Real-Time Construction Simulation Coupling a Concrete Temperature Field Interval Prediction Model with Optimized Hybrid-Kernel RVM for Arch Dams

Abstract

Joint grouting simulation is an important aspect of arch dam construction simulation. However, the current construction simulation model simplifies the temperature factors in joint grouting simulation, which leads to the difference between the simulation results and the actual construction schedule. Furthermore, the majority of existing temperature prediction research is based on deterministic point predictions, which cannot quantify the uncertainties of the prediction values. Thus, this study presents a real-time construction simulation method coupling a concrete temperature field interval prediction model to address these problems. First, a real-time construction simulation model is established. Secondly, this paper proposes a concrete temperature interval prediction method based on the hybrid-kernel relevance vector machine (HK-RVM) with the improved grasshopper optimization algorithm (IGOA). The hybrid-kernel method is adopted to ensure the prediction accuracy and generalization ability of the model. Additionally, the improved grasshopper optimization algorithm (IGOA), which utilizes the tent chaotic map and cosine adaptive method to improve the algorithm performance, is developed for the parameter optimization of HK-RVM. Thirdly, concept drift detection based on variable window technology is proposed to update the prediction model. Finally, an arch dam project in China is used as a case study, by which the superiority and applicability of the proposed method are proven.

An ANN-Based Short-Term Temperature Forecast Model for Mass Concrete Cooling Control

“…Xie et al [15] used DT algorithms to predict the change of concrete maximum temperature (e.g., at the center or mid-depth). Song et al [14] proposed a concrete temperature interval prediction method based on a hybrid-kernel relevance vector machine (HK-RVM) for grouting. (3) Prediction of temperature history.…”

“…However, few studies focus on mass concrete temperature forecasting due to poor cooling monitoring and temperature control technology. Available studies [14][15][16] simplify the problem and forecast only characteristic concrete temperature parameters, such as the maximum temperature during the early cement hydration age. Recently, rapid intelligent construction of hydropower projects in China has collected multi-source concrete cooling data, which allows for the development of concrete temperature forecasts [9,[16][17][18][19] .…”

A Review of Smart Construction Site: Research Hotspots and Development Trend

“…In terms of the research on intelligent technologies, Jayasree, V. [24] constructed an intelligent helmet that can monitor the behavior of construction workers. Son g W ang [25] built a real-time construction simulation model to predict the factors of temperature in flood dam engineering. Lee Jongse [26] constructed an efficient digital twin space through 3D modelling to reduce the accident rate of the construction site and to shorten the construction period.…”

A Hybrid Prediction Model for Local Resistance Coefficient of Water Transmission Tunnel Maintenance Ventilation Based on Machine Learning

“…At the same time, the choice of kernel parameters for the HKRVM model will affect the prediction accuracy of the model. Scholars use swarm intelligent optimization algorithms such as the grey wolf optimization algorithm [29], grasshopper optimization algorithm [30,31], particle swarm optimization algorithm [32], whale optimization algorithm [33], and bat optimization algorithm [34] to optimize the kernel function of the HKRVM model. The artificial jellyfish search algorithm (AJS) [35], as a swarm intelligent optimization algorithm proposed in recent years, has fewer adjustment parameters and a simple search process.…”