Research Projects
Perceived Risks Extended Safety Program Development for Construction Projects through Quantitative Hybrid Kinematic-Electroencephalography Vigilance Recognition
PI, #11209620, The General Research Fund (GRF), Jan 2021- Dec 2023, HKD 767,000
This project aims to develop a novel framework for the development of a Perceived Risks Extended (PRE) safety program that will complement existing construction safety management practices. Previous studies have shown that the discrepancy between actual on-site risks and workers’ perceived risks is the primary cause of unsafe behaviors. Therefore, this research aims to (1) develop quantitative methods using Hybrid Kinematic-EEG sensing to assess perceived risks, (2) explore the relationship between perceived risks and unsafe behaviors, and (3) propose the PRE safety assessment to evaluate the effectiveness and efficiency of safety programs. This research will uncover the mechanism behind on-site unsafe behaviors and provide an effective tool to evaluate existing safety programs. With the proposed framework, construction and safety managers can develop effective and responsive safety programs or supplement programs to further enhance project safety. In turn, this will ensure the safety and health of construction workers, prevent loss due to injuries and fatalities, and reduce the costs of ineffective safety training and regulation programs.
Developing Brain-Computer Interfacing Theory and Models for Construction Hazard Detection through EEG Bispectrum Analysis
PI, #51508487, The National Natural Science Foundation of China (NSFC), Jan 2016- Dec 2019, RMB 230,000
Construction companies can suffer financial losses due to labor fatalities and injuries. Since over 70% of all accidents are related to human activities, detecting and mitigating human-related risks is crucial for improving safety conditions within the construction industry. Previous studies have shown that the psychological and emotional conditions of workers can contribute to fatalities and injuries. Research in the areas of neural science and psychology suggest that inattentional blindness is a major cause of unexpected human-related accidents. Due to the limitations of human mental workload, laborers are susceptible to unexpected hazards while focusing on complex construction tasks. The ability to detect the mental conditions of workers could reduce unforeseen injuries. This project aims to develop a measurement approach to evaluate hazards through neural time-frequency analysis. The project has developed a prototype of a wearable electroencephalography (EEG) safety helmet that can collect the neural information required for the measurement approach.
Mitigating Human-related Hazards in Construction Projects: An Agile Framework for Jobsite Safety Assessment through Integrating Multiple Data Sources
PI, #9048083, Hong Kong General Research Fund (GRF) – Early Career Scheme, 2016- 2019, HKD 456,050
Construction companies face financial losses and reputational damage due to labor fatalities and injuries. Since more than 70% of all accidents are related to human activities, detecting and mitigating risks associated with human behavior can improve the negative public perception of the construction industry. However, conventional observational approaches to detecting these risks are time-consuming and unreliable, given the dynamic nature of construction environments and the unpredictability of human behavior. Sensing technologies, such as RGB-D cameras, IMUs, and RFIDs, have been introduced to automatically detect human motion in relation to their surrounding environment. However, these technologies currently lack a universal data integration and processing framework, resulting in noisy, incomplete, and incompatible data. This proposed research aims to develop an agile framework that can (1) accurately collect multiple human behavior-related sensing data, (2) efficiently detect anomalies and hazards, and (3) correctly assess job site safety conditions. The use of this framework will facilitate the detection of risks associated with human behavior in construction sites, leading to improved safety conditions and reduced financial and reputational losses.
Developing active building energy management system base on coupled Wi-Fi and BLE indoor positioning system (IPS)
PI, JCYJ20150518163139952, The Shenzhen Science and Technology Funding Programs ( Special Program for Energy Conservation), Oct 2016 – Sep 2018, RMB 340,000
Building occupancy information is a fundamental requirement for modern building service systems’ control and operation. Inaccurate occupancy information can lead to insufficient comfort levels and energy waste. The current occupancy detection system relies on indirect and low-resolution environmental sensors, which can potentially mislead facility managers and lead to inefficiencies in building energy use. In this project, we propose a novel occupancy detection system using a coupled indoor positioning system. The system integrates Wi-Fi and BLE networks and implements a stochastic positioning algorithm to collect high-resolution occupancy data. The system can not only identify the geospatial distribution of occupants but also track their movements in a network-covered space.
Study on the Theory, Models, and Applications of Eco-feedback in Energy Saving Behavior of Building Residents under Complex Social Networks
PI, JCYJ20150318154726296, The Shenzhen Science and Technology Funding Programs, Jan 2016- Dec 2017, RMB 480,000
The dissemination of energy eco-feedback among building occupants through peer networks has been shown to influence an individual’s energy consumption decisions. In this project, we plan to apply social network theory to explore the potential for changing occupants’ energy use behavior. By analyzing multilayer and large social networks, we aim to develop a comprehensive understanding of energy consumption patterns within the current social environment. We will then create social network models and simulate the decision-making processes and information transmission among building occupants.
Developing Effective Eco-feedback Platform to Benchmark Building Energy Consumption and Carbon Dioxide Emission and Promote Energy Saving Behavior and Awareness in Hong Kong
PI, #9211074 (25/2014), Hong Kong Environment and Conservation Fund (ECF), Mar 2015 – Mar 2017, HKD 455,000
This project aims to develop an effective eco-feedback platform that enables building occupants to compare their energy consumption and carbon dioxide (CO2) emissions with similar buildings. The goal is to raise awareness and encourage action to conserve energy and reduce CO2 emissions. The proposed project will make a direct and practical contribution to environmental and resource conservation in Hong Kong. Additionally, the project will introduce an innovative platform that promotes the adoption of new technologies, such as smart energy meters and eco-feedback systems. While initially targeting middle and high school participants, the project will ultimately benefit the entire building industry and energy market in Hong Kong. The objectives of the proposed project are as follows: (1) To develop a platform that can automatically collect data from smart energy meters, allowing occupants to access real-time energy consumption and carbon emission data and benchmark their performance against other buildings. (2) To investigate various types of feedback information and identify the most effective feedback to promote conservation behavior. (3) To sustain conservation behavior through the design of the platform and feedback portfolio. (4) To release the developed platform to the public and disseminate research results to academic communities, industrial practitioners, and policy-making organizations.
Green Connections: Pilot Study on Solid Waste Management on CityU Campus to Minimize Landfill solid waste
PI (with Dr. Xiaowei Luo), #6986020, City University of Hong Kong Campus SustainableFund (CSF), Jan 2015- Jun 2016, HKD 295,900
In recent decades, the amount of solid waste generated in Hong Kong has increased significantly. This proposal seeks to explore solutions for reducing solid waste at the source on the CityU campus. The project will involve waste auditing and analysis, with the aim of conducting a systematic review of solid waste management practices at CityU. Once the audit is complete, the research team will analyze the current practices of solid waste management and develop customized action plans for improvement. The project will result in a reduction in the amount of solid waste generated on campus, the strengthening of the University’s waste management practices, and an increased awareness of the 3R principles (reduce, reuse, recycle) among the CityU community.
Gyroscopic Stabilizers for Construction Cranes and Gondolas
PI, #9231133, Hong Kong Construction Industry Council (CIC), Jan 2015- Jun 2016, HKD 1,070,400
Tower cranes are commonly used on construction sites due to their efficiency. However, they pose a significant safety hazard to both the crane itself and the workers involved in the construction process due to the natural sway of payloads. Furthermore, external factors such as wind can cause additional sway and intensify the oscillation amplitude of the crane load on the construction site. This project aims to propose a hybrid control mechanism that combines electronic and mechanical gyroscopes to generate a balancing torque, thereby keeping the crane load stable.
Tracking Occupancy, Monitoring Consumption Behavior, and Estimating Occupants’ Energy Load in Commercial Buildings: A Wi-Fi Based Indoor Positioning Approach
PI, #7200365, City University of Hong Kong Startup Grant, Oct 2013 – Sep 2015, HKD 191,504