基于数字孪生和深度学习的港口水工结构耐久性寿命预测技术

Chang Guo
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摘要

数字孪生具有高保真行为模拟的特点,深度学习具有强大的数据挖掘能力。我国的港口和水利建筑设施目前是世界上最大的,其中大部分是混凝土结构。然而,混凝土结构的冻融损伤会更多,而对水工结构耐久性试验方法的研究较少,特别是对水工结构冻融损伤修复后寿命预测的研究较少。基于此,本文开展了数字孪生与深度学习相结合驱动的港口水工结构耐久性寿命预测技术研究。针对港口水工建筑物在恶劣工作环境下的状态预测与维护难题,结合数字孪生的高保真行为仿真特性和深度学习强大的数据挖掘能力,本文主要研究数字孪生与深度学习的相互驱动。本文基于多个物理参数和空间参数,构建了采煤机的多个数字孪生体。通过虚拟空间的多重视觉显示和数据分析,预测健康状态,建立基于深度机器学习的端口。水工构筑物关键部位剩余寿命预测模型实现了网络实时监测;将数据驱动下的建筑构件剩余寿命和剩余寿命值进行集成,将数字孪生状态与构件剩余寿命和水工建筑港口耐久性评估进行集成。研究结果表明,虽然少量钢渣可以显著提高混凝土的耐热性和抗压强度,但当钢渣的含量超过一定意义时,混凝土的耐热性和抗压强度就会发生变化。当钢渣含量达到10%时,效果最好。当钢渣掺量达到20%时,对28d混凝土抗压强度和强度不会产生大的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Durability Life Prediction Technology of Port Hydraulic Structure Driven by the Fusion of Digital Twins and Deep Learning
Digital twin has the characteristics of high- fidelity behavior simulation, and deep learning has powerful data mining capabilities. Our country's port and hydraulic building facilities are now the world's largest, most of which are concrete structures. However, there will be more freeze-thaw damage to concrete structures, and there is less research on the durability test methods of hydraulic structures, especially the life prediction of hydraulic structures after freeze-thaw damage repairs. Based on this, this article has launched a research on the durability life prediction technology of port hydraulic structures driven by the integration of digital twins and deep learning. Aiming at the difficult problem of predicting and maintaining the status of port hydraulic buildings in a harsh working environment, combined with the high-fidelity behavior simulation characteristics of digital twins and the powerful data mining capabilities of deep learning, this paper mainly studies the mutual driving of digital twins and deep learning. In this paper, multiple digital twins of the shearer are constructed based on multiple physical and spatial parameters. Through multiple visual displays and data analysis in the virtual space, the health status is predicted, and a port based on deep machine learning is established. The residual life prediction model of the key parts of hydraulic structures realizes real-time monitoring on the network; integrates the residual life and residual life value of the building parts driven by data, and integrates the status of the digital twin and the residual life of the parts and the port Durability assessment of hydraulic construction buildings. The research results show that although a little steel slag can significantly improve the heat resistance and compressive strength of concrete, when the content of steel slag exceeds a certain meaning, the heat resistance and compressive strength of concrete will change. When the steel slag content reaches 10%, the effect is best. When the steel slag content reaches 20%, the 28d concrete compressive strength and strength will not have a big impact.
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