Infrastructure Engineering - Research Publications
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ItemMultimodal relationships: shared and automated vehicles and high-capacity public transit(The Institution of Engineering and Technology, 2021-12-01)Shared mobility is gaining increasing attention in private and public sectors. Serving as a source of information on how best to shape shared vehicle systems of the future, this book contributes knowledge on key facets of shared mobility.
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ItemInternet of Things for Structural Health Monitoring(CRC Press, 2016-05)The Internet revolution led to the interconnection between people at an unprecedented scale and pace. The ability of the sensor networks to send data over the Internet further enhanced the scope and usage of the sensor networks. The Internet uses unique address to identify the devices connected to the network. Structural Health Monitoring (SHM) implies monitoring of the state of the structures through sensor networks in an online mode and are pertinent to aircraft and buildings. SHM can be further divided into two categories: global health monitoring and local health monitoring. Continuous online SHM would be an ideal solution. SHM is performed by using acoustic sensors, ultrasonic sensors, strain gauges, optical fibers, and so on. Video cameras can also be used for SHM. SHM can be achieved in real-time and rich analytics. With the advent of smart sensors—sensors with programmable microprocessors, memory, and processing—has reduced load of central data processing, communication overhead while proving continuous SHM status.
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ItemParking Occupancy Detection and Slot Delineation Using Deep Learning: A Tutorial(TU Wien Academic Press, 2021)This chapter describes a simple method for parking occupancy detection and an automatic parking slot delineation method using CCTV images. These methods will be presented in the form of MATLAB tutorials with code snippets to allow the interested reader to implement the method and obtain results on a sample dataset. The first tutorial will involve fine-tuning a pre-trained deep neural network for vehicle detection in a sequence of CCTV camera images to determine the occupancy of the parking spaces. In the second tutorial, we perform spatio-temporal analysis of the detections made by a state-of-the-art deep learning object detector (Faster-RCNN) for automatic parking slot delineation.
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ItemComputer Vision Techniques for Urban Mobility(TU Wien Academic Press, 2021)This chapter provides an overview of computer vision techniques with applications in urban mobility and transport systems. Focusing on imagery and Light Detection and Ranging (LiDAR) point clouds as the main data modalities, the chapter reviews relevant computer vision tasks, including classification, segmentation, object detection and tracking. Example applications of these techniques to data captured by stationary sensors installed in the environment as well as mobile sensors onboard vehicles will then be discussed.
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ItemSensors for Parking Occupancy Detection(TU Wien Academic Press, 2021)This chapter provides an overview of sensor technologies and methodologies for determining the occupancy of parking spaces. It covers a range of sensors including active and passive sensors that can be installed overhead, in or on the ground in both indoor and outdoor environments. The chapter also provides a comparison of sensors, and Discusses considerations for sensor selection and open challenges in parking occupancy detection.
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ItemStructural Health Monitoring of Bridges Using Advanced Non-destructive Testing Technique(Springer, Singapore, 2020-01-01)This paper presents an integrated framework for structural health monitoring of bridges by using advanced non-destructive testing (NDT) technique in conjunction with computational modelling. First, the structural characteristics of the Eltham Trestle Bridge under train loading were monitored using the combination of the 3D optical measurement system and IBIS-S. The results demonstrate that, in conjunction with computational modelling, the NDT can capture the structural health conditions of the bridge by analysing the natural frequencies and deformation profiles of the critical members of the bridges. Then, the developed framework also takes into account the impact of extreme events (e.g. truck impacts and earthquakes) by using a reliability-based model. Finally, using the Montague Street Bridge as a case study, it shows that proposed framework has the capability of predicting the residual life of a bridge subject to both progressive deterioration and extreme events throughout its service life.
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ItemAutomation in structural health monitoring of transport infrastructure(Springer Singapore, 2021-01-01)Roads are among the most important assets in the world. Road structure improvements make a crucial contribution to economic development and growth and bring important social benefits. Automation in structural health monitoring allow the accurate prediction of ongoing damage caused by long-term traffic loading. This permits optimal road structure management and ensures the longevity and safety of road structures. This chapter discusses a variety of advanced automation techniques in structural health monitoring of road structures, such as data acquisition, data processing, and life-cycle assessment. It demonstrates that the implementation of automation in road asset management can increase the productivity and extend the service life of road structures, and enhance the durability of crucial road structures and increase transport infrastructure sustainability.
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ItemForeword(Open Book Publishers, 2019-11-01)
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ItemUrban Analytics Data Infrastructure: Critical SDI for Measuring and Monitoring The National and Local Progress of SDGs(CRC Press - Taylor & Francis Group, 2020-01-01)This chapter describes an innovative Spatial Data Infrastructure to support urban analytics and urban research capabilities focused on Australian cities, called Urban Analytics Data Infrastructure (UADI). The UADI provides opportunity for multi-disciplinary, and cross-jurisdictional analytics. The chapter highlights the UADI capabilities to be adopted for deriving the SDG indicators as a response to the UN-GGIM strategic framework 2017 { 2021 technical requirements.