This study is part of the search for a sediment management methodology and the basic material that is designed for the manufacture of fired brick, in which we tackle the problem of choosing the mud samples from dredging the Algerian dams in particular (Bouhanifia dam western Algeria) and the choice of clay that comes from different deposits. We propose an approach for sampling using a computer optimization model that allows solving and optimizing a characterization to retain the best adapted sample (chemically) using the genetic algorithm.Keywords: Sludge- Dredging- Sample- Chemical Characterization- Brick-Optimization Methods- Genetic Algorithm

Bouhamou, N.

Mahmoudi, N.

Marouf, H.

Semcha, A.

English

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Journal of Fundamental and Applied Sciences is licensed under a Creative Commons Attribution-NonCommercial 4.0International License. Libraries Resource Directory. We are listed under Research Associations category.GENETIC PROGRAMMING FOR BRICK'S CHEMICAL ANALYSIS MODELLINGH. Marouf1*, A. Semcha2, N. Mahmoudi3, N. Bouhamou11Département de Génie Civil, Faculté des Sciences et de Technologie, Université deMostaganem, Algérie2Département de Génie Civil, Faculté des Sciences, Université de Adrar, Algerie3Département de Génie Mécanique, Institue des Sciences et de Technologie, CentreUniversitaire de Relizane, AlgérieReceived: 18 December 2017 / Accepted: 22 April 2018 / Published online: 01 May 2018ABSTRACTThis study is part of the search for a sediment management methodology and the basicmaterial that is designed for the manufacture of fired brick, in which we tackle the problemof choosing  the mud samples  from dredging  the Algerian dams in particular(Bouhanifia dam western Algeria) and the choice of clay that comes from different deposits.We propose an approach for sampling using a computer optimization model that allowssolving and optimizing a characterization to retain the best adapted sample (chemically) usingthe genetic algorithm.Keywords: Sludge- Dredging- Sample- Chemical Characterization- Brick-OptimizationMethods- Genetic Algorithm.Author Correspondence, e-mail: hafidamaarouf@yahoo.frdoi: http://dx.doi.org/10.4314/jfas.v10i2.231. CONTEXT AND PROBLEMATICThe problem of siltation of dam reservoirs still has to be borne by the managers. At the sametime, previous research conducted in the context of managing the fallout from dredgingJournal of Fundamental and Applied SciencesISSN 1112-9867Available online at http://www.jfas.infoResearch ArticleMarouf et al. J Fundam Appl Sci. 2018, 10(2), 321-335 322operations has given promising prospects [1-14].Procedures of study are summarized in characterization of the material, manufacture and atthe end of the durability tests. Each researcher opted for a study that allowed him somesatisfaction with the results obtained.In this context, our study targets the Bou Hanifia dam, which is of great economic importancefor the region of Mascara (Western Algeria), and which has a high rate of siltation. Areflection for a rational use of these sediments has been conducted in building materials and inparticular in fired brick. During the chemical characterization of the sample taken, the samplemust meet the recommended thresholds for the basic material used in the manufacture of thebrick. While relying on an optimization method which is the genetic algorithm which consistsin exploring domains with very many solutions. This model allows us to identify the bestsample taken with percentages of oxides that meet the recommended thresholds andconstraints.1.1. The Principle of the Genetic AlgorithmGenetic algorithms have the distinction of being inspired by the evolution of species in theirnatural setting. Species adapt to their living environment that can evolve, individuals of eachspecies reproduce, creating new individuals, some undergo modifications of their DNA, somedisappear....A genetic algorithm will reproduce this model of evolution in order to find solutions for agiven problem [15]. The genetic algorithm of our example is as follows:• A population will be a set of dredged silt samples• An individual will be a solution to a given problem• A gene will be part of a solution, so an individual• A generation is an iteration of our algorithmFigure 1 illustrates the operation of the genetic algorithm.Marouf et al. J Fundam Appl Sci. 2018, 10(2), 321-335 323Fig.1. The Steps of the Genetic Algorithm [16-17]2. PARAMETERS OF THE GENETIC ALGORITHM2.1. FitnessFitness in genetic sense measures the quality of the individual expressed as a number or avector. Plus a value i is close to the solution plus she is the best.In our work we have to propose two methods to calculate fitness. 11nFitness Value oxides x weight See table 1.The different values of the oxides given in table 1 are extracted from the recommendedthresholds of clay designed for the manufacture of a fired brick.Table1. The recommended thresholds of clayOxides Val Min (%) Val Max(%) WeightSiO2 35 85 0.45Al2O3 9 25 0.2Fe2O3 3 9 0.1CaO 0 25 0.11MgO 0 5 0.01SO3 0 3 0.002K2O+Na2O 1 5 0.03TiO2 0.3 2 0.01Fire loss 0 13 0.088Marouf et al. J Fundam Appl Sci. 2018, 10(2), 321-335 324With respect to the weights given for each oxide, the latter reflects the importance of eachoxide value in the chemical characterization of clay.The weights are inspired by a synthesized reading of the various tested chemical analyzes.The sum of the different weights given equal to 1.Fitness2 SiO2 /Al2O3.  (Silica and Alumina are the two main constructive components ofclay).2.2. Selection RateThe selection rate indicates the rate of individuals selected from the initial population, if theselection rate is 100% then the entire population is selected, and if it is 0% the new generationis the exact copy of the individuals from the old population, in our example it is fixed at 0.8because the optimal rate varies between 0.8 and 0.9 [17].2.3. Crossing RateThe crossing rate indicates the rate of participation in breeding, the proportion of thepopulation that breeds by crossing. If the crossing rate is 100%, then the whole populationparticipates in the crossing. On the other hand, if it is 0%, the new generation in full is theexact copy of the individuals of the old population, in our example it is fixed at 0.7 becausethe optimal rate varies between 0.25 and 0.7  [17].For an efficient use of a material for the construction of vase-based brick, we use the model ofgenetic algorithm which is heuristic metas; which aims to solve an optimization problem as itcan determine the different percentages of oxides during a chemical analysis). Ourcontribution is to propose a model for optimization.2.4. Mutation RateThe mutation rate indicates the rate that each gene of each individual a mutation during areproductive phase. If the mutation rate is 0%, the individuals that are produced just aftercrossing do not change. On the other hand, if the probability of mutation is 100%, the wholechromosome of the individual is changed: In our example we set it to 0.1 because the ratevaries between 0.01 and 0.1 to prevent the algorithm from converging to a local minimum.2.5. Number of IterationsIt is a number that limits the number of evolutions of the population generated by theMarouf et al. J Fundam Appl Sci. 2018, 10(2), 321-335 325algorithm. The search is thus stopped after a certain number of generations: In our example itis fixed at 100 iterations. Because the optimal number of iterations must be between 10 and500 iterations.Fig.2. The Proposed ModelFollowing the steps of the genetic algorithm and optimizing the chemical analyzes of thesamples, three constraints were posing: First is to give a minimum threshold for fitness 2 which is 2.7, (the classic value ofbentonites [18-19] and a maximum value of 5 which is an approximate value. Second constraint is to minimize the Alumina content to 15%. Third constraint consists of applying the total sum of the oxides during the 100%chemical analysis with a tolerance interval of ± 2%.The creation of the initial population see table 2.Marouf et al. J Fundam Appl Sci. 2018, 10(2), 321-335 326Table 2. The Generation of the Initial PopulationIn the table 3 we note the selection of initial population with the first fitnessDamMarine Recommended(Threshold)Initial Population Fitness  EvaluationN° SiO2 Al2O3 Fe2O3 CaO MgO SO3 K2O+N2 O3 TiO2 L F4 45.92 16.271 6.88 17.128 2.983 1.229 2.668 0.4424 7.317 47.774 15.127 7.713 9.784 2.874 0.336 2.281 1.255 12.1214 48.401 16.827 5.147 14.412 3.481 0.678 3.219 0.919 7.84717 55.895 16.409 8.327 2.411 3.801 1.064 1.665 1.136 10.22622 50.87 16.383 4.41 9.129 1.84 2.824 3.355 0.833 9.82126 57.616 15.656 3.018 9.799 0.892 1.273 3.374 1.786 6.85829 65.907 18.371 6.757 0.116 2.696 1.987 3.914 0.455 0.68331 52.544 15.62 6.417 14.174 3.862 2.471 1.425 1.661 2.68333 49.93 15.693 7.232 12.716 3.382 1.414 4.038 1.504 4.99634 59.518 15.057 6.647 1.399 1.112 2.508 3.051 1.531 10.17635 56.92 16.046 4.75 4.319 2.790 1.955 1.919 0.826 10.50740 47.044 16.413 3.292 14.803 4.96 1.263 3.029 1.766 6.655Marouf et al. J Fundam Appl Sci. 2018, 10(2), 321-335 327Table3. The Generation of the Initial Population with the First Fitness2.6. Best IndividualThe best people are a collection of the best people who have almost the same percentages ofoxides (see table 4)Table 4. Best IndividualSelectionCrossing Transfer Best individual Better CompositionN° SiO2 Al2O3 Fe2O3 CaO MgO SO3 K2O+N2 O3 TiO2 L F Fitness32 48.8378 15.889 7.451 16.524 4.547 1.238 1.071 1.467 2.261 0.027 55.895 16.409 8.327 2.411 3.801 1.064 1.665 1.136 10.226 0.080 49.93 16.3125 7.232 12.716 3.382 1.414 4.038 1.504 4.996 28.46585 45.92 16.271 6.88 17.128 2.983 1.229 2.668 0.424 7.31 28.792156 62.786 17.08 6.968 4.304 3.365 1.09 2.614 1.319 0.798 31.563158 57.995 16.3176 4.61 6.738 3.862 1.586 2.852 1.751 3.621 31.69722 52.544 15.62 6.417 14.147 3.862 2.471 1.425 1.661 2.683 0.0Marouf et al. J Fundam Appl Sci. 2018, 10(2), 321-335 3282.7. Better CompositionThe better composition is the optimal solution. See table 5Table 5. Better CompositionThe variation of the fitness in figure 3SelectionCrossing Transfer Best individual Better CompositionN° SiO2 Al2O3 Fe2O3 CaO MgO SO3 K2O+N2 O3 TiO2 L F Fitness39 46.137 16.413 3.292 14.803 4.96 1.263 3.029 1.766 6.655 26.74857 46.101 16.848 3.292 14.803 4.96 1.263 3.029 1.766 6.655 26.81944 46.279 16.413 3.292 14.803 4.96 1.263 3.029 1.766 6.655 26.82034 44.979 16.271 6.88 17.128 2.983 1.229 2.668 0.424 7.31 26.82720 45.101 16.271 6.88 17.128 2.983 1.229 2.688 0.424 7.31 26.88194 45.142 16.271 6.88 17.128 2.983 1.229 2.688 0.424 7.31 26.900Crossing Transfer Best individual Better CompositionSiO2 Al2O3 Fe2O3 CaO MgO SO3 K2O+N2 O3 TiO2 L F Fitness46.137 16.413 3.292 14.803 4.96 1.263 3.029 1.766 6.655 26.748Marouf et al. J Fundam Appl Sci. 2018, 10(2), 321-335 329Fig.3. variation of the first FitnessTable 6. The Generation of the Initial Population with the Second FitnessSelectionCrossing Transfer Best individual Better CompositionN° SiO2 Al2O3 Fe2O3 CaO MgO SO3 K2O+N2 O3 TiO2 L F Fitness43 59.682 17.701 4.064 9.251 0.889 0.232 2.515 1.337 3.508 0.0050 51.713 18.356 7.519 3.025 4.826 2.25 1.696 1.432 10.533 2.817191 61.526 16.589 6.844 3.889 3.461 1.347 1.321 1.795 3.12 3.708202 58.604 15.437 4.61 6.738 3.862 1.922 2.852 1.751 3.621 3.796160 53.172 15.062 4.306 16.513 2.729 0.073 1.633 1.007 5.966 3.611138 60.154 17.12 6.233 1.781 3.022 1.725 2.173 1.542 5.966 3.514103 52.544 16.0328 6.417 14.174 3.862 2.471 1.425 0.427 2.597 3.158Table7. Best IndividualMarouf et al. J Fundam Appl Sci. 2018, 10(2), 321-335 330Table8. Better CompositionFig.4. Variation of second FitnessSelectionCrossing Transfer Best individual Better CompositionN° SiO2 Al2O3 Fe2O3 CaO MgO SO3 K2O+N2 O3 TiO2 L F Fitness145 57.128 21.131 4.079 11.677 1.048 0.766 2.801 0.382 1.151 2.703115 45.92 16.978 6.88 17.128 2.983 1.229 2.668 0.424 7.31 2.704177 57.455 21.238 4.079 11.677 1.048 0.766 2.801 0.382 1.151 2.705100 46.742 17.265 3.292 14.803 4.96 1.263 3.029 1.766 6.655 2.707132 47.044 17.356 3.292 14.803 4.96 1.263 3.029 1.766 6.655 2.710113 57.310 21.131 4.079 11.677 1.048 0.766 2.801 0.382 1.151 2.712Crossing Transfer Best individual Better CompositionSiO2 Al2O3 Fe2O3 CaO MgO SO3 K2O+N2 O3 TiO2 L F Fitness57.128 21.131 4.079 11.677 1.048 0.766 2.801 0.382 1.151 2.703Marouf et al. J Fundam Appl Sci. 2018, 10(2), 321-335 3313. DISCUSSIONAlong the iterations the first fitness varies timidly but unstable, against the second fitnessappears with more sensitive wave variation and tends to stabilize.The wave stability allows us to define an optimal approach, so the second function or thefitness 2 is more accurate than the fitness13.1. Comparison with Clays used in Neighborhood BrickworksTable9. The Generation of the Initial Population Brickworks in the West of AlgeriaSelectionCrossing Transfer Best individual Better CompositionN° SiO2 Al2O3 Fe2O3 CaO MgO SO3 K2O+N2O3+ P2O5TiO2 L F1 54.09 12.24 4.94 9.93 2.56 0.11 2.62 0.66 12.862 48.12 15.5 5.49 9.97 3.01 0.52 3.23 0.75 13.413 47.86 12.67 5.34 12.26 3.15 0.37 2.73 0.68 14.964 48.17 14.46 5.78 10.49 3.01 0.03 3.21 0.71 14.315 47.83 16.83 6.1 9.04 2.68 0.07 3.38 0.79 13.196 47.71 15.81 5.91 9.94 2.88 0.46 3.09 0.78 13.417 47.91 14.4 5.94 10.4 2.73 0.4 3.00 0.78 14.448 62.33 11.04 5.58 7.32 1.51 0.01 1.70 0.58 9.939 53.88 10.75 5.39 11.94 1.79 0.02 1.92 0.57 13.7410 52 9.99 4.96 13.49 2.02 0.06 1.97 0.53 14.97Marouf et al. J Fundam Appl Sci. 2018, 10(2), 321-335 332Table10. Better Composition with the first FitnessFig.5. Variation of first Fitness of BrickworksTable11. Better Composition with the second FitnessCrossing Transfer Best individual Better CompositionSiO2 Al2O3 Fe2O3 CaO MgO SO3 K2O+N2 O3 TiO2 L F Fitness47.71 15.81 5.91 9.94 2.88 0.46 3.09 0.78 13.41 26.501Crossing Transfer Best individual Better CompositionSiO2 Al2O3 Fe2O3 CaO MgO SO3 K2O+N2O3+P2O5TiO2 L F Fitness47.83 16.83 6.1 9.04 2.68 0.07 3.38 0.79 13.19 2.841Marouf et al. J Fundam Appl Sci. 2018, 10(2), 321-335 333Fig.6. Variation of second Fitness of Brickworks3.2.Comparison with the chemical composition of the Bouhanifia sludge In table 12Table12. Comparison between Dam Sludge and Brickworks Clay4. CONCLUSIONS AND PERSPECTIVESIn conclusion, this paper presented the steps of an optimization approach to a set of samplesthat meet the recommended thresholds for the chemical analysis of clay used for theCrossing Transfer Best individual Better CompositionSiO2 Al2O3 Fe2O3 CaO MgO SO3 K2O+N2 O3 +P2O5TiO2 L F Fitness47.83 16.83 6.1 9.04 2.68 0.07 3.38 0.79 13.19 2.841Chemical Composition of Dam’s Sludge49.36 12.26 5.13 10.03 2.34 1.77 3.33 0.63 15.15 4.02Marouf et al. J Fundam Appl Sci. 2018, 10(2), 321-335 334manufacture of a fired brick. The model used is the genetic algorithm that gave us the optimalchemical analysis of a sample suitable for the design of a fired brick. The intendedexperimental goal through the choice of this model is the comparison between differentsamples analyzed and to correct the typical chemical composition. To answer the need of ourresearch, we propose to continue the development of the algorithm used in complex functionalgorithm. We plan to include as functions the mechanical and physical parameters thatcharacterize the baked brick.REFERENCES[1] Cappuyns et al, 2015 “Dredged sediments as a resource for brick production: Possibilitiesand barriers from a consumers’ perspective” Waste Management 38 (2015) 372–380.[2] Yang Xu et al , 2014 “The use of urban river sediments as a primary raw material in theproduction of highly insulating brick” Ceramics International40(2014)8833–8840.[3] Lafhaj Z., Construction and Building Materials 22 (2008) 755–762.[4] Andrea Mezencevova et al, 2012 « Utilization of Savannah Harbor river sediment as theprimary raw material in production of fired brick” Journal of Environmental Management 113(2012) 128-136[5] Kung-Yuh Chiang et al, 2008 « Study on the characteristics of building bricks producedfrom reservoir sediment” Journal of Hazardous Materials 159 (2008) 499–504[6] Mazen Samara et al, 2009 « Valorization of stabilized river sediments in fired claybricks:Factory scale experiment” Journal of Hazardous Materials 163 (2009) 701–710[7] Faycal El Fgaier et al,2013 « Use of clay bricks incorporating treated river sediments in ademonstrative building: Case study” Construction and Building Materials 48 (2013) 160–165[8] Y.M. 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Larhyss Journal 03(2004) 7-12.[14] Martinez G., J. Envir. Manag 95 (2012) S343-S348[15]Schwartz, P. (2005) Les algorithmes génétiques. Developpez.com. Available from:http://khayyam.developpez.com/articles/algo/genetic/[16]Dipama, J. (2010) Optimisation multi objectif des systèmes énergétiques, Thèse dedoctorat,Université de Montréal, Canada.[17] Khadidja Yachba, Shahin Gelareh, Karim Bouamrane, Transport andTelecommunication Vol. 17, no. 4, 2016[18] A. Qlihaa, S. Dhimni, F. Melrhaka, N. Hajjaji, A. Srhiri J. Mater. Environ. Sci. 7 (5)(2016) 1741-1750 Qlihaa et al ISSN : 2028-2508 CODEN : JMESCN[19] M. Gourouza, A. Zanguina, I. Natatou, A. Boos, Rev. CAMES – Sciences Struct. Mat.Vol. 1, Déc. 2013How to cite this article:Marouf H, Semcha A, Mahmoudi N, Bouhamou N. Genetic programming for brick's chemicalanalysis modelling. J. Fundam. Appl. Sci., 2018, 10(2), 321-335.

Genetic programming for brick's chemical analysis modelling

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Genetic programming for brick's chemical analysis modelling

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