Application of Linguistic Clustering to Define Sources of Risks in Technical Projects

Kifokeris, Dimosthenis; Xenidis, Yiannis

doi:10.1061/ajrua6.0000938

Cited by 6 publications

(4 citation statements)

References 96 publications

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“…For classifying accident reports, supervised learning and unsupervised learning methods were both used in previous studies. For example, Kifokeris and Xenidis [ 39 ] applied an unsupervised clustering algorithm on a vast risk notion set to deduce risk sources of a project. Chi et al.…”

Section: Application Areas Of Tmmentioning

confidence: 99%

Overview and analysis of the text mining applications in the construction industry

Yan

et al. 2022

Heliyon

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Section: Application Areas Of Tmmentioning

confidence: 99%

Overview and analysis of the text mining applications in the construction industry

Yan

et al. 2022

Heliyon

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“…To identify construction accidents and their causes, previous studies have proposed knowledge management systems using NLP techniques such as rule-based and conditional random field methods and deep learning techniques [44,45]. Similar studies have classified and identified not only types of job hazards and site accidents but also sources of project risk and human errors based on unsupervised machine-learning techniques (e.g., symbiotic gated recurrent unit (SGRU) and support vector machine (SVM)) [34,[46][47][48]. In a recent study, D'Orazio et al [49] developed a maintenance severity ranking system that supports decision making regarding prioritization of end-user maintenance requests.…”

Section: Text-mining Approachesmentioning

confidence: 99%

Identification of Occupant Dissatisfaction Factors in Newly Constructed Apartments: Text Mining and Semantic Network Analysis

Noh,

Jo,

Han

et al. 2023

Buildings

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With apartment buildings representing a rapidly growing share of the residential market in South Korea, the effect of construction defects throughout the life cycle of construction projects, and particularly during the occupancy stage, has emerged as a significant social issue that may ultimately lead to an increase in defect disputes between new occupants and general contractors. An important step toward mitigating the likelihood of these defect disputes is to identify and address the factors that give rise to occupant dissatisfaction during the defect repair process. However, a reliable method by which to identify these factors has yet to be developed. In this respect, the main objective of the research presented in this paper is to develop a method for identifying occupant dissatisfaction factors in the construction defect repair stage. The developed method comprises the following procedures: (i) text pre-processing, which involves data cleaning, normalization, tokenization, morphological analysis, and removal of stopwords; (ii) term frequency–inverse document frequency for keyword extraction; and (iii) semantic network analysis to recognize relationships between words. The method was implemented using a dataset of 12,874 comments in Korean text format obtained from apartment building occupants. Based on the processing and analysis of this dataset, the occupant dissatisfaction factors were found to be: (i) inaccurate and inadequate repair work (represented by such keywords as “Repair”, “Visit”, and “Accuracy”); (ii) failure to keep promises (e.g., “Fulfillment”, “Promise”, and “Change”); and (iii) unprofessional conduct on the part of representatives in the repair service center (e.g., “Response”, “Attitude”, and “Receipt”).

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“…SML has even been tested to predict construction costs [38] and the attributes of structural materials [39]. There are far fewer notable examples of UML deployment, such as in aiding design integration by implementing construction knowledge and experience [40], and deriving construction project risk sources [41]. Moreover, HML implementation has been relatively scarce (e.g.…”

Section: Machine Learning Modelling Within the Construction Sectormentioning

confidence: 99%

“…[25]). There have been mixed systems, utilizing SML and UML either complementarily or interchangeably, such as cooling control systems in office buildings [42], and the use of the results of [41] in a SML system appraising the constructability of technical projects [43,44].…”

Section: Machine Learning Modelling Within the Construction Sectormentioning

confidence: 99%

Building Information Models’ Data for Machine Learning Systems in Construction Management

Kifokeris¹,

Roupé²,

Johansson³

et al. 2019

Proceedings of the Creative Construction Conference 2019

Self Cite

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Qualitative and quantitative data are important in construction management. However, despite the capabilities of construction informatics, such data and its sources have scarcely been fully and systematically utilized for predictive purposes. Building Information Models (BIM) are such a data source. Within BIM, information structures enabling interoperability and providing utilizable data throughout the various Levels of Development (LODs) of a buildingfor example, Industry Foundation Classes (IFCs)can be fully and meaningfully exploited through data mining, and more particularly, via machine learning. In this paper, the capabilities of the information structures found in IFCs to be used as data sources for developing machine learning predictive models, will be examined. In addition, and by conceptually tying such data with constructability, their suitability for predictingthrough such machine learning modelsthe delivery cost and time overheads of a construction project, will be considered.

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Application of Linguistic Clustering to Define Sources of Risks in Technical Projects

Cited by 6 publications

References 96 publications

Overview and analysis of the text mining applications in the construction industry

Overview and analysis of the text mining applications in the construction industry

Identification of Occupant Dissatisfaction Factors in Newly Constructed Apartments: Text Mining and Semantic Network Analysis

Building Information Models’ Data for Machine Learning Systems in Construction Management

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