8月2日：香港城市大学田华明博士学术报告通知

报告人简介：

田华明博士，2021年12月获太阳集团网址博士学位，现任香港城市大学博士后。主要从事岩土工程（如填海项目）数据分析、机器学习等领域研究，在工程地质与岩土工程领域权威期刊《Journal of Geotechnical and Geoenvironmental Engineering》、《Canadian Geotechnical Journal》、《Engineering Geology》、《Acta Geotechnica》、《岩土工程学报》等发表多篇论文。

报告简介：

数字化转型与数字孪生建模是岩土工程发展的必然趋势。本报告将凝练岩土工程数字孪生建模的关键技术与挑战，提出数据与物理双驱动的机器学习方法，展示如何通过一体化、数字化和自动化方式构建高精度地质模型，精准映射物理原理至数字空间，并融合时变、多源、高维空间监测数据，实时、持续地提升模型预测能力。探讨如何解决传统机器学习大量训练数据依赖、时空预测外插能力不足、计算效率低下以及可解释性差等难题。以澳大利亚、土耳其、美国、香港等实际工程案例，展示如何利用所提方法助力岩土工程数字化转型与数字孪生建模。

8月8日【水科学讲坛】第58讲：北京航空航天大学潘翀教授学术报告通知

报告人简介：

潘翀，北京航空航天大学航空科学与工程学院教授，国家杰出青年基金获得者。主要从事先进流场测量技术、壁湍流拟序结构和和湍流控制相关研究，在Journal of Fluid Mechanics、Physics of Fluids、AIAA Journal、Experiment in Fluids等国内外力学和航空航天权威期刊发表SCI论文80余篇，SCI他引1000余次。获授权国家发明专利20余项、软件著作权12项。曾获国家技术发明二等奖、教育部技术发明一等奖、教育部自然科学一等奖等。任中国空气动力学会理事、中国力学学会流体力学专业委员会委员、北京力学学会流体力学专业委员会副主任委员，《空气动力学报》和《气动研究与实验》副主编。

报告简介：

流体测量为认识、把握复杂流动的时空演化规律提供大数据。机器学习的最终目的是用已有数据取代未来实验/试验。在仍然需要流体测量实验/试验的现阶段，如何用机器学习的方法来辅助测量并提升测量能力，是一个重要且有趣的机器学习应用方向。本报告将汇报我们在这一方向上的几个尝试，包括神经网络空间标定模型、粒子三维重构的NN-LOS算法、混合蚁群粒子匹配算法和机器学习增强的表面形貌测量方法。

8月13日：美国俄亥俄州立大学Ryan Winston副教授学术报告通知

报告人简介：

Ryan Winston，男，美国俄亥俄州立大学食品、农业和生物工程系，土木、环境和大地测量工程系副教授。曾任俄亥俄州立大学食品、农业和生物工程系的研究助理教授，北卡罗莱纳州立大学推广助理。美国土木工程师协会注册专业工程师，《Sustainable Water in the Built Environment》副主编。

主要从事城市雨水径流水文水质研究以及雨水控制措施的效果研究。先后主持了多次绿色基础设施相关会议，多项创新雨水管理重点项目，开展了雨水控制措施维护相关的检查、维护课程。其团队的研究内容主要包括“哥伦布蓝图”绿色基础设施项目、绿色码头、生物滞留柱结构及材料研究、酸性矿井排水研究等。

报告简介：

Bioretention is among the most frequently implemented green stormwater infrastructure practices. Beyond basic maintenance checks, however, little monitoring is done, despite functioning to clean stormwater for years. This seminar will focus on a field research study aimed at understanding pollutant accumulation and soil development in older bioretention cells. We visited 29 bioretention facilities in Ohio, Michigan, and Kentucky and sampled soils in the forebay (if present), and near the inlet, middle, and outlet of each bioretention cell. Further, three soil depths (0-5, 10-15, and 36-50 cm) were sampled to understand how pollutants accumulate spatially and with depth, with the ultimate goal of informing maintenance. The outcomes of this study include an understanding of phosphorus, metals, microplastics, PAHs, PCBs, alkylphenols, phthalates, and PFAS accumulation in older bioretention media. Further, we evaluate hydraulic function of the media using quick infiltration tests and relate the hydraulics to soil physical properties, including the development of an organic horizon on top of the filter media. Practical recommendations will be provided from the study including when and where to maintain field-scale bioretention systems.

8月13日：美国俄亥俄州立大学Joey Smith博士候选人学术报告通知

报告人简介：

Joey Smith，男，美国俄亥俄州立大学博士候选人，太阳集团网址高级访问学者。在俄亥俄州立大学获得生态工程和汉语语言工程硕士学位。博士研究结合水文学、土壤学和社会科学，分析海绵城市绿色基础设施对社会和自然环境的积极作用。

报告简介：

Bioretention cells are engineered stormwater controls essential for mitigating urbanization's adverse effects but require regular maintenance to ensure their continued hydraulic function. This study examines the accumulation of particulates and organic matter in over 50 bioretention cells, aged less than one to 16 years, in the Midwestern United States and 3 cells, aged three to five years, in Wuhan, China. The presentation reviews past studies on soil evolution within bioretention cells and their predictions of design lifespan, highlighting the reliability of laser diffraction particle size analysis over conventional methods. Findings indicate that mean particle size varies with depth and location, showing larger particles at the forebay/inlet and smaller particles at the middle/outlet. The mean surface intake rate was lowest at the inlet (777.2 mm/hr) and highest at the outlet (1389.4 mm/hr). Key factors influencing soil clogging in bioretention cells include the presence or absence of a forebay, percentage of plant coverage, and average mulch depth. This approach aims to guide maintenance practices effectively and ensure the long-term functionality of bioretention systems in urban stormwater management.

8月24日【水科学讲坛】第59讲：新加坡科技设计大学校长方国光院士学术报告通知

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.