Abstract
The compaction construction process is a critical operation in civil engineering projects. By establishing a construction simulation model, the compaction duration can be predicted to assist construction management. Existing studies have achieved adaptive modelling of input parameters from a Bayesian inference perspective, but usually assume the model as parametric distribution. Few studies adopt the nonparametric distribution to achieve robust inference, but still need to manually set hyper-parameters. In addition, the condition of when the roller stops moving ignores the impact of randomness of roller movement. In this paper, a new adaptive compaction construction simulation method is presented. The Bayesian field theory is innovatively adopted for input parameter adaptive modelling. Next, whether rollers have offset enough distance is used to determine the moment of stopping. Simulation experiments of the compaction process of a high earth dam project are demonstrated. The results indicate that the Bayesian field theory performs well in terms of accuracy and efficiency. When the size of roller speed dataset is 787,490, the Bayesian field theory costs only 1.54 s. The mean absolute error of predicted compaction duration reduces significantly with improved judgment condition. The proposed method can contribute to project resource planning, particularly in a high-frequency construction monitoring environment.
Highlights
The compaction construction operation is widespread in the construction of civil engineering projects and is a crucial process for ensuring project quality [1,2]
We show how to accomplish adaptive modeling of the probability distribution of the simulation input parameter based on the Bayesian field theory method
To visually demonstrate the effectiveness of the compaction construction simulation method proposed in this paper, different from the improved compaction construction simulation method proposed in this paper, different previous from the research, actual the roller trajectory the simulated obtained from the intelligent previous the research, actual roller of trajectory of thelayer simulated layer obtained from thecompaction intelligent construction monitoring system and the simulated roller trajectory are presented in 6 and 7 for compaction construction monitoring system and the simulated roller trajectoryFigures are presented in visual
Summary
The compaction construction operation is widespread in the construction of civil engineering projects and is a crucial process for ensuring project quality [1,2]. Unlike the common nonparametric classical statistical inference method, such as the kernel density estimation method, and the common nonparametric Bayesian statistical inference method, such as the Dirichlet process mixture modeling method, the Bayesian field theory approach does not require the manual identifying of values of critical parameters, specifying of boundary conditions, or making of invalid mathematical approximations in the small data regime, while realizing the optimal estimation [29]. In another aspect, defining involved events is the basis for establishing simulation logic when building the construction simulation model.
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