Construction simulation has been widely applied in schedule analysis. However, traditional simulation is based on static models built in the planning or design phase, which focuses on overall project-level schedule analysis. To provide activity-level simulation for on-site schedule management, a construction phase oriented dynamic simulation method is proposed, which takes roller compacted concrete (RCC) dam placement process as an example. Considering various innerlayer and inter-layer activities and different construction organization modes, a detailed placement process simulation model is built. Based on construction data collected by real-time monitoring, a construction activity modeling method is given. Additionally, Dirichlet process mixture (DPM) models are applied for simulation parameter updates, which endows density estimation with considerable flexibility and robustness. A fast inference algorithm is also proposed to realize the fast posterior computation of DPM models. The proposed method is tested by an RCC dam project in southwest China. The results show that the proposed method can reflect the dynamic features of the actual placement process in the construction phase and provide accurate schedule predictions for on-site construction management.
Effective construction scheme planning is critical for schedule management, but heavy rain can affect construction processes. In previous studies, stochastic rainfall characteristics are often ignored, and their impact on macro‐ and microconstruction states are not depicted comprehensively. This research presents a construction simulation model to design reasonable construction schemes considering impact of stochastic rainfall. First, a rainfall model suitable for areas with heavy rainfall and uneven seasonal rainfall distribution is built. Then, multiaspect indicators are defined to intuitively quantify rainfall impact. Two case studies are conducted to evaluate applicability of the proposed method. Results demonstrate that the developed rainfall model aligns closely with observed data. Simulation findings reveal that if stochastic rainfall characteristics are ignored, the schedule and queuing probability of trucks will be underestimated, while machinery utilization will be overestimated. This research provides an effective simulation tool for determining adaptive measures to mitigate impacts of rainfall events.
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