Search results

This paper aims to develop an effective framework which combines Bayesian optimized convolutional neural networks (BOCNN) with Monte Carlo simulation for slope reliability analysis.

The Bayesian optimization technique is firstly used to find the optimal structure of CNN based on the empirical CNN model established in a trial and error manner. The proposed methodology is illustrated through a two-layered soil slope and a cohesive slope with spatially variable soils at different scales of fluctuation.

The size of training data suite, T, has a significant influence on the performance of trained CNN. In general, a trained CNN with larger T tends to have higher coefficient of determination (R²) and smaller root mean square error (RMSE). The artificial neural networks (ANN) and response surface method (RSM) can provide comparable results to CNN models for the slope reliability where only two random variables are involved whereas a significant discrepancy between the slope failure probability (P_f) by RSM and that predicted by CNN has been observed for slope with spatially variable soils. The RSM cannot fully capture the complicated relationship between the factor of safety (FS) and spatially variable soils in an effective and efficient manner. The trained CNN at a smaller the scale of fluctuation (λ) exhibits a fairly good performance in predicting the P_f for spatially variable soils at higher λ with a maximum percentage error not more than 10%. The BOCNN has a larger R² and a smaller RMSE than empirical CNN and it can provide results fairly equivalent to a direct Monte Carlo Simulation and therefore serves a promising tool for slope reliability analysis within spatially variable soils.

A geotechnical engineer could use the proposed method to perform slope reliability analysis.

Slope reliability can be efficiently and accurately analyzed by the proposed framework.

The operational phase of a building's lifecycle is receiving increasing attention, as it consumes an enormous amount of energy and results in tremendous detrimental impacts on the environment. While energy simulation can be applied as a tool to evaluate the energy performance of a building in operation, the emergence of Building Information Modeling (BIM) technology is expected to facilitate the evaluation process with predefined and enriched building information. However, such an approach has been confronted by the challenge of interoperability issues among the related application software, including the BIM tools and energy simulation tools, and the results of simulation have been seldom verified due to the unavailability of corresponding experimental data. This study aims to explore the interoperability between the commonly used energy simulation and BIM tools and verifies the simulation approach by undertaking a case study.

With Autodesk Revit and EnergyPlus selected as the commonly used BIM and energy simulation tools, respectively, a valid technical framework of transferring building information between two tools is proposed, and the interoperability issues that occur during the data transfer are studied. The proposed framework is then employed to simulate the energy consumption of a single-family house, and sensitivity analysis and analysis on such parameters as schedule are conducted for building operations to showcase its applicability.

The simulation results are compared with monitored data and the results from another simulation tool, HOT2000; the comparison reveals that EnergyPlus and HOT2000 predict the total energy consumption with a difference from the monitoring data of 8.0 and 7.1%, respectively.

This research shows how to efficiently use BIM to support building energy simulation. Relevant stakeholders can learn from this research to avoid data loss during BIM model transformation.

This research explores the application of BIM for building energy simulation, compares the simulation results among different tools and validates simulation results using monitored data.

The purpose of this study is to develop a model extending Oriani’s formula by introducing a normalised concentration to simulate hydrogen diffusion in a multi-material system such as coated steels, under the presence of traps.

Implemented through the finite element method based on the analogy between mass diffusion and heat transfer, the governing equation was applied to investigate the combined effects of hydrogen traps and surface oxide films on hydrogen permeation in ferritic steels.

This study shows that the effective diffusivity varies over several orders of magnitude depending on the traps and films. This explains the divergence of measured hydrogen diffusivities in steels. It is revealed that hydrogen permeation in steels with Pd or Ni film is a trapping-dominant transport process, while hydrogen permeation in steel with oxide film is a process controlled by both trapping effect and retarding effect of oxide film. The oxide film enhances total hydrogen concentration within the steel substrate and is therefore detrimental. The Pd or Ni film has a little influence on total hydrogen concentration distribution depending on trapping energy.

Hydrogen flux curves and transient hydrogen concentration distributions can be directly obtained through the developed model. The proposed approach can also be extended to investigate other interstitial (i.e. carbon, oxygen and nitrogen) diffusion with traps revisited in complex systems.

Different aspects of the globalisation of Chinese basic education have been examined in recent studies. This paper aims to contribute to this research base by examining the relationship between global quality assurance and evaluation (QAEVAL) trends and reforms in the Chinese basic education system.

Relevant research literature in English and Chinese was reviewed to assesses whether a movement of increasing convergence with global QAEVAL policy contents and policy instruments has taken place in China following the introduction of the so-called “equality education”. A guiding theory regarding the basic features of QAEVAL was used to conceptually structure the findings of the authors.

The findings indicate that several subtle convergences are evident in China with global trends in QAEVAL, but a few notable exceptions are also evident.

Albeit the research indicates certain convergence, the authors acknowledge that further research is still needed to examine causal mechanisms of QAEVAL policy convergences and the processes and logic through which global trends are localised.

Both the importance of QAEVAL in global education governance as well as China’s importance to world affairs are increasing. However, so far, the effects of QAEVAL policies on China’s education system have not been thoroughly examined. This paper can serve as a concise starting point for examining the topic more deeply.

The purpose of this paper is to conceptualize the indigenous concept of suzhi at individual and organizational levels, and identify its dimensions for human resource management (HRM) research and practice in China.

Based on a comprehensive review of suzhi literature, Chinese cultural and historical literature, as well as Western mainstream HRM research, a multidimensional suzhi framework is conceptualized.

As an indigenous expression, suzhi can be and has been adopted for Chinese HRM research and practice. As a multidimensional construct, one’s cognitive suzhi is jointly determined by corresponding moral suzhi, wenhua (knowledge-based) suzhi and zhuanye (professional) suzhi. Cognitive suzhi, in turn, determines one’s behavioral suzhi that drives employees to enhance organizational performance, and this relationship is moderated by psychological suzhi.

The proposed framework provides new insight for Chinese indigenous management research, particularly in developing suzhi measurement for different dimensions. It also informs HRM practices in recruiting, selection, performance analysis and employee career development.

The complexity of suzhi dimensions from an organizational HRM perspective is analyzed. The resulting suzhi framework offers new insight for HRM research and practices in China.

Building information modeling (BIM) is a striking development in the architecture, engineering and construction (AEC) industry, which provides in-depth information on different stages of the building lifecycle. Real estate valuation, as a fully interconnected field with the AEC industry, can benefit from 3D technical achievements in BIM technologies. Some studies have attempted to use BIM for real estate valuation procedures. However, there is still a limited understanding of appropriate mechanisms to utilize BIM for valuation purposes and the consequent impact that BIM can have on decreasing the existing uncertainties in the valuation methods. Therefore, the paper aims to analyze the literature on BIM for real estate valuation practices.

This paper presents a systematic review to analyze existing utilizations of BIM for real estate valuation practices, discovers the challenges, limitations and gaps of the current applications and presents potential domains for future investigations. Research was conducted on the Web of Science, Scopus and Google Scholar databases to find relevant references that could contribute to the study. A total of 52 publications including journal papers, conference papers and proceedings, book chapters and PhD and master's theses were identified and thoroughly reviewed. There was no limitation on the starting date of research, but the end date was May 2022.

Four domains of application have been identified: (1) developing machine learning-based valuation models using the variables that could directly be captured through BIM and industry foundation classes (IFC) data instances of building objects and their attributes; (2) evaluating the capacity of 3D factors extractable from BIM and 3D GIS in increasing the accuracy of existing valuation models; (3) employing BIM for accurate estimation of components of cost approach-based valuation practices; and (4) extraction of useful visual features for real estate valuation from BIM representations instead of 2D images through deep learning and computer vision.

This paper contributes to research efforts on utilization of 3D modeling in real estate valuation practices. In this regard, this paper presents a broad overview of the current applications of BIM for valuation procedures and provides potential ways forward for future investigations.

The purpose of this study is to propose a new magnetic gearing device and the proposed transmission model can be applied in the field of wind power and wave energy generation gearboxes.

A novel radial differential field-modulated two-stage magnetic gear is introduced, using a unique radial differential linkage to integrate two single-stage magnetic gears. This design incorporates a magnetic isolation ring to enhance transmission efficiency by minimizing magnetic interference and preventing power circulation. The two-stage modulating ring rotor operates synchronously via a connecting bridge, ensuring system stability and efficiency. This configuration not only boosts the gear ratio but also maintains a compact structure, improving power density and efficiency. Leveraging the magnetic field modulation and differential transmission, a finite element model of this gear is developed and its electromagnetic performance is analyzed.

The torque density of the new radial differential field-modulated two-stage magnetic gear has increased by 44.97% compared to the traditional tandem two-stage magnetic gear. It achieves a high transmission ratio of 64 and maintains comparable power density, indicating strong torque transfer capabilities suitable for low-speed, high-torque applications. During steady-state operation, the torque pulsation difference between the two stages is minimal, ensuring stable working torque.

This research not only propels the forefront of magnetic gear technology through heightened efficiency and streamlined design but also bears profound societal significance in fostering sustainable energy paradigms. By facilitating superior energy conversion efficiencies in wind turbines and wave energy converters, it plays a pivotal role in mitigating carbon emissions and accelerating the global pivot towards cleaner, renewable energy landscapes.

A magnetic gear transmission device is proposed, which can achieve high power density and large transmission ratio at the same time, and this study provides a useful reference for the design optimization of new high-performance multistage magnetic gears.

The purpose of this paper is to evaluate doctoral candidates’ innovative ability tendency.

This study uses the theory of gray target contribution to analyze the influence degree of doctoral candidates’ individual personality factor toward their innovative ability and calculate gray impact quantitative values.

Based on the theory of contribution degree of gray target, a nine-factor model of innovative personality of doctoral candidates is built. IP=f (B, H, G, Q1, Q2, A, I, F, O), (therein: B – intelligence, H – social boldness, G – perseverance, Q1 – experimental, Q2 – independence, A – gregariousness, I – sensibility, F – excitability, O – anxiety).

This study based on gray target contribution theory builds nine-factor doctoral candidates’ innovative personality model to test the innovative ability tendency of doctoral candidates, which makes cultivating units, mentors and doctoral candidates to know their innovative ability tendency well, perfecting their own knowledge structure in time, effectively improving their innovative ability. The system can also be applied to the work of doctoral candidates as a reference tool to evaluate the innovative ability of applicants.

This study quantitatively evaluates the influence of doctoral candidates’ personality index on the tendency of doctoral candidates’ innovative ability.

Analyzing different scenarios at the design stage of construction projects has always been a challenging task. One of the main parameters that helps owners in making better decisions in designing their buildings is to look after the cost perspective on different design scenarios. Thus, this study aims to propose a semi-automated BIM-based cost estimation approach that enables practitioners to estimate the cost of projects based on different design scenarios by an accurate and agile system.

This study proposes an integrated framework, through which the cost estimation standard of Iran (FehrestBaha) is linked to the materials quantity take-offs (QTO) from BIM models. The performance of the system is based on connecting the classification standards of UniFormat and MasterFormat to the cost estimation standard of FehrestBaha. A BIM-based extension in the Revit environment is developed to automate the cost estimation process.

To evaluate the efficiency of the proposed approach in cost estimation, it is implemented to estimate the cost of the architectural discipline in a real construction project. The results indicate that the proposed BIM-based approach estimated the cost of the architectural discipline with an acceptable level of accuracy.

The proposed approach could be used by practitioners to have an agile and accurate BIM-based cost estimation of different scenarios during design process. The semi-automated system considerably reduces the time of cost estimation in comparison to the traditional manual approaches, particularly in complex structures. Owners are able to easily trace changes in project cost according to any changes in components and materials of the BIM model. Furthermore, the proposed approach provides a practical roadmap for BIM-based cost estimation based on cost estimation standards in different countries.

Unlike the traditional manual cost estimation approaches, the proposed BIM-based approach is not highly dependent on the knowledge of experienced estimators, which therefore facilitates its implementation. Furthermore, automating both QTO process and the required calculations in this approach increases the accuracy of cost estimation while decreasing the probability of human errors or omission occurrence.

This paper's aim is to investigate how Chinese consumers' purchase intention of private label drugs is associated with price advantage, appearance, store trust, manufacturer trust, and drug quality.

A survey study was conducted in two large cities in China. Subjects were asked to decide whether they would purchase a private label drug over a national brand drug and to evaluate a set of related factors. Data were collected from 230 consumers.

A total of 45 percent of the variance in private label drug purchase intention is explained by the five predictors. Price advantage, store trust, manufacturer trust, and drug quality are all significantly related to purchase intention, whereas appearance is not. None of the control variables (age, gender, health literacy, and income) has a significant relationship with purchase intention. Product quality and service quality are significant predictors of store trust, accounting for 44 percent of its variance.

The private label drug market has great potential in China, yet little is known about what factors influence Chinese consumers' intention to purchase private label drugs.

This paper is one of the first attempts to achieve an in‐depth understanding in this area. The findings of this research will benefit drug retailers and manufacturers who are interested in the Chinese market.