Optimization of mass concrete construction using genetic algorithms

Fairbairn, Eduardo de Moraes Rego; Silvoso, Marcos Martinez; Filho, Romildo Dias Toledo; Alves, José L. D.; Ebecken, Nelson F. F.

doi:10.1016/j.compstruc.2003.08.008

Cited by 63 publications

(25 citation statements)

References 29 publications

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“…The ambient temperature varied between 21-16°C during the five days when the test was conducted. Having related the properties of the concrete used for the test block and the adiabatric curves documented for different concrete mixes by Fairbairn et al (2003) as shown in Fig. 2, it was reasonably determined that the adiabatic maximum temperature parameters is about 45°C and the adiabatic rate parameters is about 0.0375.…”

Section: Ajeasmentioning

confidence: 99%

Numerical Prediction Model for Temperature Distributions in Concrete at Early Ages

Ali¹

2012

American Journal of Engineering and Applied Sciences

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A finite element-finite difference numerical model is developed for predicting non-uniform temperature development in hydrating concrete with respect to time and space. The results obtained from this model can be used by structural and construction engineers to predict critical thermal stresses induced due to differential temperatures between the core and the surface of the concrete at early ages and between the zero-stress temperatures and the minimum equilibrating ambient temperatures that the concrete experiences during its service life. The prediction of zero-stress temperatures also enables to quantify the extent of builtin curl developed in concrete structures. The finite element is used to space discretization while the finite difference is used to obtain transient solutions of the model. The numerical formulations are then programmed in Matlab. The numerical results were compared with experimental results found in literature and demonstrated very good agreement.

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Section: Ajeasmentioning

confidence: 99%

Numerical Prediction Model for Temperature Distributions in Concrete at Early Ages

Ali¹

2012

American Journal of Engineering and Applied Sciences

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“…According to Fairbairn et al [15], the values of these weights depend on factors, such as, the type of construction, local conditions, variations in the unitary costs, etc. However, regardless of the uncertainty of these unitary costs, it is always possible to estimate them when the construction is being planned.…”

Section: Fitness Functionmentioning

confidence: 99%

“…These costs were normalized by the higher cost of type 2 concrete and are given bellow: c conc = {0.984; 1.000; 0.864; 0.942} (19) The relation between the placing temperature and the cost for cooling the concrete is difficult to quantify and depends on a number of factors that are particular to the construction site and to the type of construction. For the present application a second order polynomial tendency curve was used, based on the experience of [7,11,15]. The unitary costs for the several cooling temperatures are displayed in Table 3.…”

Section: Unitary Costsmentioning

confidence: 99%

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Optimization of Mass Concrete Construction Using a Twofold Parallel Genetic Algorithm

et al. 2018

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This paper presents a solution strategy, based on a parallel Genetic Algorithm (GA), to optimize the construction of massive concrete structures. The optimization process aims at minimizing the construction cost, considering the following design variables: the concrete mixes, the placing temperature, the height of the lifts, and the time intervals between placing the lifts. The cracking tendency is taken into account by a penalty scheme imposed to the fitness function of the GA. A thermo-chemo-mechanical model is used to calculate the transient fields of hydration, temperature, stress, strain, and cracking tendency. This model is implemented in a finite element code that is, in turn, parallelized. To demonstrate the efficiency of the proposed methodology, the simulation of the construction of a structure similar to the real thick foundation of an industrial building is presented. It shows that the optimization procedure here presented is feasible and is ready to be used in real engineering applications.

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“…erefore, researchers have employed a variety of optimization approaches to find the best possible solutions in a single objective [1,17,[21][22][23][24][25][26]. Recommendations based on all the performance measures of interest to the user are more appropriate when compared with only the selection of a single solution pertaining to the measured objective.…”

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 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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Optimization of mass concrete construction using genetic algorithms

Cited by 63 publications

References 29 publications

Numerical Prediction Model for Temperature Distributions in Concrete at Early Ages

Numerical Prediction Model for Temperature Distributions in Concrete at Early Ages

Optimization of Mass Concrete Construction Using a Twofold Parallel Genetic Algorithm

Optimal Cement Mixtures Containing Mineral Admixtures under Multiple and Conflicting Criteria

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