A Scientometric Review of System Dynamics Applications in Construction Management Research

Wu, Zezhou; Yang, Kaijie; Lai, Xiaofan; Antwi‐Afari, Maxwell Fordjour

doi:10.3390/su12187474

Cited by 39 publications

(28 citation statements)

References 81 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The scientometric analysis is a technique that uses mathematical formulas and visualization to discover structural patterns and trace major research boundaries, allowing for a more comprehensive yet concise capture and mapping of scientific knowledge [82,83]. Several authors have used this approach across research areas such as green building [84,85], system dynamics applications [86], building information modeling [87], public-private partnership [88], and sustainability [82,89].…”

Section: Methodsmentioning

confidence: 99%

Research Trends on Machine Learning in Construction Management: A Scientometric Analysis

Van

Quoc

2021

JASTT

View full text Add to dashboard Cite

Machine learning plays a vital role in construction industry which could make improve project’s safety, productivity, and quality. Many studies have attempted to explore the potential opportunities to adopt this technology in different aspects of the construction sector. However, no comprehensive study to review the global research trends on this technological advancement in construction management domain. The goal is to investigate and summarize the state-of-the-art knowledge body in this topic in a systematic manner. To achieve this, this paper considered 161 studies on machine learning in construction management related to bibliographic records retrieved from the Scopus database by adopting scientometric analysis approach. This paper found that since 2014, there has been a considerable increase in the number of publications on this domain. Researchers from the United States, China, and Australia have been the main contributors to this research area through regional analysis. This study also revealed that approximately 34% of all countries in the world are engaged in this domain research. In addition, five main aspects in construction management have been applied machine learning techniques, namely, assess and reduce risk, safety management for construction sites, cost estimation and prediction, Schedule management, and building energy demand prediction. Furthermore, three potential construction management research areas that can apply this technology were proposed for further studies. The findings will help both professionals and researchers more understanding how machine learning knowledge is evolving and its role played in the construction management domain, and this study thus offers a useful reference point to how can develop this area in the future.

show abstract

Section: Methodsmentioning

confidence: 99%

Research Trends on Machine Learning in Construction Management: A Scientometric Analysis

Van

Quoc

2021

JASTT

View full text Add to dashboard Cite

show abstract

“…Based on Park's work, Ansari [28] compared alternative change management policies, including funding, outsourcing activities, schedule adjustments, and labor control by taking into account the time, cost, quality, resources, and financial indicators. An extensive review on schedule changes using the SD model can be found in a previous publication [29]. However, in the SD model, upstream changes are immediately accommodated by changing downstream tasks through feedback loops, without considering a chain reaction of changes.…”

Section: Literature Reviewmentioning

confidence: 99%

Risk Propagation Model and Simulation of Schedule Change in Construction Projects: A Complex Network Approach

et al. 2020

View full text Add to dashboard Cite

Construction schedules play an important role in construction project management. However, during construction activities, risks may arise due to unexpected schedule changes, resulting in the ineffective delivery of projects. This study aims to reveal the law of schedule change risk propagation and to analyze the effects on the risk propagation through numerical simulations. First, construction projects are represented by activity-on-node (AON) networks. A model of risk propagation is then built based on a susceptible-infected (SI) model considering the effects of the nodal characteristics on the propagation process. Next, the model is tested on a real-world project to examine cascading failures with varying parameters. The experimental results demonstrate that the model is effective in identifying the activities most capable of affecting a project schedule and evaluating the impact of schedule change risk propagation. This study will provide a basis for enhancing the robustness of AON networks and controlling the propagation of schedule change risks.

show abstract

“…On the other hand, scientometric analysis is being used by a growing number of scholars to address subjective problems in literature reviews [34][35][36][37]. In contrast, there is a paucity of scientometric analysis in the current studies that explore trends of BIM applications in the PDR projects field.…”

Section: Introductionmentioning

confidence: 99%

A Bibliometric Analysis and Review of Building Information Modelling for Post-Disaster Reconstruction

et al. 2021

View full text Add to dashboard Cite

Post-disaster reconstruction (PDR) is a dynamic, complex system that is chaotic in nature, and represents many challenges and issues. Recently, building information modelling (BIM) has been commonly utilized in the construction industry to solve complex and dynamic challenges. However, BIM has not been thoroughly considered for managing PDR, and there is a lack of comprehensive scientometric analyses that objectively examine the trends in BIM applications in PDR. A literature search was performed considering studies published from 2010 to March 2021 using the Scopus database. A total of 75 relevant studies were found to meet the inclusion criteria. The collected literature was analyzed using VOSviewer through scientific journals, authors, keywords, citations, and countries. This is the first study in its vital significance and originality that aims to investigate the current states of research on BIM applications in PDR and provide suggestions for potential research directions. The findings showed that “Reconstruction” and “Safety Management” have emerged as mainstream research themes in this field and recently attracted scholars’ interest, which could represent the directions of future research. Five major research domains associated with BIM were identified based on the most frequently used keywords, namely “Disasters”, “Earthquakes”, “HBIM”, “Damage Detection”, and “Life Cycle”. Moreover, a proposed conceptual framework of BIM adoption for PDR is provided. Accordingly, the outcomes of this study will help scholars and practitioners gain clear ideas of the present status and identify the directions of future research.

show abstract

A Scientometric Review of System Dynamics Applications in Construction Management Research

Cited by 39 publications

References 81 publications

Research Trends on Machine Learning in Construction Management: A Scientometric Analysis

Research Trends on Machine Learning in Construction Management: A Scientometric Analysis

Risk Propagation Model and Simulation of Schedule Change in Construction Projects: A Complex Network Approach

A Bibliometric Analysis and Review of Building Information Modelling for Post-Disaster Reconstruction

Contact Info

Product

Resources

About