Prediction of tunnel mechanical behaviour using multi-task deep learning under the external condition

IF 6.5 3区 工程技术 Q1 ENGINEERING, GEOLOGICAL
Bo Du, Tao Zou, Junchen Ye, X. Tan, Ke-Yu Cheng, Wei-zhong Chen
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引用次数: 1
外部条件下隧道力学行为的多任务深度学习预测
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来源期刊
CiteScore
8.70
自引率
10.40%
发文量
31
期刊介绍: Georisk covers many diversified but interlinked areas of active research and practice, such as geohazards (earthquakes, landslides, avalanches, rockfalls, tsunamis, etc.), safety of engineered systems (dams, buildings, offshore structures, lifelines, etc.), environmental risk, seismic risk, reliability-based design and code calibration, geostatistics, decision analyses, structural reliability, maintenance and life cycle performance, risk and vulnerability, hazard mapping, loss assessment (economic, social, environmental, etc.), GIS databases, remote sensing, and many other related disciplines. The underlying theme is that uncertainties associated with geomaterials (soils, rocks), geologic processes, and possible subsequent treatments, are usually large and complex and these uncertainties play an indispensable role in the risk assessment and management of engineered and natural systems. Significant theoretical and practical challenges remain on quantifying these uncertainties and developing defensible risk management methodologies that are acceptable to decision makers and stakeholders. Many opportunities to leverage on the rapid advancement in Bayesian analysis, machine learning, artificial intelligence, and other data-driven methods also exist, which can greatly enhance our decision-making abilities. The basic goal of this international peer-reviewed journal is to provide a multi-disciplinary scientific forum for cross fertilization of ideas between interested parties working on various aspects of georisk to advance the state-of-the-art and the state-of-the-practice.
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