近年國內環保意識抬頭，對於開挖檔土措施要求甚高。當工程需要較高之環保標準時切削樁係較佳之選擇。但其施工所需機具常採用租借方式且甚為昂貴，故其施工成本一部份主要來自於機具設備之租借時間。此外傳統切削樁工程常因單元施作順序不當導致延誤工期，不僅增加機具租借之成本，亦使承包商需額外承擔逾期罰款所需費用。故發展切削樁之施工最佳排序模式用以減少施工時間與衝突係管理階層所期待的。故本研究目的在於利用電腦模擬方式發展全套管切削樁施工作業最佳排程模式，以(1)縮短施工時間、(2)運用SOMO最佳化演算法應用於營建產業。過往研究為解決優化問題進而發展出許多最佳化演算法。而近十年來較為熱門之法則係以群體智能為基礎之演算法，而本研究在演算法之選擇上採用1995年提出之粒子群演算法。此外，近年自我組織特徵映射網路(SOM)亦十分熱門，其中SOMO最佳化演算法於2005年提出，屬於近年最新之最佳化方法之一。因此本研究採用此兩種演算法則建立最佳化模式，最後並比較其成果。預期之研究貢獻包含：兩種全套管切削樁優選施工排序模式之建立、切削樁工程案例蒐集、PSO與SOMO之國外文獻蒐集彙整、比較PSO演算法與SOMO演算法之優劣等。 ; Construction time matters for activities where rental equipment must be utilized. The building of a secant pile wall requires the rental of equipment and finding the optimal sequence to minimize the construction time is one way to lower construction costs. In this study we develop an effective and efficient optimization algorithm, which we call Self-Organizing Feather Map-based Optimization (SOMO), to minimize the construction time. The algorithm is applied to a case study to obtain the optimal sequences for both primary and secondary bored piles for a secant pile wall. The new SOMO algorithm is developed based on the ability of the human brain to produce topologically ordered mapping, so as to exploit better solutions via updating the weighting vectors of the neurons in a self-organizing topological way, that occurs in the evolution of the feature map for optimization. Next, Particle Swarm Optimization (PSO) is used as a basis in comparison with SOMO. It is expected that practicability of SOMO applied to construction optimization problems can be achieved. ; 研究期間 9708 ~ 9807
