混凝土廣泛應用在各種營建結構體中。隨著材料安全性、結構耐久性、施工工作性、經濟性以及生態性等不同需求，而發展出HPC、SCC、RCA等不同類型混凝土。目前混凝土相關研究以透過多次試驗，變更配比設計來改變混凝土性質，以達到強度要求，此法為目前最有效的方式；然而在混凝土強度預測一直缺乏一套準確的方法，過去多以實驗試誤法或是經驗法為主，經驗法易造成誤差過大，試誤法則是耗時費力且成本過高。再者，混凝土配比設計除了考量強度、工作度外，經濟性配比亦是一重要影響因素，因此如何發展一套準確的混凝土強度預測模式，進而推論最佳化經濟性配比，有效預測混凝土配比強度，降低生產成本，為目前混凝土研究之重要課題之一。
本研究首先建立HPC、SCC以及RCA案例資料庫，以人工智慧SOS-LSSVM演算法，發展混凝土抗壓強度預測模式(Concrete Compressive Strength Prediction Model, CCSPM)與最佳化經濟性配比模式(Otimaization of Economical Concrete Ingredient Model, OECIM)。CCSPM藉由案例訓練學習，求得不同混凝土配比與抗壓強度間之映射關係，用以預測不同混凝土配比之抗壓強度；OECIM則結合CCSPM，搜尋強度符合要求之最經濟性配比。再者，透過不同AI強度預測模式比較，驗證本研究建所建立之預測模式，在準確性與相關性上，皆優於其他模式。此外，本研究檢核最佳化經濟性配比之輸出，其結果不僅強度滿足需求，各別材料之比率也符合傳統配比方法要求，且誤差在合理範圍內，而模式求得之配比成本亦與目前市價相當。

Concrete is widely used in a variety of building structures, and derived from various types. The more the manufacturing brings the more waste. Using recycled crushed concrete as aggregate in new concrete structures can improve the sustainability of the concrete industry in which recycling of material is counted as one of the most effective factors.
Accurate early estimation of concrete compressive strength can save time and cost for designers and structural engineers. However, the relationship between the compressive strength and concrete mixtures is highly nonlinear making it difficult to model the compressive strength, and optimal concrete aggregate is an multiple and complex problems. Very few studies have examined the appropriate and efficient approach to handle the collection of historical database. In addition, there has been few research study about optimail concrete mix design considering economics aspect at the same time.
This research established two model by using Symbiotic Organisms Search - Least Squares Support Machine (SOS-LSSVM) Algorithm, whitch is Concrete Compressive Strength Prediting Model and Optimal Concrete Ingredients Model. Try to provide accurate strength prediction model, and make the design of the new reference direction.

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