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Seismic centrifuge modeling of a gentle slope of layered clay, including a weak layer
No full textThis 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
Timed Loops for Distributed Storage in Wireless Networks
No full textIoT 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
Broad learning robust semi-active structural control: A nonparametric approach
No full textWe 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
Techno-economic analysis of recuperated Joule-Brayton pumped thermal energy storage
No full textThis 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
Flexible Planning for Intercity Multimodal Transport Infrastructure
No full textPlanning 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
Feature-Based Diversity Optimization for Problem Instance Classification.
No full textUnderstanding the behaviour of heuristic search methods is a challenge. This even holds for simple local search methods such as 2-OPT for the Travelling Salesperson Problem (TSP). In this article, we present a general framework that is able to construct a diverse set of instances which are hard or easy for a given search heuristic. Such a diverse set is obtained by using an evolutionary algorithm for constructing hard or easy instances which are diverse with respect to different features of the underlying problem. Examining the constructed instance sets, we show that many combinations of two or three features give a good classification of the TSP instances in terms of whether they are hard to be solved by 2-OPT
Convergence guarantees for gaussian process means with misspecified likelihoods and smoothness
No full textGaussian processes are ubiquitous in machine learning, statistics, and applied mathematics. They provide a exible modelling framework for approximating functions, whilst simultaneously quantifying uncertainty. However, this is only true when the model is well-specifoed, which is often not the case in practice. In this paper, we study the properties of Gaussian process means when the smoothness of the model and the likelihood function are misspecified. In this setting, an important theoretical question of practical relevance is how accurate the Gaussian process approximations will be given the chosen model and the extent of the misspecification. The answer to this problem is particularly useful since it can inform our choice of model and experimental design. In particular, we describe how the experimental design and choice of kernel and kernel hyperparameters can be adapted to alleviate model misspecification
Neural random subspace
No full textThe random subspace method, also known as the pillar of random forests, is good at making precise and robust predictions. However, there is as yet no straightforward way to combine it with deep learning. In this paper, we therefore propose Neural Random Subspace (NRS), a novel deep learning based random subspace method. In contrast to previous forest methods, NRS enjoys the benefits of end-to-end, data-driven representation learning, as well as pervasive support from deep learning software and hardware platforms, hence achieving faster inference speed and higher accuracy. Furthermore, as a non-linear component to be encoded into Convolutional Neural Networks (CNNs), NRS learns non-linear feature representations in CNNs more efficiently than contemporary, higher-order pooling methods, producing excellent results with negligible increase in parameters, floating point operations (FLOPs) and real running time. Compared with random subspaces, random forests and gradient boosting decision trees (GBDTs), NRS demonstrates superior performance on 35 machine learning datasets. Moreover, on both 2D image and 3D point cloud recognition tasks, integration of NRS with CNN architectures achieves consistent improvements with only incremental cost
Regulation of notch sensitivity of lattice materials by strut topology
No full textWe propose a local reinforcement technique for lattices in the vicinity of a stress-raiser such as a notch, in order to elevate the macroscopic strength and ductility. A spatially non-uniform waviness distribution of sinusoidally-shaped struts is assumed in the vicinity of the notch, and the sensitivity of macroscopic tensile response to strut waviness distribution is studied by finite element analysis. Optimized lattice structures are determined in order to maximise the macroscopic tensile strength or ductility from these various strut waviness distributions. Both hexagonal and triangular lattices are studied as these geometries are representative of bending-dominated and stretching-dominated lattices, respectively