Design of High-Performance Concrete Mixture Using Neural Networks and Nonlinear Programming

• Published in: Journal of computing in civil engineering Vol. 13; no. 1; pp. 36 - 42
• Main Author: Yeh, I-Cheng
• Format: Journal Article
• Language: English
• Published: Reston, VA American Society of Civil Engineers 01.01.1999
• Subjects: Applied sciences; Buildings. Public works; Computation methods. Tables. Charts; Concretes. Mortars. Grouts; Exact sciences and technology; General (composition, classification, performance, standards, patents, etc.); Materials; Structural analysis. Stresses; TECHNICAL PAPERS; Experimental test; Statistical analysis; Network architecture; Concrete mix design; Concrete workability; Non linear programming; Compressive strength; Neural network; Optimization; Performance requirement; Numerical simulation; High strength concrete; Method study
• ISSN: 0887-3801; 1943-5487
• DOI: 10.1061/(ASCE)0887-3801(1999)13:1(36)

A method of optimizing high-performance concrete mix proportioning for a given workability and compressive strength using artificial neural networks and nonlinear programming is described. The basic procedure of the methodology consists of three steps: (1) Build accurate models for workability and strength using artificial neural networks and experimental data; (2) incorporate these models in software allowing an evaluation of the specified properties for a given mix; and (3) incorporate the software in a nonlinear programming package allowing a search of the optimum proportion mix design. For performing optimum concrete mix design based on the proposed methodology, a software package has been developed. One can conduct mix simulations covering all the important properties of the concrete at the same time. To demonstrate the utility of the proposed methodology, experimental results from several different mix proportions based on various design requirements are presented.