Predicting compressive strength of lightweight foamed concrete using extreme learning machine model

By Zaher Mundher Yaseen, Ravinesh C. Deo, Ameer A. Hilal, Abbas M. Abd, L. Cornejo-Bueno, Sancho Salcedo-Sanz, Moncef L. Nehdi. Advances in Engineering Software, 2018

397 citations recorded by the source corpus.

The utilization of extreme learning machine model.Predicting compressive strength of foamed concrete.Multiple concrete components were investigated.ELM showed a realistic and trustful model for the applied application. In this research, a machine learning model namely extreme learning machine (ELM) is proposed to predict the compressive strength of foamed concrete. The potential of the ELM model is validated in comparison with multivariate adaptive regression spline (MARS), M5 Tree models and support vector regression (SVR). The Lightweight foamed concrete is produced via creating a cellular structure in a cementitious matrix during the mixing process, and is widely used in heat insulation, sound attenuation, roofing, tunneling and geotechnical applications. Achieving product consistency and accurate predictability of its performance is key to the success of this technology. In the present study, an experimental database encompassing pertinent data retrieved from several previous studies has been created and utilized to train and validate the ELM, MARS, M5 Tree and SVR machine learning models. The input parameters for the predictive models include the cement content, oven dry density, water-to-binder ratio and foamed volume. The predictive accuracy of the four models has been assessed via several statistical score indicators. The results showed that the proposed ELM model achieved an adequate level of prediction accuracy, improving MARS, M5 Tree and SVR models. Hence, the ELM model could be employed as a reliable and accurate data intelligent approach for predicting the compressive strength of foamed concrete, saving laborious trial batches required to attain the desired product quality.

Subfields: Civil and Structural Engineering

View Paper: https://www.semanticscholar.org/paper/de322f3fd6f01c5567240219039ef9e1d86ad7e0

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In Civil and Structural Engineering

Citations in the corpus

Foam Concrete: A State-of-the-Art and State-of-the-Practice Review (2020, background): “,ese findings are mainly based on the artificial neural network [122–124], extreme learning machine [125], and regression analysis based empirical models [126].”
Emerging materials intelligence ecosystems propelled by machine learning (2020, background): “…or free energies), electrical (dielectric strength 52 , electrical conductivity 53 , electric breakdown 54,55 ), mechanical (tensile strengths 56,57 , elastic constants 58 , fracture toughness 59 ), thermal (heat capacity 60 , vibrational spectra and entropy 61 , thermal conductivity 62 )…”
Classification-Based Regression Models for Prediction of the Mechanical Properties of Roller-Compacted Concrete Pavement (2020, methodology): “It is time consuming and costly to use the regulation methods for the calculation of the mix design and it is necessary to comply with the conditions and assumptions of the regulations for all constituent materials of concrete [8-10].”
A novel hybrid extreme learning machine–grey wolf optimizer (ELM-GWO) model to predict compressive strength of concrete with partial replacements for cement (2020, neutral): “[23] compared the performance of an extreme learning machine (ELM), a support vector regression (SVR), a multivariable adaptive regression spline (MARS), and M5 tree model in compressive strength prediction of lightweight foamed concrete.”
Short-term water quality variable prediction using a hybrid CNN–LSTM deep learning model (2020, methodology): “4 The structure of the hybrid CNN–LSTM model (Liu et al. 2019a) which can occur with r , RMSE , MAE and BIAS metrics (Willmott 1981; Yaseen et al. 2018).”