Fuzzy Logic, Neural Network, and Adaptive Neuro-Fuzzy Inference System in Delegation of Standard Concrete Beam Calculations

Machine learning (ML) has been proven effective in various scientific and industrial domains. Nevertheless, its practical application in the construction industry requires further investigation. Leveraging ML capabilities conserves human resources, reduces errors, and speeds up computation and interpretation tasks. The efficacy of ML algorithms depends on factors like ability, result accuracy, analysis cost, and sensitivity to parameter count and available data volume. This study explores the potential of using ML to delegate structural calculation processes, which is an aspect with limited attention. Concrete beam section calculations based on the American Concrete Institute (ACI) standards were chosen as a case study to assess ML’s capacity to emulate a structural designer’s role. Initially, manual design steps and standard considerations for a concrete beam section were parametrically coded in MATLAB. Validation against structural design references ensured code accuracy in calculating shear and bending capacities. The parametric results served as initial data (lookup table) for training ML operators. Various ML techniques, including fuzzy logic (FL), neural network (NN), and adaptive neuro-fuzzy inference system (ANFIS), were coded in MATLAB. A comparative analysis of the three ML operators assessed their performance in replacing standard calculations. Parametric examples illustrated each operator’s precision in delegation compared to direct calculations. The study also explored the impact of the number of parameters and lookup table size on the accuracy of each ML operator. The findings revealed that while all three operators could delegate standard calculations, their precision varied. Notably, when the lookup table was optimal, ANFIS operators demonstrated the ability to represent standard calculations with varying parameter counts and high precision. Focused on beam calculations, this study provides insights into ML operator performance. The outcomes, including selecting the most capable operator and their sensitivity to parameters and lookup table size, offer valuable guidance for researchers interpreting experimental and numerical analysis results.

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