Abstract
Nowadays, the construction industry is on a fast track to adopting digital processes under the Industrial Revolution (IR) 4.0. The desire to automate maximum construction processes with less human interference has led the industry and research community to inclined towards artificial intelligence. This chapter has been themed on automated construction monitoring practices by adopting material classification via machine learning (ML) techniques. The study has been conducted by following the structure review approach to gain an understanding of the applications of ML techniques for construction progress assessment. Data were collected from the Web of Science (WoS) and Scopus databases, concluding 14 relevant studies. The literature review depicted the support vector machine (SVM) and artificial neural network (ANN) techniques as more effective than other ML techniques for material classification. The last section of this chapter includes a python-based ANN model for material classification. This ANN model has been tested for construction items (brick, wood, concrete block, and asphalt) for training and prediction. Moreover, the predictive ANN model results have been shared for the readers, along with the resources and open-source web links.
Highlights
Instead of manual practices for construction progress assessment, the research community is fascinated by techniques such as photogrammetry, laser scanning, time-lapse photography, etc
There are various machine learning (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
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]
Summary
The construction progress measuring practices are considered indispensable tools for effective project control [1]. Instead of manual practices for construction progress assessment, the research community is fascinated by techniques such as photogrammetry, laser scanning, time-lapse photography, etc. These strategies have adopted 4D Building Information Modelling (BIM) as a framework to execute model-based progress tracking of construction projects [3]. 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. 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 models [9]
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