Prediction model for compressive arch action capacity of RC frame structures under column removal scenario using gene expression programming

Azim, Iftikhar; Yang, Jian; Javed, Muhammad Faisal; Iqbal, Muhammad Farjad; Mahmood, Zafar; Wang, Feiliang; Liu, Qingfeng

doi:10.1016/j.istruc.2020.02.028

Cited by 128 publications

(53 citation statements)

References 82 publications

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“…e performance of the developed PSO-MEP model was assessed by measuring various statistical indicators including correlation coefficient (R), root mean squared error (RMSE), Nash-Sutcliffe efficiency (NSE), mean absolute error (MAE), relative root mean squared error (RRMSE), relative squared error (RSE), and performance index (ρ). Moreover, another measure to reduce the model overfitting is to select the best model by minimizing the objective function (OF) as suggested by Gandomi et al and Azim [34,69]. e same approach has been applied in this study, and OF is termed as fitness function.…”

Section: Performance Evaluationmentioning

confidence: 99%

Machine Learning‐Based Modeling with Optimization Algorithm for Predicting Mechanical Properties of Sustainable Concrete

Shah

Memon

Niazi

et al. 2021

Advances in Civil Engineering

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In this research, multiexpression programming (MEP) has been employed to model the compressive strength, splitting tensile strength, and flexural strength of waste sugarcane bagasse ash (SCBA) concrete. Particle swarm optimization (PSO) algorithm was used to fine-tune the hyperparameter of the proposed MEP. The formulation of SCBA concrete was correlated with five input parameters. To train and test the proposed model, a large number of data were collected from the published literature. Afterward, waste SCBA was collected, processed, and characterized for partial replacement of cement in concrete. Concrete specimens with varying proportion of SCBA were prepared in the laboratory, and results were used for model validation. The performance of the developed models was then evaluated by statistical criteria and error assessment tests. The result shows that the performance of MEP with PSO algorithm significantly enhanced its accuracy. The essential input variables affecting the output were revealed, and the parametric analysis confirms that the models are accurate and have captured the essential properties of SCBA. Finally, the cross validation ensured the generalized capacity and robustness of the models. Hence, the adopted approach, i.e., MEP-based modeling with PSO, could be an effective tool for accurate modeling of the concrete properties, thus directly contributing to the construction sector by consuming waste and protecting the environment.

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Section: Performance Evaluationmentioning

confidence: 99%

Machine Learning‐Based Modeling with Optimization Algorithm for Predicting Mechanical Properties of Sustainable Concrete

Shah

Memon

Niazi

et al. 2021

Advances in Civil Engineering

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show abstract

“…The developed models are evaluated employing statistical checks, i.e., correlation coefficient (R), Nash Sutcliff efficiency (NSE), mean absolute error (MAE), relative root mean squared error (RRMSE), relative squared error (RSE), performance index (ρ), and root means squared error (RMSE) [44,76]. The high values of R and NSE and low values of RMSE and MAE indicate better performance.…”

Section: Performance Measuresmentioning

confidence: 99%

Performance Evaluation of Soft Computing for Modeling the Strength Properties of Waste Substitute Green Concrete

et al. 2021

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The waste disposal crisis and development of various types of concrete simulated by the construction industry has encouraged further research to safely utilize the wastes and develop accurate predictive models for estimation of concrete properties. In the present study, sugarcane bagasse ash (SCBA), a by-product from the agricultural industry, was processed and used in the production of green concrete. An advanced variant of machine learning, i.e., multi expression programming (MEP), was then used to develop predictive models for modeling the mechanical properties of SCBA substitute concrete. The most significant parameters, i.e., water-to-cement ratio, SCBA replacement percentage, amount of cement, and quantity of coarse and fine aggregate, were used as modeling inputs. The MEP models were developed and trained by the data acquired from the literature; furthermore, the modeling outcome was validated through laboratory obtained results. The accuracy of the models was then assessed by statistical criteria. The results revealed a good approximation capacity of the trained MEP models with correlation coefficient above 0.9 and root means squared error (RMSE) value below 3.5 MPa. The results of cross-validation confirmed a generalized outcome and the resolved modeling overfitting. The parametric study has reflected the effect of inputs in the modeling process. Hence, the MEP-based modeling followed by validation with laboratory results, cross-validation, and parametric study could be an effective approach for accurate modeling of the concrete properties.

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“…The predictive ability of genetic programming and gene expression programming (GP and GEP, respectively) models has attracted interest from researchers investigating structural engineering problems [ 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 ]. For example, Mansouri et al [ 24 ] applied the GEP approach to improving the shear strength estimation of exterior RC beam–column connections.…”

Section: Introductionmentioning

confidence: 99%

A Prediction Model for the Calculation of Effective Stiffness Ratios of Reinforced Concrete Columns

et al. 2021

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Nonlinear dynamic analyses of reinforced concrete (RC) frame buildings require the use of effective stiffness of members to capture the effect of cracked section stiffness. In the design codes and practices, the effective stiffness of RC sections is given as an empirical fraction of the gross stiffness. However, a more precise estimation of the effective stiffness is important as it affects the distribution of forces and various demands and response parameters in nonlinear dynamic analyses. In this study, an evolutionary computation method called gene expression programming (GEP) was used to predict the effective stiffness ratios of RC columns. Constitutive relationships were obtained by correlating the effective stiffness ratio with the four mechanical and geometrical parameters. The model was developed using a database of 226 samples of nonlinear dynamic analysis results collected from another study by the author. Subsequent parametric and sensitivity analyses were performed and the trends of the results were confirmed. The results indicate that the GEP model provides precise estimations of the effective stiffness ratios of the RC frames.

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Prediction model for compressive arch action capacity of RC frame structures under column removal scenario using gene expression programming

Cited by 128 publications

References 82 publications

Machine Learning‐Based Modeling with Optimization Algorithm for Predicting Mechanical Properties of Sustainable Concrete

Machine Learning‐Based Modeling with Optimization Algorithm for Predicting Mechanical Properties of Sustainable Concrete

Performance Evaluation of Soft Computing for Modeling the Strength Properties of Waste Substitute Green Concrete

A Prediction Model for the Calculation of Effective Stiffness Ratios of Reinforced Concrete Columns

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