Curriculum

Geospatial data (i.e., data that is tied to specific locations or geo coordinates) is widely used in many fields (e.g., geography, urban planning, environmental science, and social science) and is particularly important for planning urban infrastructures. This course offers mathematical fundamentals and practical examples for modeling and simulating geospatial data. Both conventional geostatistical methods and machine learning methods are covered in this course to analyze spatiotemporal patterns of geospatial data, optimize resource allocation, and make data-driven decisions for sustainable development of urban infrastructures.

This course explores the principles and applications of remote sensing and smart sensor networks in the context of sustainable urban development. Students will learn about satellite remote sensing, AI-driven data analysis, GIS integration, and emerging technologies like digital twins and drone-based sensing. Through lectures, hands-on exercises, and case studies, the course equips students with the skills to address urban challenges such as climate resilience, smart city planning, and data-driven decision-making. By the end, students will be able to apply advanced remote sensing techniques and analyze their societal, ethical, and policy implications for digital and sustainable cities.

This course will provide an in-depth study of digital twin technology and its applications in engineering systems and smart city development. Digital twins improve visualization, support real-time monitoring and control, enhance prediction and decision making, and facilitate system design and operation. We will discuss the concepts, roles, architecture, and components of digital twins. We will also introduce integration of digital twins with different technologies, such as artificial intelligence, system federation, internet of things, physics-based simulators, and blockchain. We will investigate and plan real-world applications of digital twin technology in a wide range of applications as well, such as city resilience, transportation, smart buildings, civil infrastructure, and energy systems.

This course aims to equips students with interdisciplinary expertise to integrate soil mechanics principles with modern computational techniques for solving complex geotechnical engineering challenges. We will introduce the theoretical foundations and practical applications of numerical methods critical to the analysis, design, and optimization of geotechnical systems and large-scale infrastructure projects. The course emphasizes the role high-performance computing (HPC) in addressing computationally intensive real-world scenarios for future development of smart and sustainable cities.

This multi-faceted course encompasses advanced technologies in infrastructure and building construction, maintenance and operations. The course provides deep learning methods in computer vision and robot sensing with hands-on coding training on solving construction management problems with these methods. Combined with tools from AI and robotics, the course equips students with leading-edge knowledge and practices to bring about successful construction reform in the context of the smart city.

The course covers three parts: (1) concrete technology for enhanced sustainability including alternative cement, rheology-3D printability, concrete fracture-durability, and high-performance concretes (fiber-reinforced, high-strength, high-modulus, or lightweight ones); (2) sustainable applications of other construction materials (FRP and bitumen); (3) non-destructive testing of construction materials (wave-based and other approach).

This course will present an overview for structural identification and structural health monitoring technologies. We will demonstrate principles to exploit structural response measurements for structural condition evaluation. We will introduce fundamental knowledge of data-driven techniques including Artificial Intelligence and Computer Vision in structural health monitoring, with the ultimate goal of understanding the next-generation approach to maintain a sustainable and resilient infrastructure system.

This course will cover the theoretical and practical aspects of applied numerical analysis methods used in Structural Engineering, with emphasis on the Finite Element Method (FEM) and Digital Twin approaches for civil engineering structures and infrastructure. The course will combine theoretical foundations with practical applications, using available FEM and Digital Twin software for hands-on learning. The course is designed to equip professionals and/or graduate students with the skills needed to effectively use FEM in engineering projects, ensuring high-quality and efficient outcomes. FEM simulations and Digital Twin technologies enhance the sustainability of civil engineering structures and infrastructure by enabling real-time monitoring, and facilitating informed decision-making throughout the asset lifecycle.

The low-altitude economy, driven by drone technologies, plays a critical role in addressing urban challenges and advancing sustainable development. This course examines the applications of drones in infrastructure inspection, environmental monitoring, and disaster management, while integrating digital tools and emerging trends like AI and urban air mobility. Students will explore how low-altitude technologies optimize resource allocation, enhance urban efficiency, and contribute to the development of smart, sustainable cities through data-driven decision-making and innovative solutions.

This course explores cutting-edge methods and technologies transforming the field of transportation planning. We move beyond traditional approaches to embrace data-driven insights and innovative solutions. The course covers a range of topics, from advanced travel demand modeling techniques incorporating discrete choice analysis and large language models (LLMs), to evaluating transportation performance through metrics of accessibility, equity, resilience, and reliability. Students will gain practical experience analyzing diverse datasets and learn how to leverage big data analytics for evidence-based decision-making. This course also integrates emerging technologies like shared mobility and connected and autonomous vehicles (CAVs) into the planning process. Through case studies, project work, and interactive discussions, students will develop the skills necessary to address complex transportation challenges and shape the future of mobility.

This course provides an introduction to the concepts and methodologies involved in designing, planning, and operating sustainable cities with net-zero carbon emissions. It begins by covering the fundamentals of urban planning. Following this, the course explores strategies to improve urban energy efficiency and reduce carbon emissions, including Building Integrated Photovoltaics (PV), energy-efficient building designs, and building-grid interactions. Finally, the course will delve into advanced building control techniques as a means to effectively manage and operate a low-carbon city.

This course is designed to provide a comprehensive understanding of how circular economy principles can be applied to develop sustainable infrastructure, promote resource circularity, reduce environmental impact, and create economic value. We will elucidate regulatory frameworks and incentives, business model and economic benefits, technological and cultural challenges, and opportunities for innovation.

Modern civil infrastructure systems, such as renewable energy systems and intelligent transportation systems, often involve interactive decision-making among multiple stakeholders. This course introduces game-theoretic models that captures the interactions among strategic decision makers using mathematical tools from optimization and game theory, with a special focus on decision makers in renewable energy and intelligent transportation systems, including energy consumers (e.g., buildings, electric vehicles), energy producers, and mobility-on-demand customers.

Selected topics of current interest in Digital and Sustainable Cities. May be repeated for credit if different topics are covered.

An independent project carried out under the supervision of a faculty member.

Students will gain practical learning experiences through participating in internships outside Hong Kong, focusing on topics related to digital and sustainable cities. During these internships, students are required to engage in practical training which will allow them to apply their theoretical knowledge in real-world situations. Upon completion of the internship, students are required to compile a final report with oral presentation of the final outcomes developed through the internship.