Design optimization of cement grouting material based on adaptive boosting algorithm and simplicial homology global optimization

2022 ◽  
pp. 104049
Author(s):  
Jiaolong Ren ◽  
Hongbo Zhao ◽  
Lin Zhang ◽  
Zedong Zhao ◽  
Yinshan Xu ◽  
...  
2021 ◽  
pp. 1-44
Author(s):  
Yunxiao Liu ◽  
Jiahang Zhang ◽  
Yinyin Chi

In this study, three different diameters of multi-walled carbon nanotubes (MWCNTs) dispersed by polyvinyl pyrrolidone (PVP) were used to reinforce superfine cement grouting materials. The effect of MWCNTs and polyvinyl pyrrolidone (PVP) on the rheological properties of grouting material were accordingly studied. It was found that the yield stress (τ0) and plastic viscosity (η) were slightly decreased when PVP content was low and increased when the PVP content increased. The effect of MWCNT diameter on τ0 was not found to be clear but was more significant on η. The smaller MWCNT diameter was, the more quickly η increase. It was also found that the thixotropic ring area was increased as the MWCNTs content increased. The addition of PVP and MWCNTs caused an increase in the number of entanglement points in different scales, which was the main reason for the viscosity and thixotropy increase. Therefore, the rheological properties of superfine cement grouting material should be adjusted when MWCNTs were added as a reinforcing component. Due to the wrapping of PVP on cement particles which isolates the contacting part between the water and the cement particles, it slows down the cement's hydration rate thus slows down the fluidity loss of the slurry.


Author(s):  
Chihsiung Lo ◽  
Panos Y. Papalambros

Abstract A powerful idea for deterministic global optimization is the use of global feasible search, namely, algorithms that guarantee finding feasible solutions of nonconvex problems or prove that none exists. In this article, a set of conditions for global feasible search algorithms is established. The utility of these conditions is demonstrated on two algorithms that solve special problem classes globally. Also, a new model transformation is shown to convert a generalized polynomial problem into one of the special classes above. A flywheel design example illustrates the approach. A sequel article provides further computational details and design examples.


2013 ◽  
Vol 838-841 ◽  
pp. 1457-1462
Author(s):  
Chun Lei Xia ◽  
Ying Ye ◽  
Guan Ming Wang ◽  
Li Cui

Silty fine sand is the second smallest sand with a particle diameter ranging from 0.0625 to 0.120 mm.This kind of sand exists in a large amount in Beijing subway excavation project. Due to the poor self-stabilization of this stratum,seeping , sand flow and collapse take place frequently. Grouting materials such as Portland cement and soluble glass (also called sodium silicate) are employed in most of excavation projects to reinforce this sand stratum. However, the reinforcement is not effective, leading to a large amount of accidents in the process of construction. The reason may be attributed to the fact that Portland cement is unable to penetrate into the stratum and the strength of soluble glass (0.6MPa) is too weak to resist the stratum pressure. To solve this problem, a modified microfine cement grouting material able to penetrate into silty fine sand stratum is developed in this paper. A combination of suspension and diluent is used to increase the penetration extension of the grouts,and the experimental results reveal that the addition of the mixture of suspension and diluent in microfine cement grouting materials improves the penetration property substantially.


Author(s):  
J. Gu ◽  
G. Y. Li ◽  
Z. Dong

Metamodeling techniques are increasingly used in solving computation intensive design optimization problems today. In this work, the issue of automatic identification of appropriate metamodeling techniques in global optimization is addressed. A generic, new hybrid metamodel based global optimization method, particularly suitable for design problems involving computation intensive, black-box analyses and simulations, is introduced. The method employs three representative metamodels concurrently in the search process and selects sample data points adaptively according to the values calculated using the three metamodels to improve the accuracy of modeling. The global optimum is identified when the metamodels become reasonably accurate. The new method is tested using various benchmark global optimization problems and applied to a real industrial design optimization problem involving vehicle crash simulation, to demonstrate the superior performance of the new algorithm over existing search methods. Present limitations of the proposed method are also discussed.


2021 ◽  
Vol 4 (1) ◽  
pp. 7-18
Author(s):  
Donata D Acula

This paper employed the intelligent approach based on machine learning categorized as base and ensemble methods in classifying the disaster risk in the Philippines. It focused on the Decision Trees, Support Vector Machine, Adaptive Boosting Algorithm with Decision Trees, and Support Vector Machine as base estimators. The research used the Exponential Regression for missing value imputation and converted the number of casualties, damaged houses, and properties into five (5) risk levels using Quantile Method. The 10-fold cross-validation was used to validate the proposed algorithms. The experiment shows that Decision Trees and Adaptive Decision Trees are the most suitable models for the disaster data with the score of more than 90%, more than 75%, more than  75%  in all the classification metrics (accuracy, precision, recall f1-score) when applied to classification risk levels of casualties, damaged houses and damaged properties respectively.


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