High strength concrete modeling by artificial neural networks
Date of Publication
2002
Document Type
Bachelor's Thesis
Degree Name
Bachelor of Science in Civil Engineering
Subject Categories
Civil Engineering
College
Gokongwei College of Engineering
Department/Unit
Civil Engineering
Honor/Award
Awarded as best thesis, 2002
Abstract/Summary
Abstract. Artificial Neural Networks of the backpropagation type was used to map the strength of High Strength Concrete given the design mix. Several ANN models were trained and simulated using 89 sets of data composed of the amount of cement, water, admixture, slag, silica fume, RHA, fine aggregates, coarse aggregates, fly ash, metakaolin, and the corresponding compressive strength of concrete at 28 days. The ANN models were validated through error metrics (root mean squared error, mean average error), minimum, mean, and maximum errors, sufficiency of number of training data, parametric studies, and statistical analysis (coefficient of regression). The results show that ANN can be used to trace the behavior of HSC and predict its strength.
Abstract Format
html
Language
English
Format
Accession Number
TU11034
Shelf Location
Archives, The Learning Commons, 12F Henry Sy Sr. Hall
Physical Description
58 numb. leaves
Recommended Citation
Flores, A. C., Ng, T. L., & Roxas, C. L. (2002). High strength concrete modeling by artificial neural networks. Retrieved from https://animorepository.dlsu.edu.ph/etd_honors/172
