Concrete corrosion in wastewater systems: Prediction and sensitivity analysis using advanced extreme learning machine

doi:10.1007/s11709-021-0697-9

Front. Struct. Civ. Eng.

2021, Vol. 15

Issue (2) : 444-460 https://doi.org/10.1007/s11709-021-0697-9

RESEARCH ARTICLE

Concrete corrosion in wastewater systems: Prediction and sensitivity analysis using advanced extreme learning machine

Mohammad ZOUNEMAT-KERMANI¹(

), Meysam ALIZAMIR², Zaher Mundher YASEEN³(

), Reinhard HINKELMANN⁴

¹. Department of Water Engineering, Shahid Bahonar University of Kerman, Kerman 76169-13439, Iran
². Department of Civil Engineering, Hamedan Branch, Islamic Azad University, Hamedan 65181-15743, Iran
³. Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
⁴. Department of Civil Engineering, Technische Universität Berlin, Berlin 13355, Germany

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Abstract

The implementation of novel machine learning models can contribute remarkably to simulating the degradation of concrete due to environmental factors. This study considers the sulfuric acid corrosive factor in wastewater systems to simulate concrete mass loss using five machine learning models. The models include three different types of extreme learning machines, including the standard, online sequential, and kernel extreme learning machines, in addition to the artificial neural network, classification and regression tree model, and statistical multiple linear regression model. The reported values of concrete mass loss for six different types of concrete are the target values of the machine learning models. The input variability was assessed based on two scenarios prior to the application of the predictive models. For the first assessment, the machine learning models were developed using all the available cement and concrete mixture input variables; the second assessment was conducted based on the gamma test approach, which is a sensitivity analysis technique. Subsequently, the sensitivity analysis of the most effective parameters for concrete corrosion was tested using three different approaches. The adopted methodology attained optimistic and reliable modeling results. The online sequential extreme learning machine model demonstrated superior performance over the other investigated models in predicting the concrete mass loss of different types of concrete.

Keywords sewer systems environmental engineering data-driven methods sensitivity analysis

Corresponding Author(s): Mohammad ZOUNEMAT-KERMANI,Zaher Mundher YASEEN

Online First Date: 30 April 2021 Issue Date: 27 May 2021

Cite this article:

Mohammad ZOUNEMAT-KERMANI,Meysam ALIZAMIR,Zaher Mundher YASEEN, et al. Concrete corrosion in wastewater systems: Prediction and sensitivity analysis using advanced extreme learning machine[J]. Front. Struct. Civ. Eng., 2021, 15(2): 444-460.

URL:

https://academic.hep.com.cn/fsce/EN/10.1007/s11709-021-0697-9
https://academic.hep.com.cn/fsce/EN/Y2021/V15/I2/444

Fig.1 Flowchart of the general process of the OS-ELM model.

Fig.2 Methodology used in this study for the prediction and sensitivity analysis phases using extreme learning models, conventional ML models, and statistical MLR model.

Tab.1 Cement properties and mixture proportions of the concrete treatments used in this study [67]

Tab.2 Statistical characteristics of concrete corrosion in terms of mass loss (g)

Fig.3 Mass loss of different concrete types exposed to 2 cm³ of 7% sulfuric acid for 150 applications.

Tab.3 GT results on the mass loss of concrete

Tab.4 Optimal parameters for the proposed network-based ML models

Tab.5 Training and testing results of the applied predictive models in simulating concrete mass loss by considering all the available input variables (Scenario I)

Fig.4 Scatter plots for the results of ML models using all the available independent input parameters, (a) MLPNN; (b) CART; (c) ELM; (d) K-ELM; (e) OS-ELM.

Fig.5 Box plots of the results of different applied predictive models based on the (a) first and (b) second scenarios.

Fig.6 Taylor diagram of the statistics for the predicted corrosion values of the applied models based on the two input scenarios.

Tab.6 Training and testing results of the applied predictive models in simulating concrete mass using the GT (Scenario II)

Fig.7 Scatter plots for the results of ML models using selected independent input parameters based on the GT, (a) MLPNN; (b) CART; (c) ELM; (d) K-ELM; (e) OS-ELM

Fig.8 Importance of each cement/concrete explanatory variable obtained from: (a) RReliefF algorithm; (b) GT.

Tab.7 Sensitivity analysis results based on the ELM model using the leave-one-out method for the cement and concrete parameters

Tab.8 Effectiveness ranks for cement/concrete independent input parameters using three different sensitivity analysis tests

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