Prediction of concrete compressive strength using deep neural networks based on hyperparameter optimization

Abstract

This paper describes deep neural network (DNN) models based on hyperparameter optimization for the prediction of the compressive strength of concrete. The novelty of this research lies in the implementation of optimized hyperparameters to train the DNN models with the aim to enhance their predictive accuracy. Utilizing the Keras Tuner library, the most effective hyperparameters for the DNN models were identified. These models are then trained and evaluated using an experimental dataset of 1030 instances, encompassing nine quantitative variables that include various concrete mix ingredients and age. The target variable for all models is the compressive strength of concrete. The assessment of model performance, based on metrics like mean squared error (MSE) and R² values, was conducted on previously unseen test data. The optimal configuration for the hidden layers in the model was identified as five layers, containing 12, 16, 16, 40, and 26 neurons, respectively. The optimal learning rate for the model was 0.001. With this set of optimal hyperparameters, the best DNN model achieved an MSE of 28.76 and an R² value of 0.89 on the testing data. The results demonstrate a significant improvement in the DNN models' performance when trained with the optimized hyperparameters. In comparison to the regression model, the performance of the DNN models was significantly better. Adopting the predictive models developed in this research offers the potential for substantial cost and time savings by circumventing the need for labour-intensive and time-consuming laboratory tests.

Keywords:

• Concrete
• deep
• neural
• networks
• hyperparameter
• optimization

Reviewing Editor:

• Sanjay Kumar Shukla, Edith Cowan University, Australia

Subjects:

• Artificial Intelligence
• Computer Science (General)
• Civil, Environmental and Geotechnical Engineering

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Mohammed Naved

Mohammed Naved is a student of Bachelor of Technology program in Civil Engineering at Jamia Millia Islamia CentralUniversity in New Delhi, India. He has completed his research internship from Heriot Watt University, United Kingdom on three-dimensional printing of concrete structures.

Mohammed Asim

Tanvir Ahmad, is a Professor in Department of Computer Engineering and also as an Additional Director at FTK-Centre of Information Technology of Jamia Millia Islamia, New Delhi. He also served as the Head of the department of Computer Engineering for 6 years from 2014 to 2021. He obtained his B.Tech degree from Bangalore University, M.Tech degree from I.P. University, New Delhi with Distinction. Thereafter he received the Ph.D. degree from Jamia Millia Islamia in the area of Text Mining. He has supervised more than 20 Ph.D. students and 15 of his students have been awarded Ph.D. degree.

He has published more than 100 papers in reputed international journals, Book Chapters and International conferences and more than 90 of his papers are indexed in the Scopus database and 30+ papers are in Science citation Index. He also holds one International and one Indian patents in the field of Data Mining.

Tanvir Ahmad

Mohammed Asim is a graduated student researcher in the Department of Computer Science at the University of California, Davis. Asim obtained his bachelor’s degree in Computer Engineering from Jamia Millia Islamia Central University, New Delhi in 2021.