Video

March 7 2024 (Thursday) 4:30-5:30pm
The extraction of private, uniformly random bits from weakly random seeds is a problem of central importance in cryptography with multiple applications. A well-known result in classical computer science states that randomness extraction is possible using classical resources only when multiple independent sources are available. On the other hand, Quantum Entanglement enables a solution to the problem even in the so-called device-independent framework. Device-Independent quantum cryptography offers the highest form of security, wherein the users do not need to even trust the devices executing the cryptographic protocol, and can instead verify correctness and security by means of simple statistical tests on the devices. In this talk, we report on the state-of-art theoretical and experimental results on device-independent quantum cryptography, with a focus on quantum randomness amplification and quantum key distribution.

January 25 2024 (Thursday) 4:30-5:30pm
Engineered/Strain-Hardening Cementitious Composites (ECC/SHCC) is an advanced fiber-reinforced concrete exhibiting multiple-cracking and strain-hardening under tension. We aimed to explore the feasibility of producing high-strength seawater sea-sand Engineered Cementitious Composites (SS-ECC) for marine and coastal applications facing the shortage of freshwater and river/manufactured sand. The effects of key composition parameters including the sea-sand size, the polyethylene fiber length, and the fiber volume dosage on the mechanical performance of SS-ECC were comprehensively investigated. The crack characteristics of SS-ECC were also assessed and modelled, which are critical for its applications with non-corrosive reinforcements. SS-ECC with tensile strength over 8 MPa, ultimate tensile strain of about 5%, and compressive strength over 130 MPa were achieved. Using seawater and sea-sand had almost no negative effects on the 28-day mechanical properties of high-strength ECC. Smaller sand size and higher fiber dosage of SS-ECC resulted in smaller crack widths under the same tensile strain. A five-dimensional representation was proposed to assess the overall performance of SS-ECC, by comprehensively considering both the crack characteristics and the mechanical properties. A probabilistic model was also proposed to describe the stochastic nature and evolution of crack width, and it can be used to estimate the critical tensile strain on SS-ECC for a given crack-width limit and cumulative probability. The findings and proposed methods can facilitate the design of SS-ECC in marine and coastal infrastructures.

February 1 2024 (Thursday) 3:00-4:00pm
Hong Kong, renowned as one of the most densely populated territories globally, grapples with its hilly terrain and limited flat land, resulting in numerous buildings constructed on slopes or adjacent to large cut slopes. This situation poses a grave risk as landslides could tragically claim multiple lives. To address this critical issue, slope stabilization plays a crucial role in mitigating the landslide hazard. In this TechTalk, the pioneering work of Professor Peter Lumb on slope stability will be reviewed. The development and validation of soil nailing as an effective measure for slope stabilization will also be described.

December 14 2023 (Thursday) 4:30-5:30pm
Structural engineering community require the experience of experts in design, simulation and structural health monitoring (SHM) of existing structures. Currently, the training process of structural engineers may take more than 10 years from undergraduate to expert. The economic design currently relies on the experience of engineers, which may not reach the optimized design outcome. In addition, high-fidelity simulation and SHM are still challenging and practical applications of the nonlinear structural simulation and SHM are mostly limited to researchers, instead of practical engineers. Conventional structural engineering widely adopts finite element solvers based on CPUs, which may be time consuming. The computing resources of GPU accelerators and GPU-based supercomputers cannot be fully utilized due to the lack of GPU-based simulation platforms.

The project develops deep-learning-based intelligent structural design, simulation and structural health monitoring platform. For structural design, dataset is collected for structural design input parameters and structural design drawings, the generative models are learned to generate preliminary structural design drawings of buildings and bridges. For structural simulation, physics-informed neural networks are developed to replicate the spatial discretization and temporal discretization of conventional finite element solvers. For SHM, the state-of-the-art neural operator is trained on finite element simulation dataset of vehicle-bridge interaction (VBI) system and fine-tuned on experimental dataset to infer the damage distribution field based on structural response field. The project can inspire the undergraduate and graduate students to learn more about the challenges and future developments of structural engineering.