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Super-high-speed guided systems such as hyperloops and MagLev are highly at risk of cyber and physical threats from either natural or man-made hazards. This study thus adopts a nonlinear finite element method to investigate and analyse blast responses of a spatial thin-shell structure formed as an essential part of the Hyperloop tunnelling system. The thin-shell structure is a longitudinal cylindrical tube used in hyperloop rail concepts that will have the capability to carry passenger pods travelling at speeds in excess of 1000 km/h. A robust parametric study has been carried out on a thin-shell metallic cylinder in accordance with experimental results to validate the blast simulation modelling approach. In addition, case studies have been conducted to simulate the effects of varied charge loading (TNT equivalent) of 10 kg, 15 kg and 20 kg. Since the hyperloop system is in its development stages, potential design modifications to adjust the thickness of the thin-shell cylinder are also simulated. Our findings demonstrate that thicker walls of 30 mm yield almost negligible dynamic displacements with lower blast pressures. However, this modification can cause serious ramifications in terms of infrastructure costs. On this ground, venting ports for blast mitigation have been proposed to alter and alleviate blast effects on the tube deformations. The novel insights reveal that increased venting port sizes can significantly increase the impulse deformations of the hyperloop tube but are key in reducing blast pressures within the asset infrastructure. These findings will inform hyperloop engineers about potential design solutions to ensure safety and reliability of future hyperloop rail travels amid the risks and uncertainties of cyber and physical threats. [ABSTRACT FROM AUTHOR]

The Modular Energy Island (MEI) offers a promising solution to unlock the plentiful Aeolian source at deep‐waters. Meanwhile, high‐speed wind and strong corrosivity in the harsh marine environment, along with increasing dynamics, escalate the corrosion‐fatigue (C‐F) deterioration issue of steel wind turbine towers on MEIs notably. Thus, a better understanding of the C‐F states is of particular concern to maximize the lifetime power generation and minimize operational costs. This study aims to provide novel insights into the C‐F deterioration states of wind turbine towers by integrating the material test data, site measurement, and multi‐physics simulation based on the concept of digital twins. The DTU 10MW reference turbine has been selected as an engineering prototype. Based on the in‐situ wind‐wave data, the structural response of the steel turbine tower at critical details is predicted via multi‐physics simulations and accordingly, stress spectra are constructed. Besides, the corrosion rate is estimated from material test data and the site‐specific climate conditions. Then, a probabilistic C‐F model is established for the deterioration state of critical components in the tower by incorporating the derived stress spectra and corrosion rate. Not only does this study highlight the escalating C‐F deterioration issue in steel wind turbine towers, but it also offers a novel basis for crucial insights into the digital twin‐based operation and maintenance (O&M) of the next‐generation MEIs by integrating models and data from various sources. [ABSTRACT FROM AUTHOR]

In recent years, adding fibres into brittle concrete to improve ductility has gained momentum in the construction industry. Despite the significant momentum, limitations do exist in design and industrial applications, contributing to the complexity of shear behaviours in fibre-reinforced concrete and the existing empirical models that can hardly provide a reasonable prediction, especially for high-strength concrete applications. A critical review reveals that current research mostly focuses on single eigenvalue analysis and pay less attention to the different synergetic effect of fibres on high-strength concrete and normal-strength concrete. This study aims to fill the research gap by the unprecedented use of reliable models for the prediction and evaluation of structural and sustainable properties of high-strength fibre-reinforced concrete beams. To this end, this study establishes three novel deep learning (ANN, BNN, and Xgboost) models for designing and optimising the shear capacity of 'high-strength' fibre-reinforced concrete beams towards the circular economy. In addition to introducing a new type of novel machine learning (BNN) model, which is capable of structural design and takes into account complex design features, our study also enhances sustainability by reducing greenhouse gas (GHG) emissions. The novel prediction models unprecedentedly elicit flexural capacity, structural stiffness, carbon emission, and price, together with the shear strength for high-strength fibre-reinforced structures. Firstly, this study focuses on multiple parameters for forecasting high-strength fibre-reinforced concrete beams. In addition, the models provide more comprehensive insights into the design and manufacture of high-strength steel fibre-reinforced concrete structures in a more environmentally friendly manner. With the help of the proposed models, it will be more cost-benefit and time-efficient for the researchers to obtain the optimum design with the consideration of both structural and sustainable performance. The established models exhibit excellent prediction accuracy, and the Bayesian neural network (BNN) is found to have the best performance: R2 is 0.937, MSE is 0.06 and MAE is 0.175 in shear strength prediction; R2 = 0.968, MSE is 0.040, and MAE is 0.110 in flexural capacity prediction; R2 is 0.907, MSE is 0.070, and MAE is 0.204 in shear stiffness prediction; R2 is 0.974, MSE is 0.022, and MAE is 0.063 in carbon emission prediction; and R2 is 0.977, MSE is 0.020, and MAE is 0.082 in price prediction. [ABSTRACT FROM AUTHOR]

As the key safety-critical component of a bridge support system, bridge bearings are extensively used to accommodate, balance, and transfer differential displacements and loads between the superstructure and substructure of a bridge during operations. Several studies have been conducted to obtain dynamic modal parameters of traditional bridge bearings only in perfectly dry environments. However, in extreme weather conditions (e.g., heavy rain, flash floods, etc.), water can ingress and change the bearings' properties. In this study, novel 3D-printed porous bridge bearings (3DPPBBs) have been fabricated by Fused Deposition Modeling (FDM) with thermoplastic polyurethane (TPU) filaments. This study is the first to determine the influences of flood conditions on their dynamic properties, which has never been done before. An idealised single degree of freedom (ISDOF) for these novel bearings is considered for the non-destructive field-testing technique of the critical bridge component. A series of experimental tests have been performed under several conditions of flooding levels. The new results unprecedentedly indicate that relatively higher dynamic damping ratios can be found with the increasing flood levels. In contrast, the natural frequencies and dynamic stiffness decrease with the same conditions. Novel insights are essential for bridge engineers to assess and monitor bridge vibrations exposed to extreme weather conditions. [ABSTRACT FROM AUTHOR]

Excessive external vibrations could affect the normal functioning and integrity of sensitive buildings such as laboratories and heritage buildings. Usually, these buildings are exposed to multiple external vibration sources simultaneously, so the monitoring and respective evaluation of the vibration from various sources is necessary for the design of targeted vibration mitigation measures. To classify the sources of vibration accurately and efficiently, the advanced hybrid models of the convolutional neural network (CNN) and long short-term memory (LSTM) network were built in this study, and the models are driven by the extensive data of external vibration recorded in Beijing, and the parametric studies reveal that the proposed optimal model can achieve an accuracy of over 97% for the identification of external vibration sources. Finally, a real-world case study is presented, in which external vibration monitoring was carried out in a laboratory and the proposed CNN+LSTM model was used to identify the sources of vibration in the monitoring so that the impact of vibration from each source on the laboratory was analyzed statistically in detail. The results demonstrate the necessity of this study and its feasibility for engineering applications. [ABSTRACT FROM AUTHOR]

The interspersed railway track is an enhanced timber railway track, spot-replacing damaged wooden sleepers with new concrete sleepers to improve the bearing capacity of existing railway lines. Although this interspersed solution is characterised by low cost and short maintenance time, the interspersed tracks have worse stability than concrete tracks and can deteriorate quickly when exposed to extreme weather conditions such as heavy rains and floods. In many cases, heavy rains and floods are accompanied by strong winds. Ballast washaway can often be observed under flood conditions while the mass of trains is unevenly distributed on two rails due to the effect of lateral wind load and rail irregularities. The current work is the first in the world to investigate the collective multi-hazard effects of ballast washway and uneven axle loads on the vulnerability of conventional and interspersed railway tracks using nonlinear FEM software, STRAND 7. The train bogie is modelled by two sets of point loads. The maximum displacement, bending moment and twists have been studied to evaluate the worst condition. The novel insights will help the railway industry develop proper operations of interspersed railway tracks against naturally hazardous conditions. [ABSTRACT FROM AUTHOR]

Most of the Artificial Intelligence (AI) models currently used in energy forecasting are traditional and deterministic. Recently, a novel deep learning paradigm, called 'transformer', has been developed, which adopts the mechanism of self-attention. Transformers are designed to better process and predict sequential data sets (i.e., historical time records) as well as to track any relationship in the sequential data. So far, a few transformer-based applications have been established, but no industry-scale application exists to build energy forecasts. Accordingly, this study is the world's first to establish a transformer-based model to estimate the energy consumption of a real-scale university library and benchmark with a baseline model (Support Vector Regression) SVR. With a large dataset from 1 September 2017 to 13 November 2021 with 30 min granularity, the results using four historical electricity readings to estimate one future reading demonstrate that the SVR (an R2 of 0.92) presents superior performance than the transformer-based model (an R2 of 0.82). Across the sensitivity analysis, the SVR model is more sensitive to the input close to the output. These findings provide new insights into the research area of energy forecasting in either a specific building or a building cluster in a city. The influences of the number of inputs and outputs related to the transformer-based model will be investigated in the future. [ABSTRACT FROM AUTHOR]

This study investigates the engineering and mechanical properties of basalt fiber-reinforced (FRF) concrete, giving special attention to residual flexural strength and dynamic modal parameters. These properties, which have not been thoroughly investigated elsewhere, are a precursor to structural design applications for dynamic compliant structures (i.e., bridges, offshore platforms, railways, and airport pavement). Accordingly, the standard notched flexural tests have been carried out to assess the basalt fiber-reinforced concrete's residual flexural strength with an additional 0.125%, 0.25%, 0.375%, and 0.5% of volume fraction of basalt fiber. In addition, dynamic modal tests were then conducted to determine the dynamic modulus of elasticity (MOE) and damping of the FRF concrete beams. The results indicate that concrete's toughness and crack resistance performance are significantly improved with added fiber in basalt fiber reinforced concrete, and the optimum fiber content is 0.25%. It also exhibits the highest increment of compressive strength of 4.48% and a dynamic MOE of 13.83%. New insights reveal that although the residual flexural performance gradually improved with the addition of basalt fiber, the damping ratio had an insignificant change. [ABSTRACT FROM AUTHOR]

Railway track maintenance plays an important role in enabling safe, reliable, and seamless train operations and passenger comfort. Due to the increasing rail transportation, rolling stocks tend to run faster and the load tends to increase continuously. As a result, the track deteriorates quicker, and maintenance needs to be performed more frequently. However, more frequent maintenance activities do not guarantee a better overall performance of the railway system. It is crucial for rail infrastructure managers to optimize predictive and preventative maintenance. This study is the world's first to develop deep machine learning models using three-dimensional recurrent neural network-based co-simulation models to predict track geometry parameters in the next year. Different recurrent neural network-based techniques are used to develop predictive models. In addition, a building information modeling (BIM) model is developed to integrate and cross-functionally co-simulate the track geometry measurement with the prediction for predictive and preventative maintenance purposes. From the study, the developed BIM models can be used to exchange information for predictive maintenance. Machine learning models provide the average R2 of 0.95 and the average mean absolute error of 0.56 mm. The insightful breakthrough demonstrates the potential of machine learning and BIM for predictive maintenance, which can promote the safety and cost effectiveness of railway maintenance. [ABSTRACT FROM AUTHOR]

Innovative digital twins (DTs) that allow engineers to visualise, share information, and monitor the condition during operation is necessary to optimise railway construction and maintenance. Building Information Modelling (BIM) is an approach for creating and managing an inventive 3D model simulating digital information that is useful to project management, monitoring and operation of a specific asset during the whole life cycle assessment (LCA). BIM application can help to provide an efficient cost management and time schedule and reduce the project delivery time throughout the whole life cycle of the project. In this study, an innovative DT has been developed using BIM integration through a life cycle analysis. Minnamurra Railway Bridge (MRB), Australia, has been chosen as a real-world use case to demonstrate the extended application of BIM (i.e., the DT) to enhance the operation, maintenance and asset management to improve the sustainability and resilience of the railway bridge. Moreover, the DT has been exploited to determine GHG emissions and cost consumption through the integration of BIM. This study demonstrates the feasibility of DT technology for railway maintenance and resilience optimisation. It also generates a virtual collaboration for co-simulations and co-creation of values across stakeholders participating in construction, operation and maintenance, and enhancing a reduction in costs and GHG emission. [ABSTRACT FROM AUTHOR]

With an emphasis on the integrated deterioration of railway track geometry and components, a new hybrid numerical-analytical method is proposed for the predictive analysis of track geometrical vertical levelling loss (VLL). In contrast to previous studies showing a dependency on the number of cycles, this research unprecedentedly incorporates the influence of operational, vehicle and track conditions. The numerical models are carried out using an explicit finite element (FE) package under cyclic loadings, and then, their outcomes are iteratively regressed by an analytical logarithmic function that accumulates permanent deformations in order to quantify VLL over a long term. The results are first compared with other previous studies, indicating a very good agreement with them. Then, field measurements have been used to further verify the results. In this study, parametric simulations are performed varying three key parameters: axle load, train velocity and ballast tangent stiffness. The parametric studies exhibit that the rate of VLL raises about 50% if the axle load increases only from 30 ton to 40 ton for a freight train running at 70 km/h on a stiffer ballast track. In contrast, for a 25-tonnes-axle-load train running from 60 km/h to 100 km/h on a similar track, the vertical levelling degradation reduces by approximately 20%. The main findings suggest that higher axle loads contribute significantly to the VLL due higher contact forces and, on the other hand, a lower train speed does not necessarily imply a low rate of VLL since the influence of train velocities on track geometry (VLL) is associated with the natural frequencies (or wavelengths) of the ballasted railway track. The insight demonstrates that the load frequencies play a key role on the deterioration of VLL. [ABSTRACT FROM AUTHOR]