A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Probabilistic models of concrete compressive strength and elastic modulus with rubber aggregates
https://orcid.org/0000-0002-3383-7662
Abstract The disposal of waste rubber products is a significant issue globally and it poses a serious threat to the environment creating long-term ecological problems. The possible use of rubber aggregates in concrete is a valid alternative to obtain a green construction material. This paper develops probabilistic models for the concrete Strength Reduction Factor () and Elastic modulus Reduction Factor () accounting for the amount of rubber aggregates as well as several variables defining the mix design. Three different rubber aggregate types are considered, namely fine and coarse replaced individually and fine and coarse replaced simultaneously. A total of 644 sets of concrete compressive strength and elastic modulus tests are collected from the literature for the model calibration. The paper presents a discussion about the formulation of the models, variance stabilizing transformations, model calibration, and model selection. Once formulated, we calibrate the probabilistic models using data from experimental tests. The unknown model parameters are estimated using a Bayesian approach implemented using A Markov Chain Monte Carlo (MCMC) simulation method. The proposed probabilistic models are used to evaluate the reliability of rubberized concrete structures. As an illustration, the proposed probabilistic models are used to estimate the reliability of an example column made of rubberized concrete under compressive axial force and of an example one-way slab made of rubberized concrete under distributed load.
Highlights Probabilistic models for concrete with substituted rubber aggregates. Probabilistic models to predict the strength and the elastic reduction factors. Bayesian inference to account for the uncertainties in model parameters. Reliability analysis under parameter uncertainty of RC structures.
Probabilistic models of concrete compressive strength and elastic modulus with rubber aggregates
https://orcid.org/0000-0002-3383-7662
Abstract The disposal of waste rubber products is a significant issue globally and it poses a serious threat to the environment creating long-term ecological problems. The possible use of rubber aggregates in concrete is a valid alternative to obtain a green construction material. This paper develops probabilistic models for the concrete Strength Reduction Factor () and Elastic modulus Reduction Factor () accounting for the amount of rubber aggregates as well as several variables defining the mix design. Three different rubber aggregate types are considered, namely fine and coarse replaced individually and fine and coarse replaced simultaneously. A total of 644 sets of concrete compressive strength and elastic modulus tests are collected from the literature for the model calibration. The paper presents a discussion about the formulation of the models, variance stabilizing transformations, model calibration, and model selection. Once formulated, we calibrate the probabilistic models using data from experimental tests. The unknown model parameters are estimated using a Bayesian approach implemented using A Markov Chain Monte Carlo (MCMC) simulation method. The proposed probabilistic models are used to evaluate the reliability of rubberized concrete structures. As an illustration, the proposed probabilistic models are used to estimate the reliability of an example column made of rubberized concrete under compressive axial force and of an example one-way slab made of rubberized concrete under distributed load.
Highlights Probabilistic models for concrete with substituted rubber aggregates. Probabilistic models to predict the strength and the elastic reduction factors. Bayesian inference to account for the uncertainties in model parameters. Reliability analysis under parameter uncertainty of RC structures.
Probabilistic models of concrete compressive strength and elastic modulus with rubber aggregates
https://orcid.org/0000-0002-3383-7662
Abstract The disposal of waste rubber products is a significant issue globally and it poses a serious threat to the environment creating long-term ecological problems. The possible use of rubber aggregates in concrete is a valid alternative to obtain a green construction material. This paper develops probabilistic models for the concrete Strength Reduction Factor () and Elastic modulus Reduction Factor () accounting for the amount of rubber aggregates as well as several variables defining the mix design. Three different rubber aggregate types are considered, namely fine and coarse replaced individually and fine and coarse replaced simultaneously. A total of 644 sets of concrete compressive strength and elastic modulus tests are collected from the literature for the model calibration. The paper presents a discussion about the formulation of the models, variance stabilizing transformations, model calibration, and model selection. Once formulated, we calibrate the probabilistic models using data from experimental tests. The unknown model parameters are estimated using a Bayesian approach implemented using A Markov Chain Monte Carlo (MCMC) simulation method. The proposed probabilistic models are used to evaluate the reliability of rubberized concrete structures. As an illustration, the proposed probabilistic models are used to estimate the reliability of an example column made of rubberized concrete under compressive axial force and of an example one-way slab made of rubberized concrete under distributed load.
Highlights Probabilistic models for concrete with substituted rubber aggregates. Probabilistic models to predict the strength and the elastic reduction factors. Bayesian inference to account for the uncertainties in model parameters. Reliability analysis under parameter uncertainty of RC structures.
Probabilistic models of concrete compressive strength and elastic modulus with rubber aggregates
Nocera, Fabrizio (author) / Wang, Junsong (author) / Faleschini, Flora (author) / Demartino, Cristoforo (author) / Gardoni, Paolo (author)
2021-12-16
Article (Journal)
Electronic Resource
English
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