3D concrete printing (3DCP) is crucial in the construction because of the low labor cost, eco-friendly behavior;however, getting a proper mixture is always a challenge. This study focuses on predicting the compressive...
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3D concrete printing (3DCP) is crucial in the construction because of the low labor cost, eco-friendly behavior;however, getting a proper mixture is always a challenge. This study focuses on predicting the compressive strength (CS) of fiber-reinforced concrete produced with 3DCP using eight machine learning (ML) algorithms to get optimized mixture. The ML models were trained and tested using a comprehensive database on CS collected from literature considering the various fiber-reinforced cementitious composites, comprising over 299 mixtures with 11 features. The results show that the trained ML models could predict CS with R2 ranging from 0.927 to 0.990 and 0.914 to 0.988 for the training and testing dataset, respectively. Furthermore, supplementary experiments were conducted to create a new dataset to validate the predictive model's accuracy, with the extreme gradient boosting (XGB) and gene expression programming (GEP). Based on the GEP, a novel empirical equation was proposed and rigorously validated using experiments. The equation exhibits a high accuracy with the GEP algorithm (R2 = 0.89), providing real-world field applications that might be improve decision-making and mixture optimization, which contributes to advancements, efficiency, and innovative solutions in 3D printing practical domains.
Photodynamic therapy (PDT) is a new medical treatment modality for cancer which employs the combination of light and a photosensitizing drug against cancerous tissue. Quantitative structure-activity relationship (QSAR...
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Photodynamic therapy (PDT) is a new medical treatment modality for cancer which employs the combination of light and a photosensitizing drug against cancerous tissue. Quantitative structure-activity relationship (QSAR) study of the photocytotoxicity of miscellaneous porphyrins with amino acid and uracil has been performed. A series of molecular descriptors have been calculated in QSAR for these photosensitizers, with selection of variables and construction of nonlinear QSAR models performed simultaneously by gene expression programming (GEP). These selected descriptors encode different aspects of the molecular structure, involving electronic, spatial, and thermodynamic effects. The main factors affecting the photocytotoxicity of the considered porphyrins have been identified.
Quantitative structure-activity relationship (QSAR) study of chemokine receptor 5 (CCR5) binding affinity of substituted 1-(3,3-diphenylpropyl)-piperidinyl amides and ureas and toxicity of aromatic compounds have been...
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Quantitative structure-activity relationship (QSAR) study of chemokine receptor 5 (CCR5) binding affinity of substituted 1-(3,3-diphenylpropyl)-piperidinyl amides and ureas and toxicity of aromatic compounds have been performed. The gene expression programming (GEP) was used to select variables and produce nonlinear QSAR models simultaneously using the selected variables. In our GEP implementation, a simple and convenient method was proposed to infer the K-expression from the number of arguments of the function in a gene, without building the expression tree. The results were compared to those obtained by artificial neural network (ANN) and support vector machine (SVM). It has been demonstrated that the GEP is a useful tool for QSAR modeling. (C) 2009 Elsevier Masson SAS. All rights reserved.
The caving and subsidence developments above a longwall panel usually result in fractures of the overburden, which decrease the strength of the rock mass and its function. The height of fracturing (HoF) includes the c...
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The caving and subsidence developments above a longwall panel usually result in fractures of the overburden, which decrease the strength of the rock mass and its function. The height of fracturing (HoF) includes the caved and continuous fractured zones affected by a high degree of bending. Among the various empirical models, Ditton's geometry and geology models are widely used in Australian coalfields. The application of genetic programming (GP) and gene expression programming (GEP) in longwall mining is entirely new and original. This work uses a GEP method in order to predict HoF. The model variables, including the panel width (W), cover depth (H), mining height (T), unit thickness (t), and its distance from the extracted seam (y), are selected via the dimensional analysis and Buckingham's P-theorem. A dataset involving 31 longwall panels is used to present a new nonlinear regression function. The statistical estimators, including the coefficient of determination (R-2), the average error (AE), the mean absolute percentage error (MAPE), and the root mean square error (RMSE), are used to compare the performance of the discussed models. The R-2 value for the GEP model (99%) is considerably higher than the corresponding values of Ditton's geometry (61%) and geology (81%) models. Moreover, the maximum values of the statistical error estimators (AE, MAPE, and RMSE) for the GEP model are 12%, 14%, and 16%, respectively, of the corresponding values of Ditton's models.
Geotechnical engineers must place a high priority on the analysis and forecasting of slope stability to prevent the disasters that can result from a failed slope. As a result, it is crucial to accurately estimate slop...
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Geotechnical engineers must place a high priority on the analysis and forecasting of slope stability to prevent the disasters that can result from a failed slope. As a result, it is crucial to accurately estimate slope stability in order to ensure the project's success. This sort of information is indispensable in the early stages of concept and design, when important decisions must be made. In this study, an optimized GEP-based model for calculating the safety factor of rock slopes (SFRS) was proposed. For this purpose, a variety of rock slopes for circular failure mode were analyzed using the PLAXIS software to generate 325 datasets. In the datasets, six effective parameters on the SFRS including unit weight, friction angle, slope angle, cohesion, pore pressure ratio, and slope height were considered. 80% of the datasets were used for training and 20% for test. As a result of finding the optimal fit between the predictions, an equation for the refined GEP model was derived. Finally, the equation's potential ability to estimate SFRS was approved by comparing its outputs with the actual ones and comparing its behavior with practice. The mutual information sensitivity analysis revealed that the unit weight parameter is the most influential variable in the proposed equation. This model can reduce the uncertainties about the stability of rock slopes and give machine learning development in the field.
The gene expression programming, a novel machine learning algorithm, is used to develop quantitative model as a potential screening mechanism for a series of 1,4-dihydropyridine calcium channel antagonists for the fir...
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The gene expression programming, a novel machine learning algorithm, is used to develop quantitative model as a potential screening mechanism for a series of 1,4-dihydropyridine calcium channel antagonists for the first time. The heuristic method was used to search the descriptor space and select the descriptors responsible for activity. A nonlinear, six-descriptor model based on gene expression programming with mean-square errors 0.19 was set up with a predicted correlation coefficient (R-2) 0.92. This paper provides a new and effective method for drug design and screening. (c) 2006 Elsevier Ltd. All rights reserved.
Purpose This paper aims to predict the fire resistance of steel-reinforced concrete columns by application of the genetic algorithm. Design/methodology/approach In total, 11 effective parameters are considered includi...
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Purpose This paper aims to predict the fire resistance of steel-reinforced concrete columns by application of the genetic algorithm. Design/methodology/approach In total, 11 effective parameters are considered including mechanical and geometrical properties of columns and loading values as input parameters and the duration of concrete resistance at elevated temperatures as the output parameter. Then, experimental data of several studies - with extensive ranges - are collected and divided into two categories. Findings Using the first set of the data along with the gene expression programming (GEP), the fire resistance predictive model of steel-reinforced concrete (SRC) composite columns is presented. By application of the second category, evaluation and validation of the proposed model are investigated as well, and the correspondent time-temperature diagrams are derived. Originality/value The relative error of 10% and the R coefficient of 0.9 for the predicted model are among the highlighted results of this validation. Based on the statistical errors, a fair agreement exists between the experimental data and predicted values, indicating the appropriate performance of the proposed GEP model for fire resistance prediction of SRC columns.
Moment redistribution can play an important role in making the design of reinforced concrete (RC) structures more realistic and economical. In this paper, a new comprehensive formula has been proposed that considers f...
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Moment redistribution can play an important role in making the design of reinforced concrete (RC) structures more realistic and economical. In this paper, a new comprehensive formula has been proposed that considers four input parameters that are thought to influence moment redistribution the most in statically indeterminate RC beams using gene expression programming (GEP). For this reason, an experimental database of 108 data points was collected from experimental studies in the literature to predict the moment redistribution of the RC beams using genetic programming. All of these collected data points belong to two-span RC continuous beams. The results of the GEP formulation were statistically compared with the experimental results obtained from the literature and the results from the equations provided by the current design code provisions. The results of the comparison revealed that the proposed GEP-based formulation has the best performance and accuracy among the proposed models. Moreover, the sensitivity analysis and parametric study were also carried out to evaluate the most critical parameters affecting on moment redistribution of RC continuous beams.
The prognostic capability of gene expression programming (GEP) and artificial neural network (ANN) are compared to estimate the engine performance and emission characteristics. A stationary diesel engine was powered w...
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The prognostic capability of gene expression programming (GEP) and artificial neural network (ANN) are compared to estimate the engine performance and emission characteristics. A stationary diesel engine was powered with linseed oil biodiesel-mineral diesel blends. A total of 60 lab-based test-run were conducted by varying the engine input operating conditions, namely fuel injection parameters, diesel/biodiesel blending ratio, and engine load. The engine output data, namely brake thermal efficiency and brake-specific fuel consumption, were calculated, while emission data for oxides of nitrogen, carbon monoxide, and unburnt hydrocarbon, were recorded. The experimental data were used for predictive model development using artificial intelligence-based GEP and ANN techniques. The developed models were tested on statistical outcomes, such as the absolute fraction of variance (0.9698-0.997 for GEP and 0.9949-0.9998 for ANN), correlation coefficient (0.9848-0.998 for GEP and 0.9974-0.9998 for ANN), establishing these two models as an efficient machine identical tool. Also, Nash-Sutcliffe efficiency (0.937-0.9999 for GEP and 0.995-0.999 for ANN) and Kling-Gupta efficiency (0.834-0.9999 for GEP and 0.989-0.999 for ANN) elevate the prediction quality of developed models. The result showed that the ANN model was slightly more accurate than the GEP-based model for the same parametric range.
Design of reinforced soil structures is greatly influenced by soil-geosynthetic interactions at interface which is normally assessed by costly and time consuming laboratory tests. In present research, using the result...
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Design of reinforced soil structures is greatly influenced by soil-geosynthetic interactions at interface which is normally assessed by costly and time consuming laboratory tests. In present research, using the results of large-scale direct shear tests conducted on soil-anchored geogrid samples a model for predicting Enhanced Interaction Coefficient (EIC) is proposed enabling researchers/engineers easily, quickly and at no cost to estimate soil-geosynthetic interactions. In this regard well and poorly graded sands, anchors of three different size and anchorage lengths from the shear surface together with normal pressures of 12.5, 25 and 50 kPa were used. Artificial Intelligence (AI) called the gene expression programming (GEP) was adopted to develop the model. Input variables included coefficients of curvature and uniformity, normal pressure, effective grain size, anchor base and surface area, anchorage length and the output variable was EIC. Contributions of input variables were evaluated using sensitivity analysis. Excellent correlation between the GEP-based model and the experimental results were achieved showing that the proposed model is well capable of effectively estimating soil-anchored geogrid enhanced interaction coefficient. Sensitivity analysis for parameter importance shows that the most influential variables are normal pressure (sigma(n)) and anchorage length (L) and the least effective parameters are average particle size (D-50) and anchor base area (A(b)).
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