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This study aims to investigate the combined use of System Dynamics (SD) applications in Enterprise Engineering (EE) research and practice. SD application in EE is becoming widely accepted as a tool to support decision-making processes and for capturing relationships within enterprises.

A systematic literature review (SLR) is conducted using a standard SLR method to provide a comprehensive review of existing literature. The search was conducted on ten platforms identifying 30 publications which were analysed through the use and development of a codebook.

The SLR showed that 90% of the result set consisted of peer-reviewed academic conferences and journal papers. The SLR identified a highly dispersed author set of 83 authors. Amongst these authors, Vinay Kulkarni was an active author who has co-authored up to four publications in this research area. The analysis further revealed that the combined use of SD applications and EE is an emerging research area that still needs to develop in maturity. While all phases of EE have received attention, the current research work is more focused on the design phase. The important gap between model development and implementation is identified.

The study elucidates the existing status of interdisciplinary research combining techniques from the SD and EE disciplines, suggesting future research topics that combine the strengths of these existing disciplines.

Simulation is a well-known technique for using computers to imitate or simulate the operations of various kinds of real-world facilities or processes. The facility or process of interest is usually called a system, and to study it scientifically, we often have to make a set of assumptions about how it works. These assumptions, which usually take the form of mathematical or logical relationships, constitute a model that is used to gain some understanding of how the corresponding system behaves, and the quality of these understandings essentially depends on the credibility of given assumptions or models, known as VV&A (verification, validation and accreditation). The main purpose of this paper is to present an in-depth theoretical review and analysis for the application of VV&A in large-scale simulations.

After summarizing the VV&A of related research studies, the standards, frameworks, techniques, methods and tools have been discussed according to the characteristics of large-scale simulations (such as crowd network simulations).

The contributions of this paper will be useful for both academics and practitioners for formulating VV&A in large-scale simulations (such as crowd network simulations).

This paper will help researchers to provide support of a recommendation for formulating VV&A in large-scale simulations (such as crowd network simulations).

Sustainable manufacturing is one of the most important and most challenging issues in present industrial scenario. With the intention of diminish negative effects associated with cutting fluids, the machining industries are continuously developing technologies and systems for cooling/lubricating of the cutting zone while maintaining machining efficiency. In the present study, three regression based machine learning techniques, namely, polynomial regression (PR), support vector regression (SVR) and Gaussian process regression (GPR) were developed to predict machining force, cutting power and cutting pressure in the turning of AISI 1045. In the development of predictive models, machining parameters of cutting speed, depth of cut and feed rate were considered as control factors. Since cooling/lubricating techniques significantly affects the machining performance, prediction model development of quality characteristics was performed under minimum quantity lubrication (MQL) and high-pressure coolant (HPC) cutting conditions. The prediction accuracy of developed models was evaluated by statistical error analyzing methods. Results of regressions based machine learning techniques were also compared with probably one of the most frequently used machine learning method, namely artificial neural networks (ANN). Finally, a metaheuristic approach based on a neural network algorithm was utilized to perform an efficient multi-objective optimization of process parameters for both cutting environment.

This paper aims to create a new searchable 3D city model to help managers improve their decision-making.

This paper identifies data management basics and the key elements used in the new model design; it further analyzes five-city models, presents its findings and proposes analytical trends for the new model. It discusses the concepts underlying existing models, explains the benefit brought by the proposed model and demonstrates its robustness.

City systems can be interconnected, thanks to data digitization and the integration of new technologies into different management processes. Although there are several 3D city models available, none of those identified in this research can be queried for several sectors.

This model design can only be successfully realized in the presence of a public mandate. Potential limitations include information security risks and political non-acceptance.

The present work proposes a searchable and high performance model having the distinctive capacity to bring together city systems and perform real-time data analysis in order to extract important information needed to guide the city, and in the context of a global vision.

This study reviews the extent of application of artificial intelligence (AI) tools in the construction industry.

A thorough literature review (based on 165 articles) was conducted using Elsevier's Scopus due to its simplicity and as it encapsulates an extensive variety of databases to identify the literature related to the scope of the present study.

The following items were extracted: type of AI tools used, the major purpose of application, the geographical location where the study was conducted and the distribution of studies in terms of the journals they are published by. Based on the review results, the disciplines the AI tools have been used for were classified into eight major areas, such as geotechnical engineering, project management, energy, hydrology, environment and transportation, while construction materials and structural engineering. ANN has been a widely used tool, while the researchers have also used other AI tools, which shows efforts of exploring other tools for better modelling abilities. There is also clear evidence of that studies are now growing from applying a single AI tool to applying hybrid ones to create a comparison and showcase which tool provides a better result in an apple-to-apple scenario.

The findings can be used, not only by the researchers interested in the application of AI tools in construction, but also by the industry practitioners, who are keen to further understand and explore the applications of AI tools in the field.

There are no studies to date which serves as the center point to learn about the different AI tools available and their level of application in different fields of AEC. The study sheds light on various studies, which have used AI in hybrid/evolutionary systems to develop effective and accurate predictive models, to offer researchers and model developers more tools to choose from.

Integrated project delivery (IPD) and building information modeling (BIM) have been discussed as prominent collaborative concepts in recent architecture, engineering and construction (AEC) literature, thus recommended for more advanced value creation. However, they have been studied predominantly as discrete even though they are typically highly interrelated. This study aims to enhance collaboration in AEC projects by tracing recent trends in IPD and BIM literature by making sense of trends and by exploring how their interplay has been discussed and conceptualized.

This systematic literature review draws on Scopus and Web of Science as the primary databases. In total, 120 academic papers and review articles were sourced. Yet, the final sample includes 71 sources from the past decade (2011–2020), focusing on both IPD and BIM.

This study identifies 11 interrelated and overlapping themes that are indicative of trends in the recent IPD and BIM literature. This research found that among the identified themes, the clusters of sustainability, transformation and increasing the competence level of staff in the AEC industry, in addition to the concept of quality, require more extensive research in the context of IPD and BIM. Additionally, this study identifies four different approaches to the interplay of IPD and BIM, indicating an absence of scholarly consistency.

Based on the systematic analysis of the recent literature, this study indicates that IPD and BIM have several joint fundamental cornerstones. It is evident that both concepts support the implementation of each other. The success of implementing either one is strongly related to the other. Additionally, we have not found earlier systematic literature reviews that examine the interplay between IPD and BIM in the recent AEC literature.

The purpose of this paper is to present a review of the implications building information modelling (BIM) is having on the building energy modelling (BEM) and design of buildings. It addresses the issues surrounding exchange of information throughout the design process, and where BIM may be useful in contributing to effective design progression and information availability.

Through review of current design procedures and examination of the concurrency between architectural and thermophysical design modelling, a procedure for information generation relevant to design stakeholders is created, and applied to a high-performance building project currently under development.

The extents of information key to the successful design of a buildings energy performance in relation to its architectural objectives are given, with indication of the level of development required at each stage of the design process.

BIM offers an extensible medium for parametric information storage, and its implementation in design development offers the capability to include BEM parameter-integrated construction information. The extent of information required for accurate BEM at stages of a building’s design is key to understanding how best to record performance information in a BIM environment.

This paper contributes to the discussion around the integration of concurrent design procedures and a common data environment. It presents a framework for the creation and dissemination of information during design, exemplifies this on a real building project and evaluates the barriers experienced in successful implementation.

The paper aims to raise awareness in the industry of design automation tools, especially in early design phases, by demonstrating along a case study the seamless integration of a prototypically implemented optimization, supporting design space exploration in the early design phase and an in operational use product configurator, supporting the drafting and detailing of the solution predominantly in the later design phase.

Based on the comparison of modeled as-is and to-be processes of ascent assembly designs with and without design automation tools, an automation roadmap is developed. Using qualitative and quantitative assessments, the potentials and benefits, as well as acceptance and usage aspects, are evaluated.

Engineers tend to consider design automation for routine tasks. Yet, prototypical implementations support the communication and identification of the potential for the early stages of the design process to explore solution spaces. In this context, choosing from and interactively working with automatically generated alternative solutions emerged as a particular focus. Translators, enabling automatic downstream propagation of changes and thus ensuring consistency as to change management were also evaluated to be of major value.

A systematic validation of design automation in design practice is presented. For generalization, more case studies are needed. Further, the derivation of appropriate metrics needs to be investigated to normalize validation of design automation in future research.

Integration of design automation in early design phases has great potential for reducing costs in the market launch. Prototypical implementations are an important ingredient for potential evaluation of actual usage and acceptance before implementing a live system.

There is a lack of systematic validation of design automation tools supporting early design phases. In this context, this work contributes a systematically validated industrial case study. Early design-phases-support technology transfer is important because of high leverage potential.

There is growing recognition that effective project control systems (PCS) are critical to the success of projects. The relationship between the individual elements of PCS and successfully achieving project objectives has yet to be explored. This research investigates the enablers and barriers that influence the elements of PCS success and drive project objectives.

This study adopts a mixed approach of descriptive analysis and regression models to explore the impact of six PCS elements on project outcomes. Petroleum and chemical projects in Saudi Arabia were selected as a case study to validate the research model.

Data from a survey of 400 project managers in Saudi’s petroleum and chemical industry reveal that successful PCS are the key to achieving all project outcomes, but they are particularly critical for meeting project cost objectives. Project Governance was identified as the most important of the six PCS elements for meeting project objectives. A lack of standard processes emerged as the most significant barrier to achieving effective project governance, while having skilled and experienced project team members was the most significant enabler for implementing earned value.

The study offers a direction for implementing and developing PCS as a strategic tool and focuses on the PCS elements that can improve project outcomes.

This research contributes to project management knowledge and differs from previous attempts in two ways. Firstly, it investigates the elements of PCS that are critical to achieving project scope, schedule and cost objectives; secondly, enablers and barriers of PCS success are examined to see how they influence each element independently.

Model-based systems engineering (MBSE) is an important approach for the transforming process from “document-centered” to “model centered” systems engineering mode in equipment development, which can effectively shorten the equipment development cycle and improve product design quality. This paper aims to understand if MBSE enables to support manufacturing and equipment development.

The paper opted a bibliometric analysis of MBSE in domain of advanced manufacturing from different perspectives such as publication volume, research team, sources and keyword co-occurrence.

Firstly, the application of MBSE in advanced manufacturing can be roughly divided into three stages. And MBSE has been widely implemented globally and has gradually formed several noteworthy teams. Secondly, this article has identified some high-quality sources, with a large number of publications and citations, the most influential publications focus on the practice or guidance of digital twins and intelligent manufacturing. Thirdly, research can be divided into six categories, including systems engineering, digitalization, intelligent manufacturing, product design, model and architecture and MBSE applications.

Because of the chosen research approach, the visualized network tends to lose certain information such as a few keywords may be inaccurately categorized.

This paper comprehensively study the research status of MBSE in advanced manufacturing and forecasts future research trends, emphasizing the combination of intelligent manufacturing and digitization.

This paper fulfills an identified need to understand the current application status and future development trends of MBSE.