Field Demonstration of Bendable Concrete Precast Pavement
The aim of this project is to reduce the construction time of road pavement and thus enhance the productivity of road construction. To achieve this goal, a new type of concrete that is bendable yet stronger and longer lasting than regular concrete is used for the creation of slim precast pavement slabs, thus halving the time needed for road works and new pavements. It is also more sustainable, requiring less maintenance. This new concrete material can greatly reduce the thickness and weight of precast pavement slabs, hence enabling speedy plug-and-play installation, where new concrete slabs prepared off-site can easily replace worn out ones (Fig. 1). In addition, this technology not only enables the construction industry to reduce labour intensive on-site work, enhances workers’ safety and reduces construction time, it also benefits road users by cutting down the inconvenience caused by road resurfacing and construction works.
This project is led by Assistant Professor Yang En-Hua from the School of Civil and Environmental Engineering in collaboration with Technical University of Munich, Jurong Town Corporation (JTC) and Land Transport Authority (LTA). The bendable concrete precast pavement will be scaled up for further testing over the next three years at suitable locations within JTC’s industrial estates where there will be heavy vehicular traffic.
Figure 1: Precast concrete paving slabs for speedy construction
Web-Based 3D GeoData Modelling and Management System (GeM2S)
The main objective of the project is to establish a Web-based three-dimensional (3D) Geological and Geotechnical Data Modelling and Management System (GeM2S) to reduce construction cost and increase productivity for future underground construction projects in Singapore. A huge amount of geological and geotechnical data has been collected in the past. The 3D model and database will make use of existing borehole data as well as validated in-situ and laboratory data which can be updated with new data when these are available in the future.
Through the system, virtual borehole and cross-section can be created online as part of Building & Construction Authority’s Geoscience Information Sharing Portal; the geological conditions at a site can also be evaluated together with the geological or geotechnical model established. Uncertainties involved in the design parameters can be reduced in this way.
The proposed 3D GeM2S system can be used by both government agencies and industries for underground space planning or infrastructure developments such as buildings, roads, MRT, or underground caverns construction.
The project is led by Prof Chu Jian from the School of Civil & Environmental Engineering in collaboration with Building & Construction Authority, Land Transport Authority, Urban Redevelopment Authority of Singapore, NTU Earth Observatory of Singapore, Hong Kong University and British Geological Survey.
Tentative gridlines for the GeM2S system
Investigating the Autocrine Regulatory Roles of Adipokines on Adipose Tissue Browning and the Implications in Metabolic Diseases
The project, led by Prof Chen Peng from the School of Chemical and Biomedical Engineering in partnership with Tan Tock Seng Hospital and Duke-NUS Medical School examines the regulatory roles of locally derived adipokines on adipose browning, using integrative and interdisciplinary approaches.
In view of the growing epidemic of obesity and related metabolic diseases and the paucity of anti-obesity drugs, new therapeutic and preventive strategies against obesity are urgently needed. Turning the bad fat (white adipose) into good fat (brown adipose) could be a novel route to combat obesity. This project hopes to achieve better understanding on adipose remodeling and browning and to develop new drug delivery methods for long-term home-based treatment.
Above Figure: Apelin enhances beige adipocyte formation from [A] human white adipocytes, and [B] human mesenchymal stem cells (HMSCs). Representative optical images (Scale bars = 50 µm, upper lane), and their corresponding 5x magnified images (Scale bars = 10 µm, lower lane) are shown accordingly.
Robotic System for Large Diameter Sewers Inspection
Singapore’s Deep Tunnel Sewerage System (DTSS) was the first deep tunnel system in the world that conveyed used water by gravity to a centralized water reclamation plant when the system was commissioned in year 2009. The Phase 1 comprises 48km long deep sewerage which is up to 6 meters in diameter. As a preventive measure, there is a need to develop technologies for the inspection of the physical condition of the DTSS’ corrosion protection lining regularly.
The project aims to develop robotic technology for the inspection of the tunnel system, with focus on the design of the mobile robotic platform, sensing system for inspection and associated umbilical management system. The mobile robotic platform must be able to cope with various tunnel conditions, including dry, and partially filled with water and sediment. At the end of the 18-month project, a robotic prototype will be developed whose mobile mechanism and inspection module will be test-bedded within a large diameter trunk sewer.
Led by Assoc Prof Yeo Song Huat from the School of Mechanical & Aerospace Engineering, this project in collaboration with PUB, is the first phase of a longer term plan of PUB’s to develop robotic technology that allows inspection, desilting of debris and localised repair within the DTSS. Such capabilities and industry technical knowhow can also be adapted to other tunnel inspections such as Abu Dhabi’s STEP system and Hong Kong’s HATS system.
Figure: 1 Superhighway for Used Water Management (from PUB website)
Figure 2: Superhighway for Used Water Management (from PUB website)
Figure 3: Conceptual Design of a Robotic Platform
Test Bedding Research for Innovative Technologies in BCA SkyLab
To drive the research and development of energy-efficient building technologies in the tropics, an advanced rotatable testbed, BCA SkyLab, was developed in Singapore in collaboration with Lawrence Berkeley National Laboratory (LBNL). As the world’s first high-rise rotatable testbed for the tropics, this facility is an outdoor testbed sitting atop a 7-storey building, with a rotatable platform that can simulate various orientations of building. The testbed allows testing and evaluation of performance of various technologies in the tropical urban environment, including lighting, air-conditioning, façade systems, shading system and control systems.
Investigations will cover an integrated system that uses auto-dimming lighting control based on outdoor illuminance level through the digitally addressable lighting interface (DALI) combined with an automated daylight-redirecting blind system, active chilled beam system and thermo-chromic glass. The infrastructure developed and data obtained through this project will shed new insights in new energy-efficient technologies and building energy efficiency standards (enhancing from the current highest standard BCA Green Mark Platinum). Led by Asst Prof Wan Man Pun from the School of Mechanical & Aerospace Engineering, the research team also consists of researchers from Building and Construction Authority (BCA).
Courtesy of Building and Construction Authority, Singapore
Picture credit: Surbana Jurong Pte Ltd
In project Paddington funded by MINDEF, Asst Prof Erik Cambria from the School of Computer Science and Engineering develops and applies sentic computing for machine learning in autonomous systems e.g., for intelligent agents and robotic applications.
Sentic computing is a multi-disciplinary approach to natural language processing and understanding at the crossroads between affective computing, information extraction, and common-sense reasoning, which exploits both computer and human sciences to better interpret and process social information on the Web.
In sentic computing, whose term derives from the Latin 'sentire' (root of words such as sentiment and sentience) and 'sensus' (as in common-sense), the analysis of natural language is based on linguistics and common-sense reasoning tools, which enable the analysis of text not only at document-, page- or paragraph-level, but also at sentence-, clause-, and concept-level.
In the project, sentic computing will be exploited to understand sentiments and emotions in different modalities and different languages. Additionally, the project will also serve multiple purposes linked to sentiment mining, including human-robot interaction, emotional conversational agents, intention awareness, and domotics.
Figure above: Flowchart of the Sentic Computing Framework. Text is first deconstructed into concepts. If these are found in SenticNet, linguistic patterns are applied. If none of the concepts is available in SenticNet, a machine learning approach is employed.
Integrated Systems for Future Air Traffic
Increase in air traffic density is a major challenge in Air Traffic Control (ATC) worldwide. Air traffic density is forecasted to double by 2025 (ICAO, 2006). However, current ATC systems are approaching maximum capacity and existing ATC practices are unlikely to be able to sustain the expectant growth (CANSO, 2012). This may cause inevitable burden on air traffic controllers (ATCOs) and ultimately compromise air traffic safety.
The project led by Prof Chen Chun-Hsien of the School of Mechanical and Aerospace Engineering and team in collaboration with reseachers from Fraunhofer IDM@NTU and partners from Civil Aviation Authority of Singapore, Shenzhen University and Chiba University aims to develop and evaluate the future work place for ATCOs.
In the planned new work place, new human computer interfaces and 3D visualisations of flight data would be developed to let ATCOs interact and better comprehend the large amount of time-critical information shown by the displays in the control room (Figure 1). For example, details of aircraft flight paths essential to fast and robust flight management and planning would be displayed in 3D. Potential conflicts would also be highlighted on the screen.
Next, a series of user studies would be performed to investigate the trust, dependence, workload, performance and situation awareness of the ATCOs. Evaluation would be done real-time using bio signals such as EEG obtained from brain computer interfaces to give more reliable measures for all studies (Figure 2). Through doing this, a better understanding of ATCOs’ working patterns can be obtained and automation support can be extended appropriately, improving the capability to meet future air traffic growth.
The project is an initial effort to improve current aviation infrastructure to enhance performance of ATCOs and reduce risks of air traffic control incidents.
ICAO. (2006). Report of the Second Meeting of Automatic Dependent Surveillance - Broadcast (ADS-B) Study and Implementation Task Force (ADS-B SI TF/2). Bangkok, Thailand: International Civil Aviation Organization.
CANSO. (2012). Accelerating Air Traffic Management Efficiency. A Call to Industry. Paper of ATM Global Environment Efficiency Goals for 2050. Civil Air Navigation Service Organization.
Figure 1. Rendering of the future air traffic work place with touch interfaces
and 3D flight management
Figure 2. The ATC setup for the user study
Digital Manufacturing: Embracing the ‘Factory-of-the-Future’
Economic globalisation and urbanisation has continued to fuel new trends and demands in manufacturing and supply chain networks. In response to challenges and to seize growth opportunities in the manufacturing and service sectors, both of which are key industries and growth engines of the Singapore’s economy, the School of Computer Science and Engineering teamed up with A*STAR SIMTech through a SIMTech-NTU joint Laboratory to harness computational intelligence and complex systems technologies to address complexities in manufacturing operations and supply chain networks that will position Singapore for “Factory-of-the-Future” and maintain leadership as master facilitative control hub for supply chain in Asia Pacific.
Notably, the Joint Lab deploys the Large Engineering Supply Chain Adaptive System (LesCaS), a NTU Licensed decision support and optimisation software designed for large scale logistics and supply chain management, and the Algorithm Development Environment for Problem-Solving (ADEP), another NTU patented system with wide range of heuristics and mathematical programming tools and codes self-configured optimiser, in its research.
The 5-year SIMTech-NTU joint laboratory programme which is funded at $2.2m (and supported by 20 PhD A*GA scholarships) led by Prof Ong Yew Soon, Director of SCSE Centre for Computational Intelligence, had attracted industry interest. The research of the joint lab is aligned with SCSE’s research foci from Computational Intelligence, Big Data Analytics, Multi-Agents Modelling and Simulation to Complex Systems Optimisation.
Two of the “Factory-of-the-Future” themed projects currently undertaken at the SIMTech-NTU joint Laboratory are: “Master Facilitative Control Tower for Risk Management of Complex Supply Chains” and “Multi-Objective Vehicle Routing for Last Mile Logistics”.
In the project on Risk Management of Complex Supply Chains prompted by the increase in scale, connectivity and vulnerability of supply chains, Prof Ong and teams from the School and SIMTech ditches the traditional approach for novel bottom up analysis of complex supply chain networks. The aim is to introduce innovations for more efficient complex manufacturing, smart production operations and resilient supply chain systems. The team’s new approach investigates how a complex supply chain works as a whole, the interplay of factors and components in complex, uncertain and varied scenarios. The technologies and models developed from the research could also be applied to the aerospace, maritime, chemical and other hi-tech industries.
Figure 1. Complex Manufacturing and Supply Chain Environment
For the project on Multi-Objective Vehicle Routing for Last Mile Logistics which saw partnership with The Logistics Institute Asia Pacific, NUS and SMU, the focus is to develop multi-objective, dynamic, eco-friendly collaborative vehicle routing for last mile logistics in the city area. Last mile logistics is currently regarded as one of more expensive, least efficient and most polluting sections of the entire logistics chain in the urban environment. Through novel route planning, routing, scheduling and optimisation algorithms and eco-indicators, the research helps logistics service providers improve efficiency, reduce environmental impact (fuel consumption and emissions) and enjoy cost and time savings.
Figure 2: Eco-friendly Collaborative Last Mile Logistics