# 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
### Dataset Acquisition
#### 4.1.1 The Traditional Approach Measurement of Construction Productivity  
A number of studies (e.g., regarding formwork labor productivity [23] and piling works [24; 43]) were conducted on monitoring, gauging, and estimating CP using a variety of traditional approaches, as shown in Figure 9.  
Figure 8: Framework for analyzing ML-CPM at the activity level. Figure 9: Traditional approaches of dataset acquisition. Direct observation has been widely adopted by researchers as it is a cost-effective approach to measure the work outputs [23; 41; 44] and the inputs do not require much processing [23; 45]. However, the activities in construction are plenty and complex and the surroundings are ever changing. Although, the tools (e.g., data sheets and time sheets) for documenting the productivity inputs are customizable and allow one to cater to the documentation of productivity information [23; 46]. The definition of work sampling is \"a technique in which a large number of observations are made over a period of time of labor, equipment, or processes to facilitate quantitative analysis of an activity\" [47]. Developed in 1927, it was a popular tool for work measurement to control inputs and it is an evolution from direct observation. This is mainly due to the fact that observation can cater to only a handful of workers for each moment [44; 48]. Meanwhile, an evolution and extension of work sampling and activity analysis is where both of these methods depend on instantaneous or direct observation [44; 47]. However, according to [PERSON] et al. and [PERSON] et al. [44; 49], work sampling and activity analysis are more towards productivity analysis and improvement tools, rather than productivity measurement methods. Daily Work Reports (DRWs) are a comprehensive, inexpensive, and reliable source of input and have served as a popular source of past data. DWRs generally contain records of environmental conditions (i.e., rainfalls, job-site conditions, weather, ground conditions, production rates, facilities costs, labor, and equipment work hours) [50; 51]. Consequently, data extraction from the report is required to be carried out to obtain the desired productivity inputs in a standardized format, as shown in Figure 10, to be useable for training ML models for productivity estimation. In addition, [PERSON] et al. [52] proposed a two-tier algorithm to optimize data extraction, as manual extraction requires much effort. Despite being a great source of productivity input, DWRs may include unavoidable human errors because observations and documentation are done by humans [52]. Figure 10: Illustration of the data extraction from DWRs [52]. Furthermore, questionnaire surveys and interviews are used to collect productivity in quantitative form (e.g., cycle time, work hours, and number of resources) and qualitative form (e.g., productivity for certain soil conditions and productivity influential factors), respectively. Interviews are considered direct data collection, where many methods can be used to fill the forms (e.g., site interviews, telephone calls, and site visits to fill the data forms). Meanwhile, questionnaire surveys can be done electronically or physically (e.g., post). Both methods are flexible depending on the researchers' preferences and needs [24]. Generally, researchers have adopted both methods to develop their productivity prediction models for various activities such as prefabricated external insulation wall systems [53], pile construction [24; 43], resource allocation [54; 55], and reinforcement bar placements [56]. Table 4 summarizes the strengths and limitations of the traditional approach to collecting construction productivity data [46; 57; 58]. For instance, traditional dataset acquisition is labor intensive, subjective due to the involvement of human judgment, cost sensitive, time consuming, and prone to errors [14; 37; 59]. However, they do have their strengths as well, as DWRs are an inexpensive source of data and direct observations are straightforward and adaptable.