Artificial neural network approach using Levenberg-Marquardt algorithm in the use of bottom ash waste in concrete hollow block design
Date of Publication
Master of Science in Civil Engineering
Gokongwei College of Engineering
Andres Winston C. Oreta
Defense Panel Chair
Ronaldo S. Gallardo
Defense Panel Member
Alexis M. Fillone
Neural Network modeling was applied for the prediction of compressive strength of Coal Bottom Ash (CBA). Levenberg-Marquardt was used for the different neural network architectures to find acceptable models than can accurately predict the compressive strength of CHB's and realistically model the behavior of CHB's with CBA as partial substitute to sand. In addition, the maximum percentage of CBA content was derived from the ANN (Artificial Neural Network) model based on PNS (Philippine National Standards) types. CBA is a waste by-product of coal-fired power plant. An experimental study utilizing CBA as a partial substitute to sand in the production of CNB's was conducted. Around 429 pieces of four-inch thick CHB's were tested with such variable mix proportions as: water-cement ratio (w/c), cement-aggregate ratio (c/a), weight of the specimen (wt), slump (sl), and coal bottom ash percent substitution (CBA) taken into consideration.
Archives, The Learning Commons, 12F Henry Sy Sr. Hall
86 numb. leaves ; 28 cm.
Neural networks (Computer science); Evolutionary computation; Algorithms; Waste products; Concrete blocks
Ongpeng, J. C. (2003). Artificial neural network approach using Levenberg-Marquardt algorithm in the use of bottom ash waste in concrete hollow block design. Retrieved from https://animorepository.dlsu.edu.ph/etd_masteral/3046