岩土工程数据---超越传统方法和设计
文章摘要
第62届朗金讲座由Lidija Zdravkovic教授主讲，主题为《Geotechnical Engineering for a Sustainable Society》，探讨了岩土工程在数据驱动时代下的挑战与机遇。讲座前安排了研讨会，讨论了岩土工程数据的应用，包括利用统计分析和贝叶斯推理进行岩土工程风险评估和设计，以及人工智能和机器学习在岩土工程中的潜在作用。讲座内容涉及气候变化对基础设施的影响、海上风能结构桩基础设计、地下热扰动和化学扰动对岩土工程基础设施的环境影响等。此外，还探讨了如何将代用模拟和数字孪生技术集成到城市开挖设计和施工中，以及在观察法中结合仪器测量和模拟进行反分析的方法。这些讨论强调了数据分析和先进技术在提高岩土工程实践的可靠性和可持续性中的重要性。
正文
第62届朗金讲座将于明日凌晨北京时间1:30am开讲，《计算岩土力学》公众 号将录制这个讲座，并于明天发布。
(1) Navigating challenges to ensure reliable geo-solutions in the data-driven generation 应对挑战，确保在数据驱动的时代提供可靠的地质解决方案
[1] (2017) Statistical analysis of rock-burst events in underground mines and excavations to present reasonable data-driven predictors.
[2] Uncertainty quantification in data-driven geotechnical stratigraphic modeling.
[3] (2024) Data-Driven Approach to Tailings Storage: Geotechnical Characterization of Iron Ore Tailings in Brazil.
(2) Global pile driving model calibration with Bayesian inference 利用贝叶斯推理进行全局打桩模型校准
[1] (2009) Bayesian Probabilistic Approach for the Correlations of Compression Index for Marine Clays.
[2] (2019) Bayesian methods to treat geotechnical uncertainty in risk-based design of open pit slopes.
[3] Bayesian analysis of the impact of rainfall data product on simulated slope failure.
[4] (2018) Evaluating the effect on seismicity of a hydraulic fracturing trial using Bayesian data analysis.
[5] (2018) Probabilistic Analysis of Tunnel Face Stability below River Using Bayesian Framework.
[6] (2020) Probabilistic analysis of tunnel collapse: Bayesian method for detecting change points.
(3) How can AI and ML replace the geotechnical engineer? 人工智能和机器学习如何取代岩土工程师？
(4) Integrating surrogate modelling into the design and construction of urban excavations 在城市开挖设计和施工中集成的代用模拟
(5) Back analysis in the observational method: bringing together instrumentation and modelling in deep excavations 观察法中的反分析：将深部开挖中的仪器测量和模拟结合起来
(6) Automated reconstruction of digital twins for underground infrastructure 地下基础设施数字孪生的自动重构
