**3. Discussion**

Material classifications via ML techniques are popular among the research community in every domain. However, in the construction progress monitoring practices, the material classification via ML is still an emerging area. Collection of data from WoS and Scopus supports this argument as with the first keywords combination out of 54 technical journal based studies, only four were related to material classification for construction progress monitoring. Moreover, with the second keywords combination out of 48 ML studies in the construction progress monitoring domain, only ten were related to material classification. There are various ML classifiers which are being adopted by the researchers such as random forest (RF), decision tree (DT), bayesian, k-nearest neighbours (KNN), gaussian mixture modes (GMM), logistic regression (LR), support vector machine (SVM), and artificial neural networks (ANN), etc. However, ANN and SVM are the most favourite techniques among researchers, when it comes to material classification [5, 8, 14]. The material classification has been performed by researchers on various sources data input such as digital images taken with the help of a camera [15], smartphones, drones [16], and 3D point cloud models generated on collected images via structure from motion (SfM) [17], or laser scanners [8].

The in-depth review was performed on the collected studies, and it can be observed that material classification methodologies are being adopted for construction progress monitoring practices since before 2010. Zhu and Brilakis [18] identified concrete material regions by testing three classifiers, i.e., C-support vector classification (C-SVC), support vector data description (SVDD), and ANN, where ANN model was found with better outcomes. The model was developed using C++ and evaluated more than hundreds of building construction site digital images. Araújo et al. [19] adopted a functional SVM (FVSM) with a pearson VII function (PUK) kernel and linear functional regression for classifying granite varieties by using spectrophotometer spectrum data. Son et al. [20] developed a model for the identification of concrete structural elements in the coloured images. In the process, the red-green-blue (RGB)

*Summary of material classification studies for construction progress monitoring of building projects.*

**Ref Year Data input Adopted**

*DOI: http://dx.doi.org/10.5772/intechopen.96354*

Information

Images

[19] 2010 Spectral

[14] 2014 Concrete

[18] 2010 Site Images ANN, SVDD, &

[20] 2012 Site Images GMM, ANN, &

[5] 2016 Site Images MLP, ANN, &

[16] 2017 Site Images CDF, SVM, RBF,

[22] 2019 Rebar Images RFSP, OTSUHT,

Data/Material Images

Force Data

Material Images

[17] 2016 3D Point Cloud/

[8] 2020 Laser Scan

[13] 2020 Proprioceptive

**Table 3.**

**189**

**techniques**

*Material Classification via Machine Learning Techniques: Construction Projects Progress…*

C-SVC

FSVM with a PUK kernel

SVM

ANN, SVM, KNN, Bayesian, & FLD

RBF

LBP, & PPHT

& IVB

DT, DA, NB, SVM, & KNN

ANN, KNN, & k-mean

[21] 2019 Site Images CNN (ANN) Structural Elements

[23] 2020 Material Images ANN Sandstorms, Paving, Gravel, Stone, Cement-

[9] 2014 Material Images C-SVM (SVM) Grass, Form Work, Marble, Gravel, Foliage, Soil-

[3] 2015 Material Images C-SVM (SVM) Brick, Asphalt, Concrete, Foliage, Granular &

**Materials classified**

Concrete

Granite

Concrete

Concrete

Loose, Soil-Compact, Paving, Soil-Vegetation, Stone-Granular, Soil-Mulch, Wood, Stone-Limestone, Brick, Cement-Smooth, Cement-Granular, Asphalt, Concrete-Precast, Concrete-Cast, & Metal-Grills

Smooth Cement based surfaces, Gravel, Formwork, Marble, Insulation, Paving, Metal, Soil, Waterproofing Paint, Stone, & Wood

Concrete, OSB Boards, & Red Brick

Coating

Insulation, Studs, Outlets, Electrical & Three States for Drywall Sheets (Installed, Painted, & Plastered)

Rebar

Granular, Brick, Soil, Wood, Asphalt, Clay Hollow Block, & Concrete Block

Mortar, Concrete, Metal, Stone, Wood, Painting, Plastic, Plaster, Ceramic, & Pottery

Rock and gravel

SVM Windows, Walls, Protrusions, Tile, Brick, Stone, &

**Table 3** illustrates a general summary of the collected studies for construction progress monitoring by adopting material classification via ML techniques.


*Material Classification via Machine Learning Techniques: Construction Projects Progress… DOI: http://dx.doi.org/10.5772/intechopen.96354*

### **Table 3.**

Overall, 14 studies were found relevant to the defined scope, which were further

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

**Database Duration Keywords combination Total**

progress) AND (monitor\* OR updat\* OR track\* OR detect\* OR recogn\*))"

OR project OR progress) AND (monitor\* OR updat\* OR track\* OR detect\* OR recogn\*))"

progress) AND (monitor\* OR updat\* OR track\* OR detect\* OR recogn\* OR classification) AND (machine learning OR ML OR ANN OR artificial neural network))"

OR project OR progress) AND (monitor\* OR updat\* OR track\* OR detect\* OR recogn\* OR classification) AND ("machine learning" OR ml OR ann OR "artificial neural network"))"

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

**Database Duration Keywords combination Total**

WoS 2010–2020 "TS = (automat\* AND (construction OR project OR

Scopus 2010–2020 "TITLE-ABS-KEY (automat\* AND (construction

WoS 2010–2020 "TS = (automat\* AND (construction OR project OR

Scopus 2010–2020 "TITLE-ABS-KEY (automat\* AND (construction

**collected papers**

**collected papers**

48 10

54 4

**Relevant papers**

**Relevant papers**

Material classifications via ML techniques are popular among the research community in every domain. However, in the construction progress monitoring practices, the material classification via ML is still an emerging area. Collection of data from WoS and Scopus supports this argument as with the first keywords combination out of 54 technical journal based studies, only four were related to material classification for construction progress monitoring. Moreover, with the second keywords combination out of 48 ML studies in the construction progress monitoring domain, only ten were related to material classification. There are various ML classifiers which are being adopted by the researchers such as random forest (RF), decision tree (DT), bayesian, k-nearest neighbours (KNN), gaussian mixture modes (GMM), logistic regression (LR), support vector machine (SVM), and artificial neural networks (ANN), etc. However, ANN and SVM are the most favourite techniques among researchers, when it comes to material classification [5, 8, 14]. The material classification has been performed by researchers on various sources data input such as digital images taken with the help of a camera [15], smartphones, drones [16], and 3D point cloud models generated on collected images via structure

**Table 3** illustrates a general summary of the collected studies for construction

progress monitoring by adopting material classification via ML techniques.

analysed for the in-depth review.

from motion (SfM) [17], or laser scanners [8].

**3. Discussion**

**Table 2.**

**188**

**Table 1.**

*Deep Learning Applications*

*Summary of material classification studies for construction progress monitoring of building projects.*

The in-depth review was performed on the collected studies, and it can be observed that material classification methodologies are being adopted for construction progress monitoring practices since before 2010. Zhu and Brilakis [18] identified concrete material regions by testing three classifiers, i.e., C-support vector classification (C-SVC), support vector data description (SVDD), and ANN, where ANN model was found with better outcomes. The model was developed using C++ and evaluated more than hundreds of building construction site digital images. Araújo et al. [19] adopted a functional SVM (FVSM) with a pearson VII function (PUK) kernel and linear functional regression for classifying granite varieties by using spectrophotometer spectrum data. Son et al. [20] developed a model for the identification of concrete structural elements in the coloured images. In the process, the red-green-blue (RGB)

colour space was transformed to non-RGB colour spaces to enhance the separability between background classes and concrete. The model was tested for three ML algorithm, i.e., GMM, ANN, and SVM. However, SVM along with hue-saturationand-intensity (HSI) colour space was found more effective. Yazdi and Sarafrazi [14] used five different classifiers, ANN, SVM, bayesian, KNN, and fisher's linear discriminate (FLD) algorithm in combinations and as separate, i.e. Bayesian, Bayesian + FLD, KNN, KNN + FLD, SVM, SVM + FLD, ANN, and ANN + FLD on the segmented concrete images. This study found the ANN model as the better option for automatic image segmentation. Dimitrov and Golparvar-Fard [9] proposed C-support vector machine (C-SVM) algorithm combined with texture and hue-saturation-value (HSV) colour features. This study tested the developed algorithm on 20 construction materials (paving, grass, gravel, stone-limestone, formwork, soil-vegetation, marble, metal-grills, soil-mulch, soil-compact, stonegranular, wood, soil-loose, asphalt, cement-granular, brick, concrete-cast, cementsmooth, foliage, and concrete-precast) with more than 150 images for each category and compared various pixel sizes (n x n), i.e., 30, 50, 75, 100, 150, and 200. Better accuracy and effective output were reported for 200 x 200 pixel-images. Han and Golparvar-Fard [3] developed a construction material library (CML) based on C-SVM classifiers with linear *<sup>x</sup>*<sup>2</sup> kernels on 100 100, 75 75, and 50 <sup>50</sup> pixel-images datasets of cement-based surfaces, paving, brick, asphalt, formwork, foliage, concrete, marble, gravel, insulation, metal, soil, wood, stone, and waterproofing paint. This study developed an appearance-based material classification technique for progress monitoring using daily photologs and BIM. Point cloud models were generated using SfM and multi-view-stereo (MVS) algorithms from the construction site images. These point cloud models were superimposed with 4D BIM models, and registered site images were back-projected for the BIM elements for extracting related image patches. Testing of the extracted patches was performed with multi-class material classification technique that was pre-trained with the extended CML dataset. Rashidi et al. [5] conducted a comparison study on SVM, radial basis function (RBF), and multilayer perceptron (MLP) by evaluating the performance on building construction materials, i.e. OSB boards, red brick, and concrete. The feature extraction was performed from image blocks to compare the efficiency for detecting building construction materials, and SVM classifier with RBF kernel results were found more precise in perceiving the images for material textures. Yang et al. [17] performed material recognition of windows, walls, protrusions, tile, brick, stone, and coating using image-based 3D modelling. SfM was used for generating the 3D point cloud model for site images. The building facade was modelled as combined planes of protrusions, windows, and wall. Planes were primarily detected from 3D point clouds using random sample consensus (RANSAC) and further recognised as distinct structural components by SVM classifier. Hamledari et al. [16] adopted CV integrated with shape and colour-based modules that automatically detect the interior components using 2D digital images, i.e. three states drywall sheets (plastered, installed, and painted), insulation, studs, and electrical outlets. Cumulative distribution function (CDF) used for electrical outlet module, SVM classifier has an RBF kernel type for drywall, local binary patterns (LBP) for insulation module, and progressive probabilistic hough transform (PPHT) for stud module. The method was validated by indoor construction site images captured by unmanned aerial vehicle (UAV), smartphone and internet sources. Braun and Borrmann [21] developed an automatically labelling process via construction images with 4D BIM and 3D point cloud approach. The 3D point cloud model was integrated with the BIM model, and automated labelling for structural elements was provided with the semantic information. The convolutional neural network (CNN) model was trained on this information to generate classification tasks.

The accuracy of the allocated labels was checked by pixel-based field comparison to manual labels. Lee and Park [22] developed automatic reinforcing-bar image analysis system (ARIAS), which could separate the background for the bar area calculation. The model was also able to count the number of bars by testing various combinations between RF and super-pixel method (RFSP), otsu threshold for extracting the bars areas (OTSU), hough transforms (HT), and iterative voting for binary image (IVB). The combination RFSP+IVB gave better output results than other combinations. Ghassemi et al. [23] proposed the material classification model based on deep learning (DL) algorithm for classifying in various illumination conditions and different camera angles/positions. Eleven construction materials (sandstorms, paving, gravel, stone, cement-granular, brick, soil, wood, asphalt, clay hollow block, and concrete block) were classified in this study, and good accuracy was achieved by VGG16 algorithm, even for images that were hard to identify by the human eye. Yuan et al. [8] proposed an automatic material classification method with the help of 2D digital images using the graphical characteristics of building materials. A coloured laser scan data was generated using a terrestrial laser scanner (TLS) with a built-in camera, which contained the surface geometries, material reflectance and surface roughness of building materials. TLS data were classified using DT, Discriminant Analysis (DA), Naive Bayes (NB), SVM, and KNN. A laser scan database for ten common construction building materials (stone, pottery, mortar, concrete, ceramic, wood, plastic, plaster, metal, and painting) was used to train and validate the model. However, better results were achieved from one-class SVM (OC-SVM) and SVDD. Fernando and Marshall [13] performed a state of the art classification methodology for rock and gravel, by identifying force data (proprioceptive force data) acquired from load-haul-dump equipment with capacity of 14-tonne and adopting ANN, KNN, and k-mean algorithms. However, good results were obtained by ANN (5-NN) model with more realistic classification. Since the system only relies on proprioceptive sensing, it is feasible in harsh, dusty, and dark conditions that hinder the use of external sensor data. **Table 4** exemplifies the

*Material Classification via Machine Learning Techniques: Construction Projects Progress…*

*DOI: http://dx.doi.org/10.5772/intechopen.96354*

**Main features Achieved outcomes Remarks/observed**

80%.

error rate.

91.68%.

ANN technique was found better with an average precision of 83.3%, average recall of 79.6%, & overall concrete detection of

FSVM with a PUK kernel was found better with 0.82% validation

The SVM model with the HSI colour space was found better with an accuracy rate of

ANN was the better choice for automatic image segmentation with correctly classified pixels up to 90.29%.

The model was trained by datasets of negative & positive concrete images.

Granite varieties were characterised using a spectrophotometer (vectorial spectral information).

108 images were collected for 50 construction projects on different timings & weather

A dataset contained 31 images of concrete with the image resolution of 2 mm.

conditions.

**limitations**

The accuracy of the model outcome was evaluated by manually tracing the concrete image pixels.

For assessing the total real colour of the stone, the data was to be collected from various points on the sample.

This study covered the segmentation of concrete images.

The detection performance was evaluated by identifying concrete & background

pixels.

**Ref Adopted technique**

[19] Functional linear regression, & FSVM with a PUK kernel

[20] GMM, ANN, & SVM

[14] ANN, SVM, KNN, Bayesian, & FLD

**191**

[18] ANN, SVDD, & C-SVC
