Artificial neural network for predicting creep of concrete

• Published in: Neural computing & applications Vol. 25; no. 6; pp. 1359 - 1367
• Main Authors: Bal, Lyes, Buyle-Bodin, François
• Format: Journal Article
• Language: English
• Published: London Springer London 01.11.2014; Springer
• Subjects: Applied sciences; Artificial Intelligence; Buildings. Public works; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Computer science; control theory; systems; Concrete making (water addition, mixing). Ready mixed concrete; Concretes. Mortars. Grouts; Connectionism. Neural networks; Data Mining and Knowledge Discovery; Devices using thermal energy; Dryers; Energy; Energy. Thermal use of fuels; Exact sciences and technology; Fracture mechanics (crack, fatigue, damage...); Fundamental areas of phenomenology (including applications); Image Processing and Computer Vision; Materials; Original Article; Physics; Probability and Statistics in Computer Science; Solid mechanics; Structural and continuum mechanics; Concrete; Artificial neural network; Modeling; Drying creep; Prediction; Relative humidity; Creep; Migration; Construction materials; Very large databases; Non parametric estimation; Delay; Backpropagation algorithm; Database; Backpropagation; Hydration; Durability; Neural network; Long term; Continuous process; Experimental result; Aggregate; Dynamic load; Crack; Drying

The concrete is today the building material by excellence. Drying accompanies the hardening of concrete and leads to significant dimensional changes that appear as cracks. These cracks influence the durability of the concrete works. Deforming a concrete element subjected to long-term loading is the...