Self-compacting concrete (SCC) flows into place and around obstructions under its own weight to fill the formwork completely and self-compact without any segregation and blocking. Elimination of the need for compactio...
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Self-compacting concrete (SCC) flows into place and around obstructions under its own weight to fill the formwork completely and self-compact without any segregation and blocking. Elimination of the need for compaction leads to better quality concrete and substantial improvement of working conditions. This investigation aimed to show possible applicability of genetic programming (GP) to model and formulate the fresh and hardened properties of self-compacting concrete (SCC) containing pulverised fuel ash (PFA) based on experimental data. Twenty-six mixes were made with 0.38 to 0.72 water-to-binder ratio (W/B), 183-317 kg/m(3) of cement content, 29-261 kg/m(3) of PFA, and 0 to 1% of superplasticizer, by mass of powder. Parameters of SCC mixes modelled by genetic programming were the slump flow, liking combined to the Orimet, JRing combined to cone, and the compressive strength at 7, 28 and 90 days. GP is constructed of training and testing data using the experimental results obtained in this study. The results of genetic programming models are compared with experimental results and are found to be quite accurate. GP has showed a strong potential as a feasible tool for modelling the fresh properties and the compressive strength of SCC containing PFA and produced analytical prediction of these properties as a function as the mix ingredients. Results showed that the GP model thus developed is not only capable of accurately predicting the slump flow, JRing combined to the Orimet, JRing combined to cone, and the compressive strength used in the training process, but it can also effectively predict the above properties for new mixes designed within the practical range with the variation of mix ingredients. (C) 2009 Elsevier Ltd. All rights reserved.
Though forecasting of river flow has received a great deal of attention from engineers and researchers throughout the world, this still continues to be a challenging task owing to the complexity of the process. In the...
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Though forecasting of river flow has received a great deal of attention from engineers and researchers throughout the world, this still continues to be a challenging task owing to the complexity of the process. In the last decade or so, artificial neural networks (ANNs) have been widely applied, and their ability to model complex phenomena has been clearly demonstrated. However, the success of ANNs depends very crucially on having representative records of sufficient length. Further, the forecast accuracy decreases rapidly with an increase in the forecast horizon. In this study, the use of the Darwinian theory-based recent evolutionary technique of genetic programming (GP) is suggested to forecast fortnightly flow up to 4-lead. It is demonstrated that short lead predictions can be significantly improved from a short and noisy time series if the stochastic (noise) component is appropriately filtered out. The deterministic component can then be easily modelled. Further, only the immediate antecedent exogenous and/or non-exogenous inputs can be assumed to control the process. With an increase in the forecast horizon, the stochastic components also play an important role in the forecast, besides the inherent difficulty in ascertaining the appropriate input variables which can be assumed to govern the underlying process. GP is found to be an efficient tool to identify the most appropriate input variables to achieve reasonable prediction accuracy for higher lead-period forecasts. A comparison with ANNs suggests that though there is no significant difference in the prediction accuracy, GP does offer some unique advantages. Copyright (c) 2006 John Wiley & Sons, Ltd.
The GPTP workshop series, which began in 2003, has served over the years as a focal meeting for genetic programming (GP) researchers. As such, we think it provides an excellent source for studying the development of G...
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The GPTP workshop series, which began in 2003, has served over the years as a focal meeting for genetic programming (GP) researchers. As such, we think it provides an excellent source for studying the development of GP over the past fifteen years. We thus present herein a trajectory of the thematic developments in the field of GP.
Whole-cell biosensors are mostly non-specific with respect to their detection capabilities for toxicants, and therefore offering an interesting perspective in environmental monitoring. However, to fully employ this fe...
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Whole-cell biosensors are mostly non-specific with respect to their detection capabilities for toxicants, and therefore offering an interesting perspective in environmental monitoring. However, to fully employ this feature, a robust classification method needs to be implemented into these sensor systems to allow further identification of detected substances. Substance-specific information can be extracted from signals derived from biosensors harbouring one or multiple biological components. Here, a major task is the identification of substance-specific information among considerable amounts of biosensor data. For this purpose, several approaches make use of statistical methods or machine learning algorithms. genetic programming (GP), a heuristic machine learning technique offers several advantages compared to other machine learning approaches and consequently may be a promising tool for biosensor data classification. In the present study, we have evaluated the use of GP for the classification of herbicides and herbicide classes (chemical classes) by analysis of substance-specific patterns derived from a whole-cell multi-species biosensor. We re-analysed data from a previously described array-based biosensor system employing diverse microalgae (Podola and Melkonian, 2005), aiming on the identification of five individual herbicides as well as two herbicide classes. GP analyses were performed using the commercially available GP software 'Discipulus', resulting in classifiers (computer programs) for the binary classification of each individual herbicide or herbicide class. GP-generated classifiers both for individual herbicides and herbicide classes were able to perform a statistically significant identification of herbicides or herbicide classes, respectively. The majority of classifiers were able to perform correct classifications (sensitivity) of about 80-95% of test data sets, whereas the false positive rate (specificity) was lower than 20% for most classifiers. Res
genetic programming (GP), a relatively new evolutionary technique, is demonstrated in this study to evolve codes for the solution of problems. First, a simple example in the area of symbolic regression is considered. ...
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genetic programming (GP), a relatively new evolutionary technique, is demonstrated in this study to evolve codes for the solution of problems. First, a simple example in the area of symbolic regression is considered. GP is then applied to real-time runoff forecasting for the Orgeval catchment in France. In this study, GP functions as an error updating scheme to complement a rainfall-runoff model, MIKE11/NAM. Hourly runoff forecasts of different updating intervals are performed for forecast horizons of up to nine hours. The results show that the proposed updating scheme is able to predict the runoff quite accurately for all updating intervals considered and particularly for updating intervals not exceeding the time of concentration of the catchment. The results are also compared with those of an earlier study, by the World Meteorological Organization, in which autoregression and Kalman filter were used as the updating methods. Comparisons show that GP is a better updating tool for real-time flow forecasting. Another important finding from this study is that nondimensionalizing the variables enhances the symbolic regression process significantly.
This paper models acidolysis of triolein and palmitic acid under the catalysis of immobilized sn-1,3 specific lipase. A gene-expression programming (GEP), which is an extension to genetic programming (GP)-based model ...
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This paper models acidolysis of triolein and palmitic acid under the catalysis of immobilized sn-1,3 specific lipase. A gene-expression programming (GEP), which is an extension to genetic programming (GP)-based model was developed for the prediction of the concentration of major reaction products of this reaction (1-palmitoyl-2,3-oleoyl-glycerol (POO), 1,31-dipalmitoyl-2-oleoyl-glycerol (POP) and triolein (OOO). Substrate ratio (SR), reaction temperature (T) and reaction time (t) were used as input parameters. The predicted models were able to predict the progress of the reactions with a mean standard error (MSE) of less than 1.0 and R of 0.978. Explicit formulation of proposed GEP models was also presented. Considerable good performance was achieved in modelling acidolysis reaction by using GEP. The predictions of proposed GEP models were compared to those of neural network (NN) modelling, and strictly good agreement was observed between the two predictions. Statistics and scatter plots indicate that the new GEP formulations can be an alternative to experimental models. (C) 2009 Elsevier Ltd. All rights reserved.
This study presents genetic programming (GP) based model to predict the torque and brake specific fuel consumption a gasoline engine in terms of spark advance, throttle position and engine speed. The objective of this...
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This study presents genetic programming (GP) based model to predict the torque and brake specific fuel consumption a gasoline engine in terms of spark advance, throttle position and engine speed. The objective of this study is to develop an alternative robust formulations based on experimental data and to verify the use of GP for generating the formulations for gasoline engine torque and brake specific fuel consumption. Experimental studies were completed to obtain training and testing data. Of all 81 data sets, the training and testing sets consisted of randomly selected 63 and 18 sets, respectively. Considerable good performance was achieved in predicting gasoline engine torque and brake specific fuel consumption by using GP. The performance of accuracies of proposed GP models are quite satisfactory (R-2 = 0.9878 for gasoline engine torque and R-2 = 0.9744 for gasoline engine brake specific fuel consumption). The prediction of proposed GP models were compared to those of the neural network modeling, and strictly good agreement was observed between the two predictions. The proposed GP formulation is quite accurate, fast and practical. Crown Copyright (C) 2010 Published by Elsevier Ltd. All rights reserved.
In this study, a robust variant of genetic programming called gene expression programming (GEP) is utilized to predict the moment capacity of ferrocement members. Constitutive relationships were obtained to correlate ...
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In this study, a robust variant of genetic programming called gene expression programming (GEP) is utilized to predict the moment capacity of ferrocement members. Constitutive relationships were obtained to correlate the ultimate moment capacity with mechanical and geometrical parameters using previously published experimental results. A subsequent parametric analysis was carried out and the trends of the results were confirmed. A comparative study was conducted between the results obtained by the proposed models and those of the plastic analysis, mechanism and nonlinear regression approaches, as well as two black-box models: back-propagation neural networks (BPNN) and an adaptive neuro-fuzzy inference system (ANFIS). Three GEP models are developed to capture the effect of randomizing the test data subsets used to develop the models. The results indicate that the GEP models accurately estimate the moment capacity of ferrocement members. The prediction performance of the GEP models is significantly better than the plastic analysis, mechanism and nonlinear regression approaches and is comparable to that of the BPNN and ANFIS models. (C) 2013 Elsevier Ltd. All rights reserved.
Storm surge is a genuine common fiasco coming from the ocean. Therefore, an exact forecast of surges is a vital assignment to dodge property misfortunes and to decrease a chance caused by tropical storm surge. genetic...
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Storm surge is a genuine common fiasco coming from the ocean. Therefore, an exact forecast of surges is a vital assignment to dodge property misfortunes and to decrease a chance caused by tropical storm surge. genetic programming (GP) is an evolution-based model learning technique that can simultaneously find the functional form and the numeric coefficients for the model. Therefore, GP has been widely applied to build models for predictive problems. However, GP has seldom been applied to the problem of storm surge forecasting. In this paper, we propose a new method to use GP for evolving models for storm surge forecasting. Experimental results on datasets collected from the Tottori coast of Japan show that GP can evolve accurate storm surge forecasting models. Moreover, GP can automatically select relevant features when evolving storm surge forecasting models, and the models evolved by GP are interpretable.
This study is a pioneer work that proposes genetic programming (GP) as a new approach for the explicit formulation of available rotation capacity of wide-flange beams which is an important phenomenon that determines t...
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This study is a pioneer work that proposes genetic programming (GP) as a new approach for the explicit formulation of available rotation capacity of wide-flange beams which is an important phenomenon that determines the plastic behaviour of steel structures. The database for the GP formulation is based on extensive experimental results from literature. The results of the GP-based formulation are compared with numerical results obtained by a specialized computer program and existing analytical equations. The results indicate that the proposed GP formulation performs quite well compared to numerical results and existing analytical equations and is quite practical for use. (c) 2006 Elsevier Ltd. All rights reserved.
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