Optimal flexural design of FRP-reinforced concrete beams using a particle swarm optimizer

Mauro Innocente, Luís L. Torres, X. Cahís, G. Barbeta, A. Catalán

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding

Abstract

The design of the cross-section of an FRP-reinforced concrete beam is an iterative process of estimating both its dimensions and the reinforcement ratio, followed by the check of the compliance of a number of strength and serviceability constraints. The process continues until a suitable solution is found. Since there are infinite solutions to the problem, it appears convenient to define some optimality criteria so as to measure the relative goodness of the different solutions. This paper intends to develop a preliminary least-cost section design model that follows the recommendations in the ACI 440.1 R-06, and uses a relatively new artificial intelligence (AI) technique called “particle swarm optimization” (PSO) to handle the optimization tasks. The latter is based on the intelligence that emerges from the low-level interactions among a number of relatively non-intelligent individuals within a population.

Original language

English

Title of host publication

Proceedings of the 8th International Symposium on Fiber-Reinforced Polymer Reinforcement for Concrete Structures

title = "Optimal flexural design of FRP-reinforced concrete beams using a particle swarm optimizer",

abstract = "The design of the cross-section of an FRP-reinforced concrete beam is an iterative process of estimating both its dimensions and the reinforcement ratio, followed by the check of the compliance of a number of strength and serviceability constraints. The process continues until a suitable solution is found. Since there are infinite solutions to the problem, it appears convenient to define some optimality criteria so as to measure the relative goodness of the different solutions. This paper intends to develop a preliminary least-cost section design model that follows the recommendations in the ACI 440.1 R-06, and uses a relatively new artificial intelligence (AI) technique called “particle swarm optimization” (PSO) to handle the optimization tasks. The latter is based on the intelligence that emerges from the low-level interactions among a number of relatively non-intelligent individuals within a population.",

author = "Mauro Innocente and Torres, {Lu{\'i}s L.} and X. Cah{\'i}s and G. Barbeta and A. Catal{\'a}n",

year = "2007",

month = "7",

language = "English",

booktitle = "Proceedings of the 8th International Symposium on Fiber-Reinforced Polymer Reinforcement for Concrete Structures",

}

TY - GEN

T1 - Optimal flexural design of FRP-reinforced concrete beams using a particle swarm optimizer

AU - Innocente, Mauro

AU - Torres, Luís L.

AU - Cahís, X.

AU - Barbeta, G.

AU - Catalán, A.

PY - 2007/7

Y1 - 2007/7

N2 - The design of the cross-section of an FRP-reinforced concrete beam is an iterative process of estimating both its dimensions and the reinforcement ratio, followed by the check of the compliance of a number of strength and serviceability constraints. The process continues until a suitable solution is found. Since there are infinite solutions to the problem, it appears convenient to define some optimality criteria so as to measure the relative goodness of the different solutions. This paper intends to develop a preliminary least-cost section design model that follows the recommendations in the ACI 440.1 R-06, and uses a relatively new artificial intelligence (AI) technique called “particle swarm optimization” (PSO) to handle the optimization tasks. The latter is based on the intelligence that emerges from the low-level interactions among a number of relatively non-intelligent individuals within a population.

AB - The design of the cross-section of an FRP-reinforced concrete beam is an iterative process of estimating both its dimensions and the reinforcement ratio, followed by the check of the compliance of a number of strength and serviceability constraints. The process continues until a suitable solution is found. Since there are infinite solutions to the problem, it appears convenient to define some optimality criteria so as to measure the relative goodness of the different solutions. This paper intends to develop a preliminary least-cost section design model that follows the recommendations in the ACI 440.1 R-06, and uses a relatively new artificial intelligence (AI) technique called “particle swarm optimization” (PSO) to handle the optimization tasks. The latter is based on the intelligence that emerges from the low-level interactions among a number of relatively non-intelligent individuals within a population.

M3 - Conference proceeding

BT - Proceedings of the 8th International Symposium on Fiber-Reinforced Polymer Reinforcement for Concrete Structures