Advanced Materials Research
Vol. 651
Vol. 651
Advanced Materials Research
Vol. 650
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Vol. 649
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Advanced Materials Research
Vol. 648
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Advanced Materials Research
Vol. 647
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Advanced Materials Research
Vol. 646
Vol. 646
Advanced Materials Research
Vol. 645
Vol. 645
Advanced Materials Research
Vol. 644
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Advanced Materials Research
Vol. 643
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Advanced Materials Research
Vols. 641-642
Vols. 641-642
Advanced Materials Research
Vols. 639-640
Vols. 639-640
Advanced Materials Research
Vols. 634-638
Vols. 634-638
Advanced Materials Research
Vol. 633
Vol. 633
Advanced Materials Research Vol. 645
Paper Title Page
Abstract: The main factors are analyzed in the paper for the culvert diseases of high-filled embankment and the main factors affecting the vertical earth pressure of culvert are also analyzed from the perspective of culvert-soil function mechanism & soil arching effect, then the suitable nonlinear formula to calculate such pressure is put forward. In order to reduce the culvert diseases, the simple load reduction measures are proposed. Suitable method of soft foundation treatment is also suggested based on the analysis of influence to the internal forces of culvert structure by the varied elastic modulus of foundation, which will offer guidance and reference for the design and construction of such projects.
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Abstract: Based on the excavation of portal section in Linwu neighborhood tunnel, items of the blasting vibration, vault settlement and level convergence are measured in the field construction. And according to the actual exposed surrounding rock, the finite element analytical model is established. The results of monitoring measurement and numerical analysis indicate that, under the circumstances of advance large pipe-shed roof support and varied surrounding rock, the original excavation method is not reasonable, still existing the optimization space. The construction of changed step method is feasible, which reflects the requirement of dynamic design and construction in New Austrian Tunneling Method.
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Abstract: The online prediction of the low carbon ferrochrome terminal composition in electro-silicothemic smelting process plays a key role in guiding the determining the tapping time, the smelting process of the power supply system, the production quality and the energy consumption and so on. By introducing the multi-scale wavelet kernel function in the support vector machine (SVM) algorithm, and according to the Bayesian classifier to certain different smelting conditions, we chose different decomposition scales. In this way, the accuracy of the terminal composition prediction during the smelting process is improved greatly. Experiments show the effectiveness of the proposed method.
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