A PSO-ANN Intelligent Hybrid Model to Predict the Compressive Strength of Limestone Fillers Roller Compacted Concrete (RCC) to Build Dams

• Published in: KSCE journal of civil engineering Vol. 25; no. 8; pp. 3008 - 3018
• Main Authors: Chakali, Youcef, Sadok, Ahmed Hadj, Tahlaiti, Mahfoud, Nacer, Tarek
• Format: Journal Article
• Language: English
• Published: Seoul Korean Society of Civil Engineers 01.08.2021; Springer Nature B.V; 대한토목학회
• Subjects: Algorithms; Artificial intelligence; Artificial neural networks; Cement; Civil Engineering; Compressive strength; Concrete; Correlation coefficients; Dam design; Dams; Engineering; Engineers; Fillers; Formulations; Genetic algorithms; Geotechnical Engineering & Applied Earth Sciences; Heuristic methods; Hybrid systems; Industrial Pollution Prevention; Limestone; Mechanical properties; Neural networks; Optimization; Particle swarm optimization; Predictions; Roller compacted concrete; Structural Engineering; Vectors; Water-cement ratio; 토목공학; Dam; Particles swarm optimization; Limestone fillers; Prediction; Roller compacted concrete; Compressive strength; Artificial neural network
• ISSN: 1226-7988; 1976-3808
• DOI: 10.1007/s12205-021-1531-6

The compressive strength of the roller-compacted concrete (RCC) is an essential indicator of quality when designing dams. RCC is optimized in most cases through experimental studies conducted vigorously. This study aims at developing a smart system to predict the compressive strength of the limestone fillers RCC that is used to build dams. The prediction is made base on the following parameters: the maximum diameter of the aggregates, the compactness of the granular mixture, the rates of both cement and limestone fillers, water/cement ratio and the RCC age. The cement strength is taken in to consideration using a corrective equation. Two metaheuristic systems are developed: artificial neural networks (ANN), and a hybrid system consisting of an ANN optimized by a particle swarm optimization (PSO) algorithm. An experimental database is built containing 500 vectors taken from RCC formulations given by lab activity reports about 04 dam projects. The best results were achieved through the PSO-ANN hybrid system. This prediction system is validated by an experimental study conducted on 20 RCC formulations, and a comparison was made with the Laboratoire Central des Ponts et Chaussees (LCPC) method. The prediction resulting from the PSO-ANN system is of a good accuracy level with a correlation factor R 2 = 0.85 and a low root mean squared error of 1.45. Finally, a user interface based on the model developed is created.