傳統的鋼筋混凝土設計中,許多的設計變數都是由工程師的經驗去給定初始值,例如柱直徑、梁寬度、配筋量與位置等,不同的設計再透過試誤法來檢核。由於設計變數之間是息息相關的,而工程師的設計通常趨於保守,其設計也未必能有效率的使各個材料間發揮其效能以在合乎規範要求的前提下節省費用。由於工程師在設計的時候不易在廣大的搜尋空間中找尋適合的設計參數,若用人力計算並一一試誤相當費時費力,因此本研究主要目的將鋼筋量、柱子直徑、混凝土強度以及鋼筋配比作為最佳化之變數,以求取最經濟且符合結構強度規定之混凝土斷面設計。研究方法為基因演算法以及粒子群演算法。本研究提出之兩套演算法應用在美國AASHTO協會橋樑設計範例之橋柱最佳化結構設計;目標為取得符合設計規範且較為經濟之設計。本研究亦比較基因演算法以及粒子群演算法的求解效果。
In the design of reinforced concrete structures, initial values of design parameters are often set subjectively, such as the diameters of pillars, widths of beams, number and locations of reinforcing bars. Different designs are then tested via a tedious trial-and-error process. Since the design parameters determine the performance and the engineers usually take a conservative approach, the overall design may not necessarily be cost efficient. Moreover, it is always difficult to find the optimal set of design parameters in a usually large search space. This study proposes a model to automatically find the optimal set of design parameters (diameter of pillar, number of reinforcing bars, strength of cement and reinforcing bar) so as to minimize the material costs while simultaneously satisfying design criteria. This study develops two evolutionary optimization algorithms, Genetic algorithm and Particle swarm optimization, to find the optimal design of a RC pillar. The goal is to find the most cost efficient design, which meets the AASHTO design code. This research also compares the performance of the two algorithms.