scholarly journals Convergence Analysis of a Markov Chain Monte Carlo Based Mix Design Optimization for High Compressive Strength Pervious Concrete

2018 ◽  
Vol 371 ◽  
pp. 012020
Author(s):  
Jiaqi Huang ◽  
Lu Jin
Keyword(s):  
Compressive Strength ◽  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Design Optimization ◽  
Convergence Analysis ◽  
Pervious Concrete ◽  
Mix Design ◽  
High Compressive Strength

scholarly journals A Markov Chain Monte Carlo Technique Based Optimal Mix Design of Porous Concrete

2013 ◽  
Vol 357-360 ◽  
pp. 959-962
Author(s):  
Lu Jin ◽  
Zhu Ge Yan
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Mix Design ◽  
Hardened Concrete ◽  
Conventional Concrete ◽  
Porous Concrete ◽  
Fine Aggregates ◽  
Optimal Mix ◽  
Permeability Rate

Porous concrete is one of the innovative and promising concrete products, which is featured with a relatively high water permeability rate. Compared with conventional concrete products, due to the lack of fine aggregates in the mix design of porous concrete, the void spaces between the coarse aggregates remains unfilled and causes a large amount of porosity in the hardened concrete mass. On the other hand, the strength of porous concrete is usually lower than that of the conventional concrete products due to the lack of fine aggregates. For the purpose of achieving a relatively high strength of porous concrete while maintaining a good permeability of pavements, the mix design of porous concrete is modeled as a Markov Chain Monte Carlo (MCMC) system and a Gibbs Sampling method based approach is developed to approximate the optimal mix design. The simulation results show that, by using the proposed approach, the system converges to the optimal solution quickly and the derived optimal mix design achieves the tradeoff between the compressive strength and the permeability rate.

scholarly journals MCMC-ODPR: Primer design optimization using Markov Chain Monte Carlo sampling

2012 ◽  
Vol 13 (1) ◽  
pp. 287 ◽  
Author(s):  
James L Kitchen ◽  
Jonathan D Moore ◽  
Sarah A Palmer ◽  
Robin G Allaby
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Design Optimization ◽  
Monte Carlo Sampling ◽  
Primer Design
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