# 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
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#### 4.1.3 Construction Productivity Influential Factors  
Precisely describing labor productivity in projects and identifying the factors influencing CLP is crucial for improving overall project performance [6]. In recent years, initiatives aimed at improving CLP have gained significant attention within the construction industry [27; 66]. According to [PERSON] and [PERSON] [45], CLP is influenced by a multitude of parameters, some with long-term effects, others with short-term or temporary impacts, and some with both long-term and ripple effects. Numerous studies have focused on either identifying and/or quantifying the impact of individual parameters as summarized into four main categories, as shown in Figure 13.  
When designing a productivity study, it is essential to consider not only the effect of parameters on productivity but also the number of parameters included in the model. A comprehensive understanding requires considering the interplay of these factors; hence, this is enabled with the aid of a ML mechanism. [PERSON] et al. [3] presented an engineering framework to simulate and enhance contract's CLP, encompassing a broad range  
Figure 13: Summary of CLP influential factors at the activity level.  
of influencing factors at the micro-level (activity level at a construction site). The links between CLP and the pertinent influential factors were quantified and mapped using ANN. In addition to ANN, [PERSON] and [PERSON] [15] conducted a sensitivity analysis to identify the determinants that have the greatest impact on the predictive capability of neural networks. The analysis revealed that the key influencing factors are worker motivation, suitable site layout, labor competence, poor decision-making, and proper planning.  
Addressing and improving these key factors has the potential to significantly enhance labor productivity in future projects. Additionally, [PERSON] and [PERSON] [45] ranked the determinants that impact the daily CLP of formwork installation operations on construction job sites, and the element that has the most impact on daily productivity is temperature. This was identified using three different variable selection techniques (Stepwise regression analyses, Neural Networks, and Fuzzy clustering). Despite several studies exploring and quantifying the relationship between CLP and influencing factors using ML algorithms having great results, not all of the above mentioned factors or other new parameters are yet to be explored. The rest of the parameters should be further analyzed to identify the most influential factors in CLP allowing relevant parties to take action accordingly.  
In terms of equipment-driven activities, construction equipment is a critical resource to drive productivity. Nevertheless, the analysis and identification of the factors influencing CEP in equipment-driven activities are limited [16; 85]. The identified factors influencing the CEP are summarized in Figure 14. As a result, the human factor was regarded as the most influential factor after the researchers conducted interviews with experts and questionnaire surveys. This is because generally, equipment are just tools operated by humans. However, there is clearly a shortage of research on the quantification of the correlation between CEP and their influencing factors.