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An improved extreme gradient boosting approach to vehicle speed prediction for construction simulation of earthwork
Abstract 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.
Highlights 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.
An improved extreme gradient boosting approach to vehicle speed prediction for construction simulation of earthwork
Abstract 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.
Highlights 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.
An improved extreme gradient boosting approach to vehicle speed prediction for construction simulation of earthwork
Lv, Fei (author) / Wang, Jiajun (author) / Cui, Bo (author) / Yu, Jia (author) / Sun, Jiaen (author) / Zhang, Jun (author)
2020-07-11
Article (Journal)
Electronic Resource
English
Extreme Gradient Boosting-Based Machine Learning Approach for Green Building Cost Prediction
| DOAJ | 2022
Winter construction -- Earthwork and foundations
| Engineering Index Backfile | 1967