**Author details**

aforementioned construction materials, and predictions were performed on random images. The utilised resources were collected from the open-source repositories, and details of their parent websites have also been shared for the readers' interests. In this era of automation, the construction sector is inclined towards the adoption of artificial intelligence, and ML is playing a vital role in the enhancement of construction processes in various ways. Likewise, material classification is one of the favourite techniques when it comes to ML, especially for project progress monitoring. Although various studies have been conducted, however, still, there is a need for dedicated research to improve algorithms and methodologies to make

these construction monitoring processes more effective and feasible for

The resources and program codes used in this chapter for the Python-based material classification model are available as open-source, and their access links are

construction stakeholders.

*Deep Learning Applications*

provided for the readers.

IoT Internet of Things ML Machine learning

DT Decision tree RF Random forest LR Logistic regression KNN K-nearest neighbours GMM Gaussian mixture modes SVM Support vector machine SfM Structure from motion

ANN Artificial neural network PMO Project management office BIM Building Information Modelling

SVDD Support vector data description C-SVC C-support vector classification

HSI Hue, saturation, and intensity CML Construction material library RANSAC Random sample consensus UAV Unmanned aerial vehicle CNN Convolutional neural network

PUK Pearson VII function RGB Red, green, blue

TLS Terrestrial laser scanner LM 48-dimensional form

**198**

AEC Architecture, engineering, and construction

ARIAS Automatic reinforcing-bar image analysis system

RFSP Random forest and super-pixel method IVB Iterative voting for binary image

rLM 18-dimensional rotationally invariant form

**Abbreviations**

**Notes**

Wesam Salah Alaloul\*† and Abdul Hannan Qureshi† Department of Civil and Environmental Engineering, Universiti Teknologi PETRONAS, Tronoh, Perak, Malaysia

\*Address all correspondence to: wesam.alaloul@utp.edu.my

† These authors contributed equally.

© 2021 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
