Metaresearching Structural Engineering Using Text Mining: Trend Identifications and Knowledge Gap Discoveries

Ezzeldin, Mohamed; El‐Dakhakhni, Wael

doi:10.1061/(asce)st.1943-541x.0002523

Cited by 22 publications

(9 citation statements)

References 45 publications

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“…LDA may minimize the number of dimensions (i.e. words) substantially in a text while preserving the crucial ties between all dimensions and their key topics in the documents that follow (Blei et al , 2003; Ezzeldin and El-Dakhakhni, 2020). Many social scientists have used topic modeling, the LDA in their research (Choi and Seo, 2020).…”

Section: Methodsmentioning

confidence: 99%

Research trends analysis using text mining in construction management: 2000–2020

Bilge

Yaman

2021

ECAM

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PurposeThis study aims to identify the trends that have changed in the field of construction management over the last 20 years.Design/methodology/approachIn this study, 3,335 journal articles published in the years 2000–2020 were collected from the Web of Science database in construction management. The authors applied bibliometric analysis first and then detected topics with the latent Dirichlet allocation (LDA) topic detection method.FindingsIn this context, 20 clusters from cluster analysis were found and the topics were extracted in clusters with the LDA topic detection method. The results show “building information modeling” and “information management” are the most studied subjects, even though they have emerged in the last 15 years “building information modeling,” “information management,” “scheduling and cost optimization,” “lean construction,” “agile approach” and “megaprojects” are the trend topics in the construction management literature.Research limitations/implicationsThis study uses bibliometric analysis. The authors accept that the co-citation and co-authorship relationship in the data is ethical. They accept that honorary authorship, self-citation or honorary citation do not change the pattern of the construction management research domain.Originality/valueThere has been no study conducted in the last 20 years to examine research trends in construction management. Although bibliometric analysis, systematic literature reviews and text mining methods are used separately as a methodology for extracting research trends, no study has used enhanced bibliometric analysis and the LDA topic detection text mining method.

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Section: Methodsmentioning

confidence: 99%

Research trends analysis using text mining in construction management: 2000–2020

Bilge

Yaman

2021

ECAM

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“…Text mining is an approach that combines several fields of research and also several software tools. According to (Ignatow and Mihalcea, 2017), this is a technique that has begun to develop in social sciences such as anthropology, communication (Ezzeldin and El-Dakhakhni, 2020), economics (Levenberg, Pulman, Moilanen, Simpson and Roberts, 2014), education (Evison, 2013), and psychology (Sklad, Diekstra, Ritter, Ben and Gravesteijn, 2012). Social scientists have spent many decades studying transcribed interviews, newspaper articles, speeches, and other forms of textual data.…”

Section: Objectivesmentioning

confidence: 99%

A Text Mining Discovery of Similarities and Dissimilarities Among Sacred Scriptures

Peuriekeu¹,

Noyum²,

Feudjio³

et al. 2021

Preprint

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The careful examination of sacred texts gives valuable insights into human psychology, different ideas regarding the organization of societies as well as into terms like truth and God. To improve and deepen our understanding of sacred texts, their comparison and their separation is crucial. For this purpose, we use of our data set has nine sacred scriptures. This work deals with separation of the Quran, the Asian scriptures Tao-Te-Ching, the Buddhism, the Yogasutras and the Upanishads as well as the four books from the Bible, namely the Book of Proverbs, the Book of Ecclesiastes, the Book of Ecclesiasticus and the Book of Wisdom. These scriptures are analyzed based on the natural language processing NLP creating the mathematical representation of the corpus in terms of frequencies called document term matrix (DTM). After this analysis, machine learning methods like supervised and unsupervised learning are applied to perform classification. Here we use the Multinomial Naive Bayes (MNB), the Super Vector Machine (SVM), the Random Forest (RF) and the K-nearest Neighbors (KNN). We obtain that among these methods MNB is able to predict the class of a sacred text with an accuracy about 85.84 %.

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“…Accordingly, the current study adopts a meta-research (conducting research-on-published research) approach to first identify and classify the main research topics using text analytics (i.e., a machine learning tool that extracts meaningful information from textual data) and subsequently uncover the latent topics and the key research gaps (i.e., ill-covered areas) within the dam system and operational safety. Topic modeling is employed to identify latent topics in different fields such as transportation [20], operational research [21], city resilience [22], and structural engineering [23]. More specifically, this study adopted two stages to represent the meta-research on dam systems and reservoir operational safety.…”

Section: Introductionmentioning

confidence: 99%

Dam System and Reservoir Operational Safety: A Meta-Research

Badr,

Li,

El-Dakhakhni

2023

Water

Self Cite

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Dams are critical infrastructure necessary for water security, agriculture, flood risk management, river navigation, and clean energy generation. However, these multiple, and often conflicting, objectives introduce complexity in managing dam operations. In addition, dam infrastructure has been evolving as complex systems-of-systems with multiple interacting components and subsystems, all susceptible to a wide range of uncertainties. Such complexities and uncertainties have triggered extensive research initiatives focused on dam systems and reservoir operational safety. Focusing on the latter, this paper meta-researches (conducts research-on-research) previously published studies to identify the critical research gaps and propose future research directions. In this respect, this paper first performs a quantitative analysis of the pertinent literature, using text mining and subsequent topic modeling, to identify and classify major and uncover latent topics in the field. Subsequently, qualitative analysis is conducted to critically review the identified topics, exploring the concepts, definitions, modeling tools, and major research trends. Specifically, the study identified seven topics: optimization models; climate change; flood risk; inflow forecasting; hydropower generation; water supply management; and risk-based assessment and management. The study also presents three main research gaps associated with the limitations in modeling concepts, modeling tools capabilities, and the lack of resilience-guided management of dam operational safety. Overall, this study presents a road map of the currently available dam and reservoir operational safety research and associated knowledge gaps, as well as potential future research directions to ensure the resilience of such critically important infrastructure, especially in the age of climate change.

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Metaresearching Structural Engineering Using Text Mining: Trend Identifications and Knowledge Gap Discoveries

Cited by 22 publications

References 45 publications

Research trends analysis using text mining in construction management: 2000–2020

Research trends analysis using text mining in construction management: 2000–2020

A Text Mining Discovery of Similarities and Dissimilarities Among Sacred Scriptures

Dam System and Reservoir Operational Safety: A Meta-Research

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