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A new fracture test method—the bottom-notched splitting (BNS) test—is proposed in this study to determine the double-K fracture parameters of concrete. In the BNS test, the concrete cube with a preset crack on the bottom surface is subjected to a line compressive load on the top surface. Numerical analyses are first carried out to simulate the stress distributions along the ligament and crack opening profiles of the BNS specimens. Then, the fitting expressions of the stress intensity factor and the crack center opening displacement are derived based on the linear elastic fracture mechanics. A series of BNS tests and three-point bending (TPB) tests with different ratios of the preset crack length to the specimen height are conducted to determine the double-K fracture parameters of the concrete. The results indicate that the preset cracks in the BNS tests could initiate and propagate throughout the whole cross-sections of the concrete cubes. By substituting the obtained initial fracture loads, maximum loads, and critical crack center opening displacements in the BNS tests into the fitting expressions, the double-K fracture parameters could be determined. By comparing with the TPB tests, the BNS tests proved to be an effective method to determine the double-K fracture parameters of concrete. The BNS tests are convenient to operate and can reduce the damage risk of the preset cracks. Meanwhile, they also would be appropriate for assessing the fracture properties of existing concrete structures in service because the cubes can be easily obtained from the samples core-drilled from existing concrete structures. [ABSTRACT FROM AUTHOR]

In the age of big data, the Internet big data can finely reflect public attention to air pollution, which greatly impact ambient PM2.5 concentrations; however, it has not been applied to PM2.5 prediction yet. Therefore, this study introduces such informative Internet big data as an effective predictor for PM2.5, in addition to other big data. To capture the multi-scale relationship between PM2.5 concentrations and multi-source big data, a novel multi-source big data and multi-scale forecasting methodology is proposed for PM2.5. Three major steps are taken: 1) Multi-source big data process, to collect big data from different sources (e.g., devices and Internet) and extract the hidden predictive features; 2) Multi-scale analysis, to address the non-uniformity and nonalignment of timescales by withdrawing the scale-aligned modes hidden in multi-source data; 3) PM2.5 prediction, entailing individual prediction at each timescale and ensemble prediction for the final results. The empirical study focuses on the top highly-polluted cities and shows that the proposed multi-source big data and multi-scale forecasting method outperforms its original forms (with neither big data nor multi-scale analysis), semi-extended variants (with big data and without multi-scale analysis) and similar counterparts (with big data but from a single source and multi-scale analysis) in accuracy. [ABSTRACT FROM AUTHOR]

This study investigated the time-dependent crack propagation of the rock–concrete interface under sustained loading. First, sustained loading tests were performed on the composite rock–concrete beams under three-point bending with respect to three interface roughness degrees, i.e., natural, 4×4 , and 7×7 interfaces, and three sustained load levels, i.e., 80% of the maximum load, the initial cracking load, and 97% of the maximum load. Then, by employing the Norton-Bailey model and the time-dependent fictitious crack model, the constitutive relationships of bilateral materials and the rock–concrete interface were established and the mechanical responses of the rock–concrete interface under sustained loading were simulated. In addition, a crack propagation criterion proposed in this study was applied in the numerical procedure to simulate the time-dependent crack propagation process under sustained loading. The criterion implied that the interface crack propagated when the difference between the average elastic strain energy densities near the crack tip caused by the external load and cohesive stress exceeded that at the initial fracture status under quasi-static loading. The results indicated that the crack propagation under sustained loading could be interpreted from the view of energy balance between the driving energy from the external load and the resistance energy from the cohesive stress. The time-dependent crack propagation under sustained loading experienced three stages, i.e., decelerated propagation, uniform propagation, and accelerated propagation. A linear relationship between the logarithms of the crack mouth opening rate in the uniform propagation period and the failure time was established to predict the failure time of the rock–concrete interface under sustained loading. [ABSTRACT FROM AUTHOR]

The push-plate kiln is a kind of kiln equipment widely used in the oxygen-free sintering of high-temperature alloy materials. Its flow field monitoring has an important application value for the manufacturing industry. However, traditional simulation methods cannot meet the requirements of real-time applications due to the high computational cost and being time-consuming. The rapid development of artificial intelligence technology will empower the traditional manufacturing industry. In this paper, we propose a data-driven digital twin framework for real-time flow field prediction by combining the CFD modeling simulation, IoT, and deep learning technologies. The framework integrates geometric, rule, physical, and neural network models to achieve the real-time simulation of physical and twin objects. The proper orthogonal decomposition (POD) and multiscale convolutional neural network (MCNN) are innovatively embedded into the framework. The POD is used to map high-dimensional data to low-dimensional features, and the MCNN is used to construct models predicting low-dimensional features for fast flow field prediction. The effectiveness of the proposed model is verified by the push-plate kiln case. The results show that the digital twin can quickly predict multi-physics fields based on the perceptual data to achieve the real-time evaluation of the operating state of the push-plate kiln. [ABSTRACT FROM AUTHOR]

Distribution centers are quite important for logistics. In order to save costs, reduce energy consumption and deal with increasingly uncertain demand, it is necessary for distribution centers to select the location strategically. In this paper, a two-stage model based on an improved clustering algorithm and the center-of-gravity method is proposed to deal with the multi-facility location problem arising from a real-world case. First, a distance function used in clustering is redefined to include both the spatial indicator and the socio-economic indicator. Then, an improved clustering algorithm is used to determine the optimal number of distribution centers needed and the coverage of each center. Third, the center-of-gravity method is used to determine the final location of each center. Finally, the improved method is compared with the traditional clustering method by testing data from 12 cities in Inner Mongolia Autonomous Region in China. The comparison result proves the proposed method's effectiveness. [ABSTRACT FROM AUTHOR]

To investigate the fracture property of the rock–concrete interface after sustained loading, creep tests were conducted on composite rock–concrete beams with two mechanical grooving interfaces, 4×4 and 7×7 interfaces, under two sustained load levels, 50% and 75% of the maximum load. After 90-day sustained loading, the creep specimens were unloaded and reloaded to failure in the subsequent three-point bending tests. The viscoelastic characteristic of the rock–concrete interface was numerically simulated by employing the Bailey–Norton creep law to describe the constitutive relationships of bilateral materials. Based on the numerical results, the elastic and creep strain energies within the whole specimen and the core region near the crack tip were calculated during the loading process. The results indicated that during the creep tests, the local elastic strain energy decreased due to the stress relaxation near the crack tip, and the local creep strain energy increased due to the accumulated creep strain. It was found that, at crack initiation status, the average elastic strain energy density in the core region after sustained loading was identical to that under static loading, which indicates that the elastic energy in the core region dominated the fracture behavior. Then, an energy-based fracture criterion was proposed to judge the crack initiation of the rock–concrete interface considering viscoelastic characteristics. It was found that the average elastic strain energy density of the rock–concrete interface was not affected by the viscoelasticity, which can be regarded as a material parameter. [ABSTRACT FROM AUTHOR]

In this paper, a new hybrid genetic–particle swarm algorithm with enhanced local search ability (L-GPS) has been proposed for the structural optimization problem. This algorithm combines the wide area search ability of the genetic algorithm, the local development capacity of the particle swarm optimization algorithm, the extremely strong local search ability of the limited memory quasi-Newton algorithm as well as the optimal switching point evaluation strategy for the self-adjustment of the global and local search. L-GPS achieves a faster convergence rate than the well-known stochastic methods for a large class of standard test functions. In addition, all the global minima of Lennard–Jones (LJ) clusters of 2–19 atoms are successfully and efficiently located. Moreover, the convergence rate is much faster than the well-known structural optimization methods. Our results indicate that L-GPS is a promising structural optimization algorithm for atomic LJ clusters. [ABSTRACT FROM AUTHOR]

In the era of big data, a variety of factors (particularly meteorological factors) have been applied to PM 2.5 concentration prediction, revealing a clear discrepancy in timescale. To capture the complicated multi-scale relationship with PM 2.5 -related factors, a novel multi-factor & multi-scale method is proposed for PM 2.5 forecasting. Three major steps are taken: (1) multi-factor analysis, to select predictive factors via statistical tests; (2) multi-scale analysis, to extract scale-aligned components via multivariate empirical mode decomposition; and (3) PM 2.5 prediction, including individual prediction at each timescale and ensemble prediction across different timescales. The empirical study focuses on the PM 2.5 of Cangzhou, which is one of the most air-polluted cities in China, and indicates that the proposed multi-factor & multi-scale learning paradigms statistically outperform their corresponding original techniques (without multi-factor and multi-scale analysis), semi-improved variants (with either multi-factor or multi-scale analysis), and similar counterparts (with other multi-scale analyses) in terms of prediction accuracy., (Copyright © 2019 Elsevier Ltd. All rights reserved.)