# Application of Machine Learning in Construction Productivity at Activity Level: A Critical Review
## 3 Overview of the Application of Machine Learning in Construction Productivity Publications  
The trend of the quinquennial publications from 1990 to 2024 of journal papers related to ML-CP are depicted in Figure 2. It can be discerned that the quantity of papers exhibited a steady ascending trajectory. In addition, the significant boost in papers happened after 2014 when the amount was more than double and closely doubled again after 2019, resulting in a remarkable growth over the last decade. Therefore, this positive trend underscores the incessant importance of research in ML-CP. Figure 1: PRISMA sequence diagram for paper extraction. Using VOSviewer version 1.6.20, the mapping of the ML-CP research domain was acquired. The network visualization of the keywords was conducted to illustrate the results of the bibliometric analysis. A total of 55 out of the 1085 keywords met the threshold to be included as a result the after setting the minimum number of occurrences to four. This was carried out through several trials to acquire the optimal clusters. In relation to the publication trends, Figure 3 illustrates the evolution of ML-CP research. Generally, it is noticeable that during the last few decades, \"mathematical models\", \"construction labor productivity\", and \"estimation\" are tending to focus. General keywords such as \"construction\" and \"productivity\" are represented in the middle spectrum, which could be due to an emphasis on the topic throughout the whole period. Unsurprisingly, \"neural network\" has been found since the early timeframe indicating its constant presence in the literature [33; 34; 35]. However, the recent gravitation towards \"machine learning\" and \"deep learning\" plausibly indicates the shift of focus in construction productivity research from \"regression analysis\" and \"mathematical models\". As sensing technologies continue to advance, more data are becoming available. Naturally, the data processing techniques have also steadily progressed. Therefore, the active exploration of ML and DL approaches among researchers in the area of construction productivity is noticeable. For example, the measurement of productivity via vision [36], kinematic [37], and audio [38] adopted ML and DL algorithms for the extraction of productivity data. Figure 3: Timeline visualization of keyword relationships associated with ML-CP. Figure 2: Quinquennial publication trends on ML-CP. In terms of journal sources, with 133 identified papers, Table 2 outlined the top ten journals with the highest count of relevant publications. Within these journals, \"Automation in Construction\" (45 papers), \"Journal of Construction Engineering and Management\" (30 papers), \"Journal of Computing in Civil Engineering\" (15 papers), and \"Advanced Engineering Informatics\" (14 papers) demonstrated high publication volumes and are considered the top-ranked construction journals. Nevertheless, future studies can utilize this information as a reference when researchers submit their scholarly works. According to statistics, the first authors of the 133 selected works are affiliated with more than 25 different countries/regions. This demonstrated the fact that the area of ML-CP is under the influence of the international academic community, with scholars across the globe contributing knowledge to this domain. Figure 4 illustrates the top ten countries/regions' contributions of papers to the research field. Standing on the peak, the United States of America (USA) lead with 32 papers, followed by Canada with 26 papers. Generally, the high volume of papers produced indicates that the academic community and the construction industry in the USA and Canada are very concerned about increasing construction productivity. In addition, China (ten papers) ranked third, while Hong Kong (eight papers) and Australia (eight papers) came fourth, followed closely by the Republic of Korea and the United Kingdom (UK) at seven papers each.