**5. Conclusions**

with the increased epochs, was better than the first model, which validates the

(precision = 0.93, F1 score = 0.93), and brick (precision = 0.88, F1 score = 0.78. Whereas, the lowest precision has been achieved by concrete block (precision = 0.35)

It can be seen that maximum precision and F1 score have been attained for wood

The output of the predictive model is much dependent on the training of the model. For the testing of the predictive model, random images were collected from the internet for each. i.e., concrete block, asphalt, wood and brick. The selected predictive model utilised the memory output of the trained model and predicted the material along with its probability. The model predictions were observed for both trained models, i.e., 150 epochs and 300 epochs. **Table 5** shows the summary and comparison of the predictive model against the input images for both trained

enhanced performance of the ANN on increasing the epochs per run.

*Graphical representation of model accuracy and loss for 300 epochs per run.*

and F1 score by asphalt (F1 score = 0.42).

*Training/testing model output for 300 epochs per run.*

**4.2 Predictive model**

**Figure 6.**

**196**

**Figure 5.**

*Deep Learning Applications*

The emergence of automation and IoT, in the construction sector, have regained the interest of professional and research community towards ML techniques. The ML technologies are now being adopted in many construction processes and one of which is construction progress monitoring. The theme of this chapter was designed to overview the application of material classification via ML techniques and their implication in the construction automated progress monitoring. For the achievement of this study objective, a small structured review was performed to collect relevant studies from WoS and Scopus. Overall, 14 studies were found relevant, where the majority of studies were performed for the multi-classification of construction materials using digital images. Moreover, the classification of material has also been performed based on proprioceptive force data. ANN and SVM models have been found most effective ML techniques for classification, and these techniques have also been integrated with BIM for effective construction processes control. For the better understanding of the readers to the practical implementation of ML techniques, a small experiment for multi-classification of construction materials (brick, asphalt, concrete block, and wood) using Python has also been included in this chapter. A simple ANN-based model was trained on the dataset of the
