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Existing deterministic models that predict the capacity of corroded reinforced concrete (RC) beams have limited applicability because they were based on accelerated tests that induce general corrosion. This research gap was addressed by performing a combined numerical and statistical analysis on RC beams, subjected to natural corrosion, to achieve a much better forecast.

Data of 42 naturally corroded beams were collected from the literature and analyzed numerically. Four constitutive models and their combinations were considered: the elastic-semi-plastic and elastic-perfectly-plastic models for steel, and two tensile models for concrete with and without the post-cracking stresses. Meanwhile, Popovics’ model was used to describe the behavior of concrete under compression. Corrosion coefficients were developed as functions of corrosion degree and beam parameters through linear regression analysis to fit the theoretical moment capacities with test data. The performance of the coefficients derived from different combinations of constitutive laws was then compared and validated.

The results showed that the highest accuracy (R² = 0.90) was achieved when the tensile response of concrete was modeled without the residual stresses after cracking and the steel was analyzed as an elastic-perfectly-plastic material. The proposed procedure and regression model also showed reasonable agreement with experimental data, even performing better than the current models derived from accelerated tests and traditional procedures.

This study presents a simple but reliable approach for quantifying the capacity of RC beams under more realistic conditions than previously reported. This method is simple and requires only a few variables to be employed. Civil engineers can use it to obtain a quick and rough estimate of the structural condition of corroding RC beams.

The objective of the paper is to explore the out-of-sample forecasting connections in income growth across the globe.

An autoregressive distributed lag (ARDL) framework is employed and the forecasting performance is analyzed across several horizons using different forecast combination techniques.

Results show that the foreign country's income provides superior forecasts beyond what is provided by the country's own past income movements. Superior forecasting power is particularly held by Belgium, Korea, New Zealand, the UK and the US, while these countries' income is rather difficult to predict by global counterparts. Contrary to conventional wisdom, improved forecasts of income can be obtained even for longer horizons using our approach. Results also show that the forecast combination techniques yield higher forecasting gains relative to individual model forecasts, both in magnitude and the number of countries.

The forecasting paths of income movement across the globe reveal that predictive power greatly differs across countries, regions and forecast horizons. The countries that are difficult to predict in the short run are often seen to be predictable by global income movements in the long run.

Even while it is difficult to predict the income movements at an individual country level, combining information from the income growth of several countries is likely to provide superior forecasting gains. And these gains are higher for long-horizon forecasts as compared to the short-horizon forecast.

In evaluating the forward-looking social implications of economic policy changes, the policymakers should also consider the possible global forecasting connections revealed in the study.

Employing an ARDL model to explore global income forecasting connections across several forecast horizons using different forecast combination techniques.

Simulation-based optimisation (SO) is a popular optimisation approach for building and civil engineering construction planning. However, in the framework of SO, the simulation is continuously invoked during the optimisation trajectory, which increases the computational loads to levels unrealistic for timely construction decisions. Modification on the optimisation settings such as reducing searching ability is a popular method to address this challenge, but the quality measurement of the obtained optimal decisions, also termed as optimisation quality, is also reduced by this setting. Therefore, this study aims to develop an optimisation approach for construction planning that reduces the high computational loads of SO and provides reliable optimisation quality simultaneously.

This study proposes the optimisation approach by modifying the SO framework through establishing an embedded connection between simulation and optimisation technologies. This approach reduces the computational loads and ensures the optimisation quality associated with the conventional SO approach by accurately learning the knowledge from construction simulations using embedded ensemble learning algorithms, which automatically provides efficient and reliable fitness evaluations for optimisation iterations.

A large-scale project application shows that the proposed approach was able to reduce computational loads of SO by approximately 90%. Meanwhile, the proposed approach outperformed SO in terms of optimisation quality when the optimisation has limited searching ability.

The core contribution of this research is to provide an innovative method that improves efficiency and ensures effectiveness, simultaneously, of the well-known SO approach in construction applications. The proposed method is an alternative approach to SO that can run on standard computing platforms and support nearly real-time construction on-site decision-making.

The purpose of this paper is to examine employees’ knowledge creation processes by leveraging a conceptual framework based on the socialisation, externalisation, combination and internalisation (SECI) model introduced by Nonaka and Takeuchi (1995). Given that many employees work within teams, in the current study, the authors examine the impact that team-level trust and intrinsic motivation have on an employee’s SECI model and, in turn, the relationship between SECI model and individual creativity. As such, this work represents one of the first works to examine team-level factors that shape individual knowledge creation and creativity. Additionally, building on and extending previous SECI research, the authors develop a scale to measure SECI models that uses peer-rated assessments.

Data were collected from 431 employees who worked in 59 teams drawn from 51 companies in a variety of industry sectors, both SME’s and corporate. To minimise common method bias, the SECI model questionnaire was adapted to the individual level through peer ratings instead of self-ratings (each employee rated three peers). To assess the hypotheses, hierarchical linear models using IBM SPSS were applied. The questionnaires were completed using both paper and online versions.

Results showed that SECI mediates the relationships between individual-level creativity and both team-level intrinsic motivation and trust. Furthermore, findings suggest that the scale developed is a reliable measure of SECI.

Knowledge creation and sharing practices should take into account both, a team’s trust and its intrinsic motivation, which would result in creativity.

This paper examines the impact that team-level factors (i.e. team trust and team intrinsic motivation) have on individual SECI and creativity across a variety of industries. As such, this work is one of the first to examine the impact of team-level factors in shaping individual knowledge creation and creativity. Given the support that the study found for this hypothesis, this work demonstrates that team trust and intrinsic motivation are salient factors in shaping individual employee knowledge creation and creativity. Given the novelty of this work, the authors hope is that this study will be the foundation upon future cross-level studies of individual-level SECI and individual creativity can be built so as to improve SECI models.

The purpose of this article is to propose a standardization model that contributes to the creation of organizational knowledge in the paper industry. This study was oriented to answer the question: how to create organizational knowledge through the standardization model of the paper industry’s production system?

This research was applied in the main production unit of the paper organization. The data were collected through the analysis of documents, systems and routines of the researched unit. In the research, the observation technique and direct documentation were used. For the operationalization of the research, the following phases were carried: understanding of the applied standardization model, literature review on the research topics, formulation of a standardization model and application of the model.

A model of standardization of production processes that contributes to the creation of organizational knowledge, in which a correlation of all its stages with the Knowledge conversion modes was found and validated through an applied research in the industry.

This study is applied in a paper industry. In the survey, there is no comparison with other companies. The adaptation of the study in other industries and organizations can increase knowledge about the connection of standardized systems with knowledge conversion modes, adjusting them to other environments or other situations.

This study stands out for empirically testing, a standardization model that favors the creation of knowledge through the analysis of the various activities in a paper industry, providing a real connection between the knowledge management literature and the organizational environment. Standardization can represent an instrument of innovation in the most diverse types of industry, as long as it comes with a proposal for something new and better than the existing model.

Special purpose companies issue stocks to raise money to finance development of real estate and infrastructure. The advantage of a stock issue is that it does not entail financial cost such as interest on a loan. However, financing obtained in this way has been insufficient due to low interest by investors because of the large variability of the stocks’ earnings rates. The purpose of this paper is to propose methods to improve investment earnings rate for financing.

The proposed methods are Markowitz’s model and a combination of Markowitz’s model and Monte Carlo simulation. The proposed methods were verified by comparison with actual earnings rate.

The earnings rate was increased by as much as 23 percent over the actual value. Then, earnings rate compared with risk was analyzed using the Sharpe ratio which is a method to measure investment performance. The performance was also increased by as much as 23 percent over the actual value. The proposed method can help activate investment by increasing investors’ interest in the stock issue.

This study verified that Markowitz’s portfolio model, which is used for econometrics, could be applied for financing of construction project. It is valuable because the previous studies did not propose the method for financing.

To predict the passenger volume reasonably and accurately, this paper fills the gap in the research of quarterly data forecast of railway passenger volume. The research results can also provide references for railway departments to plan railway operation lines reasonably and efficiently.

This paper intends to establish a seasonal cycle first order univariate grey model (GM(1,1) model) combing with a seasonal index. GM (1,1) is termed as the trend equation to fit the railway passenger volume in China from 2014 to 2018. The railway passenger volume in 2019 is used as the experimental data to verify the forecasting effect of the proposed model. The forecasting results of the seasonal cycle GM (1,1) model are compared with the traditional GM (1,1) model, seasonal grey model (SGM(1,1)), Seasonal Autoregressive Integrated Moving Average (SARIMA) model, moving average method and exponential smoothing method. Finally, the authors forecast the railway passenger volume from 2020 to 2022.

The quarterly data of national railway passenger volume have a clear tendency of cyclical fluctuations and show an annual growth trend. According to the comparison of the modeling results, the authors know that the seasonal cycle GM (1,1) model has the best prediction effect with the mean absolute percentage error of 1.32%. It is much better than the other models, reflecting the feasibility of the proposed model.

As the previous grey prediction model could not solve the series prediction problem with seasonal fluctuation, and there are few research studies on quarterly railway passenger volume forecasting, GM (1,1) model is taken as the trend equation and combined with the seasonal index to construct a combination forecasting model for accurate forecasting results in this study. Besides, considering the impact of the epidemic on passenger volume, the authors introduce a disturbance factor to deal with the forecasting results in 2020, making the modeling results more scientific, practical and referential.

The aim of this paper is to assess the performance of different widely-adopted models to forecast Italian hotel occupancy. In particular, the paper tests the different models for forecasting the demand in hotels located in urban areas, which typically experience both business and leisure demand, and whose demand is often affected by the presence of special events in the hotels themselves, or in their neighborhood.

Several forecasting models that the literature reports as most suitable for hotel room occupancy data were selected. Historical data on occupancy in five Italian hotels were divided into a training set and a test set. The parameters of the models were trained and fine-tuned on the training data, obtaining one specific set for each of the five Italian hotels considered. For each hotel, each method, with corresponding best parameter choice, is used to forecast room occupancy in the test set.

In the particular Italian market, models based on booking information outperform historical ones: pick-up models achieve the best results but forecasts are in any case rather poor.

The main conclusions of the analysis are that the pick-up models are the most promising ones. Nonetheless, none of the traditional forecasting models tested appears satisfactory in the Italian framework, although the data collected by the front offices can be rather rich.

From a managerial point-of-view, the outcome of the study shows that traditional forecasting models can be considered only as a sort of “first aid” for revenue management decisions.

States that there is a need for a practical instrument to measure the present situation of work‐life balance. Describes the development process of the Family and Business Audit within the Flemish context. Details the setting up and aims of the system before outlining its application in some detail and other existing instruments also emploiyed. Provides a number of short case studies to demonstrate its effectiveness.

The purpose of this paper is to propose a system dynamic simulated process model for maintenance work management incorporating the Fourth Industrial Revolution (4IR) technologies.

The extant literature in physical assets maintenance depicts that poor maintenance management is predominantly because of a lack of a clearly defined maintenance work management process model, resulting in poor management of maintenance work. This paper solves this complex phenomenon using a combination of conceptual process modeling and system dynamics simulation incorporating 4IR technologies. A process for maintenance work management and its control actions on scheduled maintenance tasks versus unscheduled maintenance tasks is modeled, replicating real-world scenarios with a digital lens (4IR technologies) for predictive maintenance strategy.

A process for maintenance work management is thus modeled and simulated as a dynamic system. Post-model validation, this study reveals that the real-world maintenance work management process can be replicated using system dynamics modeling. The impact analysis of 4IR technologies on maintenance work management systems reveals that the implementation of 4IR technologies intensifies asset performance with an overall gain of 27.46%, yielding the best maintenance index. This study further reveals that the benefits of 4IR technologies positively impact equipment defect predictability before failure, thereby yielding a predictive maintenance strategy.

The study focused on maintenance work management system without the consideration of other subsystems such as cost of maintenance, production dynamics, and supply chain management.

The maintenance real-world quantitative data is retrieved from two maintenance departments from company A, for a period of 24 months, representing years 2017 and 2018. The maintenance quantitative data retrieved represent six various types of equipment used at underground Mines. The maintenance management qualitative data (Organizational documents) in maintenance management are retrieved from company A and company B. Company A is a global mining industry, and company B is a global manufacturing industry. The reliability of the data used in the model validation have practical implications on how maintenance work management system behaves with the benefit of 4IR technologies' implementation.

This research study yields an overall benefit in asset management, thereby intensifying asset performance. The expected learnings are intended to benefit future research in the physical asset management field of study and most important to the industry practitioners in physical asset management.

This paper provides for a model in which maintenance work and its dynamics is systematically managed. Uncontrollable corrective maintenance work increases the complexity of the overall maintenance work management. The use of a system dynamic model and simulation incorporating 4IR technologies adds value on the maintenance work management effectiveness.