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The purpose of this paper is to introduce a decision support aid for deciding an overseas construction project (OCP) using an adaptive neuro fuzzy inference system (ANFIS).

This study presents an ANFIS approach as a decision support aid for assessment of OCPs. The processing data were derived from 110 simulation cases of OCPs. In total, 21 international factors observed from a Delphi survey were determined as assessment variables to examine the cases. The experts were involved to evaluate and judge whether the company should Go or Not Go for an OCP, based on the different parameter scenarios given. To measure the performance of the ANFIS model, root mean square error (RMSE) and coefficient of correlation (R) were employed.

The result shows that optimum ANFIS model indicating RMSE and R scores adequately near between 0 and 1, respectively, was obtained from parameter set of network algorithm with two input membership functions, Gaussian type of membership function and hybrid optimization method. When the model tested to nine real OCPs data, the result indicates 88.89 percent accurate.

The use of simulation cases as data set in development the model has several advantages. This technique can be replicated to generate other case scenarios which are not available publicly or limited in terms of quantity.

This study evidences that the developed ANFIS model can predict the decision satisfactorily. Therefore, it can help companies’ management to make preliminary assessment of an OCP.

The steel construction market has undergone gradual development in the past decades given its profound impacts on environment, economy and society. The purpose of this paper is to facilitate a better understanding of the major drivers and issues behind the market development of the steel construction industries around the world.

A three-step desktop research was conducted to select relevant research outputs published in the past 20 years. The research methodology in conducting these studies and their research trends were analyzed. Then the potential influencing factors for the market development of steel construction were identified through a content analysis of the selected studies.

A total of 59 articles were identified accordingly. These influencing factors were divided into five main themes: contextual, institutional, industrial, project-related and individual factors. In terms of the frequencies of these factors appeared in previous studies, “continuous development of standards, codes, and specifications” and “advance in product and process technology” were the top two driving forces in the market development of steel construction, while “cost issues” was the most frequently reported obstacle.

The study takes an initiative to establish a practical classification framework that can be dedicated to illuminating the critical issues or success factors affecting the development of the steel construction market. This framework can help policymakers, industry practitioners and researchers achieve sustaining success in steel construction in the developed, emerging and inactive markets.

The highest number of accidents in proportion to the employment rate is found in construction industry among all industries in Pakistan. The purpose of this paper is to identify and prioritize the contributory factors of accident causation that can significantly reduce the rate of accident in the construction industry.

In total, 32 contributory factors of accident causation were identified through a triangulation strategy comprising eight face-to-face semi-structured interviews with the academic and industry experts coupled with a comprehensive literature review. Delphi survey was then conducted among the four respondent groups (clients, contractors, safety official and academia) to prioritize these factors. A consensus was achieved among the respondent groups after conducting two rounds of Delphi survey. Finally, the results were validated using the technique of inter-rater agreement (IRA) analysis.

All the shortlisted accident causation factors were graded as “important” to “extremely important”. Moreover, a “moderate” to “strong level” agreement was developed among the respondent groups. The three most significant factors were highlighted as “poor enforcement of safety rules and regulations by the Government agencies”, “insufficient allocation of safety budget and safety incentives by the client”, and “insufficient provision of safety training and resources by the contractor”.

The findings will help the key stakeholders to prioritize their energies towards achieving zero accident in the construction industry. Moreover, addition of academic experts as one of the respondent groups will enhance the linkages between the academia and the industry practitioners.

Besides highlighting the underlying causes of construction accidents in Pakistan, a detailed methodology is presented in this study for the analysis and validation of the Delphi survey data, which can be extrapolated in other regions and industries for elements prioritization. The findings of the study can also be generalized for other developing countries having similar work environment. The results validation through the use of IRA analysis is an addition to the field of construction safety research. The study also authenticates the applicability of IRA analysis to assess the agreement level among the respondents.

This chapter explores a descriptive theory of multidimensional travel behaviour, estimation of quantitative models and demonstration in an agent-based microsimulation.

A descriptive theory on multidimensional travel behaviour is conceptualised. It theorizes multidimensional knowledge updating, search start/stopping criteria and search/decision heuristics. These components are formulated or empirically modelled and integrated in a unified and coherent approach.

The theory is supported by empirical observations and the derived quantitative models are tested by an agent-based simulation on a demonstration network.

Based on artificially intelligent agents, learning and search theory and bounded rationality, this chapter makes an effort to embed a sound theoretical foundation for the computational process approach and agent-based micro-simulations. A pertinent new theory is proposed with experimental observations and estimations to demonstrate agents with systematic deviations from the rationality paradigm. Procedural and multidimensional decision-making are modelled. The numerical experiment highlights the capabilities of the proposed theory in estimating rich behavioural dynamics.

This paper aims to study the influence of aerodynamics force of trains passing each other on the dynamic response of vehicle bridge coupling system based on numerical simulation and multi-body dynamics and put forward the speed threshold for safe running of train under different crosswind speeds.

The computational fluid dynamics method is adopted to simulate the aerodynamic force in the whole process of train passing each other by using dynamic grid technology. The dynamic model of vehicle-bridge coupling system is established considering the effects of aerodynamic force of train passing each other under crosswind, the dynamic response of train intersection on the bridge under crosswind is computed and the running safety of the train is evaluated.

The aerodynamic force of trains' intersection has little effects on the derailment factor, lateral wheel-rail force and vertical acceleration of train, but it increases the offload factor of train and significantly increases the lateral acceleration of train. The crosswind has a significant effect on increasing the derailment factor, lateral wheel-rail force and offload factor of train. The offload factor of train is the key factor to control the threshold of train speed. The impact of the aerodynamic force of trains' intersection on running safety cannot be ignored. When the extreme values of crosswind wind speed are 15 m·s⁻¹, 20 m·s⁻¹ and 25 m·s⁻¹, respectively, the corresponding speed thresholds for safe running of train are 350 km·h⁻¹, 275 km·h⁻¹ and 200 km·h⁻¹, respectively.

The research can provide a more precise numerical method to study the running safety of high-speed trains under the aerodynamic effect of trains passing each other on bridge in crosswind.

Today’s marketplace has witnessed intense competitive pressures and high levels of uncertainty and disruption. Therefore, supply chains require agility to obtain a sustainable competitive advantage and cope with uncertainties as well as disruptions. Although a wide range of studies exists on supply chain agility (SCA) from the perspective of antecedents or consequences, there is little research on the investigation of enablers of SCA and their relations among them. Furthermore, the literature has investigated proactive and reactive enablers for enhancing SCA, but most studies have not sufficiently framed their analysis of both aspects synchronically. This paper aims to find out the interrelationships among the proactive and reactive enablers for enhancing SCA.

An extensive literature review has been conducted to identify SCA enablers and a Delphi study has been performed to elucidate SCA enablers in the manufacturing industry in Turkey. Interpretive structural modeling (ISM) has been used to identify the contextual relationship among the SCA enablers, and the model has been validated based on Matriced Impact Croises Multiplication Appliquee a un Classement (MICMAC) analysis.

On theoretical and practical levels, the proposed ISM model in this study can help organizations analyze and interpret interrelationships among enablers of SCA. For managers, it can provide better insights and understanding of the facilitators of SCA to enhance the effectiveness of the supply chain and cope with uncertainties and turbulence. According to results, enhancing “supply and demand side competency”, “delivery speed” and “strategic sourcing” are the most significant enablers of SCA.

The study extends the existing literature related to the enablers of SCA by modeling the proactive and reactive enablers of SCA based on the Al Humdan et al. (2020) classification. Arranging the enablers of SCA in a hierarchy and classifying the enablers into different levels with the help of the ISM-MICMAC approach is an exclusive effort to achieve successful management of the supply chain.

The present study investigates the effect of foreign direct investment (FDI) and governance quality on carbon emissions in the Economics Community of West African States (ECOWAS).

To achieve the objective of this research, panel data for dependent and explanatory variables over the period 2005–2016, collected in the World Development Indicators (WDI) database and World Governance Indicators (WGI), are analyzed using the generalized method of moments (GMM). Also, the panel-corrected standard errors (PCSE) method is applied to the four segments of the overall sample to analyze the stability of the results.

The findings of this study are: (1) FDI inflows have a negative effect on carbon emissions in ECOWAS and (2) The interaction between FDI inflows and governance quality have a negative effect on carbon emissions. These results show the decreasing of environmental damage by increasing institutional quality. However, the estimation results on the country subsamples show similar and non-similar aspects.

This study suggests that policymakers in the ECOWAS countries should strengthen their environmental policies while encouraging FDI flows to be environmentally friendly.

The subject has rarely been explored in West Africa, with gaps such as the lack of use of institutional variables. This study contributes to the literature by drawing on previous work to examine the role of good governance on FDI and the CO2 emission relationship in the ECOWAS, which have received little attention. However, this research differs from previous work by subdividing the overall sample into four groups to test the stability of the results.

The purpose of this paper is to briefly summarize and review the theories and methods of complex structures’ dynamic reliability. Complex structures are usually assembled from multiple components and subjected to time-varying loads of aerodynamic, structural, thermal and other physical fields; its reliability analysis is of great significance to ensure the safe operation of large-scale equipment such as aviation and machinery.

In this paper for the single-objective dynamic reliability analysis of complex structures, the calculation can be categorized into Monte Carlo (MC), outcrossing rate, envelope functions and extreme value methods. The series-parallel and expansion methods, multi-extremum surrogate models and decomposed-coordinated surrogate models are summarized for the multiobjective dynamic reliability analysis of complex structures.

The numerical complex compound function and turbine blisk are used as examples to illustrate the performance of single-objective and multiobjective dynamic reliability analysis methods. Then the future development direction of dynamic reliability analysis of complex structures is prospected.

The paper provides a useful reference for further theoretical research and engineering application.

The digital revolution has brought many challenges and opportunities for the manufacturing firms. The impact of Industry 4.0 technology adoption on sustainable manufacturing and circular economy has been under-researched. This paper aims to review the latest articles in the area of Industry 4.0, sustainable manufacturing and circular economy and further developed a research framework showing key paths.

Qualitative research is performed in two stages. In the first stage, a review of the extant literature is performed to identify the barriers, drivers, challenges and opportunities. In the second stage, a research framework is proposed to integrate Industry 4.0 technology (big data analytics powered artificial intelligence) adoption, sustainable manufacturing and circular economy capabilities.

This research extends the knowledge base by providing a detailed review of Industry 4.0, sustainable manufacturing, and circular economy and proposes a research framework by integrating these three contemporary concepts in the context of supply chain management. Through an exploration of this integrative research framework, the authors propose a future research agenda and seven research propositions.

It is important to understand the interplay between institutional pressures, tangible resources and human skills for Industry 4.0 technology (big data analytics powered artificial intelligence) adoption. Industry 4.0 technology (big data analytics powered artificial intelligence) adoption can positively influence sustainable manufacturing and circular economy capabilities. Managers must also put more attention to sustainable manufacturing to develop circular economic capabilities.

Factory workers and the local communities generally suffer from various adverse effects resulting from the traditional manufacturing process. The quality of the environment is deteriorating to such an extent that people even staying miles away from the factory are also affected due to environmental pollution that is generated from factory operations. Hence, sustainable manufacturing is the only choice left to manufacturers that can help in the transition to a circular economy. The research framework can help firms to enhance circular economy capabilities.

This review paper contains the most updated work on Industry 4.0, sustainable manufacturing and circular economy. It also proposes a research framework to integrate these three concepts.

This paper aims to examine the effect of eXtensible Business Reporting Language (XBRL) adoption on financial reporting quality at the country-level (developing and developed countries).

This study uses data from 98 developed and developing countries between 2005 and 2018. This study collected data from various sources such as the World Economic Forum, World Development Indicators, World Governance Indicators and XBRL website.

The results show that XBRL is associated with an increased financial reporting quality. However, the relationship is stronger in developing countries than in developed countries. This study also finds that the results remain the same after accounting for years of XBRL experience and the effect of accounting globalisation. The results are consistent with the assumption that XBRL-formatted financial statements improve information efficiency through increased searching efficiency, quality of display and comparability. The results are robust to alternative econometric modifications such as controlling for country, year effects and endogeneity.

The results can potentially assist the XBRL promoters and regulators in expeditiously assessing the benefits of XBRL and advocating its adoption by many countries. The findings offer more motivations for regulators around the world to mandate this new filing standard format.

This study contributes to the literature by providing empirical evidence on the consequences of XBRL at the country level. This study provides evidence on an important question of whether the XBRL, new information technology in the accounting field, can play a useful role in improving financial reporting.