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

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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.

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Abbreviations

AA :

acid application number

ANN:

artificial neural network

C2S:

dicalcium silicate

C3A:

tricalcium aluminate

C3S:

tricalcium silicate

C4AF:

tetracalcium aluminoferrite

CART:

classification and regression tree

CW :

weight of cement in concrete

ELM:

extreme learning machine

GT:

gamma test

K-ELM:

kernel extreme learning machine

MAE :

mean absolute error

ML:

machine learning

MLPNN:

multi-layer perceptron neural network

NSE :

Nash-Sutcliffe efficiency

OS-ELM:

online sequential extreme learning machine

PC:

Portland cement

PC40:

Portland concrete with W/C ratio equals to 0.4

PC50:

Portland concrete with W/C ratio equals to 0.5

RBF:

radial basis function

RMSE :

root mean square error

RReliefE:

regressional relief F

SC:

slag concrete

SF %:

percentage of silica fume in the concrete mixture

SFC:

silica fume concrete

SFSC:

silica fume and slag concrete

SI %:

percentage of slag in the concrete mixture

SLFN:

single-layer feed-forward neural network

SR:

sulfate resisting cement

W/C :

water-cementitious ratio

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Acknowledgements

This research was financially supported by the Alexander von Humboldt Foundation within the framework of a Georg Forster Research fellowship.

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Authors and Affiliations

  1. Department of Water Engineering, Shahid Bahonar University of Kerman, Kerman, 76169-13439, Iran

    Mohammad Zounemat-Kermani

  2. Department of Civil Engineering, Hamedan Branch, Islamic Azad University, Hamedan, 65181-15743, Iran

    Meysam Alizamir

  3. Institute of Research and Development, Duy Tan University, Da Nang, 550000, Vietnam

    Zaher Mundher Yaseen

  4. Department of Civil Engineering, Technische Universität Berlin, Berlin, 13355, Germany

    Reinhard Hinkelmann

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  1. Mohammad Zounemat-Kermani
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Zounemat-Kermani, M., Alizamir, M., Yaseen, Z.M. et al. Concrete corrosion in wastewater systems: Prediction and sensitivity analysis using advanced extreme learning machine. Front. Struct. Civ. Eng. 15, 444–460 (2021). https://doi.org/10.1007/s11709-021-0697-9

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