**2. Research methodology**

effectively process [4] and retrieve valuable information from video clips and digital images. Moreover, the applications of computer vision (CV) and image processing systems are now considered in the architecture, engineering and construction (AEC) industry as an emerging field of research with steady growth [5]. Whereas, automated building material classification has gained the interest of the research community with respect to the AEC industry. Automated material classification may increase the performance output of various activities, including defect identification, on-site material control and progress monitoring [6]. The as-is BIM includes the geometric as well as non-geometric information on the building components, including the building materials, which is necessary for energy simulations and 3D structure visualisations. This evolution has led machine learning (ML) techniques to gain popularity for material classification models. Material classification can be performed via laser scan data and image-based data detection; however, the latter is more popular among the research community. The general concept of image-based approaches relies on utilising visual characteristics of building materials such as projection, roughness, colour and shape for automated detection. However, image-based approaches are highly affected by lighting environment. The varying light conditions have a substantial impact on the visual properties of the materials, which create difficulties in classifying the image-based building material. Moreover, the weak textures on surfaces and uncertain points of view often adversely affect the effectiveness and precision process of image-based content classification [7, 8]. Material classification is considered a vital activity of any vision-based framework to generate conceptual as-built 3D models for automatic progress monitoring in construction projects. In the case of construction material, related details can be obtained primarily from the appearance-based details found in 2D images. Digitalised material classification extracts the appearance-based information for construction progress tracking and perform segmentation process for the effective generation of automated 3D as-built

The implementation of ML techniques is vital for dynamic operations of the systems with continuous and automated learning [10]. Other than pattern recognition, ML technologies are adopted for the self-learning of the big-data based systems connected via the internet of things (IoT) integrated with digital technologies [11]. Likewise, for the construction progress detection technologies, the trend of integration with ML techniques for the digitalisation of the monitoring process has also been increased in recent times [12]. ML algorithms are generally divided into supervised and unsupervised types, which are analysed for learning and prediction of empiric results. Supervised learning algorithms minimise the error between the targeted data and output data, whereas unsupervised algorithms are adopted for clustering data when data training is not preferable. However, both types of ML algorithms can be utilised for the material classifications based on the site conditions and circumstances for the availability of input data [13]. Construction material classification via ML techniques has gained a lot of attention among professionals and researchers in the construction sector. Various studies can be found related to material classification for construction progress monitoring. However, still, improvements are required in the methodologies and algorithms towards effective and efficient outcomes. Therefore, this chapter aims to overview the applications of construction material classifications via ML techniques for progress monitoring/ tracking/detection of construction projects by conducting a short systematic review. Moreover, a simple artificial neural network (ANN) based material classification algorithm has also been discussed at the end of this chapter, which will help the readers to understand practical implications of ML techniques in the

models [9].

*Deep Learning Applications*

construction sector.

**186**

For the achievement of the study objective, i.e., overview the applications of material classification via ML techniques in the construction progress monitoring domain, the literature was collected from Web of Science (WoS) and Scopus for the last ten years. Two different keywords combinations were designed for the collection of studies from the aforementioned databases. The first keywords combination was designed to explore overall automated construction project monitoring technologies, and the final results were sorted for material classification techniques via ML. The second keywords combination was designed specifically for automated construction monitoring practices using material classification via ML techniques. The study's scope was narrowed down to journal articles and building construction projects, for the last ten years data, i.e. 2010 to 2020. **Figure 1** shows the study flowchart of the adopted methodology for this chapter.

Using the first keywords combination, overall 54 studies were collected on construction automated progress monitoring technologies, out of which four studies were based on material classification via ML techniques. The summary of the data searching and collection for the first keywords combination is shown in **Table 1**.

Likewise, with the second keywords combination, 48 studies were collected and out of which ten were found relevant to the designed scope of this chapter. **Table 2** shows the summary of the data searching and collection for the second keywords combination.

**Figure 1.** *Study methodology framework.*


**Table 1.**

*Data collection summary of material classification-ML techniques with first keywords combination.*


**Table 2.**

*Data collection summary of material classification-ML techniques with second keywords combination.*

Overall, 14 studies were found relevant to the defined scope, which were further analysed for the in-depth review.
