Concrete compressive strength prediction modeling utilizing deep learning long short-term memory algorithm for a sustainable environment

• Published in: Environmental science and pollution research international Vol. 28; no. 23; pp. 30294 - 30302
• Main Author: Latif, Sarmad Dashti
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2021; Springer Nature B.V
• Subjects: Algorithms; Aquatic Pollution; Atmospheric Protection/Air Quality Control/Air Pollution; Blast furnace practice; Blast furnace slags; cement; compression strength; Compressive Strength; Concrete; Concrete properties; Construction Materials; data collection; Deep Learning; Design parameters; Earth and Environmental Science; Ecotoxicology; Environment; Environmental Chemistry; Environmental Health; Environmental impact; Environmental science; environmental sustainability; Fly ash; furnaces; humidity; Learning algorithms; Long short-term memory; Machine learning; Mathematical models; neural networks; Neural Networks, Computer; prediction; Prediction models; Research Article; Root-mean-square errors; Severe weather; Slag; slags; Statistical analysis; Superplasticizers; Support vector machines; Waste Water Technology; Water Management; Water Pollution Control; Weather; Deep learning; Concrete strength prediction; Support vector machine (SVM); Long short-term memory (LSTM); High-performance concrete (HPC)
• ISSN: 0944-1344; 1614-7499; 1614-7499
• DOI: 10.1007/s11356-021-12877-y

One of the most critical parameters in concrete design is compressive strength. As the compressive strength of concrete is correctly measured, time and cost can be decreased. Concrete strength is relatively resilient to impacts on the environment. The production of concrete compressive strength is greatly influenced by severe weather conditions and increases in humidity rates. In this research, a model has been developed to predict concrete compressive strength utilizing a detailed dataset obtained from previously published studies based on a deep learning method, namely, long short-term memory (LSTM), and a conventional machine learning (ML) algorithm, namely, support vector machine (SVM). The input variables of the model include cement, blast furnace slag, fly ash, water, superplasticizer, coarse aggregate, fine aggregate, and age of specimens. To demonstrate the efficiency of the proposed models, three statistical indices, namely, the coefficient of determination ( R 2 ), mean absolute error (MAE), and root mean square error (RMSE), were used. Findings shows that LSTM outperformed SVM with R 2 =0.98, R 2 = 0.78, MAE=1.861, MAE=6.152, and RMSE=2.36, RMSE=7.93, respectively. The results of this study suggest that high-performance concrete (HPC) compressive strength can be reliably measured using the proposed LSTM model.