An improved extreme gradient boosting approach to vehicle speed prediction for construction simulation of earthwork
Construction simulation is an effective tool to provide schedule plans. Vehicle speed is one of the most significant factors in earthwork construction simulation. However, neglecting the strong correlation with contextual factors, random distribution methods will lead to inaccurate prediction of veh...
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Published in | Automation in construction Vol. 119; p. 103351 |
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Main Authors | , , , , , |
Format | Journal Article |
Language | English |
Published |
Amsterdam
Elsevier B.V
01.11.2020
Elsevier BV |
Subjects | |
Online Access | Get full text |
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Summary: | Construction simulation is an effective tool to provide schedule plans. Vehicle speed is one of the most significant factors in earthwork construction simulation. However, neglecting the strong correlation with contextual factors, random distribution methods will lead to inaccurate prediction of vehicle speed. To address such issues, an improved extreme gradient boosting (XGBoost) approach to vehicle speed prediction is proposed for earthwork construction simulation. Firstly, to improve the global searching ability, an improved grey wolf optimization algorithm (IGWO) is put forward. Secondly, XGBoost is optimized by IGWO to construct an IGWO-XGBoost model. Then, the prediction model is embedded in the earthwork construction simulation model. The case study proves that the simulation results of the proposed method are more consistent with an actual construction schedule. It is expected that the vehicle speed prediction embedded into a simulation program facilitated an accurate development of schedule plan, thereby improving the efficiency of construction management.
•A new vehicle speed prediction model integrated contextual factors was proposed.•The extreme gradient boosting is optimized by improved grey wolf optimization algorithm.•An earthwork construction simulation model considering vehicle speed prediction is developed.•Integration of simulation and prediction increases the simulation result reliability. |
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ISSN: | 0926-5805 1872-7891 |
DOI: | 10.1016/j.autcon.2020.103351 |