Predicting the compressive strength of silica fume concrete using hybrid artificial neural network with multi-objective grey wolves

By Ali Behnood, Emadaldin Mohammadi Golafshani. Journal of Cleaner Production, 2018

317 citations recorded by the source corpus.

Abstract The use of silica fume as a partial replacement for Ordinary Portland Cement provides a wide variety of benefits such as reduced pressure on natural resources, reduced CO2 footprint, and improved mechanical and durability properties. Compressive strength of concrete is one of the most important parameters in the design of concrete structures. Artificial Neural Network (ANN), as one of the powerful tools in the artificial intelligence field, has been widely used to predict various properties of concrete mixtures. One of the major drawbacks of the developed ANN models in predicting the concrete properties is their high complexity. In this study, estimating the compressive strength of silica fume concrete using the ANN method was considered as a two-objective optimization problem. These two objectives were the accuracy and the complexity of the developed ANN models. In this regard, a new multi-objective optimization method called Multi-Objective Grey Wolves Optimization (MOGWO) method was served to find a simple ANN model with acceptable error. After solving the optimization problem, a total number of 31 optimized ANN models were achieved. An ANN model with just one hidden layer with five neurons and the Pearson correlation coefficient of 0.9617 for all data was chosen as the final ANN model. Moreover, a sensitivity analysis was carried out to investigate the capability of the final ANN model in predicting the trend of the compressive strength of silica fume concrete with changing the effective variables on the compressive strength.

Subfields: Civil and Structural Engineering

View Paper: https://www.semanticscholar.org/paper/2a587e0ce88074c475ba3dd1493338a6bb490f14

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In Civil and Structural Engineering

Citations in the corpus

Prediction Compressive Strength of Concrete Containing GGBFS using Random Forest Model (2021, background): “Overall, the MSE value of RF is the lowest in the case of simulation with 1 leaf size and about 500 grown trees [500–1].”
Estimation of the dynamic modulus of asphalt concretes using random forests algorithm (2020, background): “ML techniques are able to learn the underlying behaviour of a system without requiring prior knowledge in terms of the nature of the relationships between the variables (Behnood and Golafshani 2018, Golafshani and Behnood 2018b).”
Compressive Strength of Fly-Ash-Based Geopolymer Concrete by Gene Expression Programming and Random Forest (2021, methodology): “Due to the complications in the proposed relationship, just a graphical interface was developed to make the model functional [45].”
A novel artificial intelligence technique to predict compressive strength of recycled aggregate concrete using ICA-XGBoost model (2020, background): “Although a number of research have been conducted for predicting CS of RAC [48–58], further research is still necessary to find a robust algorithm capable of revealing the different aspects of its design and which can allow its generalised use.”