Flexible Planning for Intercity Multimodal Transport Infrastructure

Planning transport infrastructure development involves high levels of uncertainty due to socioeconomic, environmental, and technological changes. Methodologies currently used in transport planning often have minimal consideration for adaptiveness, leading to costly redesigns or cancellation of entire projects. Presented herein is the investigation of the applicability of dynamic adaptive policy pathways, which is a methodology predominantly used in the field of flood-risk planning, to long-term transport infrastructure planning. Specifically, the paper investigates whether this methodology could facilitate ongoing adaptation to variations in service demand and capacity. It demonstrates this by examining future demand and capacity of road and rail travel between Manchester, United Kingdom, and London using publicly available data and information sources. The study shows that dynamic adaptive policy pathways is useful for identifying periods of time of significant capacity vulnerability for the examined transport network in the coming decade. The method is demonstrated to be valuable for identifying the points in time when policy-makers will have to make decisions and for assessing the impact of transport mode switching. This can have implications of cost-saving and improved service delivery

Broad learning robust semi-active structural control: A nonparametric approach

We propose a novel algorithm for dynamic response suppression via semi-active control devices, which we refer to as broad learning, robust, semi-active control (BLRSAC). To configure the semi-active controller, a nonparametric reliability-based output feedback control strategy is introduced. In particular, an adaptive broad learning network is developed to formulate the control strategy using the clipped-optimal control technique. The learning network is augmented incrementally to adopt additional training data based on the inherited information of the trained learning network. By utilizing a robust failure probability, the training dataset is obtained adaptively to include the training input–output pairs with optimal structural control performance. The robust failure probability we propose incorporates both predicted failure probability and the uncertainty of the underlying structure. Therefore, the resultant control strategy can handle the inevitable uncertainty of the actual control situation to achieve optimal structural control. To examine the efficacy of the proposed BLRSAC algorithm, illustrative examples of a shear building and a three-dimensional braced frame under various external excitation and structural damaging conditions are presented

Timed Loops for Distributed Storage in Wireless Networks

IoT deployments that have limited memories lack sustained computation power and have limited connectivity to the Internet due to intermittent last-mile connectivity, particularly in rural and remote locations. For maintaining congestion-free operations, most of the collected data from these networks are discarded, instead of being transmitted remotely for further processing. In this article, we propose the paradigm Timed Loop Storage to distribute the data and use the underutilized bandwidth of local network links for sequentially queuing packets of computational data that are being operated on in parts in one of the IoT nodes. While the sequenced packets are executed sequentially on the target IoT device, the remaining packets, which are currently not being operated on, distribute and keep looping over the network links until they are required for processing. A time-synchronized packet deflection mechanism on each node handles data transfer and looping of individual packets. In our implementation, although we observe that the proposed approach requires data rates of 6 Mbps, it incurs only 45 Kb usage of primary storage systems even for sizeable data, ensuring scalability of the connected IoT devices' temporary storage capabilities, thereby making it useful for real-life applications

Techno-economic analysis of recuperated Joule-Brayton pumped thermal energy storage

This article describes a techno-economic model for pumped thermal energy storage systems based on recuperated Joule-Brayton cycles and two-tank liquid storage. Models have been developed for each component, with particular emphasis on the heat exchangers. Economic metrics such as the power and energy capital costs (i.e., per-kW and per-kWh capacity) and levelized cost of storage are evaluated by gathering numerous cost correlations from the literature, thereby enabling estimates of uncertainty. It is found that the use of heat exchangers with effectivenesses up to 0.95 is economically worthwhile, but higher values lead to rapidly escalating component size and system cost. Several hot storage fluids are considered; those operating at the highest temperatures (chloride salts) improve the round-trip efficiency but the benefit is marginal and may not warrant the additional material costs and risk when compared to lower-temperature nitrate salts. Cost-efficiency trade-offs are explored using a multi-objective optimization algorithm, yielding optimal designs with round-trip efficiencies in the range 59–72% and corresponding levelized storage costs of 0.12 ± 0.03 and 0.38 ± 0.10 $/kWhe. Lifetime costs are competitive with lithium-ion batteries for discharging durations greater than 6 h under current scenarios

Seismic centrifuge modeling of a gentle slope of layered clay, including a weak layer

This article presents a model preparation methodology for simulating the seismic behavior of a gentle slope in clay with the presence of a soft, weak layer employing centrifuge testing. The model consisted of a three-layered slope of relatively soft clay with a 3° inclination, representative of Brazilian marine subsoils. In-flight characterization of the undrained shear strength and shear wave velocity profiles were achieved through T-bar penetrometer and air hammer tests. The model was subjected to a series of earthquake simulations at different amplitudes, and the response was tracked with accelerometers and displacement transducers. Additional data were obtained using a particle image velocimetry (PIV) methodology also described in this work. The results show that the proposed model preparation methodology enables the simulation of the strength contrast between the weak and relatively stronger surrounding layers using a laminar container. The additional displacement and acceleration data obtained from the PIV were in good agreement with the corresponding displacement transducer and accelerometer measurements. From the spectral analysis, a shift in the fundamental period was observed as the strain amplitude was increased, suggesting that strain rate effects mobilize higher stresses and a strength rate correction should be considered for the calibration of numerical models and comparison with existing methods for calculation of dynamic displacements in slopes

LIBS2ML: A library for scalable second order machine learning algorithms

LIBS2ML is a library based on scalable second order learning algorithms for solving large-scale problems, i.e., big data problems in machine learning. LIBS2ML has been developed using MEX files, i.e., C++ with MATLAB/Octave interface to take the advantage of both the worlds, i.e., faster learning using C++ and easy I/O using MATLAB. Most of the available libraries are either in MATLAB/Python/R which are very slow and not suitable for large-scale learning, or are in C/C++ which does not have easy ways to take input and display results. So LIBS2ML is completely unique due to its focus on the scalable second order methods, the hot research topic, and being based on MEX files. Thus it provides researchers a comprehensive environment to evaluate their ideas and it also provides machine learning practitioners an effective tool to deal with the large-scale learning problems. LIBS2ML is an open-source, highly efficient, extensible, scalable, readable, portable and easy to use library. The library can be downloaded from the URL: \url{https://github.com/jmdvinodjmd/LIBS2ML}

Fibre optic sensing of ageing railway infrastructure enhanced with statistical shape analysis

Developing early-warning sensor-based maintenance systems for ageing railway infrastructure, such as masonry arch bridges, can be a challenging task due to the difficulty of identifying degradation/damage as the source of small, gradual changes in sensor data, as opposed to other environmental and loading effects. This paper offers a new method of applying statistical modelling and machine learning to enhance the interpretation of fibre optic sensing data, and, therefore, improve deterioration monitoring of railway infrastructure. Dynamic strain and temperature monitoring data between 2016 and 2019 from a fibre Bragg grating (FBG) network installed in a Victorian railway viaduct are first presented. The statistical shape analysis adopted in this study is modified to track changes in the shape of FBG signals directly linked to train speed and dynamic strain amplitudes. The method is complemented by a support vector machine, which is trained to identify different classes of trains. After distinguishing train types, dynamic strain was found to be clearly correlated to temperature, verifying previous findings. No correlation with train speed was observed. The integrated system is then able to compensate for changes in the structural performance due to variations in train loading and ambient temperature, and identify changes in dynamic deformation caused by degradation, in an order comparable to the signal noise (± 2 micro-strain). As a result, the new procedure is shown to be capable of detecting small magnitudes of local degradation well before this degradation manifests itself in typical global measures of response

Design and model for ‘falling particle’ biosensors

Particle-immobilized enzymes have proven benefits when integrated into biosensors, typically via packed-bed approaches in microfluidic channels. These benefits include dramatically improving sensitivity by increasing the effective surface area to volume ratio and enhancing shelf-life through their thermal stability. However, microfluidic approaches require complex fabrication steps to create weirs or pillars that hold the particles in place and an external pump to control sample flow. In a global trend for affordable diagnostics, there is a need to benefit from the improved performance of particle-based systems while also simplifying the fabrication and readout techniques. Here, we present a new biosensor format, where the bio-functionalized particles are moved through the fluid sample in which they are suspended. We deliver a first study into the main design considerations for this falling particle biosensor, detailing the interdependencies between the kinetics of the enzyme reaction, the mass transport of the substrate to the enzyme on the surface of the particle, and the falling behavior of the settling particles. We detail, through a mathematical model, validated by experimental results, how particle size and enzyme loading are able to influence the outcome measured and establish that this falling particle model does not deviate from the kinetic regime, but that particle size and enzyme loading can be used to tune the signal resolution and deliver simple but highly effective sensors

Non-isothermal phase-field simulations of laser-written in-plane SiGe heterostructures for photonic applications

Advanced solid-state devices, including lasers and modulators, require semiconductor heterostructures for nanoscale engineering of the electronic bandgap and refractive index. However, existing epitaxial growth methods are limited to fabrication of vertical heterostructures grown layer by layer. Here, we report the use of finite-element-method-based phase-field modelling with thermocapillary convection to investigate laser inscription of in-plane heterostructures within silicon-germanium films. The modelling is supported by experimental work using epitaxially-grown Si0.5Ge0.5 layers. The phase-field simulations reveal that various in-plane heterostructures with single or periodic interfaces can be fabricated by controlling phase segregation through modulation of the scan speed, power, and beam position. Optical simulations are used to demonstrate the potential for two devices: graded-index waveguides with Ge-rich (>70%) cores, and waveguide Bragg gratings with nanoscale periods (100–500 nm). Periodic heterostructure formation via sub-millisecond modulation of the laser parameters opens a route for post-growth fabrication of in-plane quantum wells and superlattices in semiconductor alloy films