more likely caused the failure of this building compared with a building without balconies. However, balconies are an external extension of RC slabs, which can provide extra resistance through tensile membrane action (TMA) or compressive membrane action (CMA). All those gaps have not been investigated yet.

Additive manufacturing became the most popular method as it enables the production of light-weight and high-density parts in effective way. Selective laser melting (SLM) is preferred by means of producing a component with good surface quality and near-net shape even if it has complex form. Titanium alloys have been extensively used in engineering covering a variety of sectors such as aeronautical, chemical, automotive and defense industry with its unique material properties. Therefore, the purpose of this review is to study the tribological behavior and surface integrity that reflects the thermal and mechanical performances of the fabricated parts.

This paper is focused on the tribological and surface integrity aspects of SLM-produced titanium alloy components. It is aimed to outline the effect of SLM process parameters on tribology and surface integrity first. Then, thermal, thermal heat, thermomechanical and postprocessing surface treatments such as peening, surface modification and coatings are highlighted in the light of literature review.

This work studied the effects of particle characteristics (e.g. size, shape, distributions, flowability and morphology) on tribological performance according to an extensive literature survey.

This study addresses this blind spot in existing industrial-academic knowledge and goals to determine the impact of SLM process parameters, posttreatments (especially peening operations) and particle characteristics on the SLMed Ti-based alloys, which are increasingly used in biomedical applications as well as other many applications ranging from automobile, aero, aviation, maritime, etc. This review paper is created with the intention of providing deep investigation on the important material characteristics of titanium alloy-based components, which can be useful for the several engineering sectors.

There are two main objectives in this study. First, we aim to develop a set of constructs for health task management support (HTMS) features to evaluate which health-related tasks are supported by mobile health application (mHealth app) functions. Second, drawing on innovation resistance theory (IRT), we examine the impacts of the newly developed HTMS dimensions on perceived usefulness, alongside other barrier factors contributing to technology anxiety.

Using a mixed-method research design, this research seeks to develop new measurement scales that reflect how mHealth apps support older adults’ health-related needs based on interviews. Subsequently, data were collected from older adults and exploratory factor analysis was used to confirm the validity of the new scales. Partial least squares structural equation modeling (PLS-SEM) was used to analyze survey data from 602 older adults.

The PLS-SEM results indicated that medical management task support, dietary task support, and exercise task support were positively associated with perceived usefulness, while perceived complexity and dispositional resistance to change were identified as antecedents of technology anxiety. Perceived usefulness and technology anxiety were found to positively and negatively influence adoption intention, respectively.

This study enriches the information systems literature by developing a multidimensional construct that delineates how older adults’ health-related needs can be supported by features of mHealth apps. Drawing on IRT, we complement the existing literature on resistance to innovation by systematically examining the impact of five types of barriers on technology anxiety.

Adjusting professionally to new work roles in a foreign work environment can be a challenging and stressful experience for expatriates. However, whether this experience translates into counterproductive behaviour remains to be examined. Hence, drawing on organisational support theory (OST; Eisenberger et al., 1986), this study aims to investigate whether work adjustment mediates the effect of diversity-oriented leadership on psychological withdrawal behaviour. The authors also propose that the relationship between diversity-oriented leadership and psychological withdrawal behaviour (via work adjustment) is moderated by organisational-based self-esteem.

Dyadic data from 148 paired surveys of full-time expatriate employees and direct supervisors working in hotels were collected. Smart-PLS procedures with SEM were used to assess the research hypotheses.

Overall, the authors received empirical support for the mediation and moderated model. Contrary to the expectations, the authors demonstrate that diversity-oriented leadership has no significant direct effect on expatriate employees’ psychological withdrawal behaviours; however, this effect is significant only via the full mediation of work adjustment. This relationship appears stronger among expatriate employees who feel important and valued by their organisation (i.e. organisational-based self-esteem).

This research is valuable in various ways, including adding to the emerging literature on expatriate employees in the UAE, which heavily relies on such employees for economic growth. Furthermore, as many organisations are hiring a diverse workforce, diversity-oriented leadership is crucial in ensuring that culturally and demographically diverse employees remain productive and become valuable and significant members of the organisation.

This research aims to forecast the ABI as a leading indicator of U.S. construction activities, applying multivariate machine learning predictive models over different horizons and utilizing the nonlinear and long-term dependencies between the ABI and macroeconomic and construction market variables. To assess the applicability of the machine learning models, six multivariate machine learning predictive models were developed considering the relationships between the ABI and other construction market and macroeconomic variables. The forecasting performances of the developed predictive models were evaluated in different forecasting scenarios, such as short-term, medium-term, and long-term horizons comparable to the actual timelines of construction projects.

The architecture billings index (ABI) as a macroeconomic indicator is published monthly by the American Institute of Architects (AIA) to evaluate business conditions and track construction market movements. The current research developed multivariate machine learning models to forecast ABI data for different time horizons. Different macroeconomic and construction market variables, including Gross Domestic Product (GDP), Total Nonresidential Construction Spending, Project Inquiries, and Design Contracts data were considered for predicting future ABI values. The forecasting accuracies of the machine learning models were validated and compared using the short-term (one-year-ahead), medium-term (three-year-ahead), and long-term (five-year-ahead) ABI testing datasets.

The experimental results show that Long Short Term Memory (LSTM) provides the highest accuracy among the machine learning and traditional time-series forecasting models such as Vector Error Correction Model (VECM) or seasonal ARIMA in forecasting the ABIs over all the forecasting horizons. This is because of the strengths of LSTM for forecasting temporal time series by solving vanishing or exploding gradient problems and learning long-term dependencies in sequential ABI time series. The findings of this research highlight the applicability of machine learning predictive models for forecasting the ABI as a leading indicator of construction activities, business conditions, and market movements.

The architecture, engineering, and construction (AEC) industry practitioners, investment groups, media outlets, and business leaders refer to ABI as a macroeconomic indicator to evaluate business conditions and track construction market movements. It is crucial to forecast the ABI accurately for strategic planning and preemptive risk management in fluctuating AEC business cycles. For example, cost estimators and engineers who forecast the ABI to predict future demand for architectural services and construction activities can prepare and price their bids more strategically to avoid a bid loss or profit loss.

The ABI data have been forecasted and modeled using linear time series models. However, linear time series models often fail to capture nonlinear patterns, interactions, and dependencies among variables, which can be handled by machine learning models in a more flexible manner. Despite the strength of machine learning models to capture nonlinear patterns and relationships between variables, the applicability and forecasting performance of multivariate machine learning models have not been investigated for ABI forecasting problems. This research first attempted to forecast ABI data for different time horizons using multivariate machine learning predictive models using different macroeconomic and construction market variables.

This research aims to explore the dark side of mobile applications by investigating the role of apps' technicality and app security in the mechanism of user satisfaction, app intention and customers' continuance tendency to make in-app purchases.

Drawing on attitude-behavior-context (ABC) theory, the study proposed a conceptual framework and examined the framework using a structural equation modeling (SEM) approach based on data collected from app users from New Zealand.

The results reveal the correlation between user satisfaction and in-app purchase with a mediator of app continuance intention (ACI). In particular, the results show that app technicality (AT) has a positive correlation with user satisfaction as an antecedent. App security and hedonic value are positively correlated with user satisfaction.

The research has three critical research implications. First, this research advances the understanding of the dark side of mobile apps by showing how app security influences customers' in-app purchases. Secondly, this study reveals and offers empirical evidence for the mechanism between app security and user satisfaction. Finally, the study provides empirical evidence of AT as a distal antecedent for in-app purchases.

This study quantitatively investigates the impacts of digital and learning orientations on supply chain resilience (SCR) and firm performance (FP), aiming to fill the gaps in understanding their specific impacts in the context of Industry 4.0 developments and supply chain disruptions.

This study utilized survey techniques and structural equation modelling (SEM) to gather and analyse data through a questionnaire based on a seven-point Likert scale. Hypotheses were formulated based on an extensive literature review and tested using Amos software.

The study confirms SCR’s significant impact on FP, aligning with existing research on resilience’s role in organizational competitiveness. This study uncovers the nuanced impacts of digital and learning orientations on SCR and FP. Internal digital orientation (DOI) positively impacts SCR, while external digital orientation (DOE) does not. Specific dimensions of learning orientation – shared vision (LOS), open-mindedness (LOO) and intraorganizational knowledge sharing (LOI) – enhance SCR, while commitment to learning (LOC) does not. SCR mediates the relationship between DOI and FP but not between DOE and FP.

This research focuses on digital and learning orientations, recommending that future studies investigate other strategic orientations and examine the specific contributions of various digital technologies to SCR across diverse contexts.

The empirical findings emphasize the significance of developing internal digital capabilities and specific learning orientations to enhance SCR and FP, aligning these initiatives with resilience strategies.

This study advances knowledge by distinguishing the impacts of internal and external digital orientations and specific learning dimensions on SCR and FP, offering nuanced insights and empirical validation.

The importance of the agri-food supply chain in both food production and distribution has made the issue of its development a critical concern. Based on configuration theory and congruence theory, this research investigates the complex impact of supply chain concentration on financial growth in agri-food supply chains.

The cluster analysis and response surface methodology are employed to analyse the data collected from 207 Chinese agri-food companies from 2010 to 2022.

The results indicate that different combination patterns of supply chain concentration can lead to different levels of financial growth. We discover that congruent supplier and customer concentration is beneficial for companies’ financial growth. This impact is more pronounced when the company is in the agricultural production stage of agri-food supply chains. Post-hoc analysis indicates that there exists an inverted U-shaped relationship between the overall levels of supply chain concentration and financial growth.

Our research uncovers the complex interplay between supply chain base and financial outcomes, thereby revealing significant ramifications for agri-food supply chain managers to optimise their strategies for exceptional financial growth.

This study proposes a combined approach of cluster analysis and response surface analysis for analysing configuration issues in supply chain management.

This study aims to address the limited understanding of the complex correlations among strut size, structural orientation and process parameters in selective laser melting (SLM)-fabricated lattice structures. By investigating the effects of crucial process parameters, strut diameter and angle on the microstructure and mechanical performance of AlSi10Mg struts, the research seeks to enhance the surface morphologies, microstructures and mechanical properties of AM lattice structures, enabling their application in various engineering fields, including medical science and space technologies.

This comprehensive study investigates SLM-fabricated AlSi10Mg strut structures, examining the effects of process parameters, strut diameter and angle on densification behavior and microstructural characteristics. By analyzing microstructure, geometrical properties, melt pool morphology and mechanical properties using optical microscopy, scanning electron microscope, energy dispersive X-ray spectroscopy and microhardness tests, the research addresses existing gaps in knowledge on fine lattice strut elements and their impact on surface morphology and microstructure.

The study revealed that laser energy, power density and strut inclination angle significantly impact the microstructure, geometrical properties and mechanical performance of SLM-produced AlSi10Mg struts. Findings insight enable the optimization of SLM process parameters to produce lattice structures with enhanced surface morphologies, microstructures and mechanical properties, paving the way for applications in medical science and space technologies.

This study uniquely investigates the effects of processing parameters, strut diameter and inclination angle on SLM-fabricated AlSi10Mg struts, focusing on fine lattice strut elements with diameters as small as 200 µm. Unlike existing literature, it delves into the complex correlations among strut size, structural orientation and process parameters to understand their impact on microstructure, geometrical imperfections and mechanical properties. The study provides novel insights that contribute to the optimization of SLM process parameters, moving beyond the typically recommended guidelines from powder or machine suppliers.