Predicting Truck Cycle Time in Earthworks Using a Machine Learning Approach

Predicting Truck Cycle Time in Earthworks Using a Machine Learning Approach

Monica Sidarta, Monica (TU Delft Civil Engineering and Geosciences)

Tavasszy, Lorant (graduation committee)
Nogal Macho, M. (mentor)
Spaan, M.T.J. (graduation committee)
Krishnakumari, P.K. (graduation committee)
Chunduri, Sreelatha (graduation committee)

Delft University of Technology

Civil Engineering | Construction Management and Engineering

2021-07-08

Inaccurate truck cycle time (TCT) prediction in earthworks impacts construction projects because more equipment and human resources must be added to complete the project. It also increases fuel consumption and emissions from the machinery. However, the current method gives inaccurate prediction because of subjectivity and human error.
This research aims to utilize the historical data for improving the accuracy of TCT prediction in earthworks. Two historical data are explored, such as manual and automated data provided by BAM, using a machine learning approach in which regression techniques: Multi Linear Regression (MLR), Support Vector Regression (SVR), and Artificial Neural Network (ANN).
The result concluded that automated data could develop predictive models because of its quality and variance. ANN develops most predictive models with feature combination one or two, where is distance is the important feature. The benefits of the predictive model are analyzed by comparing them with the traditional method in predicting truck cycle time. The models are more accurate in predicting truck productivity and have lower inefficient truck cycle time than the traditional method. The reduction of inefficient truck cycle time can reduce the cost for fuel and drivers, fuel emission. Models also have intangible benefits to stakeholders, such as gaining partners trust and a better strategic plan to complete the project.
In conclusion, the historical data can improve the prediction accuracy of TCT and give benefits to stakeholders. And practitioners are suggested to raise awareness about the importance of data and improve earthmoving documentation for better predictive models.

Machine Learning
Truck Cycle Time
Artifical neural networks
Multiple linear regression
Support vector regression

http://resolver.tudelft.nl/uuid:0f3c5ea9-c2b5-447d-b282-d6d25843f97d

Student theses

master thesis

© 2021 Monica Monica Sidarta