Development of Advanced Computer Aid Model for Shear Strength of Concrete Slender Beam Prediction

• High-strength concrete (HSC) is highly applicable to the construction of heavy structures. However, shear strength (Ss) determination of HSC is a crucial concern for structure designers and decision makers. The current research proposes the novel models based on the combination of adaptive neuro-fuzzy inference system (ANFIS) with several meta-heuristic optimization algorithms, including ant colony optimizer (ACO), differential evolution (DE), genetic algorithm (GA), and particle swarm optimization (PSO), to predict the Ss of HSC slender beam. The proposed models were constructed using several input combinations incorporating several related dimensional parameters such as effective depth of beam (d), shear span (a), maximum size of aggregate (ag), compressive strength of concrete (fc), and percentage of tension reinforcement (ρ). To assess the impact of the non-homogeneity of the dataset on the prediction result accuracy, two possible modeling scenarios, (i) non-processed (initial) dataset (NP) and (ii) pre-processed dataset (PP), are inspected by several performance indices. The modeling results demonstrated that ANFIS-PSO hybrid model attained the best prediction accuracy over the other models and for the pre-processed input parameters. Several uncertainty analyses were examined (i.e., model, variables, and data), and results indicated predicting the HSC shear strength was more sensitive to the model structure uncertainty than the input parameters.

Ämnesord

TEKNIK OCH TEKNOLOGIER  -- Samhällsbyggnadsteknik -- Geoteknik (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Civil Engineering -- Geotechnical Engineering (hsv//eng)

Nyckelord

structure monitoring

shear strength prediction

machine learning

hybrid ANFIS model

high-strength concrete

Soil Mechanics

Geoteknik

Publikations- och innehållstyp

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