Bearing capacity and failure mechanism of strip footings on anisotropic sand

Sand typically exhibits anisotropic internal structure (or fabric), and the fabric anisotropy has a dramatic influence on the mechanical behavior of sand. Meanwhile, the fabric evolves when sand is subjected to external loading. This eventually makes the response of strip footings on sand dependent on fabric anisotropy and fabric evolution. A numerical investigation on this effect is presented using a critical state sand model accounting for fabric evolution. The model parameters are determined based on plane strain and triaxial compression test data, and the model performance is validated by centrifuge tests for strip footings on dry Toyoura sand. The bearing capacity of strip footings is found to be dependent on the bedding plane orientation of dense sand. However, this effect vanishes as the sand density decreases, though the slope of the force-displacement curve is still lower for vertical bedding. Progressive failure is observed for all the simulations. General shear failure mode occurs in dense and medium dense sand, and the punching shear mode is the main failure mechanism for loose sand. In general shear failure, unsymmetrical slip lines develop for sand with an inclined bedding plane due to the noncoaxial sand behavior caused by fabric anisotropy. For strip footing on sand with horizontal bedding, the bearing capacity and failure mechanism are primarily affected by the sand density. The bearing capacity of a strip footing is higher when the sand fabric is more isotropic for the same soil density. An isotropic model can give significant overestimation on the bearing capacity of strip footings

A hybrid LSTM neural network for energy consumption forecasting of individual households

Irregular human behaviors and univariate datasets remain as two main obstacles of data-driven energy consumption predictions for individual households. In this study, a hybrid deep learning model is proposed combining an ensemble long short term memory (LSTM) neural network with the stationary wavelet transform (SWT) technique. The SWT alleviates the volatility and increases the data dimensions, which potentially help improve the LSTM forecasting accuracy. Moreover, the ensemble LSTM neural network further enhances the forecasting performance of the proposed method. Verification experiments were performed based on a real-world household energy consumption dataset collected by the 'UK-DALEat project. The results show that, with a competitive training efficiency, the proposed method outperforms all compared state-of-art methods, including the persistent method, support vector regression (SVR), long short term memory (LSTM) neural network and convolutional neural network combining long short term memory (CNN-LSTM), with different step sizes at 5, 10, 20 and 30 minutes, using three error metrics

Fractional elastoplastic constitutive model for soils based on a novel 3D fractional plastic flow rule

A novel three-dimensional (3D) fractional plastic flow rule that is not limited by the coordinate basis of the differentiable function is proposed based on the fractional derivative and the coordinate transformation. By introducing the 3D fractional plastic flow rule into the characteristic stress space, a 3D fractional elastoplastic model for soil is established for the first time. Only five material parameters with clear physical significance are required in the proposed model. The capability of the model in capturing the strength and deformation behaviour of soils under true 3D stress conditions is verified by comparing model predictions with test results

Real-Time Parameter Identification for Forging Machine Using Reinforcement Learning

It is a challenge to identify the parameters of a mechanism model under real-time operating conditions disrupted by uncertain disturbances due to the deviation between the design requirement and the operational environment. In this paper, a novel approach based on reinforcement learning is proposed for forging machines to achieve the optimal model parameters by applying the raw data directly instead of observation window. This approach is an online parameter identification algorithm in one period without the need of the labelled samples as training database. It has an excellent ability against unknown distributed disturbances in a dynamic process, especially capable of adapting to a new process without historical data. The effectiveness of the algorithm is demonstrated and validated by a simulation of acquiring the parameter values of a forging machine

Physical-layer key distribution using synchronous complex dynamics of DBR semiconductor lasers

Common-signal-induced synchronization of semiconductor lasers with optical feedback inspired a promising physical key distribution with information-theoretic security and potential in high rate. A significant challenge is the requirement to shorten the synchronization recovery time for increasing key rate without sacrificing operation parameter space for security. Here, open-loop synchronization of wavelength-tunable multi-section distributed Bragg reflector (DBR) lasers is proposed as a solution for physical-layer key distribution. Experiments show that the synchronization is sensitive to two operation parameters, i.e., currents of grating section and phase section. Furthermore, fast wavelength-shift keying synchronization can be achieved by direct modulation on one of the two currents. The synchronization recovery time is shortened by one order of magnitude compared to close-loop synchronization. An experimental implementation is demonstrated with a final key rate of 5.98 Mbit/s over 160 km optical fiber distance. It is thus believed that fast-tunable multi-section semiconductor lasers opens a new avenue of high-rate physical-layer key distribution using laser synchronization.Comment: 13 pages, 5 figure