Prediction and multi-objective optimization of high-strength concrete parameters via soft computing approaches

Baykasoğlu, Adil; Öztaş, Ahmet; Özbay, Erdoğan

doi:10.1016/j.eswa.2008.07.017

Cited by 119 publications

(48 citation statements)

References 27 publications

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“…Furthermore, the best tradeoffs among the competing criteria were identified with the application of Pareto-optimality conditions, as advocated in prior research [14,15,42]. is method is exact (as opposed to a heuristic approach [18]) and has being utilized Advances in Civil Engineeringpreviously to solve engineering and science problems [14,15]. In this research, we applied the method to find the proportion (or fractional) combinations of a cement mixture that belong to the best possible balances in the presence of the conflicting performance measures, or more formally, the solutions in the Pareto-efficient frontier.…”

Section: Design Of Experimentsmentioning

confidence: 99%

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Optimal Cement Mixtures Containing Mineral Admixtures under Multiple and Conflicting Criteria

García

Soto-Toro

Cabrera-Ríos

et al. 2018

Advances in Civil Engineering

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In modern construction industry, fabrication of sustainable concrete has turned the decision-making process into a challenging endeavor. One alternative is using fly ash and nanostructured silica as cement replacements. In these modern mixtures, proper concrete bulk density, percentage of voids, and compressive strength normally cannot be optimized individually. Hereby, a decision-making strategy on the replacement of those components is presented while taking into account those three performance measurements. e relationships among those components upon concrete fabrication required a design of experiments of mixtures to characterize those mineral admixtures. is approach integrates different objective functions that are in conflict and obtains the best compromise mixtures for the performance measures being considered. is optimization strategy permitted to recommend the combined use of fly ash and nanosilica to improve the concrete properties at its early age.

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Section: Design Of Experimentsmentioning

confidence: 99%

“…Often, researchers have used regression models to predict performance measures, such as compressive strength, density, and porosity [17][18][19][20]. Sometimes, via neural networks, some were able to predict concrete behavior [19].…”

Section: Introductionmentioning

confidence: 99%

Optimal Cement Mixtures Containing Mineral Admixtures under Multiple and Conflicting Criteria

García

Soto-Toro

Cabrera-Ríos

et al. 2018

Advances in Civil Engineering

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“…In the design of improvement of complex systems, multiple decision criteria need to be considered [1,15]. As an example, one can think of a military information architecture [16] or the improvement of the performance of an industrial device [2,3].…”

Section: Problem Of Interest and Notationsmentioning

confidence: 99%

Multi-criteria improvement of options

Imoussaten¹,

Montmain²,

Trousset³

et al. 2011

Proceedings of the 7th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT-2011)

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Designing the way a complex system should evolve to better match the customers' requirements provides an interesting class of applications for muticriteria techniques. The required models to support the improvement design of a complex system must include both preference models and system behavioral models. A MAUT model captures the decisions related to customers' preferences whereas a fuzzy representation is proposed to model the relationships between systems parameters and performances to capture operational constraints. This latter part of the improvement design is supported by a branch and bound algorithm to efficiently compute the most relevant actions to be performed.

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“…Statistical regression analysis techniques can be used to utilize experimental results and to estimate concrete strength from the mix components. Although several models were developed for prediction and/or optimization of concrete properties (Bouzoubaa and Fournier, 2003;Gupta et al, 2006;Hossain and Lachemi, 2006;Lee, 2003;Lim et al, 2004;Muthukumar et al, 2003;Nataraja et al, 2006;Simon, 2003;Sobolev, 2004;Tesfamariam and Najjaran, 2007), few of them includes the prediction of slump of fresh HPC (Patel, 2003;Baykasoglu et al, 2009;Marcia et al, 1997;Sonebi, 2001Sonebi, , 2004Yeh, 1999), very few of them deal with silica fume incorporated HPC. Some of them consider only linear models and do not consider nonlinear models.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Strength and Slump of Silica Fume Incorporated High-Performance Concrete

Zain¹,

Karim²,

Islam³

et al. 2015

Asian J. of Scientific Research

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This study describes the development of statistical models to predict strength and slump of silica fume incorporated High-Performance Concrete (HPC). Experimental data of silica fume incorporated HPC mixes were used to develop and validate models. The HPC having compressive strength range of 40-113 MPa and slump range of 180-250 mm were used. Statistical models were developed by regression analysis. The results of prediction by the models showed good agreement with those of experiments and other researchers. The developed models can be used to predict slump and 28 days compressive strength of silica fume incorporated HPC.

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Prediction and multi-objective optimization of high-strength concrete parameters via soft computing approaches

Cited by 119 publications

References 27 publications

Optimal Cement Mixtures Containing Mineral Admixtures under Multiple and Conflicting Criteria

Optimal Cement Mixtures Containing Mineral Admixtures under Multiple and Conflicting Criteria

Multi-criteria improvement of options

Prediction of Strength and Slump of Silica Fume Incorporated High-Performance Concrete

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