报告题目：数字岩土工程（Databases and data-centric geotechnics）

报  告 人：方国光   教授

邀  请 人：李典庆   教授

时      间： 2024年8月24日（星期六）上午10:00

地      点： 水资源国重大楼A区202会议室

报告人简介：

方国光，新加坡科技设计大学校长、郑曾文讲席教授，新加坡工程院（SAEng）院士，新加坡国家科学院（SNAS）院士，新加坡政府科学顾问委员会成员，新加坡-天津经济贸易理事会成员，新加坡民航局董事会成员，曾任新加坡国立大学高级副教务长、新加坡总理公署国家研究基金会副首席科学顾问。

方教授长期从事数字岩土工程和机器学习方法研究。主要学术荣誉包括：2005年和2020年两次获得美国土木工程师学会（ASCE）诺曼奖章，2017年获洪堡研究奖，2023年获Harry Poulos Award奖，2024年获Alfredo Ang Award，《Georisk》期刊的创刊主编和《Geodata and AI》期刊主编，新加坡注册工程师和东盟特许专业工程师，等。

本次报告内容是方教授于2024年7月10在澳洲岩土力学协会所做的Harry Poulos讲座报告，今天首次在中国演讲。

报告简介：

This lecture was first presented in the 2023 Harry Poulos Award and Lecture, 10 July 2024, Sydney (https://australiangeomechanics.org/meetings/databases-and-data-centric-geotechnics/).

Research in data-centric geotechnics is accelerating as a result of tremendous advances in machine learning and AI. ChatGPT from OpenAI, Gemini from Google Deepmind, and other generative AIs have moved the divide between what a machine can do and what a human can do in a major way. There is near complete consensus that machine learning and AI have the potential to transform the way we work, live, and play in many fundamental ways. It is prudent for geotechnical engineers to understand and to explore the power and the impact of these new tools, particularly their value propositions to practice (Phoon and El-Din Anwar 2024).

Data is now considered to be an asset that is as valuable as our physical infrastructure. This advantage is not well appreciated by most engineers, although it is pivotal to digital transformation. Machine learning and AI universally depend on data for training and validation. Data-centric geotechnics is an emerging area that is underpinned by three elements: (1) data centricity, (2) fit for (and transform) practice, and (3) geotechnical context. The purpose of this lecture is to present the latest research findings in data-driven site characterization (one application area in data-centric geotechnics) to illustrate how data can support decision making in real world projects when data-driven methods are developed with the above elements in mind.

欢迎相关专业教师和研究生的光临！