Postgraduate Courses
DISC
Digital and Sustainable Cities
- DISC 5101Geospatial Data Analytics for Urban Infrastructure Planning[3 Credit(s)]DescriptionGeospatial 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 datadriven decisions for sustainable development of urban infrastructures.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Explore geospatial data and identify spatial patterns of the data.
- 2.Evaluate the appropriateness of different data analytic methods for geospatial data available during planning of urban infrastructures.
- 3.Apply appropriate spatial data analytic methods to make data-driven decisions for sustainable development of urban infrastructures.
- 4.Incorporate machine learning for practical applications of geospatial data analytics.
- DISC 5102Remote Sensing and Smart Sensor Networks[3 Credit(s)]DescriptionThis 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.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Explain the fundamental principles, types, and platforms of remote sensing, including their applications in sustainable urban development.
- 2.Evaluate the environmental, economic, and societal impacts of remote sensing and smart sensor networks in addressing urban challenges.
- 3.Apply advanced technologies, such as AI, GIS, digital twins, and drone-based sensing, to analyze and solve complex urban design and management problems.
- 4.Utilize remote sensing software tools and methodologies to preprocess, analyze, and interpret spatial data for evidence-based decision-making.
- 5.Assess the role of remote sensing in enhancing climate resilience through applications such as disaster management, carbon footprint analysis, and environmental monitoring.
- 6.Integrate remote sensing data with other digital tools (e.g., GIS, digital twins) to develop innovative solutions for smart city applications.
- 7.Propose strategies for the ethical and sustainable implementation of emerging remote sensing and smart sensor technologies in urban contexts.
- 8.Critique emerging trends and innovations in remote sensing and smart sensor networks, demonstrating a commitment to continuous learning and professional development.
- DISC 5103Digital Twins for Engineering Systems and Smart Cities[3 Credit(s)]DescriptionThis 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.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Describe and design the architecture and components of digital twin systems.
- 2.Integrate digital twin technology with related digital technologies.
- 3.Investigate and evaluate the practical implementation of digital twins in real-world scenarios.
- 4.Plan and manage digital twin applications in engineering and smart city contexts.
- DISC 5104High-performance Computing for Geotechnical Engineering[3 Credit(s)]DescriptionThis 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 of high-performance computing (HPC) in addressing computationally intensive real-world scenarios for future development of smart and sustainable cities.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Use state-of-the-art computational methods for rigorously modelling geotechnical systems.
- 2.Integrate geotechnical fundamentals for critical analysis and validation of simulations results.
- 3.Leverage HPC resources and techniques for simulation of large-scale, complex geotechnical systems.
- 4.Acquire insights into emerging trends in geotechnical modelling, including AI-driven geotechnics.
- DISC 5105Autonomous Construction and Robotics[3 Credit(s)]DescriptionThis 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.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Apply AI tools to building and construction data.
- 2.Evaluate the appropriateness of AI and robotics in building and infrastructure construction, maintenance, and operations.
- 3.Incorporate AI and robotics for practical construction engineering and management issues.
- DISC 5201Sustainable Construction Materials[3 Credit(s)]DescriptionThe 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, highstrength, 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).Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Identify a proper selection of raw materials of concrete, FRP, and bitumen for increased sustainability.
- 2.Identify the factors that influence the rheological properties, 3D-printing properties, strength, and environmental durability of concrete, FRP, and bitumen.
- 3.Identify the corrosion behavior and maintenance method of steel reinforcement in concrete.
- 4.Describe the physical laws and practical method of common non-destructive testing for construction materials.
- 5.Identify sustainable material design and structural applications of high-performance concretes (fiber-reinforced, high-strength, high-modulus, and lightweight).
- DISC 5202Computer Vision for Structural Health Monitoring[3 Credit(s)]DescriptionThis 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.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Measure structural response (strain, acceleration, deflection) with sensors and analyze to assess structural performance.
- 2.Apply computer vision techniques for automated defect identification in structural images.
- 3.Implement state-of-the-art AI algorithms to detect structural anomalies from monitoring datasets.
- 4.Evaluate the effectiveness of SHM technologies for infrastructure condition monitoring.
- DISC 5203Computer Methods for Structural Engineering[3 Credit(s)]DescriptionThis 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.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Utilize numerical simulations for structural-mechanical problems.
- 2.Apply FEM theory in practical problems of civil engineering structures.
- 3.Systematically approach simulation tasks.
- 4.Verify and validate simulation results.
- 5.Identify, consider and model nonlinear effects in simulations.
- 6.Establish digital twins with the aid of machine learning and available data.
- DISC 5204Low Altitude Economy in Sustainable Cities[3 Credit(s)]DescriptionThe 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.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Analyze the role of low-altitude technologies, particularly drones, in addressing urban challenges and advancing sustainable development goals.
- 2.Evaluate the suitability of drone-based solutions for specific urban applications, such as infrastructure inspection, environmental monitoring, and disaster management.
- 3.Apply drone-collected data and digital tools to design and implement innovative solutions for sustainable urban planning and management.
- 4.Incorporate emerging trends and technologies, such as AI, automation, and urban air mobility, into the design of future-ready smart city solutions.
- DISC 5205Smart Transportation Planning[3 Credit(s)]DescriptionThis 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.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Grasp core concepts of smart transportation systems, demand modeling, data analytics, and emerging mobility technologies.
- 2.Utilize advanced analytical tools, including discrete choice modeling modules, large language model (LLM) applications, big data analytics platforms, and visualization tools.
- 3.Design a research question, methodology and data approach for a real problem, and apply project design and data analysis methods to real problems with real data.
- 4.Learn state-of-the-art planning methods, analytical tools and emerging technologies to solve current real-world issues and propose policy implications.
- DISC 5206Sustainable Design, Planning, and Operation for Net-Zero City[3 Credit(s)]DescriptionThis 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.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Identify fundamental concepts of urban planning.
- 2.Model the urban energy system.
- 3.Model the interaction between urban micro-climate and building energy system.
- 4.Design and operate low-carbon energy-efficient building.
- 5.Design and model the application of renewables in cities.
- DISC 5207Circular Economy in Infrastructure Development[3 Credit(s)]DescriptionThis 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.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Evaluate infrastructure sustainability using Life Cycle Assessment methodologies.
- 2.Implement circular economy principles to optimize infrastructure resource efficiency and minimize waste.
- 3.Design infrastructure solutions integrating sustainable materials and innovative technologies.
- 4.Analyze policy frameworks and regulatory incentives relevant to circular infrastructure development.
- 5.Develop circular economy models promoting resource circularity within infrastructure projects.
- 6.Assess technological, economic, and cultural challenges in adopting circular economy practices.
- 7.Synthesize multidisciplinary insights to propose innovative solutions for sustainable infrastructure.
- DISC 5208Game-Theoretic Models for Smart Urban Infrastructure Systems[3 Credit(s)]DescriptionModern 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 capture 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.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Formulate and solve electricity market problems in renewable energy systems based on game-theoretic models and equilibrium analysis.
- 2.Formulate and solve multi-agent decision making problems in mobility-on-demand service based on game-theoretic models and optimization tools.
- 3.Identify the potential for improvement in transport and energy systems and design strategies to realize the potential.
- 4.Identify a broad impact of civil engineering on smart city development and environmental sustainability.
- DISC 6000Special Topics in Digital and Sustainable Cities[3 Credit(s)]DescriptionSelected topics of current interest in Digital and Sustainable Cities. May be repeated for credit if different topics are covered.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Identify the fundamental knowledge in different areas related to the course topic.
- 2.Apply theories and methods to solve problems in the various areas of the course topic.
- DISC 6500Industrial Placement[6 Credit(s)]DescriptionStudents 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. Graded PP, P or F.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Develop an understanding of different technical issues in digital and sustainable cities.
- 2.Develop effective written and oral communication skills.
- DISC 6980MSc Project[6 Credit(s)]DescriptionAn independent project carried out under the supervision of a faculty member. May be graded PP.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Develop and apply project planning and management skills effectively.
- 2.Critically analyze a selected topic to identify, formulate and solve problems and apply solutions for implementations.
- 3.Develop effective written and oral communication skills.
