# Application of Machine Learning in Construction Productivity at Activity Level: A Critical Review
## 2 Research Methodology  
The research findings were examined using a mixed-review methodology which incorporated both bibliometric analysis and qualitative (systematic review) techniques. The rationale behind the selected approach is that bibliometric analysis provides a statistical outlook of the relevant previous literature, thus enabling the mapping of the inter-relationships (publications, authors, countries, and research themes) in specific research areas [25]. Meanwhile, a systematic review is commonly utilized to analyze the main outcomes of various studies or to discern patterns in the knowledge framework of the literature [26]. Relevant works of literature related to construction productivity and ML were extracted from the Scopus database since it is among the largest online academic databases and a commonly applied search engine [27; 28]. In addition, this review focuses on peer-reviewed journals as the Scopus database has extensive citation records and wider literature coverage in comparison to Web of Science [29]. To achieve a comprehensive review, the timeframe of the papers was determined to be from 1990 to 2024. This is because construction productivity, particularly labor productivity, has been a popular research area for the last couple of decades [30; 31]. With the advancement of construction technology and information technology, as well as artificial intelligence, there is a significant increase in research working towards automation in construction productivity measurement, as well as estimation. Subsequently, considering the need to produce an evidence-based outcome, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) process was selected as the literature selection process is transparent, elevating the quality of this review [32].  
According to PRISMA guidelines, the extraction of papers from the database is performed using the following categories: identification, screening, eligibility, and included. Figure 1 shows the paper extraction process via the PRISMA sequence diagram.  
In step one, an extensive search was carried out within \"titles, abstract, keywords\" from the Scopus database. The following rule was applied to the chosen keywords: (\"construction productivity\") AND (\"machine learning\" OR \"neural network\" OR \"artificial intelligence\"). The initial search resulted in 799 papers. In step two, a total of 641 papers were excluded. The filtering of papers by restricting to subject areas (\"engineering\", \"computer science\", \"business\", \"management and accounting\", \"social sciences\", \"environmental science\", \"mathematics\", \"energy\", \"multidisciplinary\", \"decision sciences\", \"arts and humanities\", \"economics, econometrics and \"finance\"), reviewed academic publications in English, and review journals was carried out. Subsequently, the title, keywords, and abstracts were thoroughly scrutinized to ensure alignment with the research direction. Twenty-seven publications were eliminated in step three after a full-text review, as the papers were not pertinent to the research topic. By identifying records from reference and citation searches, two additional papers, which are closely related to the research topic but previously not targeted were added. Eventually, 133 papers were deemed relevant for subsequent bibliometric analysis and systematic review.