Infrastructure Engineering - Research Publications
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ItemNo Preview AvailableProgressive collapse and robustness of modular high-rise buildings(TAYLOR & FRANCIS LTD, 2022-01-01)
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ItemNo Preview AvailableDual-mechanism auxetic-core protective sandwich structure under blast loading(ELSEVIER SCI LTD, 2022-11-01)
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ItemNo Preview AvailableThe low frequency structure-borne sound problem in multi-storey timber buildings and potential of acoustic metamaterials: A review(PERGAMON-ELSEVIER SCIENCE LTD, 2022-10)
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ItemEnergy Efficient Time Synchronization in WSN for Critical Infrastructure Monitoring(SPRINGER-VERLAG BERLIN, 2011-01-01)Wireless Sensor Networks (WSN) based Structural Health Monitoring (SHM) is becoming popular in analyzing the life of critical infrastructure such as bridges on a continuous basis. For most of the applications, data aggregation requires high sampling rate. A need for accurate time synchronization in the order of 0.6 − 9 μs every few minutes is necessary for data collection and analysis. Two-stage energy-efficient time synchronization is proposed in this paper. Firstly, the network is divided into clusters and a head node is elected using Low-Energy Adaptive Clustering Hierarchy based algorithm. Later, multiple packets of different lengths are used to estimate the delay between the elected head and the entire network hierarchically at different levels. Algorithmic scheme limits error to 3-hop worst case synchronization error. Unlike earlier energy-efficient time synchronization schemes, the achieved results increase the lifetime of the network.
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ItemVision transformer-based autonomous crack detection on asphalt and concrete surfaces(ELSEVIER, 2022-08)Previous research has shown the high accuracy of convolutional neural networks (CNNs) in asphalt and concrete crack detection in controlled conditions. Yet, human-like generalisation remains a significant challenge for industrial applications where the range of conditions varies significantly. Given the intrinsic biases of CNNs, this paper proposes a vision transformer (ViT)-based framework for crack detection on asphalt and concrete surfaces. With transfer learning and the differentiable intersection over union (IoU) loss function, the encoder-decoder network equipped with ViT could achieve an enhanced real-world crack segmentation performance. Compared to the CNN-based models (DeepLabv3+ and U-Net), TransUNet with a CNN-ViT backbone achieved up to ~61% and ~3.8% better mean IoU on the original images of the respective datasets with very small and multi-scale crack semantics. Moreover, ViT assisted the encoder-decoder network to show a robust performance against various noisy signals where the mean Dice score attained by the CNN-based models significantly dropped (<10%).
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ItemReal-time monitoring of construction sites: Sensors, methods, and applications(ELSEVIER, 2022-04)The construction industry is one of the world's largest industries, with an annual budget of $10 trillion globally. Despite its size, the efficiency and growth in labour productivity in the construction industry have been relatively low compared to other sectors, such as manufacturing and agriculture. To this extent, many studies have recognised the role of automation in improving the efficiency and safety of construction projects. In particular, automated monitoring of construction sites is a significant research challenge. This paper provides a comprehensive review of recent research on the real-time monitoring of construction projects. The review focuses on sensor technologies and methodologies for real-time mapping, scene understanding, positioning, and tracking of construction activities in indoor and outdoor environments. The review also covers various case studies of applying these technologies and methodologies for real-time hazard identification, monitoring workers’ behaviour, workers’ health, and monitoring static and dynamic construction environments.
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ItemNo Preview AvailableBallistic performance of a lightweight nacre-inspired armour panel – a numerical study(Elsevier, 2022-08-01)This paper provides a numerical investigation on the ballistic performance of ceramic composite armour panels with porous architectures inspired by nacre from mollusc shells. The development of such armour panels can lead to reduced weight and improve ballistic performance through projectile rotation induced by non-uniform contact stresses. The porous tablets of nacre have the potential to reduce the mass of armour panels without compromising their ballistic performance. Preliminary simulations were conducted to assess the performance of several porous bio-inspired structures that have the potential to survive projectile impact with less mass. Several bio-inspired panels composed of various porous designs were generated based on the different void architectures of nacre's tablets. Their performances were benchmarked against a monolithic panel under the same projectile impact condition and having the same 4.5 mm overall panel thickness but higher mass. It was found that for the cases considered, the biomimetic panels with porous architectures possess better ballistic performance compared to the corresponding monolithic panel by arresting the projectile having an initial impact velocity of 500 m/s, whilst reducing its mass by up to 18%. A further study was conducted on bio-inspired panels with a higher thickness but the same mass as the solid monolithic panel under the same projectile impact condition. It was found that the bio-inspired panel exhibits significantly improved ballistic performance when subjected to a fragment simulating projectile having an initial velocity chosen to be the ballistic limit velocity of the corresponding monolithic panel.
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