A Review of Data Mining Strategies by Data Type, with a Focus on Construction Processes and Health and Safety Management

Antonella Pireddu (), Angelico Bedini, Mara Lombardi, Angelo L. C. Ciribini and Davide Berardi
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Antonella Pireddu: Department of Technological Innovations and Safety of Plants, Products and Anthropic Settlements (DIT), Italian National Institute for Insurance against Accidents at Work, Inail, 00144 Rome, Italy
Angelico Bedini: Department of Technological Innovations and Safety of Plants, Products and Anthropic Settlements (DIT), Italian National Institute for Insurance against Accidents at Work, Inail, 00144 Rome, Italy
Mara Lombardi: Department of Chemical Engineering Materials Environment (DICMA), Sapienza-University of Rome, 00184 Rome, Italy
Angelo L. C. Ciribini: Department of Civil Engineering, Architecture, Land, Environment and Mathematics (DICATAM), Brescia University, 25121 Brescia, Italy
Davide Berardi: Department of Chemical Engineering Materials Environment (DICMA), Sapienza-University of Rome, 00184 Rome, Italy

IJERPH, 2024, vol. 21, issue 7, 1-26

Abstract: Increasingly, information technology facilitates the storage and management of data useful for risk analysis and event prediction. Studies on data extraction related to occupational health and safety are increasingly available; however, due to its variability, the construction sector warrants special attention. This review is conducted under the research programs of the National Institute for Occupational Accident Insurance (Inail). Objectives: The research question focuses on identifying which data mining (DM) methods, among supervised, unsupervised, and others, are most appropriate for certain investigation objectives, types, and sources of data, as defined by the authors. Methods: Scopus and ProQuest were the main sources from which we extracted studies in the field of construction, published between 2014 and 2023. The eligibility criteria applied in the selection of studies were based on the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA). For exploratory purposes, we applied hierarchical clustering, while for in-depth analysis, we used principal component analysis (PCA) and meta-analysis. Results: The search strategy based on the PRISMA eligibility criteria provided us with 63 out of 2234 potential articles, 206 observations, 89 methodologies, 4 survey purposes, 3 data sources, 7 data types, and 3 resource types. Cluster analysis and PCA organized the information included in the paper dataset into two dimensions and labels: “supervised methods, institutional dataset, and predictive and classificatory purposes” (correlation 0.97–8.18 × 10 −1 ; p -value 7.67 × 10 −55 –1.28 × 10 −22 ) and the second, Dim2 “not-supervised methods; project, simulation, literature, text data; monitoring, decision-making processes; machinery and environment” (corr. 0.84–0.47; p -value 5.79 × 10 −25 –-3.59 × 10 −6 ). We answered the research question regarding which method, among supervised, unsupervised, or other, is most suitable for application to data in the construction industry. Conclusions: The meta-analysis provided an overall estimate of the better effectiveness of supervised methods (Odds Ratio = 0.71, Confidence Interval 0.53–0.96) compared to not-supervised methods.

Keywords: clustering; principal component analysis (PCA); meta-analysis; construction industry; data mining; machine learning; prediction models; workplace safety; smart technology (ST); state of the art. (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2024
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