# 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
There is typically no \"single\" optimal classifier; therefore, each situation calls for a different assessment of the learning algorithm using cross-validation [77]. [PERSON] and [PERSON] [76] researched equipment productivity and adopted five learning algorithms (logistic regression, k-NN, decision tree, ANN, and SVM) to enhance the robustness of the results. In their test cases, ANN emerged as having the best relative overall accuracy after training and testing performed through a stratified 10-fold cross-validation. Meanwhile, in [PERSON] et al. [75], research in measuring the operational efficiency of equipment (excavator) used naive Bayes, ANN, and decision tree; the accuracy of each classifier differs according to the number of samples. This can be further improved by having a larger sample size to alleviate the effect of external noises. Two DL techniques for compactor and excavator activity recognition were compared [71]. Only accelerometer data were used in this research. They achieved a lower accuracy of 77.6% in comparison to other research and emphasized the need for a more complete dataset in order to further increase the accuracy. This clearly identifies the importance of the dataset volume for DL applications. However, a novel Fractional Random Forest method with comparable performance to a DL-based method's (up to 94%) activity recognition model that worked with small datasets was proposed by [PERSON] et al. [78]. The importance of fractional feature augmentation was highlighted and was able to elevate the performance of the activity recognition model regardless of the type of ML model. Vision-based  
Mainly, construction equipment for earthmoving activities (e.g., excavators, dozens, dump trucks, and backhoes) have been explored for the purpose of automated measurement of productivity via vision-based techniques. Noticeably, the vision-based dataset acquisition approach is more popular for measuring equipment productivity rather than labor productivity, due to the countless complexities of human movements compared to equipment. Recent work in computer vision and pattern recognition has shown that traditional ML methods, such as nearest neighbor models, SVM, and linear regression, in tasks like object detection, object tracking, and action recognition are inferior to the DL approaches [21; 79; 80]. This superiority can be attributed to the capabilities of advanced neural networks, including Recurrent Neural Networks (RNNs) and CNNs, which excel at modeling sophisticated connections of input data (i.e., images and videos) and output predictions (e.g., object categories and action categories) [81]. Given the benefits that DL algorithms offer over traditional ML methods, their application for recognizing construction activities has been the focus of recent research. Specifically, [PERSON] et al. [82] developed models incorporating the Faster R-CNN and Double-layer Long Short-Term Memory (DLSTM) layers using data gathered in experimental arrangements. This approach yielded precision rates of 90.9% and recall rates of 89.2%. [PERSON] and [PERSON] [81] conducted a study where excavators engaged in earthmoving tasks were analyzed through vision-based activity recognition. Three models, CNN-LSTM, CNN, and CNN-DLSTM, were analyzed, where the CNN-DLSTM model achieved the highest accuracy. Despite the advancement of neural networks, as shown in Figure 12, there is no single fit algorithm for every scenario, due to different techniques (object detection, tracking, and action recognition) requiring different algorithms. Thus, hybrid methods and further improvement of architectures are commonly found in vision-based dataset preparation to extract productivity data. ### Audio-based  
The development of audio-based methods for recognizing construction equipment activity is relatively new [78]. Because construction heavy equipment frequently produces distinctive audio patterns while carrying out different tasks, these audio signals may serve as valuable sources of information.